Research on Influencing Factors of Residential Building Carbon Emissions and Carbon Peak: A Case of Henan Province in China
Round 1
Reviewer 1 Report
The concept is good...but needs not well organised needs to rewrite... after rewritting it may considered obviously after checking it.
Comments for author File: 
Comments.pdf
the concept is good...but needs not well organised needs to rewrite... after rewritting it may considered obviously after checking it.
Author Response
Dear Editor and Reviewer:
Thank you for handling our manuscript and for the reviewers’ comments on “A ‘Static Setting + Dynamic Simulation’ Model on Influencing Factors of Residential Building Carbon Emissions and Carbon Peak：A Case of Henan Province in China”. Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval. Revised portion are marked in RED in the manuscript.
It is hoped that the current version of the manuscript satisfies the Reviewer and the Editor.
Sincerely
Dr. Xin Yang
Xinyang Normal University
Nanhu Road 237#, Xinyang, Henan, 464000, P.R.China
E-mail: [email protected]
Responds to the reviewer’s comments:
Comment 1: The title of this paper needs to be simplified.
Response 1: Thanks for carefully reviewing our manuscript and providing comments.
We have changed the title as “Research on Influencing Factors of Residential Building Carbon Emissions and Carbon Peak：A Case of Henan Province in China”.
Comment 2: Abstract needs to be rewritten… because it does not reflect about the whole concept of the paper.
Response 2: Thank you for bringing this issue to our attention. According to the reviewer’s feedback, we have meticulously rewritten the abstract. Specifically as follows:
Building is considered to have significant emission reduction potential. Residential building carbon emissions, as the most significant type of building-related carbon emissions, represent a crucial factor in achieving both carbon peak and carbon neutrality targets for China. Based on carbon emission data from Henan Province, a large province located in central China, between 2010 and 2020, this study employs the Kaya-LMDI decomposition method to analyze seven driving factors of carbon emission evolution, encompassing energy, population and income, and assesses the historical reduction in CO2 emissions from residential buildings. Based on carbon emission data from Henan Province, a large province located in central China between 2010 and 2020, this study employed the Kaya-LMDI decomposition method to analyze seven driving factors of carbon emission evolution, encompassing energy consumption, population growth and income level, and evaluated the historical reduction in CO2 emissions from residential buildings. Then, by integrating Kaya identity static analysis with Monte Carlo dynamic simulation, various scenarios were established to infer the future evolution trend, peak time and potential for carbon emission reduction in residential buildings. The analysis results are as follows: (1) The carbon emissions of residential buildings in Henan have exhibited a rising trend from 2010 to 2020, albeit with a decelerating growth rate. (2) Per capita household disposable income is the main driving factors for the increase in carbon emissions, but the household housing purchase index inhibits the most to the growth of carbon emissions for the residential buildings in Henan, with the total carbon emission reduction of residential buildings reaches 106.42 million tons of CO2 during the research period. (3) During the period from 2020 to 2050, residential buildings in Henan Province will exhibit an "inverted U-shaped" trend in carbon emissions under the three static scenarios. The base scenario predicts that carbon emissions will reach their peak of 131.66 million tons in 2036, while the low-carbon scenario forecasts a peak of 998.8 million tons in 2030 and the high-carbon scenario projects a peak of 138.65 million tonnes in 2041. (4) Under the dynamic simulation scenario, it is anticipated that residential buildings in Henan Province will reach their carbon peak in 2036±3, with a corresponding carbon emission of 155.34 million tons. This study can serve as a valuable reference for future development of low-carbon pathways within the building sector.
Comment 3: For “2. Literature review”：Does not require this portion... Add it in Intriduction part. Add some more reference.
Response 3: Thank you very much for pointing out this issue. We have integrated the “literature review” into the “introduction”, and included some latest references (references 16, 17,18, 21, and 22).
Comment 4: For“3. Carbon emission accounting”：What is this...Here Materials and methodology should be there......
Response 4: Thank you for carefully reviewing our manuscript and providing comments. We have made revisions to “2. Data Sources and research methodology”.
Comment 5: For “4. Construction of the Kaya-LMDI model”: Need to rewrite...
Response 5: Thank you for your valuable comment. We have rewritten to
“3. Evaluation of historical reductions in CO2 emissions from residential buildings”.
