An Integrated Air Quality Improvement Path of Energy-Environment Policies in the Guangdong-Hong Kong-Macao Greater Bay Area
Chuanbao Wu
Round 1
Reviewer 1 Report
The manuscript entitled “An Integrated Air Quality Improvement Path of Energy-Environment Policies in the Guangdong-Hong Kong-Macao Greater Bay Area” investigates the air pollutant emissions reduction benefits of energy related clean air method and the co-benefits of energy consumption conservation. The results provide valuable recommendation on future integrated strategies and policies towards air quality improvement in the Greater Bay Area. It is also meaningful to highlight the co-benefits of energy system transitions brought by clean air measures. The authors are suggested to review and revise this manuscript based on the following comments to further improve the quality of this manuscript.
1. Pay attention to the line space and font (lines 439-536) of the main body, which should be uniformed.
2. In line 13,72, 261 and 506, the official expression of “the 13th Five-Year Period” should be “the 13th Five-Year Plan period”
3. Same as mentioned in 1., “the 14th Five-Year Period” ->“the 14th Five-Year Plan period”
4. In introduction, line 83, “energy system transformation” -> “energy system transition”
5. In section 2.1, please explain to me why the air quality target was set to reach 21 μg/m3 of PM2.5 annual average concentration?
6. In section 2.3.1, please carefully check the expression of “the GBA will further promote the transformation to a cleaner, more efficient and safer modern energy system”.
7. In section 3.1, please explain to me why “The emission reduction benefit of promoting new energy vehicles, transportation structure adjustment and energy efficiency improvement of vehicles were not as effective as measures the industrial and energy sectors”
8. In section 3.2, line 324, it is appropriate to implement China Vehicles Emission Standard to classify old vehicles in Hong Kong and Macao?
9. In section 3.2, please make sure the description of transportation development in your baseline scenario is consistent with the scenario description in Table 2. It seems that the phasing out plan of diesel vehicles in the base case is tightened than the scheme in Hong Kong from 2015 to 2020, as you mentioned the policies in transportation sector in SBUA will be consisted with the period of 2015 to 2020.
10. In section 3.3, please revise the expression of “the reduction…. are better than….”.
11. Please specify which figure is referred to the statement you made in the results part, as some figures were divided into multi panels
12. In discussion, “energy consumption volume” or “energy consumption amount”?
13. In discussion, the authors mentioned the limitation of Lin’s estimation about the energy consumption amount in the Greater Bay Area by 2025. Please explain to me how did the study estimate the energy consumption development in a more proper way?
14. In discussion, what’ the meaning of “Co-benefits of motivating the industries to optimize the energy use structure with the implementation of energy-environment policy worth to be further explored by the policymakers”?
15. In conclusion, line 536, “clean energy transformation” -> “clean energy transition”
Author Response
Dear Reviewer:
Thank you very much for your time involved in reviewing the manuscript and your very generous comments. Appended to this letter is our point-by-point response to your comments. The comments are reproduced, and our responses are given directly afterward in red.
We would like to show you our great appreciation for all your time involved and offer us an opportunity to improve the manuscript. We hope you will be satisfied with our responses and revisions.
Point 1: Pay attention to the line space and font (lines 439-536) of the main body, which should be uniformed.
Response 1: Thanks for your careful checks. We are sorry for our carelessness. The line space and font of the manuscript is carefully uniformed.
Point 2: In line 13,72, 261 and 506, the official expression of “the 13th Five-Year Period” should be “the 13th Five-Year Plan period”
Response 2: Thank you for you correction. According to your suggestion, we have corrected the “the 13th Five-Year Period” into “the 13th Five-Year Plan period”.
Point 3: Same as mentioned in 2., “the 14th Five-Year Period” ->“the 14th Five-Year Plan period”
Response 3: Again, thank you for you correction. According to your suggestion, we have corrected the “the 14th Five-Year Period” into “the 14th Five-Year Plan period”.
Point 4: In introduction, line 83, “energy system transformation” -> “energy system transition”
Response 4: Thank you for your meaningful suggestion. We agree that it would be more appropriate to use energy system transition in here. The revised expression in line 94 is as follows: “…green energy system transition in the GBA.”
Point 5: In section 2.1, please explain to me why the air quality target was set to reach 21 μg/m3 of PM2.5 annual average concentration?
