Investigating the Impacts of Urban Built Environment on Travel Energy Consumption: A Case Study of Ningbo, China
Round 1
Reviewer 1 Report
This paper is interesting and well-written. I have a few comments.
1. Can you include additional built environments, such as job accessibility and distance to transit? You may want to look at papers written by Dr. Cevero or Ewing.
2. Can you elaborate more on rationales to classify travel energy consumption into high-energy and low-energy?
3. Can you divide age groups of 18-60 into more finer groups?
4. Can you offer R-squared and adjusted R-squared in the results?
5. Why didn’t high-energy and low-energy travel consumption add up observations of 22112?
6. Can you add VIF test results?
7. Can you use multilevel regression model instead of conventional ols model used in this study?
Author Response
Response to Reviewer 1Comments
First of all, I would like to thank the reviewer for your valuable comments. It is of great significance to improve the quality of manuscripts. My modifications are as follows：
Point 1: Can you include additional built environments, such as job accessibility and distance to transit? You may want to look at papers written by Dr. Cevero or Ewing.
Response 1: The variable distance from works sites represents job accessibility In addition, I have considered the variable distance to transit in my research. In the research, distance to bus stations was considered. However, in the analysis process, it was found that the variable had multiple collinearity (VIF>10) with other variables during regression analysis. In order to prevent affecting the analysis results, this variable was removed.
I have referred to the literature of Cevero and Ewing, and I have referred to many of their studies. The biggest inspiration of this study from the literature of Cevero and Ewing is to use the 5D dimension to characterize the built environment. The cited articles are listed in Reference 9 and 35.
Point 2: Can you elaborate more on rationales to classify travel energy consumption into high-energy and low-energy?
Response 2:In order to reveal the relationship between the built environment and the energy consumption of transportation trips in detail, the study not only analyzes the relationship between the built environment and the overall energy consumption of transportation trips, but also further analyzes the impact of the built environment on the energy consumption of different travel purposes and different travel modes. In order to propose the optimization design strategy of the built environment under the guidance of low carbon, the energy consumption of different travel modes in this study is divided into high energy consumption and low energy consumption travel modes based on the use of vehicles.
In this study, when conducting traffic travel research, the travel modes are divided into 9 categories for the respondents to choose, including walking, cycling, electric vehicles, motorcycles, buses, shuttle buses, driving cars, taking cars, taxis and others. This study mainly analyzes the impact of the built environment on the energy consumption of transportation trips. Since walking and bicycle trips are inefficient, the study needs to exclude these two types of travel samples, and then classify them according to the level of energy consumption of different travel modes. According to the energy intensity factors of different transportation modes, the energy consumption of private cars and taxis is relatively high, while that of buses, shuttle buses and electric vehicles is relatively low. Therefore, the transportation modes in this section are divided into high energy consumption mode and low energy consumption mode.
Point 3: Can you divide age groups of 18-60 into more finer groups?
Response 3:The age division of this study mainly considers to divide the population into three categories: minors, adults and the elderly. Generally, based on the analysis results, corresponding strategies are proposed for the three categories of people. For adults, this study has made a more detailed division according to different occupations, including students, workers in enterprises and institutions, self-employed workers, etc., so as to grasp the travel characteristics of adults in different age groups.
Point 4: Can you offer R-squared and adjusted R-squared in the results?
Response 4:Yes, I will add it in the article.
Point 5: Why didn’t high-energy and low-energy travel consumption add up observations of 22112?
Response 5:In the second feedback, I have stated the division of high energy consumption and low energy consumption travel. Since there are also zero energy consumption travel modes such as walking and cycling, they are not within the scope of research, so the sum of high energy consumption and low energy consumption is not 22112. However, the sum of commuting travel and non commuting travel samples in this study is 22112
Point 6:Can you add VIF test results?
Response 6: Yes, I will add it in the article.
Point 7: Can you use multilevel regression model instead of conventional ols model used in this study?
Response 7: The ols model was selected because a large number of literatures were consulted. After comparison and analysis, this method is more reliable. Although the multilevel regression model is not used, the independent variables in this study are divided into four categories: built environment, family socio-economic characteristics, residents' personal lifestyle, and residents' energy-saving attitude. During regression analysis, each type of variable is gradually introduced to judge the degree of impact of each type of variable on travel energy consumption. The regression analysis results presented in this article are the final results of all variables introduced.