Comment 6: For “7. Discussions and innovations” : this should be before conclusion.
Response 6: Thanks for this insightful comment. The order of “7. Discussions and innovations” and “6. Conclusion” has been reversed.
Comment 7: For the last paragraph: the concept is good...but needs not well organized needs to rewrite... after rewriting it may be considered obviously after checking it.
Response 6: Thank you for your valuable feedback. We have rewritten this section, and provided a comprehensive overview of the research perspectives, limitations and future prospect presented in this study. Specifically as follows:
In conclusion, this study delves into the trajectory of carbon emissions from residential buildings in Henan Province under the "two-carbon" target. This study has greatly enriched the theoretical knowledge system and empirical research methods of the influencing factors of carbon emission change in the building sector and peak simulation prediction, and provided a research reference for the assessment of historical CO2 emission reduction and the scenario analysis of carbon emission peak. However, the basic emission data and the framework of peaking model need to be further expanded and improved. First of all, the quantitative analysis in this study is based on building carbon emission data from the Energy Statistical Yearbook. However, the decomposition of energy consumption and emissions (heating, cooling, lighting, etc.) at building terminals has not been included. In the future, expanding data collection and accounting of building terminal energy use and emissions is imperative, so as to evaluate the historical emission reduction level of terminal energy use carbon emissions. Secondly, this study simplifies the carbon emission model of residential buildings based on the Kaya identity and subsequently simulates their future development trajectory and corresponding peak state of carbon emissions. In future studies, it is recommended to expand the existing emission model parameters, particularly with regards to a series of economic factors that affect building carbon emissions, in order to reliably and reasonably predict future development trends.
Comment 7: For “References”: Add some more latest reference.
Response 7: Thanks for this suggestion. We have incorporated some latest relevant references (references 16, 17,18, 21, and 22) into the text.
Furthermore, in addition to the aforementioned revisions, we have thoroughly refined both the language and structure of the manuscript. These modifications do not affect the content or framework of the paper. While we have not provided a comprehensive list of changes here, they are clearly marked in red within the revised document. We have made every effort to enhance this manuscript and sincerely appreciate the editor's and reviewer's valuable feedback.
Author Response File: 
Author Response.docx
Reviewer 2 Report
The accuracy and reliability of estimates of this model can only as good as the inputs. For this reason, the inputs must be clear and sound, and free from any uncertainty. After reading this paper, I have more questions than answers. Below are my general and some specific comments for this research.
This study titled: “A static setting +dynamic simulation model on influencing factors of residential building carbon emissions and carbon peak: Case study of a province in China.”
GENERAL COMMENTS
The authors must address the following general comments to improve the delivery and substance of this paper.
1. The validity of the estimation in this research is difficult to assess because of the following reasons
a. The calculation is basically based on unfamiliar model, and therefore the model must be explained in a simple language, in such a way that it could be understood by a broader audience, especially for the intended audience of this work, including readers without technical knowledge.
b. Lack of description of the type of model being used in this study (e.g., black box model, process-based model, empirical model, factor model, stochastic model). It is important to describe the type of model being used in the study because different types of models have different types of inputs, limitations, and level of reliability.
c. Uncertainty on the inputs of this model, particularly the data being used in the estimate.
d. Lack of flexibility in the estimate
2. The authors stated they used energy balance approach, which means the calculation of net C balance should be based on the difference between input and output. But it is not clear how they generated the inputs and outputs.
3. While the authors provided some values of CO2 emissions in years to come, these are estimated values, and therefore, the estimates must be accompanied by standard deviation or standard error of the mean
4. This is a projection of potential total C emissions in the next decades in Henan Province. Because this is a prediction, the authors must provide flexibility in the estimates by providing “best-case scenario”, “worst-case scenario”, and “moderate or average case scenario.”
5. The authors must also explain the significance of this approach, and how this approach compared with the default IPCC method for greenhouse gas inventory in urban areas.
SOME DETAILED COMMENTS
1. Introduction
a. The discussion is mainly focused on China policies on carbon mitigation policies, but not connecting this to the IPCC framework on GHG reduction, which China is now considered as the leading CO2 polluter. I suggest including discussion of China’s commitment in reducing CO2 emissions within the national borders with reference to the Conference of Parties (COP).
b. While the authors provided some information about the Henan Province, there is no general information regarding the carbon footprint in this province based on previous studies, and if there is none, some information regarding the potential rise of CO2 taking into account the rapid economic growth, urbanization, population, and carbon emitting activities in the province.
c. The authors must also clearly point out the research gaps and significance of this project.
