Peak Carbon Dioxide Emissions Strategy Based on the Gray Model between Carbon Emissions and Urban Spatial Expansion for a Built-Up Area
Round 1
Reviewer 1 Report
This paper is a well-researched study on the relationship between urban spatial expansion and carbon emission. In this paper, based on grey model, the future trend of carbon emission is predicted. The paper is scientifically sound and should obtain broad international interest. The manuscript is clearly laid out and all the key elements are present. The abstract and key words are appropriate. Its presentation and explanation is accurate. All the illustrations are clearly and suitably captioned.
The manuscript can be accepted for publication after a minor revision. My suggestions for revision are as follows:
1. COVID-19 has a great impact on carbon emissions in China. In view of the current policy, the impact will continue for a long time. In this situation, the grey model obviously has shortcomings. Discussion and analysis should be strengthened to clarify the problem.
2. In the 2.2 section, grey relational model is not original. So, the specific algorithm only needs to be referenced. The method may need improvement.
Author Response
Dear Reviewer,
We are grateful for your time and effort on reviewing the manuscript. The new manuscript has been revised based on your comments and the corrections made are explained below.
Hopefully, this revised version would be received favorably.
We are looking forward to hearing from you.
Sincerely yours,
Authors
☆ ☆ ☆ ☆ ☆
This paper is a well-researched study on the relationship between urban spatial expansion and carbon emission. In this paper, based on grey model, the future trend of carbon emission is predicted. The paper is scientifically sound and should obtain broad international interest. The manuscript is clearly laid out and all the key elements are present. The abstract and key words are appropriate. Its presentation and explanation is accurate. All the illustrations are clearly and suitably captioned.
The manuscript can be accepted for publication after a minor revision. My suggestions for revision are as follows:
- COVID-19 has a great impact on carbon emissions in China. In view of the current policy, the impact will continue for a long time. In this situation, the grey model obviously has shortcomings. Discussion and analysis should be strengthened to clarify the problem.
Response:
Thank you so much for your suggestion. COVID-19 does have an impact on urban carbon emissions by affecting economic development and urban space expansion. In this paper, our grey correlation analysis describes the correlation results of data from 1949 to 2016, so there is no problem in correlation analysis. However, when predicting the future carbon emission trend, it is necessary to consider the scenario of economic decline and its impact on land expansion, which has been added in Table 5 and Table 8. (P10 L311 and P12 L382)
The COVID-19 scenario analysis has been added in the 5.1 Discussion section. In line 437 to line 442, you will read:
(P14 L437—L442) (4) Considering the impact of COVID-19 on China's GDP, the overall urban economy is in a downwards trend. Changsha's GDP maintained a growth trend of 12.8% before 2019. Influenced by COVID-2019, the GDP growth rate in 2020, 2021 and 2022 decreased to 7.1%, 7.5% and 4.8%, respectively. Forecast for 2030, the GDP will reduse to 1900 billion CNY. The economic downturn is likely to slow urban sprawl, so binding targets for carbon emissions will be adjusted accordingly.
- In the 2.2 section, grey relational model is not original. So, the specific algorithm only needs to be referenced. The method may need improvement.
Response:
Thank you very much for your suggestion. This section has been briefly described with references to specific algorithms. (See P4 L173—P6 L214)
A carefully prepared paper. Still some methodology issues are missing - one of them is why the city of Changsha was chosen? Does it have some particular outstanding parameters, or possibly the parameters are typical with other cities of its size.
Response:
Thank you very much for your question. Supplementary reasons are given for choosing Changsha for the case study. Basically three reasons have motivated this choice, which are the importance of the city in terms of national development strategy, making it worth paying attention to, and energy shortage restricts the development of cities to a certain extent, which makes research very urgentand, third but also indispensable the full accessibility of the research team to non-open-source data like urban land use data. The modified paragraphs are shown below.