Response 5: Thank you for your question. The PM2.5 concentration target was set in 21μg/m3 for two reasons. The annual average PM2.5 concentration will target to 22μg/m3 in 2025. Aiming at building the “Air Quality Improvement Pioneering Demonstration Area”, the PM2.5 concentration target in the GBA should be more aggressive than the provincial goal. The second reason is that according to the recent released clean air actions in Guangdong, the Ref. [14], as pollutant concentrations decrease, further improvements in air quality become more difficult. Three-year average PM2.5 concentration in the GBA from 2019 to 2021 was 22μg/m3, indicating an annual average decline rate of approximately 1.2%. if the target is set to be 21μg/m3. We believe that an annual average decline rete of 1.2% will be more affordable and feasible for the region with the air pollution pressure coming from the recovery of industrial production and economic development in the GBA after the COVID-19 pandemic.
Point 6: In section 2.3.1, please carefully check the expression of “the GBA will further promote the transformation to a cleaner, more efficient and safer modern energy system”.
Response 6: Your suggestion really means a lot to us. Yes, it would be more understandable if we change this expression into “…the GBA will further accelerate the transition toward a cleaner and high-efficient energy system” in section 2.3.1 (line 155-160).
Point 7: In section 3.1, please explain to me why “The emission reduction benefit of promoting new energy vehicles, transportation structure adjustment and energy efficiency improvement of vehicles were not as effective as measures the industrial and energy sectors”
Response 7: It is our pleasure to explain this question to you. First of all, although Guangdong actively promotes the development of new energy vehicles and actively promotes the low-carbon transformation of transportation structure, the progress of promoting clean transportation development in Hong Kong and Macao lag behind that of Guangdong Province. When we evaluate the policy impact from the perspective of the entire GBA, measures on transportation sectors thus performed not as effective as measures the industrial and energy sectors. Second, the rapid increase in the number of motor vehicles and the increase in the demand for urban passengers and freight have led to the rapid growth of air pollutant emissions in the transportation sector. The fast-growing air pollutant emissions in the transportation sectors out weight the reduction benefits brought from clean air measure in this sector to some extent.
Point 8: In section 3.2, line 324, it is appropriate to implement China Vehicles Emission Standard to classify old vehicles in Hong Kong and Macao?
Response 8: Thank you for your meticulous concerns. Yes, it is appropriate to implement China Vehicles Emission Standard to classify old vehicles in Hong Kong and Macao. In fact, the Chia â…¡ and China â…¢ emission standard are equal to the EU â…¡ and EU â…¢ emission standard.
Point 9: In section 3.2, please make sure the description of transportation development in your baseline scenario is consistent with the scenario description in Table 2. It seems that the phasing out plan of diesel vehicles in the base case is tightened than the scheme in Hong Kong from 2015 to 2020, as you mentioned the policies in transportation sector in SBUA will be consisted with the period of 2015 to 2020.
Response 9: We are sorry for the misrepresentation. We have corrected the misrepresentation into a clear one in Table 2 as follows “The Energy-related clean air measures in transportation sector will remain consistent with current policies”.
Point 10: In section 3.3, please revise the expression of “the reduction…. are better than….”.
Response 10: Thank you for your correction. We have corrected the expression in the revisions as follows: “…Benefiting from the stringent implementation of regional energy-environment policies, the emission reduction of air pollutants achieved by SA and SO in transportation and industrial sectors are significantly higher than that achieved by SBAU in 2025.” （Line 358-360）
Point 11: Please specify which figure is referred to the statement you made in the results part, as some figures were divided into multi panels
Response 11: Your suggestion really means a lot to us. Yes, it would be more understandable if we specify the particular figures when interpreting the results. According to your suggestion, we have specified the particular reference figures in result interpretations.
Point 12: In discussion, “energy consumption volume” or “energy consumption amount”?
Response 12: Thank you for you correction. According to your suggestion, we have corrected the “energy consumption volume” into “energy consumption amount” (Line 473).
Point 13: In discussion, the authors mentioned the limitation of Lin’s estimation about the energy consumption amount in the Greater Bay Area by 2025. Please explain to me how did the study estimate the energy consumption development in a more proper way?