2. Literature Review
This section is quite complicated and confusing. After reading this section, I failed to get the message on the importance of this research, how other researchers utilized this model in predicting the trend of CO2 in the future, how this model being developed, what are important inputs and outputs of this model, what is a Kaya-LMDI decomposition method, what is an energy splitting method, and many other important technicalities that should be focus of this section. While the authors tried to discuss about other models, they failed to compare these models with the model being used in this study, and the advantages of the current model compared with the other models. Although the last two paragraphs attempted to identify the gaps, the foundation of these gaps remain uncertain. I suggest the authors should be clear on its literature by directing the focus of references to the discussion on how this model works in previous studies, and the gaps that needs to be addressed.
3. Carbon emission accounting
a. Basically, the inputs of this study were taken from secondary data from one source, which is the Henan Energy Balance Sheet and Henan Energy Statistical Yearbook. The authors should take some samples on the ground to validate the data reflected in these secondary data sources.
b. On the CO2 accounting methods, basically this study is only looking at the CO2 emissions, not net CO2 balance because the study lacked the data on C sinks. Because this is a projection, the authors must provide the best-case, worst-case, and average-case scenarios in the analysis.
c. If the balance sheet does not show information on building energy consumption, and not calculated because their proportion is low and on decreasing trend, then how could this research generate a reliable estimate if the main focus of this study is the urban and rural buildings?
The English grammar is fine, although needs further improvement. However, coherence in the construction and flow of ideas are not so good, which oftentimes cause confusion. Further improvement is needed to make the storyline of the paper easy to follow and for easy understanding of the message.
Author Response
Jun. 10, 2023
Sustainability
Dear Editor and Reviewer:
Thank you for handling our manuscript and for the reviewers’ comments on “A ‘Static Setting + Dynamic Simulation’ Model on Influencing Factors of Residential Building Carbon Emissions and Carbon Peak：A Case of Henan Province in China”. Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval. Revised portion are marked in RED in the manuscript.
It is hoped that the current version of the manuscript satisfies the Reviewer and the Editor.
Sincerely
Dr. Xin Yang
Xinyang Normal University
Nanhu Road 237#, Xinyang, Henan, 464000, P.R.China
E-mail: [email protected]
Responds to the reviewer’s comments:
The accuracy and reliability of estimates of this model can only as good as the inputs. For this reason, the inputs must be clear and sound, and free from any uncertainty. After reading this paper, I have more questions than answers. Below are my general and some specific comments for this research.
This study titled: “A static setting+dynamic simulation model on influencing factors of residential building carbon emissions and carbon peak: Case study of a province in China.”
The authors must address the following general comments to improve the delivery and substance of this paper.
- The validity of the estimation in this research is difficult to assess because of the following reasons:
Comment a: The calculation is basically based on unfamiliar model, and therefore the model must be explained in a simple language, in such a way that it could be understood by a broader audience, especially for the intended audience of this work, including readers without technical knowledge.
Response a: Thanks for carefully reviewing our manuscript and providing comments.
We have incorporated a comprehensive explanation of the model into the introduction.
Specifically as follows:
The Kaya model is a classic extension of the IPAT model, originally proposed by Japanese scholar Yoichi Kaya. This model establishes links between economic development, population growth, policy implementation and carbon dioxide emissions resulting from human activities. It analyzes the relationship between regional carbon emissions and various factors such as energy consumption structure, emission intensity of different energy sources, energy utilization efficiency, economic development and human activities, which is recommended by IPCC to analyze the change characteristics and influencing factors of carbon dioxide emissions or greenhouse gas emissions. The Logarithmic Mean Divisia Index Decomposition (LMDI) method is a widely used form of the classic Divisia Index Decomposition Analysis within the index decomposition methodology. It was initially proposed by Ang B W, a distinguished scholar from Singapore. The LMDI decomposition method is highly practical and easily implemented, with no residual values remaining after the decomposition process to ensure unique results. Additionally, it guarantees homology across different operational decomposition methods. After comparing and analyzing various index decomposition methods, Ang concludes that the Kaya-LMDI model, which characterizes carbon emissions based on the Kaya identity, is an excellent method for decomposing and analyzing factors influencing carbon emissions. This model analyzes the influencing factors of carbon emissions from three dimensions: population, energy use intensity, and social affluence. It can accurately identify the main factors affecting changes in carbon emissions.
