A Prediction Method of GHG Emissions for Urban Road Transportation Planning and Its Applications
Round 1
Reviewer 1 Report
Dear Authors
First of all I would like to tell you that your research work is interesting and can provide solutions to reduce the emission of greenhouse gases in cities.
I think it would be convenient to introduce some improvements in your article so that it can be referenced in the future by researchers who are researching in the same field.
I think it would be interesting in the introduction part to introduce some reference to the compliance of the 17 SDG as well as to introduce that one of the requirements to consider the future paradigm of green and smart cities should be the reduction of emissions coming from transport.
The conclusion part should be further developed, I think it would be interesting if the authors could make more proposals for improvement and introduce more future lines of research in this important field of work related to transport and sustainability.
Below I propose a series of articles that I think you can use to find ideas, reflections and hypotheses to help you improve your interesting research.
I would like to send you my best wishes to improve your work.
Proposal of keywords
Smart cities, negative transport externalities
Martín Martín, J. M., Guaita Martínez, J. M., Molina Moreno, V., & Sartal Rodríguez, A. (2019). An analysis of the tourist mobility in the island of Lanzarote: Car rental versus more sustainable transportation alternatives. Sustainability, 11(3), 739.
Rubio, E. V., Rubio, J. M. Q., & Moreno, V. M. (2017). Environmental fiscal effort: Spatial convergence within economic policy on taxation. Revista de economía mundial, (45), 87-100.
Janik, A.; Ryszko, A.; Szafraniec, M. Greenhouse Gases and Circular Economy Issues in Sustainability Reports from the Energy Sector in the European Union. Energies 2020, 13, 5993.
Ruiz-Guerra, I., Molina-Moreno, V., Cortés-García, F. J., & Núñez-Cacho, P. (2019). Prediction of the impact on air quality of the cities receiving cruise tourism: the case of the Port of Barcelona. Heliyon, 5(3), e01280.
Helfaya, A.; Whittington, M. Does designing environmental sustainability disclosure quality measures make
a difference? Bus. Strategy Environ. 2019, 28, 525–541.
Author Response
Response to Reviewer 1 Comments:
We appreciate your time and help in reviewing our manuscript. Thank you very much for your affirmation of our work in this paper and many thanks for your detailed comments. We have revised the paper very carefully according to your suggestions, and all the modified contents have been marked in red font in the updated manuscript.
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General comments:
First of all, I would like to tell you that your research work is interesting and can provide solutions to reduce the emission of greenhouse gases in cities. I think it would be convenient to introduce some improvements in your article so that it can be referenced in the future by researchers who are researching in the same field.
|
Response:
We appreciate your time and help in reviewing our manuscript. Thank you very much for your affirmation of our work in this paper and many thanks for your detailed comments. We have revised the paper very carefully according to your suggestion, and all the modified contents have been marked in red font in the updated manuscript.
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1. I think it would be interesting in the introduction part to introduce some reference to the compliance of the 17 SDG as well as to introduce that one of the requirements to consider the future paradigm of green and smart cities should be the reduction of emissions coming from transport. |
Response:
Many thanks for your suggestions.
We have improved the introduction part according to your valuable suggestions. The modified contents have been marked in red font in the updated manuscript as follows:
In 2015, 17 Sustainable Development Goals (SDGs) were adopted by 193 countries for sustainable development over the coming years. All countries are working to develop some policies to achieve equitable compliance with the SDGs at global level in 2030[1]. Each organization evaluates the SDGs to accurately assess the relative priority of each of the 17 SDGs to its core organizational objectives[2]. Climate action is always identified as a priority SDG to achieve sustainable development[3]. In the whole world, reducing carbon emissions in the transportation sector is always a “core objective” to fight against climate change. How to effectively control traffic negative externalities has always been a research hotspot.