Response 13: Thank you for questioning. As you can find in the material and method part, we did the predict by setting year 2020 as the base year, in compare with Lin et al. who implemented 2017 as the base year. The condition in 2017 might be out-of-date today since it did not involve the impact of the pandemic. Besides, to improve the reliability of the scenario analysis, the prediction of energy consumption in this study was estimated not only based on statistical prediction models, but also cross-checked with relevant regional research results.
Point 14: In discussion, what’ the meaning of “Co-benefits of motivating the industries to optimize the energy use structure with the implementation of energy-environment policy worth to be further explored by the policymakers”?
Response 14: Thank you for questioning. Here, we would like to propose a view that policymakers should consider the synergy of measures when formulating policies. For example, through our research, we can find that clean air actions help promote the optimization of energy structure. Thus, considering the need for energy system transition when formulating environmental policies will help achieve a win-win situation in the GBA.
Point 15: In conclusion, line 536, “clean energy transformation” -> “clean energy transition”
Response 15: Thanks for your careful checks. We agree that it would be more appropriate to use clean energy transition in here. The revised expression in line 581 is as follows: “a coordinated mechanism for clean air action and clean energy transition.”
Thank you again for your time and your review work.
Best regards,
Yixi Li
Author Response File: 
Author Response.docx
Reviewer 2 Report
Dear Editor,
I have reviewed the manuscript entitled "An Integrated Air Quality Improvement Path of Energy-Environment Policies in the Guangdong-Hong Kong-Macao Greater Bay Area ". I believe that the manuscript needs the following moderate revisions before it is considered for publication by this journal:
- The English language of the paper should be improved.
- The study examines the air pollutant emission reduction effects of energy-related clean air measures in the GBA during the 13th Five-Year Period, with SO2, NOx, primary PM2.5, and VOCs in 2020 decreasing by about 23.6%, 32.1%, 39.8%, and 37.6% compared to 2015. As the authors know, one of the most important parts of the introduction is to indicate the research question as well as the study's main goals but unfortunately, these sections were not clearly mentioned.
- In terms of materials and methods, authors need to explain how quality assurance and quality control of data were implemented.
- Please note the consistency and the upper case and lower case in the entire manuscript.
- What about Strengths and limitations in your work? Please add it to the discussion.
Author Response
Dear Reviewer:
Thank you very much for your professional review work on our manuscript! We appreciate your generous and meticulous comments on our manuscript. Your comments are valuable and very helpful for revising and improving our manuscript. Appended to this letter is our point-by-point response to your comments. The comments are reproduced, and our responses are given directly afterward in different colors (responses in red and excerpted revisions in blue). We hope that our response can address your concerns well.
Response to reviewer:
Point 1: The English language of the paper should be improved.
Response 1: Thank you for your comment on the language quality. The grammar, spelling, punctuation and phrasing of this paper is carefully checked during the revision. The readability of the paper is improved in the revised version.
Point 2: The study examines the air pollutant emission reduction effects of energy-related clean air measures in the GBA during the 13th Five-Year Period, with SO2, NOx, primary PM2.5, and VOCs in 2020 decreasing by about 23.6%, 32.1%, 39.8%, and 37.6% compared to 2015. As the authors know, one of the most important parts of the introduction is to indicate the research question as well as the study's main goals but unfortunately, these sections were not clearly mentioned.
Response 2: We appreciate your kind reminder. The research question and the study’s main goals are clearly described in introduction as follows.
Revised manuscript – Line 74-94:
“…Therefore, it is necessary to assess the air pollutant emission reduction benefits of energy related clean air measures already implemented in the entire GBA. It is also important to predict the policies’ influence on future’s air quality and propose feasible policy paths for policymakers before drafting policies. Besides, since no coordinated regional air quality improvement plan has been promulgated for the GBA, concerns on how to strengthen integrated clean air actions are also in need from the perspective of the entire GBA under the goal of jointly building a “Air Quality Improvement Pioneering Demonstration Area”.
To support the “Air Quality Improvement Pioneering Demonstration Area” initiative in the GBA, this research first conducts an ex-post assessment on the impact of those major clean air measures in the perspective of entire GBA during the 13th Five-Year Plan period. Then, based on the prediction of the development trends by 2025 in the GBA, three scenarios implemented with different energy related clean air measures are set on the base of 2020 to compare the benefits of air quality improvement and the green development progress of energy system. This study also adapts the Community Multiscale Air Quality Model (CMAQ) model to simulate the change of annual average concentration of PM2.5 in the GBA. Finally, based on results from scenario analysis, the study puts forward the energy-environmental strategy suggestions based on air quality improvement and green adjustment strategies of the energy system in the GBA for the middle of the 14th Five-Year Plan period and demonstrates recommendations and feasible policy paths for the building of “Air Quality Improvement Pioneering Demonstration Area” and promoting the integration of clean air action and green energy system transition in the GBA.”