Comment b: Lack of description of the type of model being used in this study (e.g., black box model, process-based model, empirical model, factor model, stochastic model). It is important to describe the type of model being used in the study because different types of models have different types of inputs, limitations, and level of reliability.
Response b: We greatly appreciate your valuable advice. The calculation method utilized in this study is a factor model based on the split of Energy Balance Sheet. Its core involves reprocessing and summarizing data from various departments involved in building energy use, as derived from the physical Energy Balance Sheet. This enables accurate determination of energy consumption and emission data for buildings. The KaYa-LMDI model employed is a grey prediction model, which means that some information is known while the rest remains uncertain. The historical carbon emission data in this study are considered as known information, whereas the future carbon emission predicted by the model falls under uncertain information. The grey prediction model predicts a system that comprises both known and unknown information, and the resulting data exhibits certain regularity. We have revised the relevant content, please refer to 2.2 and 3.3.
Comment c: Uncertainty on the inputs of this model, particularly the data being used in the estimate.
Response c: Thanks for your insightful comment. In the dynamic simulation, the carbon peak is predicted to be 2036±3 years, and the reliability of uncertain data in the dynamic simulation can be reflected in Table 4. Static simulation data is obtained based on historical data trends. In the dynamic simulation, the carbon peak is projected to occur in 2036±3 years, with Table 4 reflecting the reliability of uncertain data in this model. The static simulation data is derived from historical trends.
Comment d: Lack of flexibility in the estimate.
Response d: Thank you for bringing this issue to our attention. We re-evaluated the flexibility of this study, the results showe that the dynamic simulation estimates demonstrate a high level of reliability, as evidenced by Table 4. The standard deviation for peak time in dynamic simulation is 3, while the standard deviation for peak value is 0.2571.
Comment 2: The authors stated they used energy balance approach, which means the calculation of net C balance should be based on the difference between input and output. But it is not clear how they generated the inputs and outputs.
Response 2: Thanks for this insightful comment. The data utilized in this paper is sourced from the fourth category of terminal consumption within the energy balance sheet, with building energy consumption falling under said category. For purposes of this study, energy consumption value shall be considered as output value in the balance table, while pre-terminal consumption shall be disregarded.
Comment 3: While the authors provided some values of CO2 emissions in years to come, these are estimated values, and therefore, the estimates must be accompanied by standard deviation or standard error of the mean.
Response 3: Thank you for carefully reviewing our manuscript and providing comments. The dynamic simulation's estimated value demonstrates a high level of reliability, as evidenced by Table 4. Specifically, the standard deviation for peak time is 3 and the standard deviation for peak value is 0.2571.
Comment 4: This is a projection of potential total C emissions in the next decades in Henan Province. Because this is a prediction, the authors must provide flexibility in the estimates by providing “best-case scenario”, “worst-case scenario”, and “moderate or average case scenario.”
Response 4: Thank you for bringing this issue to our attention. This study’s static simulation yields three predictions for future carbon emissions: the high-carbon scenario, the low-carbon scenario, and the base case, as depicted in Figure 4, which corresponds to “best-case scenario”, “worst-case scenario”, and “moderate or average case scenario”. During the period from 2020 to 2050, residential buildings in Henan Province will exhibit an "inverted U-shaped" trend in carbon emissions under the three static scenarios. The base scenario predicts that carbon emissions will reach their peak of 131.66 million tons in 2036, while the low-carbon scenario forecasts a peak of 998.8 million tons in 2030 and the high-carbon scenario projects a peak of 138.65 million tonnes in 2041. Under the dynamic simulation scenario, it is anticipated that residential buildings in Henan Province will reach their carbon peak in 2036±3 years, with a corresponding carbon emission of 155.34 million tons.
Comment 5: The authors must also explain the significance of this approach, and how this approach compared with the default IPCC method for greenhouse gas inventory in urban areas.