Your comments have greatly improved the quality of this article. Finally, I would like to thank all editors and reviewers for their efforts. I also take this opportunity to wish all editors and reviewers a Merry Christmas in advance.
Author Response File: 
Author Response.docx
Reviewer 2 Report
Lines 53 - 60: Repeated text
Line 87: "Schwanen and Mokhtarian"
Line 114: Please be more specific about how Chinese cities are different from American and European cities rather than assuming that the differences are obvious to the reader.
Line 165: Add details on your sample subject recruitment methodology, or provide details on the survey set used if this is secondary data.
Line 191: Indicate whether travel distance was calculated as Euclidean or network distance. Euclidean distance will understate travel distance through a road network.
Line 191: Add information on which GIS software was used.
Line 212: cite your source (Jiang?)
Lines 213 - 217: Restatement of table information in text is redundant and unnecessary
Line 226: Your bus factor appears to need adjustment based on the load factor information from lines 249 - 255.
Line 237: For bus, the same journey carrying rate will need to be a multiplier since the group is allocated a larger share of bus energy.
Line 241: Because the different modes mapped in figure 3 are categorical rather than quantitative, you might consider using different colors for the different modes rather than shades of a single color that implies intensity.
Line 241: Clarify what locations are being mapped since travel is a line rather than a point. Are these origins, destinations, or some kind of dominant mode at particular locations?
Line 309: Clarify how address multicollinearity, preferably including VIF values. Throwing all these variables into a single equation seems like it might be overspecification. It seems like you might have some confounders in there as well.
Line 351: Add R-squared values.
Line 365: Indicate the ranges of the normalized variables. Your variables need to be normalized for the correlation coefficients to be comparable.
Line 360 - 520: Clarify your terminology to avoid conflating significance with importance.
The variables may be statistically significant, but their impact on the dependent variable may not be important. Please clarify how the results of your analysis make a novel contribution to our knowledge about the relationship between urban form and transport energy consumption. For example, the observation in section 3.4 of increased work travel energy use associated with distance from the urban core in a hub-based urban architecture adds little to the long established (and obvious) association of energy use and commute distance.
Indeed, your findings seem to largely be validations within a specific Chinese context of existing relationships described in your literature review for cities outside China. Please make this obvious along with a clearer statement of the answers to the research questions stated in your introduction. Given the growing body of research specifically on Chinese cities, you should make certain that these observations have not already been tested elsewhere. This will be determinative of whether this paper belongs in a peer-reviewed journal.
Author Response
Response to Reviewer 2 Comments
First of all, I would like to thank the reviewer for your valuable comments. It is of great significance to improve the quality of manuscripts. My modifications are as follows：
Point 1: Lines 53 - 60: Repeated text.
Response 1: I'm going to delete the duplicate content.
Point 2: Line 87: "Schwanen and Mokhtarian".
Response 2:I will change the text of Schwann T to Schwanen and Mokhtarian.
Point 3: Line 114: Please be more specific about how Chinese cities are different from American and European cities rather than assuming that the differences are obvious to the reader.
Response 3:The differences between China and European and American cities are mainly reflected in the following aspects: the residential types in European and American communities are mainly single house and apartment, the development density and plot ratio are relatively low, most of the residential buildings in the communities are low rise, the residential space is relatively large, and there are large green spaces and water systems outside. The residential density and plot ratio in Chinese communities are relatively high. Not only are the residential floors much higher than those in foreign communities, but also the outdoor green space and water system are relatively small. In terms of travel mode, European and American cities need more private cars than Chinese cities. In terms of road network, the road network density of European and American cities is also higher than that of Chinese cities.
Point 4: Line 165: Add details on your sample subject recruitment methodology, or provide details on the survey set used if this is secondary data.
Response 4:The data in this study are first-hand data obtained from household survey. The survey objects are all the permanent residents aged 6 or above and 80 or below who are selected to live together in the family sample. In order to successfully interview the households that need to be surveyed, the survey is conducted from 19:00 to 21:00 every day. In addition, if there is no one in the family during the survey, it will be replaced by families with adjacent numbers. The questionnaire is composed of four parts: the first part mainly involves the respondents' family conditions, including the number of permanent residents, ownership of transportation vehicles, etc; The second part mainly involves the basic personal information of the interviewees, including gender, age, education level, annual income, etc; The third part mainly involves the personal travel of the interviewees, including starting and ending points, travel purposes, travel modes, etc; The fourth part mainly involves the respondents' attitudes towards improving the urban transport system.