As an essential part of urban traffic, road traffic undertakes the vast majority of traffic travel tasks. It plays an important role in alleviating urban traffic pressure and improving citizens' travel environment. The increasing vehicle usage has brought about a sharp increase in GHG emissions of vehicles, which curbs the low-carbon transportation development in China. Road traffic has become a key area for energy-saving and pollutant reduction in China. According to the IEA statistics of International Energy Agency in 2019[4], China's total carbon emissions in the past two years have exceeded 10 billion tons, of which the emissions in the transportation sector have reached about 10 percent of China's total emissions or 1 billion tons. Most of the emissions from the transportation sector were attributed to road transport with approximately 80 percent of all emissions and a total of 800 billion tons. The carbon emissions of road traffic have seriously exceeded the self-purification capacity of the natural environment. As a highly concentrated place for residents to produce and live, the destruction of the ecological environment will undoubtedly cause serious harm to the physical, psychological, and life of urban residents. One of the requirements to consider the future paradigm of green and smart cities should be the reduction of emissions coming from transport[5], [6]. Current rate of progress in China is insufficient to achieve equitable compliance with the SDGs at national-level in 2030. Therefore, how to achieve sustainable development of road transportation (low-carbon road transportation) has become a considerable challenge for transportation planning designers[7], [8].
[1] Rubio, E. V., Rubio, J. M. Q., & Moreno, V. M., “Environmental fiscal effort: Spatial convergence within economic policy on taxation,” Revista de economía mundial, no. 45, pp. 87–100, 2017.
[2] Helfaya, A.; Whittington, M., “Does designing environmental sustainability disclosure quality measures make a difference ?,” Bus. Strategy Environ, vol 28, pp. 525–541, 2019.
[3] Janik, A.; Ryszko, A.; Szafraniec, M., “Greenhouse Gases and Circular Economy Issues in Sustainability Reports from the Energy Sector in the European Union,” Energies, vol. 13, pp:5993, 2020.
[4] F. Birol, “CO2 emmisions from fuel combustion,” pp. 1–165, 2019.
[5] Martín Martín, J. M., Guaita Martínez, J. M., Molina Moreno, V., & Sartal Rodríguez, A. “An Analysis of the Tourist Mobility in the Island of Lanzarote : Car Rental Versus More Sustainable Transportation Alternatives,” Sustainability, vol. 11, no. 3, pp. 739, 2019.
[6] Ruiz-Guerra, I., Molina-Moreno, V., Cortés-García, F. J., & Núñez-Cacho, P., “Prediction of the impact on air quality of the cities receiving cruise tourism : the case of the Port of Barcelona,” Heliyon, vol. 5, no. 3, 2019.
[7] K. Nakamura and Y. Hayashi, “Strategies and instruments for low-carbon urban transport: An international review on trends and effects,” Transp. Policy, vol. 29, pp. 264–274, 2013.
[8] C. Brand, J. Anable, and M. Tran, “Accelerating the transformation to a low carbon passenger transport system: The role of car purchase taxes, feebates, road taxes and scrappage incentives in the UK,” Transp. Res. Part A Policy Pract., vol. 49, pp. 132–148, 2013.
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2. The conclusion part should be further developed, I think it would be interesting if the authors could make more proposals for improvement and introduce more future lines of research in this important field of work related to transport and sustainability. |
Response:
Many thanks for your suggestion.
We further developed the conclusion part according to your suggestions
There are some limitations to this study that would need further improvements. First, more available traffic data needs to be collected for the purpose of more accurate prediction. This issue will be easier to achieve in the future connected and automated environment. In addition, this study is performed under traditional traffic environment. Therefore, further study may focus on the analysis of the impact of connected and automated vehicles on GHG emissions and try to propose a prediction method of emissions under connected and automated environment to further expand the research of this study. There are also some other interesting topics that calls for more attentions in the connected and automated environment in the near future. The transportation planning method as well as the innovation and development of traffic control in a connected environment are tow interesting research topics for the sustainable transportation development. Nevertheless, this paper provides a general framework for the traffic GHG emission prediction method.
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3. Proposal of keywords: Smart cities, negative transport externalities. |
Response:
Many thanks for your suggestion.
We have added these keywords in our revised manuscript as follows:
Low-carbon transportation planning; Sustainable development; GHG emissions; Smart cities; Negative transport externalities
Reviewer 2 Report
This paper presents a novel prediction method of hourly GHG emissions over the urban roads network, based on a case study in Changxing county. It is well explained and structured. The methodology can be very useful for other studies. It is simple but, as the authors determine, quite accurate in its predictions, especially with good data. In my opinion, it can be published after several changes:
Abstract, line 17: change “determining” and write “determine”.
Section 2.2, line 175: correct “MVOES”.
Table 2: what is “bin”, clarify
Section 2.3, line 212: change “attempts” and write “attempt”.
Section 2.3.1, line 257. Figure 5? Or Figure 2? And where do you obtain these data? A reference is needed.