Response 5: Thank you for your valuable feedback. The Kaya-LMDI model, which characterizes carbon emissions based on the Kaya identity, is an excellent method for decomposing and analyzing factors influencing carbon emissions. This model analyzes the influencing factors of carbon emissions from three dimensions: population, energy use intensity, and social affluence. It can accurately identify the main factors affecting changes in carbon emissions. The carbon emissions calculation method utilized in this study is based on the IPCC greenhouse gas inventory approach for urban areas, with both calculations relying on the formula: Emission = energy consumption × emission factor. The variance lies in the data processing source. The data utilized in this paper is sourced from the national energy balance sheet, which is a reliable source. The method employed for dividing energy consumption is clear and has been shown to have minimal discrepancies when compared to other accounting methods, thus indicating high reliability. Moreover, the Kaya model is recommended by IPCC to analyze the change characteristics and influencing factors of carbon dioxide emissions or greenhouse gas emissions.
The pertinent information has been incorporated into the introduction section.
SOME DETAILED COMMENTS
- Introduction
Comment a： The discussion is mainly focused on China policies on carbon mitigation policies, but not connecting this to the IPCC framework on GHG reduction, which China is now considered as the leading CO2 polluter. I suggest including discussion of China’s commitment in reducing CO2 emissions within the national borders with reference to the Conference of Parties (COP).
Response a: Thank you for your valuable suggestion. We have rediscussed this section. Specifically as follows:
To achieve significant and sustained reductions in emissions and ensure a livable and sustainable future for all, rapid and comprehensive transformations across all industries and systems are imperative, as proposed by the Intergovernmental Panel on Climate Change (IPCC). The international community’s commitment to carbon emission reduction has been intensifying. As one of the world’s big economies, China shoulder significant responsibilities and tasks for environmental protection and carbon reduction, while developing at a rapid pace. In recent years, the China’s government has attached great importance to carbon emission reduction, promulgating a series of standard specifications and issuing relevant work guidance schemes. In the Paris Agreement, China has committed to achieving a peak in CO2 emission around 2030 and striving for an early peak.
Please see the introduction section.
Comment b： While the authors provided some information about the Henan Province, there is no general information regarding the carbon footprint in this province based on previous studies, and if there is none, some information regarding the potential rise of CO2 taking into account the rapid economic growth, urbanization, population, and carbon emitting activities in the province.
Response b: Thank you for your valuable suggestion. The research subject of this paper is residential buildings in Henan Province, which situates in the central part of China, is a developing region with a population density of approximately 595 individuals per square kilometer and a permanent resident count of 98.72 million. It ranks third in terms of population size in China and has one of the highest carbon dioxide emission rates from its residential buildings.
We have incorporated the aforementioned analysis into the introduction section.
Comment c：The authors must also clearly point out the research gaps and significance of this project.
Response c: Thank you for bringing this issue to our attention. The existing reports have laid a solid methodological foundation for a comprehensive and systematic understanding of the status of carbon emissions from buildings and for further research. There are still some shortcomings in the current research on carbon emissions from residential buildings in China. First, most of the research on building carbon emissions focuses on the national level. However, China has a vast territory, and the buildings in each place have their own characteristics. The research conclusions at the national level are not fully suitable for learning at the provincial and municipal levels, and a detailed and systematic analysis at the provincial level is lacking. Second, the data sources of building energy consumption calculation models are different, resulting in large differences in calculation results. Third, there is a lack of analysis of carbon emission peaking scenarios for residential buildings under the influence of uncertainty.
Therefore, this study aims to evaluate the potential for carbon emission reduction in the building sector of Henan Province by analyzing historical data on carbon emissions from residential buildings and identifying factors that influence carbon emission intensity. With consideration given to the impact of energy consumption intensity, population, energy emission coefficient, per capita GDP, urbanization level and tertiary industry, a novel Kaya-LMDI model is formulated based on the Kaya identity. Additionally, Monte Carlo simulation was introduced into the scenario analysis method. A “static setting + dynamic simulation” model was built to predict the trajectory and corresponding peak state of carbon emissions from residential buildings in Henan between 2020 and 2050 under the influence of uncertainty, thereby compensating for the inadequacies of previous research.
We have incorporated the aforementioned analysis into the introduction section. Please see the last two paragraphs of the introduction section.
