Identification of Key Factors to Reduce Transport-Related Air Pollutants and CO₂ Emissions in Asia

Round 1

Reviewer 1 Report

Comments

1. I suggest that the introduction section need to be improved. There is no background about AIM/Transport model.
2. In order to make the article more complete, I suggest that some supplementary information (for example Chapters 1 and 2) should be placed in the manuscript.
3. There are many abbreviations (for example IVA, SSP2, GAINS…) in the text without full names, which makes the article difficult to read.
4. In subsection 2.3, it is not appropriate that the description of Table 2 is before Table 1. Please check. Further, the data source of Table 1 should be presented.
5. Similarly, the data sources of Table 3 and Figure 3 should be presented. Moreover, the current layout of Table 3 is difficult to read. What does “-” mean?
6. The titles of Figures 7-10 is suggested to be short and specific.
7. In my opinion, Concluding section should highlight the insights that can be gained from the results of data analysis, discuss its limitations, and present some ideas for future research.
8. Other minor comments are as follows:

• Line 58: “Kishimoto et al.” should be “Kishimoto et al. [14]”.
• Line 60: please delete “[14]”.
• Line 180: “3.1. Subsection” should be deleted.
• Line 363: “5. Conclusions” should be deleted.
• Lines 364-365, I think the sentence “This section is not mandatory, but can be …”is unnecessary.
• In Figure 1 of Supplementary Information, “Mode 2” is missing.
• SI Chapter 4: please add the description of Table 5.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

The authors should include a brief review of the literature,b which also includes the recent work of the main international organizations on the subject. This task will allow to complete and improve the results and conclusions sections, comparing their results with those established generically by literatura. A recent approach to these questions can be seen, for example, in:

Vallés-Giménez, J.  & A. Zárate-Marco (2020): "A Dynamic Spatial Panel of Subnational GHG Emissions: Environmental Effectiveness of Emissions Taxes in Spanish Regions", Sustainability, MDPI, Open Access Journal, vol. 12(7), pages 1-22, April.

Author Response

Thanks very much for taking your time to review this manuscript. I really appreciate your comments. The introduction part of this manuscript has been revised to make it more complete. Two main parts are added. (1) The background of top-down integrated assessment model, AIM/CGE, which is one of our main data sources; and the background of our model, AIM/Transport model. (2) The introduction of a series of studies which have explored GHG and air pollutants mitigation scenarios. These revisions can be found from line 56 to 78. Besides, The Vallés-Giménez’s article you recommended to me is much helpful to sort out ideas and provide scenario background of this study. I mentioned it in the introduction part. Thanks a lot.

Author Response File: Author Response.pdf

Reviewer 3 Report

The research relates to air pollution and it centers in the Asian geographical area. This interesting analysis finds that emission control policies have a major effect on air pollution reduction, especially with respect to short-term reduction. Carbon pricing policies also have significant emission reduction benefits and should be prioritized for mid-term reduction, both for carbon and for air pollutants overall.

The reduction benefits of public transportation, higher energy efficiency, higher load factor, and advanced technology are similar and noticeable; however, the effects differ considerably among individual countries and the authors do not provide even suggestions or reasons about the causes. As it poses a meaningful and significant impact on the results, we consider that it should be provided.

Author Response

Thanks very much for taking your time to review this manuscript. I really appreciate all your comments. I have added the explanation of the reason why the effects differ among countries in the manuscript, which can be found from line 349 to 351 in the subsection 3.3.1 Nation level, and line 438 to 439 in the discussion part. All the scenarios assume a same standard line for all countries, so the different baseline situation in each country causes the gaps between baseline scenario and these scenarios among countries. Besides the different discrepancy, the different transportation structure (size, mode and technology) and energy structure (fuel share) also lead to the different effectiveness. For example, time is more sensitive in Japan than in other countries, which cause the Speed_High scenario is more effective in Japan. And the effectiveness of APS_Strong scenario may have more relationship with weighted transport demand in each mode (transport demand in each technology multiply emission factors of that technology). As a conclusion, this should be a complex question, and we mentioned the cause sometimes in this manuscript (such as, line 300 to 301, line 324 to 325, line 349 to 351 and line 438 to 439). In the section 3.3 Regional aggregates, we aggregate the results of each country to try to simulate the effectiveness of each scenario at regional level, so that we could analyze the general effectiveness of each policy. And emission control policy and carbon price policy win in this part.

And please allow me to explain the specific assumptions of each scenario below.

There are eight scenarios in this study except baseline scenario, including seven single-policy scenario and one comprehensive scenario, SD scenario. Mass_Transit scenario assumes the value of mode preference parameter will converge to Japan's value by 2100. Here we considered Japan is the representative of high mass_transit ratio's country. Considering the current situation of different market shares of different transportation modes (car, bus, train, airplane, etc.) and technology (gasoline, EV, HEV, etc.) in each country, the discrepancies between baseline scenario and Mass_Transit scenario among countries are different; Ene_Efficiency_High scenario assumes that the energy efficiency improvement rate will double in new light duty vehicle efficiency from baseline. Speed_High scenario assumes the vehicle speed in some mode, mainly bus and railway will achieve a higher value by 2050 (same in all country). Advanced_Tech_High scenario assumes the value of advanced technologies preference parameter turns to high. Occupancy_High scenario assumes the load factor of passenger car converges to 2 in 2100. Carbon_Pricing scenario assumes a carbon tax is imposed in each country to achieve the target of 50% of total anthropogenic GHG emissions reduction by 2050. The value of carbon tax is then calculated by AIM/CGE. Carbon tax is then added to technology-wise price in AIM/Transport and market share is computed by multinomial logit equation. APS_Strong scenario applies the strong group of emission factor, compared to the central group in baseline scenario. Emission factors in strong group assume a faster growth of pollution control technology development, with greater effectiveness achieved compared with emission factors in the “central” group mapped with SSP2 scenario. So that the different effectiveness in each scenario among countries is the comprehensive result of the discrepancy between the central group of emission factor and strong group, and also the weighted transport demand in each mode (transport demand in each technology and emission factors of that technology). Sustainable development scenario is a comprehensive scenario which applies all the single policy above. This scenario provides a single possibility and shows us which factor will show the greatest effectiveness, so that policymakers and relevant researchers can pay more attention on it.

Round 2

Reviewer 1 Report

I recommend that this article can be accepted for publication in Sustainability.