Section 4.1.2: Where do you obtain the for the different figures in Figure 8? This should be included, for example, in Supplementary material. If the data used for the model are not shown… it is not possible to know if calculations are right or wrong. At least the reference where the data are obtained must be included.
Section 4.2: Lines 442-444. Coefficients of chemical compounds should be written as subscripts.
Figure 9: Abscissa (X) axis should be named (magnitud and units). And, as before, coefficients of chemical compounds should be written as subscripts. Please check this in all sections.
Author Response
Response to Reviewer 2 Comments:
We appreciate your time and help in reviewing our manuscript. Thank you very much for your affirmation of our work in this paper and many thanks for your detailed comments. We have revised the paper very carefully according to your suggestions, and all the modified contents have been marked in red font in the updated manuscript.
|
General comments:
This paper presents a novel prediction method of hourly GHG emissions over the urban roads network, based on a case study in Changxing county. It is well explained and structured. The methodology can be very useful for other studies. It is simple but, as the authors determine, quite accurate in its predictions, especially with good data. In my opinion, it can be published after several changes
|
Response:
Thank you very much for your affirmation of our work in this paper and many thanks for your detailed comments. We appreciate your time and help in reviewing our manuscript, and the insightful comments you provided that have helped significantly improve the quality of this study. We have revised the paper very carefully according to your suggestions, and detailed explanations of all the issues are as follows. All the modified contents have been marked in red font in the updated manuscript.
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1. Abstract, line 17: change “determining” and write “determine”. |
Response:
Many thanks for pointing out this issue. We have revised this word to “determine”.
For transportation planning designers, a quick and accurate estimation of carbon emissions under different transportation planning schemes is a prerequisite to determine the optimal low-carbon transportation development plan.
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2. Section 2.2, line 175: correct “MVOES”. |
Response:
Many thanks for pointing out this issue. We have revised this word to “MOVES”.
we choose to calibrate only CO2 emission rates in this study. For CH4 and N2O, we directly apply the emission rates database embedded in MOVES.
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3. Table 2: what is “bin”, clarify. |
Response:
Many thanks for your suggestion. We have added a note to the explanation of bin below Table 2. In addition, we added the average speed bin description in MOVES in the supplementary file (Table S1).
Table 2. Benchmark Fuel Consumption and Speed Correction Functions for LDPV, LDT, and MC.
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Vehicle category |
Fuel type |
Benchmark speed bin |
Benchmark fuel consumption (L/100km) |
Speed correction functions |
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LDPV |
Gasoline |
5 |
8.86 |
4.38495x-0.90741 |
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LDT |
Gasoline |
5 |
10.87 |
4.27765x-0.89292 |
|
MC |
Gasoline |
5 |
3.82 |
3.98090x-0.74419 |
Note: 16 average speed bin are defined in MOVES for emission rates, ranging from 4- to 120+km/h (see Table S1 in the supplementary data file)
Table S1. The average speed bin description in MOVES
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Average speed bin ID |
Average speed bin ID Description (in unit mph) |
Average speed bin ID Description (in unit km/h) |
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1 |
Speed<2.5mph |
Speed<4.0km/h |
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2 |
2.5mph<=speed<7.5mph |
4.0km/h<=speed<12.1km/h |
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3 |
7.5mph<=speed<12.5mph |
12.1km/h<=speed<20.1km/h |
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4 |
12.5mph<=speed<17.5mph |
20.1km/h<=speed<28.2km/h |
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5 |
17.5mph<=speed<22.5mph |
28.2km/h<=speed<36.2km/h |
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6 |
22.5mph<=speed<27.5mph |
36.2km/h<=speed<44.3km/h |
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7 |
27.5mph<=speed<32.5mph |
44.3km/h<=speed<52.3km/h |
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8 |
32.5mph<=speed<37.5mph |
52.3km/h<=speed<60.4km/h |
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9 |
37.5mph<=speed<42.5mph |
60.4km/h<=speed<68.4km/h |
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10 |
42.5mph<=speed<47.5mph |
68.4km/h<=speed<76.4km/h |
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11 |
47.5mph<=speed<52.5mph |
76.4km/h<=speed<84.5km/h |
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12 |
52.5mph<=speed<57.5mph |
84.5km/h<=speed<92.5km/h |
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13 |
57.5mph<=speed<62.5mph |
92.5km/h<=speed<100.6km/h |
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14 |
62.5mph<=speed<67.5mph |
100.6km/h<=speed<108.6km/h |
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15 |
67.5mph<=speed<72.5mp |
108.6km/h<=speed<116.7km/h |
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16 |
72.5mph<=speed |
116.7km/h<=speed |
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4. Section 2.3, line 212: change “attempts” and write “attempt”. |
Response:
Many thanks for pointing out this issue. We have revised this word to “attempt”.