Comment 2: Literature Review
This section is quite complicated and confusing. After reading this section, I failed to get the message on the importance of this research, how other researchers utilized this model in predicting the trend of CO2 in the future, how this model being developed, what are important inputs and outputs of this model, what is a Kaya-LMDI decomposition method, what is an energy splitting method, and many other important technicalities that should be focus of this section. While the authors tried to discuss about other models, they failed to compare these models with the model being used in this study, and the advantages of the current model compared with the other models. Although the last two paragraphs attempted to identify the gaps, the foundation of these gaps remain uncertain. I suggest the authors should be clear on its literature by directing the focus of references to the discussion on how this model works in previous studies, and the gaps that needs to be addressed.
Response 2: Thank you for your valuable suggestion. We have redescribed this section. Specifically as follows:
The Kaya model is a classic extension of the IPAT model, originally proposed by Japanese scholar Yoichi Kaya. This model establishes links between economic development, population growth, policy implementation and carbon dioxide emissions resulting from human activities. It analyzes the relationship between regional carbon emissions and various factors such as energy consumption structure, emission intensity of different energy sources, energy utilization efficiency, economic development and human activities, which is recommended by IPCC to analyze the change characteristics and influencing factors of carbon dioxide emissions or greenhouse gas emissions. The Logarithmic Mean Divisia Index Decomposition (LMDI) method is a widely used form of the classic Divisia Index Decomposition Analysis within the index decomposition methodology. It was initially proposed by Ang B W, a distinguished scholar from Singapore. The LMDI decomposition method is highly practical and easily implemented, with no residual values remaining after the decomposition process to ensure unique results. Additionally, it guarantees homology across different operational decomposition methods. After comparing and analyzing various index decomposition methods, Ang concludes that the Kaya-LMDI model, which characterizes carbon emissions based on the Kaya identity, is an excellent method for decomposing and analyzing factors influencing carbon emissions. Peng et al. conducted an analysis on the proportion of carbon emissions from China's construction industry and related sectors in the country's total social carbon emissions, revealing that the construction industry accounted for 16%. A Kaya model-based study focusing on various types of public buildings/municipalities indicated that public buildings contributed to 8% of China's overall energy consumption. However, relying solely on the Kaya model for detecting changes in carbon emissions poses challenges in accurately determining their actual impact on total emissions and fails to account for variations in economic and social trends. Qi et al. conducted a calculation of carbon emissions from both producer and consumer perspectives, while also analyzing the decision-making process behind inter-provincial net carbon emission transfers. Additionally, the LMDI method was employed to decompose the factors influencing the province's net carbon emissions into technology, structure, input-output and scale effects. However, utilizing solely the LMDI model still presents certain limitations in elasticity analysis. et al. first proposed a bottom-up approach for measuring the values of commercial buildings in China based on decomposing the extended Kaya identity via the LMDI method. Subsequently, a comparative analysis of the contribution rate elasticity of drivers, assessed by both the LMDI method and ridge regression, effectively examines the robustness of China's commercial building measurement model. In addition, Lu et al. utilized the decomposition of Kaya identity and mixed LMDI to comprehensively analyze the factors influencing building energy consumption growth throughout its full life cycle from 2007 to 2015. Therefore, the Kaya-LMDI analyzes the influencing factors of carbon emissions from three dimensions: population, energy use intensity, and social affluence. It can accurately identify the main factors affecting changes in carbon emissions.
Based on feedback from another reviewer, the literature review section was incorporated into the introduction section. Please see the introduction section.
- Carbon emission accounting
Comment a: Basically, the inputs of this study were taken from secondary data from one source, which is the Henan Energy Balance Sheet and Henan Energy Statistical Yearbook. The authors should take some samples on the ground to validate the data reflected in these secondary data sources.
Response a: Thank you for bringing this issue to our attention. We have cross-referenced the data from Henan Energy Balance Sheet and Henan Energy Statistical Yearbook with the research report on building energy consumption and carbon emissions 2010-2020 published by China Building Energy Efficiency Association, thus ensuring their authenticity and reliability. However, we acknowledge that this is a limitation of our study, and in future research, we will conduct field sampling to further validate the secondary data sources.
Comment b: On the CO2 accounting methods, basically this study is only looking at the CO2 emissions, not net CO2 balance because the study lacked the data on C sinks. Because this is a projection, the authors must provide the best-case, worst-case, and average-case scenarios in the analysis.
