Impact of Urbanisation on the Spatial and Temporal Evolution of Carbon Emissions and the Potential for Emission Reduction in a Dual-Carbon Reduction Context

Round 1

Reviewer 1 Report

Good topic! However, multiple scenarios may be helpful for your study with the consideration of R&D, subsidies, and carbon trading.

Author Response

Good topic! However, multiple scenarios may be helpful for your study with the consideration of R&D, subsidies, and carbon trading.

 

Reply: Thanks very much for your kind suggestion. The revision is marked in red in the manuscript.

 

Based on the background of urbanisation and taking urban agglomerations as the research object, the study investigates the spatial and temporal mechanisms and dynamics of carbon emissions through the construction of carbon emission models, the identification of influencing factors and the processing of spatial data, and proposes relevant measures for carbon emission control mechanisms. The model idea proposed in the study can effectively provide new perspectives and ideas for the differentiated formulation of emission reduction policies, and the government ought to focus more on the dynamic changes of urbanised carbon emissions in future development, so as to release the potential of urban emission reduction.

 

Especially, for the prediction of carbon emissions, the research will process the original data series to obtain the baseline series, and design the high-carbon scenario and the low-carbon scenario to predict the data. The design of these three scenarios has been able to effectively achieve the prediction and analysis of carbon emissions, and currently many mathematicians have conducted empirical analysis from these three aspects.

Reviewer 2 Report

The authors should add references of the literature review that support the variables under analysis,  and should additionally focus on the objective of the research. 

The authors should add comparative models in other countries. 

The Authors should include the data from which the variables are composed.

The authors should complement the references. 

The regression model should be reviewed in terms of the variables, and the supposes of use of the model.  

Author Response

Thanks very much for your kind suggestion. The revision is marked in blue in the manuscript.

 

1. The authors should add references of the literature review that support the variables under analysis, and should additionally focus on the objective of the research.

Reply: Thanks very much for your kind suggestion. The research supplements and perfects the original literature content by adding the literature content of relevant variable analysis. For example:

Wang Q scholar explored the nonlinear impact of population aging on carbon emissions with the help of panel threshold regression (PTR) model, and set explanatory variables, threshold variables, and control variables. The results showed that the intensification of population aging caused the inverse U-shaped correlation between urbanization and carbon emissions of high-income groups^[15]. Xu B scholar carried out empirical analysis of carbon dioxide in heavy industry with the help of geographical weighted regression model, and put forward suggestions on carbon reduction measures for regional cities according to different development conditions ^[16]. Qin H scholars conducted a dynamic analysis of the driving process of China's urban carbon dioxide emissions with the help of geographical weighted regression, and divided the impact degree of the driving factors by two-step clustering. The results showed that the population density and the proportion of the secondary industry were positively correlated with carbon dioxide emissions, while the number of buses per 10000 people was negatively correlated with carbon dioxide emissions, and the spatial heterogeneity of different influencing factors was more prominent^[17]. Xu G and his research team introduced the nonlinear autoregressive model (NARX) of external input into the problem of carbon emission peak, predicted carbon dioxide under different scenarios with the help of nonlinear artificial neural network, and ranked the factors affecting carbon dioxide with the help of average impact value, and put forward relevant suggestions and contents for carbon reduction^[18].

The literatures:

[15]Wang Q, Wang L. The nonlinear effects of population aging, industrial structure, and urbanization on carbon emissions: A panel threshold regression analysis of 137 countries[J]. Journal of Cleaner Production, 2021, 287: 125381.

[16]Xu B, Lin B. Investigating the differences in CO2 emissions in the transport sector across Chinese provinces: Evidence from a quantile regression model[J]. Journal of Cleaner Production, 2018, 175: 109-122.

[17]Qin H, Huang Q, Zhang Z, et al. Carbon dioxide emission driving factors analysis and policy implications of Chinese cities: Combining geographically weighted regression with two-step cluster[J]. Science of The Total Environment, 2019, 684: 413-424.

[18]Xu G, Schwarz P, Yang H. Determining China's CO2 emissions peak with a dynamic nonlinear artificial neural network approach and scenario analysis[J]. Energy Policy, 2019, 128: 752-762.

2. The authors should add comparative models in other countries.

Reply: Thanks very much for your kind suggestion. The research realizes the empirical analysis of carbon emissions through the construction of anthropogenic carbon emissions measurement model and the application of geographical weighted regression model. And the spatial analysis of carbon emissions is mainly verified by regression model, which can explain the interpretation relationship and spatial correlation between various variables

3. The Authors should include the data from which the variables are composed.

Reply: Thanks very much for your kind suggestion. The research has added relevant content data, such as Tables 2 and 3.

4. The authors should complement the references.

Reply: The research has supplemented some appropriate documents on the basis of the original documents. As follows in the revised paper:

[15]Wang Q, Wang L. The nonlinear effects of population aging, industrial structure, and urbanization on carbon emissions: A panel threshold regression analysis of 137 countries[J]. Journal of Cleaner Production, 2021, 287: 125381.

[16]Xu B, Lin B. Investigating the differences in CO2 emissions in the transport sector across Chinese provinces: Evidence from a quantile regression model[J]. Journal of Cleaner Production, 2018, 175: 109-122.

[17]Qin H, Huang Q, Zhang Z, et al. Carbon dioxide emission driving factors analysis and policy implications of Chinese cities: Combining geographically weighted regression with two-step cluster[J]. Science of The Total Environment, 2019, 684: 413-424.

[18]Xu G, Schwarz P, Yang H. Determining China's CO2 emissions peak with a dynamic nonlinear artificial neural network approach and scenario analysis[J]. Energy Policy, 2019, 128: 752-762.