(Section 2.1 Background of the Study Area, P3 L131— P4 L166)
The Located in the middle of China, Changsha is an important central city in the middle reaches of the Yangtze River, one of the pilot areas for the comprehensive reform of the "Two-oriented Society", an important grain production base in China, and an important node city in the middle reaches of the Yangtze River City cluster and the Yangtze River Economic Belt, playing an increasingly prominent role in the national development strategy. In recent years, Changsha's vigorous economic development has brought about the rapid outwards expansion of the city. From 1949 to 2016, the urban population grew from 383,500 to 3,362,500, the GDP from CNY2.87 million to CNY851,013 million, and built-up area increased from 6.7 km2 to 476.34 km2. During this period, the city’s master plan went through six revisions in 1979, 1996, 2003, 2008, 2013 and 2016, respectively. As of 2021, Changsha's GDP has reached CNY1,214,250 million, jumping to 15th place in the country. The population, economic and built-up area indices are shown in Table 1. Nevertheless, Changsha has always had the problems of a single energy structure and tight energy consumption, and its energy consumption and carbon emissions rank in the forefront of Hunan Province. According to the energy statistics yearbook data of Hunan Province, from 2005 to 2015, the total energy consumption of Changsha increased from 1851.785 t-ce to 9709.27 t-ce. Compared with the total energy consumption of Hunan Province increasing from 4455.207 t-ce to 15468.61 t-ce, the growth rate of Changsha is far higher than that of Hunan Province.
The National Development and Reform Commission announced nearly 100 low-carbon pilot cities three times in 2010, 2014 and 2017. Changsha was one of the third batches of pilot cities announced. However, Changsha's booming economy has brought several drawbacks, such as the rapid outwards expansion of urban land and excessive energy consumption. Energy problems restrict the development of Changsha city to some extent. In the new historical development stage, how to take effective ways to reduce energy consumption and reduce carbon emissions is an important issue to be solved. To achieve the two types of societal goals and accelerate the low-carbon city pilot projects, it is necessary to optimize Changsha's territorial planning and reach peak carbon emissions by 2030. The intensity of carbon emissions needs to be reduced by 60% to 65% from the 2005 level.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
This is an interesting study and could be published in the present version.
Author Response
We are grateful for your time and effort on reviewing the manuscript.
Thank you for your recognition of the article.
Kind regards,
Authors.
Author Response File: Author Response.pdf
Reviewer 3 Report
The paper titled Peak Carbon Dioxide Emissions Strategy Based on Grey Model between Carbon Emissions and Urban Spatial Expansion for Build-up Area is an interesting paper which discussed the relationship between carbon emission and urban expansion. However, there are many unsatisfied points, and there are lots of expression errors. Especially, as one important part of the research, the carbon emission measurement and prediction results are inaccurate, sorry I suggest it to be rejected.
Abstract:
1 The expressions about the data collection in the abstract suggested to be deleted, here only the methods and the results are suggested to be introduced in the abstract.
Introduction:
1 The literature reviews in the abstract suggested to be summarized and rewritten, there listed lots of previous study on land use and carbon emissions, it is suggested to summary the previous study into different categories to make them more clearly.
2 Second paragraph of introduction, “but the land use aspect has not been considered enough”, what the mean of land use aspect here? There has plenty of study on land use and carbon emission, why the land use aspect is not considered enough?
3 The main research aims and contents suggested to be expressed briefly at the end of Introduction.
Areas and research methods:
1 Part 2.1, “Changsha at the crossroad between the Chengdu-Chongqing Lining east-west”, the expression of the position of Changsha maybe error
2 The carbon emission data suggested to be added into the background of research area.
3 The expressions of the grey relational model is too redundant, the basic principles of the model are suggested to be introduced more concise.
Index calculation
1 The expressions of methods in this part, especially the formulas about the index calculation process are suggested to move to the method part.
2 Why the time nodes of 1949, 1979, 1996, 2003, 2008, 2013 and 2016 are selected to calculate the index and carbon emission in this part?
3 What are the means of Im, Is and Ig in the Table 2 and how they were calculated need to be clarify.
4 Part 3.1.3, the urban compactness calculation is shown in Table 2, however, there’s no information about the results in the table. The results of urban compactness calculation are suggested to be added.
5 The data of carbon emission from references cited in part 3.2.1 are not the research data in Changsha city, the data from the case study of other cities is not comparable with the facts in Changsha city and will cause the inaccuracy of the results.
6 There are no uncertainty test about the carbon emission prediction and the title of Table 5 is wrong.
Grey Correlation Analysis Results
1 The results of this part are suggested to be combined with the results of part 3, the title of these is suggested to be Results.