Point 3: In terms of materials and methods, authors need to explain how quality assurance and quality control of data were implemented.
Response 3: Thank you for your suggestions. Descriptions on how quality assurance and quality control of data are explained in materials and methods.
Take the quality assurance of simulation results from CMAQ model as an example (line 257-266):
“…To assure the data quality, the simulated concentration of PM2.5 from CMAQ was compared with observation data. The simulated PM2.5 concentration was corrected by the observed concentration, as well as by the base case simulations, which would help to control the quality of predicted outputs. The formula of simulation error elevations was as follows:
Conci,j=Oi,j× (CSi/CBi )
where Conci,j represented the predicted value of the PM2.5 concentration in city i and in simulated day j after error corrections. Oi,j represented the PM2.5 observation data from the monitoring network, CBi represents the simulation value of the benchmark scenario, CSi represented the simulation value of the development scenarios.”
Point 4: Please note the consistency and the upper case and lower case in the entire manuscript.
Response 4: Thank you for your kind reminder. The consistency and the upper case and lower case in the entire revised manuscript are carefully checked.
Point 5: What about Strengths and limitations in your work? Please add it to the discussion.
Response 5: Thanks for your suggestion. The strengths and limitations in our work have been added to the discussion now.
Revised discussion – Line 504-521:
“… In the process of building the “Air Quality Improvement Pioneering Demonstration Area”, it will be an interesting and novel topic for regional researchers to assess the policy’s impact on air quality improvement under the spatial scale of entire GBA region. This study first carries out an ex-post assessment to quantify the impact of those major clean air measures in the entire GBA during the 13th Five-Year Plan period on pollutant reduction. Aiming at proposing an integrated energy related clean air path for policymakers, the study then designs a future policy scenario analysis to predict the air quality improvement potential as well as the co-benefit of accelerating energy system transition that could be reach. The results can help to predict future pollutant emission trends and can also provide feasible recommendations for the governments to further strengthen the efforts on air pollution prevention and control. On the other hand, only simulating the change of PM2.5 concentration in the GBA might not be able to reflect the change on the overall air quality since the photochemical smog problem in the GBA has not yet been solved. Study on regional formation mechanism of O3 and policy’s impact on O3 pollution control will be carried out next. Also, due to the lack of regional up-to-date emission factors and coefficients, the pollutant emission reduction estimated in this study might deviate from the actual conditions. Thus, studies on updating regional air pollutant emission factors will also be necessary in the future.”
We would like to take this opportunity to thank you for all your time involved and this great opportunity for us to improve the manuscript. We hope you will find this revised version satisfactory.
Best regards,
Yixi Li
Author Response File: Author Response.docx
Reviewer 3 Report
General comment:
It is good peace of work and a well-written paper. The paper is in the scope of the journal and touches the important problem of air pollution.
Before publication, major corrections are needed. It is hard to connect results with equations in material and methods in the first reading. Please take a look at this and re-write it to make clear what is what and from where. Please also add what is the novelty of your study (besides the analysis in this particular region). The authors made a prediction for the next years without a current major global change in terms of people's transportation behavior etc related to the pandemic. Maybe this is an outlier to be removed but maybe not. I expect at least a discussion about this.
Figures - please explain all colors on them
Introduction:
In general the story there is well-written and easy to read but at the same time, the main idea of the paper is not lost. However, I miss a broader view of the problem in the context of research already carried out in the world, especially in the context of the policies' influence on the final air quality. For example in Krakow (see for reference MDPI Sensors special issue Sensors for Air Quality Monitoring) there is a 9-year observation of the „no coal” policy impact on air pollution. There is also another good paper about policies by Cristiana Tudor and Sova in ISPRS Int. J. Geo-Inf
Materials and methods
1st paragraph - maybe a chart will be a better idea to see decreasing in pollution concentration
How (or is?) the impact of the pandemic and current situation on the oil&gas market included in the predictions? Please comment on this.