In order to ensure the quality of the survey, each investigator of the research team must attend a half day training seminar. During the training, one of our questionnaire designers explained and discussed every question in the questionnaire for each investigator, and each investigator received training on sampling strategy and interview skills. At first, we conducted a pilot survey on 20 families to find out any situation that may affect the accuracy of the survey, including misunderstanding of the survey questions, ambiguity of the answers to the questionnaire or other questions. According to the feedback from the pilot survey, we slightly modified the survey method, and then conducted a formal household survey.
Point 5: Line 191: Indicate whether travel distance was calculated as Euclidean or network distance. Euclidean distance will understate travel distance through a road network.
Response 5:As for the calculation of travel distance, we use the Traffic Analysis Zone of Ningbo to carry out the calculation. The calculation of travel distance is divided into three steps: the first step is to obtain the starting point and destination of residents' travel within the traffic zone through household survey; Second, calculate the distance between any two traffic zones based on GIS software, and import the calculated data into Excel software; The third step is to use the VLOOKUP function of Excel software to match the distance between the starting point and the destination of residents' travel, and then calculate the distance of a single one-way trip. It should be noted that since the travel route only knows the starting point and the end point, it is impossible to obtain its specific travel path. According to people's previous travel habits, this study calculates residents' travel distance according to the optimal path, that is, the straight line distance between the center point of the traffic community where the travel starting point is located and the center point of the traffic community where the end point is located is the traffic travel distance.
Although there are some errors in this method, this is the most accurate travel distance that the research team can obtain at present.
Point 6:Add information on which GIS software was used.
Response 6: The original GIS software that has been researched and applied is ArcGIS10.8
Point 7: Line 212: cite your source (Jiang?)
Response 7: The ols model was selected because a large number of literatures were consulted. After comparison and analysis, this method is more reliable. Although the multilevel regression model is not used, the independent variables in this study are divided into four categories: built environment, family socio-economic characteristics, residents' personal lifestyle, and residents' energy-saving attitude. During regression analysis, each type of variable is gradually introduced to judge the degree of impact of each type of variable on travel energy consumption. The regression analysis results presented in this article are the final results of all variables introduced.
Point 8: Lines 213-217: Restatement of table information in text is redundant andunnecessary
Response 8: Deleted redundant and unnecessary information in Lines 213-217 and tables.
Point 9.Line 226: Your bus factor appears to need adjustment based on the load factor information from lines 249 - 255.
Response 9: I have corrected the table to 26.6.
Point 10: Line 237: For bus, the same journey carrying rate will need to be a multiplier since the group is allocated a larger share of bus energy.
Response 10: The energy intensity factor of the bus in the study has taken into account the intra city carrying rate and has been multiplied by the coefficient.
Point 11:Line 241: Because the different modes mapped in figure 3 are categorical rather than quantitative, you might consider using different colors for the different modes rather than shades of a single color that implies intensity.
Response 11: Different patterns and colors are used in the figure
Point 12:Line 241: Clarify what locations are being mapped since travel is a line rather than a point. Are these origins, destinations, or some kind of dominant mode at particular locations?
Response 12:The position being drawn is C1-C9, which has been shown in the figure and is a dominant mode for a specific location
Point 13:Line 309: Clarify how address multicollinearity, preferably including VIF values. Throwing all these variables into a single equation seems like it might be overspecification. It seems like you might have some confounders in there as well.
Response 13:Added in Table 3
Point 14:Line 351: Add R-squared values.
Response 14:Added in Table 3
Point 15:Line 365: Indicate the ranges of the normalized variables. Your variables need to be normalized for the correlation coefficients to be comparable.
Response 15:The variables in this study have all referred to relevant literature, and have been integrated and optimized on the basis of existing literature. The range of normalized variables is 0-1.
Point 16:Line 360 - 520: Clarify your terminology to avoid conflating significance with importance.
Response 16:I reexamine the terminology in the article.