2 There only discussed the relationship between carbon emissions, urban land area and urban expansion. However, the specific of the results explanation and comparison of similar studies are lacked. Besides, what the results can be used for is also not pointed out in this part.
Discussion and conclusion
1 What the results can be used for, can the results help to raise policies on urban expansion carbon emission control is suggested to be added to the discussion.
2The conclusion is not just of the repeat of the results, besides, the significance and the shortcomings of the study also needed in this part.
Author Response
Dear Reviewer,
We are grateful for your time and effort on reviewing the manuscript. The new manuscript has been revised based on your comments and the corrections made are explained below.
Hopefully, this revised version would be received favorably.
We are looking forward to hearing from you.
Sincerely yours,
Authors
☆ ☆ ☆ ☆ ☆
The paper titled Peak Carbon Dioxide Emissions Strategy Based on Grey Model between Carbon Emissions and Urban Spatial Expansion for Build-up Area is an interesting paper which discussed the relationship between carbon emission and urban expansion. However, there are many unsatisfied points, and there are lots of expression errors. Especially, as one important part of the research, the carbon emission measurement and prediction results are inaccurate, sorry I suggest it to be rejected.
Abstract:
1 The expressions about the data collection in the abstract suggested to be deleted, here only the methods and the results are suggested to be introduced in the abstract.
Response:
Thank you so much for your suggestion. The expressions about the data collection in the abstract have been deleted. Abstract of the new modification you will read:
(P1 L12—29) Abstract: Urban spatial expansion affects almost every dimension of sustainable urban development. A good grasp of the relationship between urban spatial evolution and carbon emissions can be the key to urban spatial governance. As a central city in the central region and a national low-carbon pilot city, Changsha has a rapid expansion of construction land and growing carbon emissions. In this paper, four variable factors and five variable factors of carbon emission of the case city Changsha in 1979, 1996, 2003, 2008, 2013 and 2016. Based on the "double carbon" constraint target, the total carbon emissions, carbon emission intensity and per capita carbon emission constraint indices are forecasted until 2030. They are 87.29 million t-CO2, 0.45 t-CO2/CNY104, and 8.73 t-CO2/person, respectively. The scale of urban land is controlled at 889.61; The constraint indices of residential, commercial service land, industrial land and road square land scales are 231.3 km2, 143.88 km2, 150.17 km2 and 135.83 km2, respectively. The land expansion intensity, urban compactness and shortest travel distance constraint indices are 6.19, 0.236 and 96086.76 km, respectively. The results of this analysis can provide scientific guidance for the next step of territorial spatial master planning and low-carbon governance.
Introduction:
1 The literature reviews in the abstract suggested to be summarized and rewritten, there listed lots of previous study on land use and carbon emissions, it is suggested to summary the previous study into different categories to make them more clearly.
Response:
Thank you so much. The literature reviews have been rewritten. Research on carbon emissions associated with urban space focuses on this relationship particularly studying land use, urban spatial form, urban compactness, urban transportation and construction control. A comprehensive review of land use and carbon emissions is studied from three aspects: land-use carbon emission effects, land-use carbon emission accounting, and low-carbon land use policies. Throughout the revised manuscript, further details and relevant literatures have been added.
(P1 L40—P2 L61) The research on land use and carbon emissions mainly includes three aspects: land use carbon emission effect, land use carbon emissions accounting and low carbon land use policy. Dong, Y. [1] and Houghton, R.A. [2] studied the impact of land use change on carbon emissions; Wei, Y.R. [3], Niu, Y.W. [4], Xu, Z. [5], Zhang, Y. [6] considered the spatiotemporal pattern, spatial differentiation characteristics, spatial correlation and influencing factors, foucused on the impact of landout mode oncarbon emissions; Chang, Q. [7], Yan, C. [8], Yang, X. [9], Zhang, R.Q. [10] analysed the relationship between construction land expansion and carbon emissions or carbon emission intensity by building a Tapio decoupling model, a gray correlation model, a Kuznets curve model, and a coupling coordination model. The calculation of land use carbon emissions is conducted by using the model method[11-12], sample plot inventory method[13-14], remote sensing estimation method[15-17] and other calculation methods to study the change in carbon emissions with the dynamic change in land use. From the perspective of low-carbon land use policy, Glaeser, E.L. [18], Mathy, S. [19], Xi, F.M. [20], Yang, X.L. [21] and Zhou, L.Y. [22] considered the policy aspect of the subject, proposed a study of the relationship between land use control regulations and carbon emissions at the policy level.