Line 217 - please add the reference to the model soft.
Please add a clear explanation of how the policies were included (quantitative) in the modeling.
Will your research be used to make decisions later on? I think it should be shared with local authorities.
Author Response
Dear Reviewer:
Thank you very much for your professional review work on our manuscript! We appreciate your generous and meticulous comments on our manuscript. Your comments are valuable and very helpful for revising and improving our manuscript. Appended to this letter is our point-by-point response to your comments. The comments are reproduced, and our responses are given directly afterward in different colors (responses in red and excerpted revisions in blue). We hope that our response can address your concerns well.
General comment 1: Before publication, major corrections are needed. It is hard to connect results with equations in material and methods in the first reading. Please take a look at this and re-write it to make clear what is what and from where. Please also add what is the novelty of your study (besides the analysis in this particular region).
Response to general comment 1: Thank you for your comments and suggestions. The equations, as well as the materials and methods were re-written in a more logical and easy-understanding way. Please find them in the revisions. Besides conducting novel analysis in the particular region, the research not only did an ex-post assessment on the energy policies, but also forecasted the effect of future policies with the use of statistical model and air quality simulation model. In addition, the study also took energy development in the future as an important input when doing policy scenario analysis and tried to find the co-benefits of energy system optimization from clean air measures. The novelty of the study was also added to the revisions.
Revisions: Materials and Methods - Line 96-266
Novelty of the study – Introduction & Discussion- Line 66-80 & Line 504 – 514
General comment 2: The authors made a prediction for the next years without a current major global change in terms of people's transportation behavior etc. related to the pandemic. Maybe this is an outlier to be removed but maybe not. I expect at least a discussion about this.
Response to general comment 2: Thank you for your professional comment. It would be another interesting topic to figure out how will the global change of people’s working and living behaviors related to the pandemic influence the air quality from the perspective of nationwide and worldwide.
But in this particular regional study, we believe that regional air quality is more affected by local activities in the GBA and activities in the neighboring regions. In fact, as far as we know, wearing a mask might be one of the most universal change of people’s behavior due to the pandemic in the GBA. And luckily wearing a mask will not cause direct air pollutant emission from the residential livings side.
On the other hand, the urgent need of taking climate action to address the problem of global warming might lead to further impact on the global change of people’s behavior. We believe that people’s behavior, especially transportation behavior in the GBA will be greatly influence by the green promotion from the governments and thus the transportation structure in the GBA is expected to be more sustainable and environmental-friendly when making the prediction. We involved the green development in the transportation sector when predicting the energy demand change in the transportation sectors as follows: we assume that 96.2%, 95.8% and 95.3% of energy consumption in the transportation sector in the three scenarios will be oil by 2025, and electricity will compose of 3.8%, 4.1% and 4.7% of the sectoral energy consumption for the three scenarios respectively. In 2025, the freight volume share rate of road, railway, navigation, aviation, and pipeline in the GBA under SBAU will reach to 59.7%, 1.4%, 37.9%, 0.2% and 0.7%. The freight volume share rate of road will decrease by 3.4% compare with 63.1% in 2020, and the share rate of railway and navigation will increase by 0.5% and 2.9% compared with 0.9% and 35% in 2020 respectively, while the share rate of aviation and pipeline will remain the same as the base year. Under SBAU, the proportion of passenger transport in the GBA by 2025 will consist of 55% road, 28% railway, 3% navigation, and 14% aviation. In SA and SO, both the passenger and freight structure of the GBA will be optimized. Compared with SBAU, the freight volume share rates of navigation and railway will be increased by 0.3% and 0.6%, and 1.5% and 4.1%, respectively in SA and SO and the passenger transport volume share rate of road and railway will reduce by 1.7% and 1% in SA, as well as 4.7% and 3% in SO. In terms of clean vehicle promotion, the market share of new energy vehicles in 2025 will reach 20% among passenger vehicles under SBAU, and the market share of new energy vehicles will reach 25% and 30% under SA and SO. We also predicted the benefit of promoting new energy vehicles, retiring old and emission-intensive vehicles in our study as you can find in the result part.
We would be glad to further discuss the global change in terms of people's transportation behavior etc. not only related to the pandemic but also to climate change if you are interested in.
Point 1: Figures - Please explain all colors on them
Response to point 1: Thank you for your interest in the color of figures.