Point 17:The variables may be statistically significant, but their impact on the dependent variable may not be important. Please clarify how the results of your analysis make a novel contribution to our knowledge about the relationship between urban form and transport energy consumption. For example, the observation in section 3.4 of increased work travel energy use associated with distance from the urban core in a hub-based urban architecture adds little to the long established (and obvious) association of energy use and commute distance.
Response 17:In order to reveal in detail the impact of the built environment on the energy consumption of transportation trips, this study has divided different models, including the overall energy consumption of transportation trips, different travel purposes and different travel modes. For the same independent variable, the analysis results will be different in different models, which is significant to reveal the impact of built environment and traffic energy consumption.
Although there are still some conclusions that are consistent with empirical judgment, this does not deny the role of statistical analysis, but verifies empirical judgment through statistical analysis, and affirms the accurate grasp of empirical judgment on rational science. Some other conclusions are inconsistent with empirical judgment. For example, although the density of road intersections has a significant negative correlation effect on the total travel and commuting travel energy consumption, it has a significant positive correlation effect on the energy consumption of high energy consumption mode travel. Such a conclusion is put forward after reasonable argument, which represents the revision and inspiration of subjective experience by rational science.
Point 18:Indeed, your findings seem to largely be validations within a specific Chinese context of existing relationships described in your literature review for cities outside China. Please make this obvious along with a clearer statement of the answers to the research questions stated in your introduction. Given the growing body of research specifically on Chinese cities, you should make certain that these observations have not already been tested elsewhere. This will be determinative of whether this paper belongs in a peer-reviewed journal.
Response 18:At present, most studies on the impact of built environment on transport travel mainly focus on transport travel behavior. For example, the impact of urban built environment on travel distance, travel time, travel mode and other travel behaviors. There are relatively many studies conducted abroad from the perspective of transport travel energy consumption, because it provides rich reference experience for this study, while China is limited by data acquisition and other conditions, There are relatively few studies on the impact of the built environment on the energy consumption of transportation trips, and more analysis is carried out at the macro level. There are relatively few studies on the specific household survey and large sample size. On the one hand, this study can be compared with foreign studies, on the other hand, it can provide reference for Chinese cities. Some of the findings observed in this study have not been presented in other studies in China.
Your comments have greatly improved the quality of this article. Finally, I would like to thank all editors and reviewers for their efforts. I also take this opportunity to wish all editors and reviewers a Merry Christmas in advance.
Author Response File: Author Response.docx
Round 2
Reviewer 1 Report
Thanks for addressing my comments.
Author Response
Thank you very much for your valuable suggestions.
Reviewer 2 Report
Line 178: Add details on your sample subject RECRUITMENT methodology. How did you find the households you included in your survey? If recruitment was not random, inferences drawn from your sample are subject to bias. If random selection was impossible, you need to clearly indicate this and indicate the possible biases in both the sample group and the analysis results. Weighting might also be appropriate if the demographics of your sample do not match the demographics of the population you are making an inference about.
Line 237: Your bus "fuel economy factor" of 0.3 L/km seems to be for the bus as a vehicle rather than a per-passenger value as with the other travel modes. Because buses, on average and by definition, carry more than one passenger, your results dramatically overstate bus energy intensity, probably by an order of magnitude. You need to divide this per-vehicle value by average bus load factor to get a per-passenger value.
Table 3: VIF values are associated with individual independent variables rather than with models. How did you detect and address multicollinearity in your models?
Table 3: These R-squared values are very low given the high number of variables in your model, especially the low-energy consumption model. Please justify making such strong inferences based on such weak models.
Regarding my prior comment about conflating significance with importance, the comment would be better stated as conflating statistical significance with practical significance:
https://online.stat.psu.edu/stat200/lesson/6/6.4
For example, on line 416, you state that, "It can be seen that population density and mixed use degree, road intersection density and distance from work place will significantly affect the total transportation travel and commuting travel energy consumption, and all variables have a greater impact on commuting travel energy consumption, but they have no significant impact on non commuting travel."
The coefficients for population density are all dramatically smaller in absolute value than land use. The coefficients for road intersection density are as much as two orders of magnitude smaller. Grouping variables together in your analysis based on statistical significance rather than practical significance overstates the importance of some of your variables. You need to temper your findings accordingly.
Author Response
Thank you very much for your valuable suggestions, The second revision is marked in blue in the manuscript, My modifications are as follows：
Author Response File: Author Response.docx