Therefore, we attempt to explore an alternative method for estimating hourly traffic volume based on road capacity and transportation planning indicators.
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5. Section 2.3.1, line 257. Figure 5? Or Figure 2? And where do you obtain these data? A reference is needed. |
Response:
Many thanks for pointing out this issue.
We have revised the figure number to “Figure 2” and added an explanation of data source as follows.
Figure.2 depicts road traffic volume conditions in different time periods in Changxing, Wuan, Qingcheng, and Jintang county. The traffic data were obtained from the bayonet system of Vehicle Management Office in each county. It can be easily found that there are obvious morning and evening peaks in these four counties. Therefore, according to the travel characteristics presented in Figure.2, the periods 7:00am-9:00am and 17:00pm-19:00pm are set as peak hour periods, 22:00pm-6:00am is selected as free flow periods, and other times are set as off-peak hours.
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6. Section 4.1.2: Where do you obtain the for the different figures in Figure 8? This should be included, for example, in Supplementary material. If the data used for the model are not shown… it is not possible to know if calculations are right or wrong. At least the reference where the data are obtained must be included. |
Response:
Many thanks for pointing out this issue.
We have added data source description in the revised manuscript, and the transportation indicators data that used for calculation of other 30 counties has been presented in the supplementary data file (Table S2). Due to the typesetting issue caused by Table S2, we will not present it here.
In order to better analyze the driving factors, when selecting the county, we should first ensure that the road traffic indicators mentioned in section 2 are all available in the comprehensive traffic planning of every county. On the basis of ensuring this precondition, taking into account different levels of economic development and different geographical characteristics become our choice to explore the impact of economic development level and regional characteristics. In the end, in addition to Changxing, we chose the remaining 30 counties as follows: Shanxi province (Long county, Chenghceng county, Ganquan county, Mian county, Ningqiang county, Ziyang county); Sichuan province (Jintang county, Maerkang county, Mianzhu county); Hubei province (Jingshan county, Anlu county, Yunxi county, Fang county, Yidu county); Anhui province (Wuwei county, Huoshan county, Mengcheng county); Shandong province (Tengzhou county, Liangshan county, Xiajin county); Guangdong province (Wuhua county); Hunan province (Tongdao county, Taojiang county); Hebei province (Zhengding county, Wuan county); Fujian province (Nanjing county); Zhejiang province (Tiantai county, Changxing county, Qingyuan county); Henan province (Suixian county); Gansu province (Qingcheng county). For convenience, we use the numbers 1 to 31 to represent the counties above in alphabetical order, as shown in Figure. 8a). The transportation planning indicators were obtained from the comprehensive transportation planning text of each county (see Table S2 in the supplementary data file).
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7. Section 4.2: Lines 442-444. Coefficients of chemical compounds should be written as subscripts. |
Response:
Many thanks for pointing out this issue.
We double-checked the manuscript and revised this issue in all sections. All the modified contents have been marked in red font in the revised manuscript.
|
8. Figure 9: Abscissa (X) axis should be named (magnitude and units). And, as before, coefficients of chemical compounds should be written as subscripts. Please check this in all sections. |
Response:
Many thanks for pointing out this issue.
We added the name of the X axis in Figure 9 as following:
Figure 9. Peak Hour Emission Coefficient of CO2, CH4, and N2O.
In addition, we double-checked the manuscript and revised the issue of subscripts in all sections. All the modified contents have been marked in red font in the revised manuscript.
Round 2
Reviewer 1 Report
Dear Authors
I have reviewed their research work and found that they have made the improvements that in my view their research should have contained.
For this reason my decision is that your paper must be published in Sustainability as I believe that it can help in making decisions to improve urban land transport.
I would like to take this opportunity to wish you a happy ending to the year 2020 and an excellent year 2021 for you and your families where health and wisdom will help you continue working in this field of sustainability.