In Figure 1 (newly added according to your meaningful suggestion), we used distinct colors to distinguish different air pollutants, i.e., SO2 in grey, NO2 in red, O3-8h in blue, PM10 in green, PM2.5 in purple, CO in yellow.
In Figure 2, we showed the changes in air pollutant emissions in the GBA during the 13th Five-Year Plan period. We used dark blue to represent the emissions of air pollutant in year 2015 and used yellow to interpret the emission increase due to economic and social development. Bar charts in red pictured the emission decrease from eight major energy related clean air measures summarized in the study. Bar charts in light blue illustrated the air pollutant emissions in year 2020.
In Figure 3, we presented the emission reduction benefits from different scenarios in 2025 compared to 2020. Charts in dark blue stood for the air pollutant emissions in 2020. Charts in blue green, in light green and in champagne represented the air pollutant emissions of SBAU, SA and SO, respectively.
Figure 4 stated the contribution of different policy measures to emission reductions in 2025 for each scenario. Blue charts meant the emissions in air pollutant equivalent in 2020, rea charts denoted the decrease of emission from policies implementation, and charts in blue green showed the emissions in air pollutant equivalent in 2025.
Figure 5 pictured the industrial air pollutant emission reduction benefits in 2025. Again, red charts denoted the decrease of emission from clean air measures implementation. The bar chart in light blue was the baseline air pollutant emission in year 2020. The bar chart in purple was the air pollutant emission under SBAU in 2025 after implementation of measures of EM1- EM8. The bar chart in purple blue was the air pollutant emission under SA in 2025 after implementation of additional measures of EM9- EM13. The bar chart in dark blue was the air pollutant emission under S0 in 2025 after implementation of enhanced measures of EM9+- EM13+.
In Figure 6 (a)-(c), we use heat maps to illustrate the spatial distribution of PM2.5 concentrations in the GBA by 2025 for each scenario. Here, the warmer the color, the higher spatial PM2.5 concentrations, while the cooler the color, the lower spatial PM2.5 concentrations in the GBA. In figure 6 (d), bar chart was used to compare the PM2.5 concentrations among cities under different scenarios, while dark blue represented PM2.5 concentrations of the cities in SBAU, blue green represented PM2.5 concentrations of the cities in SA, and champagne represented PM2.5 concentrations of the cities in So. In addition, the red line in figure 6 (d) stood for the PM2.5 concentration goal of 21μg/m3.
Point 2: Introduction - In general the story there is well-written and easy to read but at the same time, the main idea of the paper is not lost. However, I miss a broader view of the problem in the context of research already carried out in the world, especially in the context of the policies' influence on the final air quality. For example in Krakow (see for reference MDPI Sensors special issue Sensors for Air Quality Monitoring) there is a 9-year observation of the „no coal” policy impact on air pollution. There is also another good paper about policies by Cristiana Tudor and Sova in ISPRS Int. J. Geo-Inf
Response to point 2: We appreciate your valuable comment on the introduction part. These two paper you mentioned are valuable and helpful to enrich the manuscripts. Although we solely focus on the regional perspective at the beginning, it is worth to review how research on the policies’ influence on the air quality conducted in the world. We also added more references on research already carried out in the world into the introduction part in the revised manuscript.
On ref. [8] Tudor, C.; Sova, R. EU Net-Zero Policy Achievement Assessment in Selected Members through Automated Forecasting Algorithms. ISPRS Int. J. Geo-Inf. 2022, 11, 232. https://doi.org/ 10.3390/ijgi11040232
On ref. [9] Danek, T.; Zareba, M. The Use of Public Data from Low-Cost Sensors for the Geospatial Analysis of Air Pollution from Solid Fuel Heating during the COVID-19 Pandemic Spring Period in Krakow, Poland. Sensors 2021, 21, 5208. https://doi.org/10.3390/s21155208
Revisions: Introduction - Line 51-58
“Studies has been carried out on the influences of energy-environment policies on air quality from both home and aboard. By forecasting the emission changes for key air pollutants, .... Tudor et al. assessed the EU Net-Zero Policy achievement of greenhouse gas and air pollutants in Central and Eastern Europe through automated forecasting algorithms [8]. Danek et al. used public data from low-cost sensor to show the significant reduction effect of the “no coal” policy on air pollution in Krakow [9].”
