Examining the Overall and Heterogeneous Impacts of Urban Spatial Structure on Carbon Emissions: A Case Study of Guangdong Province, China

Round 1

Reviewer 1 Report

Interesting article, needs minor changes, comments:

1)     In the introduction, the authors wrote: This paper utilizes urban-level panel data from 2000 to 2020 in Guangdong Province ..... However, in point 2.2. authors wrote "Data for five periods (2000, 2005, 2010, 2015, and 2020) were collected".

It should be specified in the introduction that data from five periods of 5 years are analyzed from the period 2000-2020.

2)     “Missing data were supplemented using linear interpolation” – rows 187-188. Explain why authors used linear interpolation?

3)     In 2.3.3. and equation 2) the authors should explain why the variables are as ln (logarithm)

4)     In Table 3. Regression results of variables with CO2 emission - there are numerical results, complete the text with these numbers (interpretation).

5)     Conclusions - the text can be extended.

Native-speaker text verification recommended

Author Response

Response to Reviewer 1:

Interesting article, needs minor changes, comments:

R: Thank you.

1. In the introduction, the authors wrote: This paper utilizes urban-level panel data from 2000 to 2020 in Guangdong Province ..... However, in point 2.2. authors wrote "Data for five periods (2000, 2005, 2010, 2015, and 2020) were collected". It should be specified in the introduction that data from five periods of 5 years are analyzed from the period 2000-2020.

R: Thank you for your valuable comments. We have revised the relevant content. (lines113-114)

2. “Missing data were supplemented using linear interpolation” – rows 187-188. Explain why authors used linear interpolation?

R: Thank you for your comments. Based on the latest administrative division plan in 2022, this study aggregated the data from the county level to the prefecture level. For the missing individual data, the mean values are interpolated based on the neighboring years before and after to reduce the impact on the overall model fitting effect. We have amended the relevant content. (lines201-203)

3. In 2.3.3. and equation 2) the authors should explain why the variables are as ln (logarithm)

R: Thank you for your comments. In general, the main purposes of logarithmic transformation are (1) the estimated coefficients can be interpreted as elasticities, which are generally used in economics models; (2) it reduces the degree of sample heteroskedasticity; and (3) it reduces the volatility of the variables to match the level of volatility of other variables. In order to reduce the variability among the data and to facilitate subsequent regression calculations, we standardize all variables by natural logarithms. We added relevant content on lines 246-248.

4. In Table 3. Regression results of variables with CO₂ emission - there are numerical results, complete the text with these numbers (interpretation).

R: Thank you for your valuable comments. We further combined the values in Table 3 when describing the overall relationship between urban structure and carbon emissions in Sec.3.3.（lines373-411）

5. Conclusions - the text can be extended.

R: Thank you for your comments. We expanded the conclusions appropriately. (lines557-560,570)

Author Response File: Author Response.docx

Reviewer 2 Report

This is a nicely written paper investigating relationships between urban morphology and local aggregate carbon emissions, paying particular attention to total area of urbanization and to land patches. I found myself wanting to hear more about three dimensional aspects of the problem (which  presumably affect development potential as well as carbon emissions and concentrations) and was pleased to see this discussed later in the paper as a topic for future research.   It is somewhat frustrating that the authors do not discuss the underlying sources of carbon emissions (industry, electricity production, building heating and cooling,  transportation, etc.) for the cities and region studied, since interventions from a policy perspective usually have to address sources and not just land development patterns, but the authors do get to this briefly toward the end of the paper. I hope that their future research will start by looking at more detailed emissions inventories!

Author Response

Response to reviewer 2:

This is a nicely written paper investigating relationships between urban morphology and local aggregate carbon emissions, paying particular attention to total area of urbanization and to land patches. I found myself wanting to hear more about three dimensional aspects of the problem (which presumably affect development potential as well as carbon emissions and concentrations) and was pleased to see this discussed later in the paper as a topic for future research. It is somewhat frustrating that the authors do not discuss the underlying sources of carbon emissions (industry, electricity production, building heating and cooling, transportation, etc.) for the cities and region studied, since interventions from a policy perspective usually have to address sources and not just land development patterns, but the authors do get to this briefly toward the end of the paper. I hope that their future research will start by looking at more detailed emissions inventories!

R: Thank you very much for your valuable comments and suggestions for future research.

Author Response File: Author Response.docx

Reviewer 3 Report

This study explores the impacts of urban spatial structure on carbon emissions using the city data of Guangdong province. The topic is interesting but the current manuscript needs serious revision.