2 Second paragraph of introduction, “but the land use aspect has not been considered enough”, what the mean of land use aspect here? There has plenty of study on land use and carbon emission, why the land use aspect is not considered enough?
Response:
Thank you so much for your suggestion. The expression "but the land use aspect has not been considered enough" is indeed not clearly expressed, and in terms of land use, it refers to the classification and binding control of various types of urban construction land. This phrase has been reformulate according to your reminder.
(P3 L116—L119)There are also many documents in the recent literature covering the relationship between land use changes and carbon emissions. When implemented at the planning level, most of them propose carbon reduction constraint control policies in terms of population size and land use scale, but there is no classified restrictive control on various types of urban construction land.
3 The main research aims and contents suggested to be expressed briefly at the end of Introduction.
Response:
Yes, it is a good suggestion, corrections are made accordingly.
(P3 L98—L128) Previous studies on the correlation between carbon emissions and urban spatial expansion are abundant, but they mostly start from a single factor reflecting the spatial characteristics, and study the relationship between between a single indicator and total carbon emissions or carbon emission intensity. There is still insufficient evidence to establish the relationship between multiple factors. There are also many documents in the recent literature covering the relationship between land use changes and carbon emissions. When implemented at the planning level, most of them propose carbon reduction constraint control policies in terms of population size and land use scale, but there is no classified restrictive control on various types of urban construction land. In this paper, on the one hand, a nonequally spaced approach is applied, which serves to gradually optimize the Grey relational model and carry out the correlation analysis between carbon emissions and urban spatial expansion. On the other hand, the future trend of urban construction land and carbon emissions is predicted. Considering the target of the "double carbon" constraint in China, we propose a carbon emission constraint index, an urban land scale constraint index and an urban spatial expansion constraint index. This proposition provides a scientific guidance for future overall territorial planning and low-carbon policy.
Areas and research methods:
1 Part 2.1, “Changsha at the crossroad between the Chengdu-Chongqing Lining east-west”, the expression of the position of Changsha maybe error1
Response:
Thank you for your reminder. Sorry, this was an error. This phrase (P3 L137—L143) has been reformulate according to your suggestion.
(P3 L137—L143) Located in the middle of China, Changsha is an important central city in the middle reaches of the Yangtze River, one of the pilot areas for the comprehensive reform of the "Two-oriented Society", an important grain production base in China, and an important node city in the middle reaches of the Yangtze River City cluster and the Yangtze River Economic Belt, playing an increasingly prominent role in the national development strategy. In recent years, Changsha's vigorous economic development has brought about the rapid outwards expansion of the city.
2 The carbon emission data suggested to be added into the background of research area.
Response:
Yes, that is a good suggestion. Accordingly, more carbon emission data for Changsha city and Hunan province has been added to the background of research area. From the revised manuscript you will read:
(P4 L151—L155) Nevertheless, Changsha …… According to the energy statistics yearbook data of Hunan Province, from 2005 to 2015, the total energy consumption of Changsha increased from 1851.785 t-ce to 9709.27 t-ce. Compared with the total energy consumption of Hunan Province increasing from 4455.207 t-ce to 15468.61 t-ce, the growth rate of Changsha is far higher than that of Hunan Province.
3 The expressions of the grey relational model is too redundant, the basic principles of the model are suggested to be introduced more concise.
Response:
Thank you very much for your suggestion. This section has been briefly described with references to specific algorithms. (See P4 L173—P6 L214)
Index calculation:
1 The expressions of methods in this part, especially the formulas about the index calculation process are suggested to move to the method part.
Response:
Thank you so much for your advice. the formulas about the index calculation process have been moved to the method part (Part 2. Areas and Research Methods). So, the original manuscript “3.1 Urban Space Indicators” has been changed into “2.3 Urban Space Indicators” in revised manuscript.
2 Why the time nodes of 1949, 1979, 1996, 2003, 2008, 2013 and 2016 are selected to calculate the index and carbon emission in this part?