Point 3: Materials and methods- 1st paragraph - maybe a chart will be a better idea to see decreasing in pollution concentration
Response to point 3: Thank you for your suggestion. We added a figure to picture the decreasing trends in air pollution concentration in the reversions as shown in Figure 1.
Revisions: Figure 1- Line 110-111
Point 4: Materials and methods- How (or is?) the impact of the pandemic and current situation on the oil & gas market included in the predictions? Please comment on this.
Response to point 4: Thank you for your question and concerns. Your question is meaningful to our future study. As we stated in the introduction, the GBA also faced the problems of high dependence on external energy. In 2020, the regional energy dependence was as high as 75%, of which 100% of coal demand, 76% of crude oil demand and 55% of natural gas demand were imported oversea or transferred from other regions in China. At this point we did not quantify the impact of the pandemic and current situation on the oil & gas market in the study as this study mainly focused on the demand side, rather than on the supply side. But we qualitatively involved the impact of the oil & gas market in the prediction. We agreed with the views of future energy structure from Song et al. (ref. [28]) and Zhang et al. (ref. [29]) when forecasting future energy development in the GBA. In Zhang et al., he pointed out that to reduce the high dependence of oil & gas, the electrification process should be accelerated. He also stated that due to the high dependence on external energy, new energy, such as hydrogen power and photovoltaic power will usher in rapid development opportunities. We also believe that energy strategies recommended by Song et al. and Zhang et al. can alleviate supply pressure caused by instability in the oil and gas market. It would also be a novel and interesting topic to study in the future. Your question is meaningful to our future study.
Point 5: Materials and methods- Line 217 - please add the reference to the model soft.
Response to point 5: Thank you for pointing out our negligence. We are sorry for the careless. The reference to the model soft was added in the revisions as ref. [33].
Revisions: Line 222 - “The chemical transport model Model-3/CMAQ version 5.0.22[33] was used to simulate the annual PM2.5 concentration that can be achieved under each energy-environment policy scenario studied in this study.”
Point 6: Materials and methods - Please add a clear explanation of how the policies were included (quantitative) in the modeling.
Response to point 6: Thank you for your comments on the materials and methods. We believe that after the rewrite and restructure of the materials and methods, it should be explained in a more straightforward way. Please check our explanation in the following paragraphs.
The policies were quantitatively included in the modeling as follows: 1) Calculate the emission of air pollutant without implementing a given policy and the emission of air pollutant after the implementation of a given policy by multiplying activity data by corresponding emission factors (as shown in equation (1) and (2)) ; 2) As shown in equation (3), the policies were quantified by the emission of air pollutant after the implementation of a given policy minus the emission without policy implementation. Taking the policy of promoting new energy vehicles as an example: If the promotion of new energy vehicles leads to an increase sales of new energy vehicles by one hundred units, it means that the purchase demand for one hundred fuel vehicles is replaced by these new energy vehicles. The policy influence on emission reduction in this case equals to the difference between the air pollutants emitted by one hundred fuel vehicles and the emissions generated by the one hundred new energy vehicles that replace the need of fuel vehicles after the implementation of the policy.
In addition, the policies were also included in the air quality simulation model as well. The emission estimation models mentioned in the material and methods (in equation (1)-(3)) were used to assess how policies would impact air pollutant emissions, the results of which were used for the modeling. When simulating each scenario in 2025, the innermost GBA region used the 2025 development scenario inventory prepared in this study. Scenario based emission inventory was then transformed to the model-ready format by SMOKE model.
Revisions: Please refer to detailed descriptions in section 2.2 (Line 120-152) and section 2.4 (Line 221-266).
Point 7: Will your research be used to make decisions later on? I think it should be shared with local authorities.
Response to point 7: Thank you for your concerns. Yes, we would have the chance to share our research with the local authorities in the form of an executive summary
We would like to take this opportunity to thank you again for all your time involved. We sincerely appreciate your valuable feedback and discussion which were used to improve the quality of our manuscript. We hope you will find our responses satisfactory.
Best regards,
Yixi Li
Author Response File: Author Response.docx
Round 2
Reviewer 3 Report
The authors made a significant paper improvement. I got also comprehensive and factual answers to comments which convinced me to recommend this paper to be published in its current form. The methods are clearly explained now, as well as the figures.
Best luck to authors, I'm waiting for your next papers related to other air pollution factors