1. The relation between the urban spatial structure and carbon emissions has been stated in many existing literature, see Zhu et al. (2022).  What is new here?

2. The introduction needs revision by adding results, contributions and policy implications.

3. The description of method (sec. 2.3.3) can be improved. 

4. As the method used in Section 2.3.3 can not identify the causal effects. This section can not identify "impact effects"

Reference:

Kai Z,Manya T,Yingcheng L. Did Polycentric and Compact Structure Reduce Carbon Emissions? A Spatial Panel Data Analysis of 286 Chinese Cities from 2002 to 2019. Land,2022,11(2).

Author Response

Response to reviewer 3:

This study explores the impacts of urban spatial structure on carbon emissions using the city data of Guangdong province. The topic is interesting but the current manuscript needs serious revision.

1. The relation between the urban spatial structure and carbon emissions has been stated in many existing literature, see Zhu et al. (2022). What is new here?

R: Thank you for your valuable comments. The reference states that “Within the literature, indicators that are often used to represent urban forms and urban spatial structure include population density, urban size, urban compactness, and urban polycentricity”. Similarly, Zhu's article focuses on Urban Polycentricity and Compactness when measuring spatial structure. The reference used two indicators, Urban Polycentricity and Compactness, to measure urban spatial structure. However, this paper is based on the theory of landscape ecology and measures urban spatial structure from three dimensions: scale, shape, and Morphological structure. The six indicators finally selected in this paper (Total Area, Maximum Patch Index, Average Patch Perimeter Area Ratio, Average Shape Index, Agglomeration Index, and Patch Cohesion Index) can be more conducive to combining with spatial constraint indicators in urban planning practice to further guide sustainable urban development.

In terms of methodology, the spatial Durbin model (SDM) differs from the Geographically and Temporally Weighted Regression (GTWR) model used in this paper. The SDM mainly explores the influence of influencing factors on the region or neighboring provinces, and the correlation of influencing factors. The GTWR, on the other hand, mainly explores the spatial heterogeneity of the influencing factors, which is one of the focuses of this paper. As described in the introduction of this paper, most existing studies have focused on mega-cities or independent urban areas such as capital cities, neglecting large-scale provincial analysis and heterogeneous research on the carbon emission determinants of cities within them. Research conclusions based on provincial divisions with urban clusters as the main units, representing more advanced forms of urbanization, would be more conducive to decision-makers in adjusting spatial strategic policies and efficiently promoting carbon reduction. Combined with your comments, this paper expresses the innovative points more clearly. (lines100-103)

2. The introduction needs revision by adding results, contributions and policy implications.

R: Thank you for your comments. Perhaps due to a difference in writing paradigms, this paper does not add results to INTRODUCTION. Following your comments, we have added results, contributions and policy implications. (lines119-125)

3. The description of method (sec. 2.3.3) can be improved.

R: Thank you for your comments. We have made the necessary additions to the section to more clearly express the modeling principles and formulas. (lines246-270)

4. As the method used in Section 2.3.3 can not identify the causal effects. This section can not identify "impact effects"

R: Thank you for your valuable comments. This paper analyzes the comprehensive relationship between urban spatial structure and socio-economic factors on carbon emissions, and their spatiotemporal variations by using a two-way fixed effects model and a geographically and temporally weighted regression model, respectively. Existing literature suggests that urban form or urban spatial structure can influence carbon emissions through a number of indirect mechanisms, such as transportation and travel, the urban heat island effect, and household-level carbon emissions. It is therefore reasonable to use the term "effect" of the former on the latter when describing the relationship between urban spatial structure and carbon emissions. This description is allowed in some journals [1-3]. In order to avoid misuse of the concepts of "causal effects" and "impact effects", the title and section descriptions of Sec.2.3.3 have been modified.

[1] Wang, S.; Wang, J.; Fang, C.; Li, S. Estimating the impacts of urban form on CO2 emission efficiency in the Pearl River Delta, China. Cities 2019, 85, 117-129.

[2] Zhu, K.; Tu, M.; Li, Y. Did polycentric and compact structure reduce carbon emissions? A spatial panel data analysis of 286 Chinese cities from 2002 to 2019. Land 2022, 11, 185.

[3] Li Z, Wu H, Wu F. Impacts of urban forms and socioeconomic factors on CO2 emissions: A spatial econometric analysis[J]. Journal of Cleaner Production, 2022, 372: 133722.

Author Response File: Author Response.docx

Round 2

Reviewer 3 Report

Thank you for your efforts