Response:
Thank you very much for your question. This paper studies the relationship between urban spatial expansion and carbon emissions, and spatial expansion is mainly represented by land use expansion. Changsha has revised the urban master plan at six time nodes in 1979, 1996, 2003, 2008, 2013 and 2016 respectively. Very accurate land use data and other data required for urban spatial expansion indicators can be extracted from the current land use map of the urban master plan. This is also explained in the background of the study area. (P3 L143—L146)
3 What are the means of Im, Is and Ig in the Table 2 and how they were calculated need to be clarify.
Response:
Thank you so much for your question. This was a negligent. The meaning represented by i is forgotten to be illustrated in the formula. (P6 L221) I is the land expansion intensity index; Ib and Ia are the land use areas at the end and the beginning respectively; H is the build-up land area; i is the area of each type of land, it refers to residential land (R), commercial service land (A and B), industrial land (M), road and transportation land (S), public green space (G), respectively. Date for the land use areas at the end and the beginning are presented in Table1. (P8 L263) Therefore, according to Formula 1, we can calculate the land expansion intensity indicators of various types of land, as shown in Table 2. IH, IR, IA&B, IM, IS and IG in Table 2 represent the expansion intensity indexes of various types of land use respectively. Of which，Is represents the road and transportation land expansion intensity index, Ig represents the public green land expansion intensity index.
4 Part 3.1.3, the urban compactness calculation is shown in Table 2, however, there’s no information about the results in the table. The results of urban compactness calculation are suggested to be added.
Response:
Thank you for your reminder. Sorry, this was an error, in fact the results of urban compactness calculation is shown in Table 3. Corrections are made accordingly (P7 L248).
5 The data of carbon emission from references cited in part 3.2.1 are not the research data in Changsha city, the data from the case study of other cities is not comparable with the facts in Changsha city and will cause the inaccuracy of the results.
Response:
Thank you so much for your question. This was a misunderstanding, in face the data of carbon emission from references cited in part 3.2.1 are the research data in Changsha city. In another paper by the team members (ref. [56]), carbon emissions results from 1949 to 2016 in Changsha city have been calculated based on a System dynamics method.
(P10 L296—L300) The system dynamics model calculates carbon emissions from the energy consumption and the portion of carbon absorbed by greenery in five systems: residential (FRes), commercial services(FCom), industrial (FInd), transportation (FTra), and green space(FG) carbon sinks. Total carbon emissions are equal to the difference between emissions and absorption. Based on this, the model is optimized by Liu, L.Y. [50]. And the city of Changsha was used as a case study for the calculation of urban carbon emissions. The calculation results are shown in Table 4.
6 There are no uncertainty test about the carbon emission prediction and the title of Table 5 is wrong.
Response:
Thank you for your reminder. Sorry, the title of Table 5 is wrong. We have changed the “projections” into the “prediction”. (see Table 5)
Grey Correlation Analysis Results
1 The results of this part are suggested to be combined with the results of part 3, the title of these is suggested to be Results.
Response:
Yes, that is a good suggestion. Accordingly, the structure of the article has been adjusted, the results of the part 4 have be combined with the results of part 3.
2 There only discussed the relationship between carbon emissions, urban land area and urban expansion. However, the specific of the results explanation and comparison of similar studies are lacked. Besides, what the results can be used for is also not pointed out in this part.
Response:
Thank you so much for your suggestion. The results explanation, research significance and comparison of similar studies have been added in the discussion and conclusion.
(P13 L401—L404) The nonequidistant stepwise optimization GM (1,1) model is used to predict the future carbon emissions of cities and calculate the correlation between urban carbon emissions and the urban expansion index.
Road traffic land itself does not produce significant carbon emissions, but the increase in residential land and commercial land caused by the improvement in road and transportation facilities is the main reason for the increase in carbon emissions.
(P14 L449—L451) A coordinated development between urban land use and public transportation, particularly the promotion of rail transit to guide land development and reduce traffic trips, is the first strategy that needs to be considered for the reduction of carbon emissions.
(P14 L444—L448) Based on the grey correlation analysis of multiple factors, the calculation of the constraint control value of urban space expansion can provide policy guidance for the next round of spatial optimization of the city- and county-level land space master plan and can also develop more precise constraint indicators to achieve the national 2030 carbon peak and carbon neutral policy goals.