Response b: Thank you for your valuable suggestion. This study’s static simulation yields three predictions for future carbon emissions: the high-carbon scenario, the low-carbon scenario, and the base case, as depicted in Figure 4, which corresponds to “best-case”, “worst-case” and “average-case” scenario. During the period from 2020 to 2050, residential buildings in Henan Province will exhibit an "inverted U-shaped" trend in carbon emissions under the three static scenarios. The base scenario predicts that carbon emissions will reach their peak of 131.66 million tons in 2036, while the low-carbon scenario forecasts a peak of 998.8 million tons in 2030 and the high-carbon scenario projects a peak of 138.65 million tonnes in 2041. Under the dynamic simulation scenario, it is anticipated that residential buildings in Henan Province will reach their carbon peak in 2036±3 years, with a corresponding carbon emission of 155.34 million tons.
Comment c: If the balance sheet does not show information on building energy consumption, and not calculated because their proportion is low and on decreasing trend, then how could this research generate a reliable estimate if the main focus of this study is the urban and rural buildings?
Response c: Thank you for bringing this issue to our attention. The carbon emissions of buildings encompass three aspects: (1) Direct carbon emissions from buildings, which are caused by the consumption of fossil fuels during building operation and primarily occur in activities such as heating water and decentralized heating. (2) Construction of indirect carbon emissions refers to the carbon emissions resulting from the consumption of secondary energy sources, namely electricity and heat, during the building operation stage. These two sources are considered as the primary contributors to carbon emissions in building operations. (3) Building implicit carbon emissions refer to the carbon emissions caused by building construction and production of building materials, also known as building construction carbon emissions or physical carbon emissions. Among them, the carbon emission of building construction includes the emission during the construction stage, maintenance and use stage, as well as demolition.
If the balance sheet does not show information on building energy consumption, and not calculated because their proportion is low and on decreasing trend, combining these three figures can provide an estimation of carbon emissions from both urban and rural buildings.
Comment 4: The English grammar is fine, although needs further improvement. However, coherence in the construction and flow of ideas are not so good, which oftentimes cause confusion. Further improvement is needed to make the storyline of the paper easy to follow and for easy understanding of the message.
Response 4: Thanks for carefully reviewing our manuscript and providing comments. We have made the whole manuscript polished in language and structure. And here we did not list the changes but marked in RED in revised paper.
We have made every effort to enhance this manuscript and sincerely appreciate the diligent efforts of both our editor and reviewer.
Author Response File: Author Response.docx
Reviewer 3 Report
The authors used Kaya-LMDI decomposition method to investigate the influencing factors of carbon emissions based on relevant data. The growth trend and future predictions of carbon emissions from residential buildings in Henan Province have been obtained. The research results have certain significance for achieving carbon emission reduction goals and carbon peaking in Henan Province. This work is in line with the research field of the journal.
Author Response
Jun. 10, 2023
Sustainability
Dear Editor and Reviewer:
Thank you for handling our manuscript and for the reviewers’ comments on “A ‘Static Setting + Dynamic Simulation’ Model on Influencing Factors of Residential Building Carbon Emissions and Carbon Peak：A Case of Henan Province in China”. Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval. Revised portion are marked in RED in the manuscript.
It is hoped that the current version of the manuscript satisfies the Reviewer and the Editor.
Sincerely
Dr. Xin Yang
Xinyang Normal University
Nanhu Road 237#, Xinyang, Henan, 464000, P.R.China
E-mail: [email protected]
The reviewer’s comments:
The authors used Kaya-LMDI decomposition method to investigate the influencing factors of carbon emissions based on relevant data. The growth trend and future predictions of carbon emissions from residential buildings in Henan Province have been obtained. The research results have certain significance for achieving carbon emission reduction goals and carbon peaking in Henan Province. This work is in line with the research field of the journal.
Responds to the reviewer’s comments:
Thank you for your generous recognition of our work. Moving forward, we will strive to conduct even more rigorous research. Best regards.
Furthermore, we have had the whole manuscript polished in language and structure. These changes will not influence the content and framework of the paper. And here we did not list the changes but marked in RED in revised paper. We tried our best to improve the manuscript and made some changes in the manuscript. We appreciate for Editor and Reviewer’s warm work earnestly and hope that the correction will meet with approval. Once again, thank you very much for your comments and suggestions.