5. The regression model should be reviewed in terms of the variables, and the supposes of use of the model.  

Reply: Tables 2 and tables3 have been added to the study to test that traffic carbon emissions are the main influencing factors.

Reviewer 3 Report

What carbon emissions are the authors talking about? Fine carbon in the form of soot, carbon dioxide, carbon monoxide, or a combination of them?

What cities are described, what are the industrial facilities there - sources of emissions? It should be proved that transport emissions are predominant in these agglomerations.

In Figure 1, vehicles are named as anthropogenic sources. Either you need to add industrial objects, or rename the drawing.

What is the duration of the period in Figure 2?

How do the figures in Figure 2 compare to carbon emissions? Based on what calculations?

In table 2, different numbers of indicators correspond to different years. Explain.

In figure 3, which cities are included in this emission scheme?

In table 5, the high-carbon and low-carbon scenarios were calculated using what formulas? If this is a forecast, then the data should start from 2023.

I would like to see more recommendations for optimizing the transport structure of this agglomeration in order to reduce transport emissions.

Author Response

Thanks very much for your kind suggestion. The revision is marked in green in the manuscript.

1. What carbon emissions are the authors talking about? Fine carbon in the form of soot, carbon dioxide, carbon monoxide, or a combination of them?

Reply: Thanks very much for your kind suggestion. The main carbon emission studied in the study is fine carbon in mixed form.

2. What cities are described, what are the industrial facilities there - sources of emissions? It should be proved that transport emissions are predominant in these agglomerations.

Reply: Thanks very much for your kind suggestion. The cities described are mainly the Beijing-Tianjin-Hebei urban agglomeration. The industrial facilities are heavy industrial products and energy supply, and the main source of emissions is traffic emissions. Relevant data content was added in the revised paper. At the same time, Beijing-Tianjin-Hebei urban agglomeration has a high level of economic development, but its urban population density and limited land area are still issues to be considered for its further development. The changes in carbon emissions caused by the adjustment of industrial structure and economic level are relatively small.

3. In Figure 1, vehicles are named as anthropogenic sources. Either you need to add industrial objects, or rename the drawing.

Reply: Thank you very much for your suggestions. The study has revised the original drawing.

4. What is the duration of the period in Figure 2?

Reply: Thanks very much for your kind suggestion. The time in Figure 2 is the number of tracking periods. The length of time indicated by the specific number of periods should be analyzed according to the specific corresponding situation. The purpose of the study is to explore the relationship between carbon emissions and various variables from a macro perspective.

The tracking period of the response function is set to five years.

5. How do the figures in Figure 2 compare to carbon emissions? Based on what calculations?

Reply: Thanks very much for your kind suggestion. The function curve in Figure 2 (a) represents the impulse response function, which represents the impulse response between carbon emissions and the governance of a variable, that is, the impact of carbon emissions on the current and future values of endogenous variables after a unit of impact, and can also be interpreted as the degree of impact of carbon emissions on variables. The higher the value, the greater the impact of carbon emissions, indicating the variable relationship under the time series. The definition of this function is evolved from the vector autoregressive model. The contribution value in Figure (b) is the improvement of the effect of corresponding carbon emissions caused by the increase of variable investment, and the governance strength of the variable has a certain explanatory power on the fluctuation of carbon emissions.

6. In table 2, different numbers of indicators correspond to different years. Explain.

Reply: Thanks very much for your kind suggestion. Table 2 analyzes the spatial correlation of the two modes of transportation in different years. The reason is that the spatial differences of freight transport and passenger transport in different years and the positive and negative correlation will reflect the dynamic variability of the two modes of transportation, providing a reference basis for the subsequent adjustment of the traffic pattern. Moreover, the change of spatial value in different years can also reflect the change of traffic carbon emissions.

7. In figure 3, which cities are included in this emission scheme?

Reply: Thanks very much for your kind suggestion. Figure 3 is mainly a study of the spatial pattern of traffic carbon emissions in the Beijing-Tianjin-Hebei urban agglomeration, which includes Beijing, Tianjin and Shijiazhuang, Tangshan, Baoding, Qinhuangdao, Langfang, Cangzhou, Chengde and Zhangjiakou in Hebei Province.

8. In table 5, the high-carbon and low-carbon scenarios were calculated using what formulas? If this is a forecast, then the data should start from 2023.

Reply: Thanks very much for your kind suggestion. The high carbon and low carbon scenarios are based on the average weakening of the original data series, the construction of vector matrix, and the least squares solution to achieve the prediction and evaluation of the data series with the help of GM (1,1) principle. It mainly refers to the formula (4-5) in the reference method part. The selected benchmark data series in the study is the cumulative processing of the data from 2008 to 2020. The prediction data series is obtained on the basis of the original benchmark series, which reflects the overall trend of change. If the data from 2023 is directly selected for prediction, it may lead to the problem of expansion of the original data error, and the prediction accuracy is difficult to guarantee.

9. I would like to see more recommendations for optimizing the transport structure of this agglomeration in order to reduce transport emissions.

Reply: Thanks very much for your kind suggestion. The study supplemented the adjustment of transportation structure and the content of carbon reduction suggestions.

Round 2

Reviewer 2 Report

The authors must add the data.

Author Response

The authors must add the data.
Reply: Thank you very much for your suggestions. The research has supplemented and improved the relevant information of the variable data (especially in Table 1, 2 and 3) in the revised manuscript to make theresults more smooth and logical.
In addition, the whole manuscript was checked to avoid grammatical errors and misspellings.

Author Response File: Author Response.docx