(P14 L52-458) The rapid urbanization and expansion of urban land is a necessity that continuously increases total carbon emissions and per capita carbon emissions. The goal of carbon peaking and carbon neutrality is not simply to limit urban expansion, nor is it simply to delineate urban spatial growth boundaries and control the total scale of construction lands. It should be more about the optimization of the urban space from the inside and change the urban development model from "incremental development" to "stock development" to reduce the disorderly expansion in favor of compact spatial development.
Discussion and conclusion：
1 What the results can be used for, can the results help to raise policies on urban expansion carbon emission control is suggested to be added to the discussion.
Response:
Thank you so much for your suggestion. The discussion section have been modified according to your suggestion.
(P14 L444—L448) Based on the grey correlation analysis of multiple factors, the calculation of the constraint control value of urban space expansion can provide policy guidance for the next round of spatial optimization of the city- and county-level land space master plan and can also develop more precise constraint indicators to achieve the national 2030 carbon peak and carbon neutral policy goals.
2 The conclusion is not just of the repeat of the results, besides, the significance and the shortcomings of the study also needed in this part.
Response:
Thank you so much for your suggestion. The conclusion section have been modified according to your suggestion, and the significance and main limitations of the study have been specified.
(P14 L444—L448) Based on the grey correlation analysis of multiple factors, the calculation of the constraint control value of urban space expansion can provide policy guidance for the next round of spatial optimization of the city- and county-level land space master plan and can also develop more precise constraint indicators to achieve the national 2030 carbon peak and carbon neutral policy goals.
(P15 L460—L465) Although the grey system theoretical model introduced in this paper has high simulation accuracy, different types of grey models need to be selected for different types of data, and the quantitative relationship expression is more complex, so MATLAB software is needed to assist in analysis and research. Therefore, the correlation model of urban spatial expansion and carbon emissions is worth further deepening to select a more appropriate quantitative model to fit.
Author Response File: Author Response.pdf
Round 2
Reviewer 3 Report
The revisions made in the re-submitted manuscript can well reply the questions raised before and I also noticed the system dynamic model cited in another paper published by the research group. However, there still exist some doubts with the carbon emission calculated with the system dynamic model in 3.2.1. Is there any model check to check the results output from the SD model and how to prove that the results from the model are accurate. I suggested the manuscript to be minor revised.
Author Response
Dear Reviewer,
We are grateful for your time and effort on reviewing the manuscript. The new version of the manuscript has been revised based on your comments and the corrections made are explained below.
Hopefully, this revised version would be received favorably.
We are looking forward to hearing from you.
Sincerely yours,
Authors
☆ ☆ ☆ ☆ ☆
The revisions made in the re-submitted manuscript can well reply the questions raised before and I also noticed the system dynamic model cited in another paper published by the research group. However, there still exist some doubts with the carbon emission calculated with the system dynamic model in 3.2.1. Is there any model check to check the results output from the SD model and how to prove that the results from the model are accurate. I suggested the manuscript to be minor revised.
Response:
Thank you so much for your question. We have already done the validation as early as when we performed the system dynamics modeling. Therefore, in the newly revised manuscript, we added the validation of the SD model output results. From the revised manuscript you will read:
(P7 L227—L243) The system dynamics model defines five corresponding su-models, namely, a residential sub-model (FRes), a commercial services sub-model (FCom), an industrial sub-model (FInd), a transportation sub-model (FTra), and a carbon sequestration sub-model (FG). Total carbon emissions are obtained as the difference between emissions and absorption. Based on this, Liu, L.Y. [56] optimized the model. The carbon emission of the city was calculated by taking Changsha city as an example. This paper analyzes the interactions between carbon emissions and several indicators, namely urban population, land expansion, and economic growth, as well as the complex relationships covered by the results. The data of three indicators in the Statistical Yearbook in 1979, 1996, 2003, 2008, 2023, and 2016 were extracted to verify the SD model outputs. The comparison between the actual values and the model outputs is shown in Figure 5. The results show that the error between them is small, so the model is feasible for urban carbon emission assessment. The results calculated using the system dynamics are shown in Table 4.
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Figure 5. Model checking |
Author Response File: Author Response.pdf