Author Response File: Author Response.docx
Reviewer 4 Report
1. Some important papers are missing from the lierature review and should be added. for example:
Swan, L. G., & Ugursal, V. I. (2009). Modeling of end-use energy consumption in the residential sector: A review of modeling techniques. Renewable and sustainable energy reviews, 13(8), 1819-1835.
Kavgic, M., Mavrogianni, A., Mumovic, D., Summerfield, A., Stevanovic, Z., & Djurovic-Petrovic, M. (2010). A review of bottom-up building stock models for energy consumption in the residential sector. Building and environment, 45(7), 1683-1697.
Ali, U., Shamsi, M. H., Hoare, C., Mangina, E., & O’Donnell, J. (2021). Review of urban building energy modeling (UBEM) approaches, methods and tools using qualitative and quantitative analysis. Energy and Buildings, 246, 111073.
2. There are a few typos and errors in the manuscript, e.g. Histerical instead of historical in Fig 4
3. The policy environment is referred to in discussion should be described.
The quality of English is acceptable. Language can be improved at some places.
Author Response
Jun. 10, 2023
Sustainability
Dear Editor and Reviewer:
Thank you for handling our manuscript and for the reviewers’ comments on “A ‘Static Setting + Dynamic Simulation’ Model on Influencing Factors of Residential Building Carbon Emissions and Carbon Peak：A Case of Henan Province in China”. Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval. Revised portion are marked in RED in the manuscript.
It is hoped that the current version of the manuscript satisfies the Reviewer and the Editor.
Sincerely
Dr. Xin Yang
Xinyang Normal University
Nanhu Road 237#, Xinyang, Henan, 464000, P.R.China
E-mail: [email protected]
Responds to the reviewer’s comments:
Comment 1: Some important papers are missing from the lierature review and should be added. for example:
Swan, L. G., & Ugursal, V. I. (2009). Modeling of end-use energy consumption in the residential sector: A review of modeling techniques. Renewable and sustainable energy reviews, 13(8), 1819-1835.
Kavgic, M., Mavrogianni, A., Mumovic, D., Summerfield, A., Stevanovic, Z., & Djurovic-Petrovic, M. (2010). A review of bottom-up building stock models for energy consumption in the residential sector. Building and environment, 45(7), 1683-1697.
Ali, U., Shamsi, M. H., Hoare, C., Mangina, E., & O’Donnell, J. (2021). Review of urban building energy modeling (UBEM) approaches, methods and tools using qualitative and quantitative analysis. Energy and Buildings, 246, 111073.
Response 1: Thank you for your valuable comment. Several pertinent references have been incorporated (references 11, 14, and 19).
Comment 2: There are a few typos and errors in the manuscript, e.g. Histerical instead of historical in Fig 4.
Response 2: According to the reviewer's feedback, we have conducted a thorough examination of the whole manuscript. We sincerely apologize for the incorrect in Figure 4 and have since replaced it with the appropriate spelling. Please refer to Figure 4 for the updated version.
Comment 3: The policy environment is referred to in discussion should be described.
Response 3: Thank you for your valuable suggestion. Relevant policies have been added to the discussion. Specifically as follows:
At present, China has put forward five goals: “building a green, low-carbon, and circular economic system, promoting sustainable development by improving energy efficiency, increasing the share of non-fossil fuel consumption, reducing carbon dioxide emissions, and enhancing ecosystem carbon sequestration capacity”.
Comment 4: The quality of English is acceptable. Language can be improved at some places.
Response 4: Thanks for carefully reviewing our manuscript and providing comments. We have made the whole manuscript polished in language and structure. And here we did not list the changes but marked in RED in revised paper.
We tried our best to improve the manuscript and made some changes in the manuscript. We appreciate for Editor and Reviewer’s warm work earnestly and hope that the correction will meet with approval. Once again, thank you very much for your comments and suggestions.
Author Response File: Author Response.docx
Round 2
Reviewer 1 Report
Now the Paper is in order and it is ok to publish
Comments for author File: Comments.pdf
Reviewer 2 Report
This revised version significantly improved the contents and structure, based on the issues and weaknesses pointed out in the first round of review. The authors also able to address my comments and suggestions. Although there are still minor issues that need to be addressed, this can be properly handled by the assigned editor of this paper.
Reviewer 4 Report
The paper reads well after the changes.
The quality of English language is acceptable.
