Carbon Emission Reduction Cost Assessment Using Multiregional Computable General Equilibrium Model: Guangdong–Hong Kong–Macao Greater Bay Area
Round 1
Reviewer 1 Report
The introduction is well written in terms of providing background to the research question and demonstrating previous uses of CGE model. However how can the CGE model answer the research question isn't explained well here.
Methods and data are explained well.
In the results - the link between Table 2 and Figure 3 isn't immediately clear. Advise to provide better x-axis titles for Figure 3. In Figure 4, the axis labels are not clear at all. These impact the readability of the text as well, because the text assumes that the reader understands the figure perfectly. In Table 4, please avoid using acronyms in the table unless they are well explained below the table like Table 3. Also following Table 3, the numbers that stand out should be bolded. Same with Tables 5-12. For microeconomic impact, I think a summary table with the findings from each regions will be good, because this section is too long and the reader won't necessarily remember what happened in the earlier tables.
Figure 5 has similar axis label issues to the others.
Author Response
Dear reviewer,
Thank you very much for your pertinent suggestions for the revisions. We revised the article according to your suggestions, as follows:
- “how can the CGE model answer the research question isn't explained well here.”
Response: A more detailed description about the principle and method of Using CGE model to calculate the emission reduction cost is given,“This feature of CGE gives it a unique advantage in estimating the cost of carbon reduction. First, under the assumption of a set emission reduction target (such as the reduction level of carbon intensity), it can simulate and calculate the carbon price that enterprises need to face to achieve the target, which is the cost of emission reduction that the enterprise must pay (that is, the actual emission reduction cost mentioned earlier). Second, it can simulate net changes in the output of enterprises in an economic network due to changes in supply and demand in the context of emission reductions. In this way, the opportunity emission reduction cost for the whole of society can be calculated more comprehensively. For example, the outputs of some low-carbon enterprises do not decrease but increase due to cost advantages in the context of carbon emission reduction, and the opportunity costs at the regional level are partially compensated for by these benefited enterprises. Third, the CGE model, as a laboratory for policy simulation, can simultaneously calculate the cost of emission reduction faced by enterprises and the socio-economic impact on the whole of society under various emission reduction schemes, which provides a good reference value for enterprises to choose emission reduction technologies and government decision-making departments to determine emission reduction targets, carbon taxes, or the price of pollutant discharge rights.”
2.“In the results - the link between Table 2 and Figure 3 isn't immediately clear. ”
Response: Table 2 is the scheme set of this study, and figure 3 shows the results considering the impact of changes in emission reduction intensity on carbon price.
3.“Advise to provide better x-axis titles for Figure 3. In Figure 4, the axis labels are not clear at all. Figure 5 has similar axis label issues to the others.”
Response: All the charts have been modified, as shown in the manuscript.
4．“ In Table 4, please avoid using acronyms in the table unless they are well explained below the table like Table 3. Also following Table 3, the numbers that stand out should be bolded. Same with Tables 5-12. For microeconomic impact, I think a summary table with the findings from each regions will be good, because this section is too long and the reader won't necessarily remember what happened in the earlier tables.”
Response: The tables of each city have been merged into one table; abbreviations of industries have been added for ease of reading. See table 1 and table 4.
Thank you again for your constructive comments which are very helpful to improve our work.
Best Regards,
Authors
Author Response File: 
Author Response.pdf
Reviewer 2 Report
I have read your manuscript and found some concerns about your manuscript. I highlight below the key issues specifically, I will focus on several substantives issues that you need to address in order to improve your work.
First, when reading your manuscript, I had a hard time seeing the rationale and justification for your study. Note that I am not saying that there is no need for your study (this would just be plain rude to say), but rather that I have to go back and forth through your introduction and read in between the lines to figure out why it would be necessary to focus on your research question and its relevance for theory and practice beyond what we already know. I think that part of this problem is due to the fact that (1) your introduction is very fragmented and without any discussion of relevant theories (you do mention the theories that you will draw upon but it is not clear how or why this theory is needed to develop your arguments). As a consequence of this, I had a hard time figuring out why your work matters in light of what we already know; (2) There is an insufficient coherent theoretical framework connecting the elements of your story; thus, leading me to wonder what you are testing and how it is related to theory; and (3) your introduction is very much focused on “filling gaps or voids” without demonstrating why or how this is a gap or void. Again, I am not mentioning these issues to discredit your work, but to demonstrate that I am not fully convinced by the strength of your arguments for the need of your study. I think you can solve this issue by rewriting your introduction to more clearly state why you would need to focus on these relationships and what the importance might be from understanding these effects from a theoretical and practical point of view; in doing so, you will position your study much stronger and convince readers of its importance from the get go.
My second concern is that, there appears to be a lack of theoretical justification for your manuscript; there appears to be a lack of coherent theoretical underpinning of what you are investigating (you are mentioning several theories throughout your manuscript but you do not sufficiently explain which theoretical tenets you are investigating, nor is there an integration into a coherent theoretical story); in the absence of this, I find it very hard – if not impossible – to judge the merit of your manuscript. I would also say that it is absolutely critical that you conduct a more comprehensive literature review. A stronger literature review would ensure that the claims you make in your work are accurate. Finally, when reading your introduction, I had a hard time seeing how this work builds upon and extends our existing knowledge beyond what we already know from the available literature; several of these associations have already been well-documented in the literature. As it stands, I found myself struggling trying to understand why this study matters from a theoretical point of view in its current form; hence I could not identify the contributions of your work to the existing body of literature. An effective introduction – and your paper generally - should answer three important questions: (1) Who cares?, (2) What do we know, what don’t we know, and so what?, and (3) What will we learn? If you can put greater emphasis on answering these questions – particularly “who cares”, “so what”, and “what will we learn” – in your introduction, theoretical development, and discussion - you can make a stronger case for your work.
Third, incorporating some empirical evidences in introduction part will convince the reader about the significance of the study.
Lastly but not least, discussion part relating with earlier findings of the study will signify the findings of this study
Author Response
Dear reviewer,
Thank you very much for your pertinent suggestions for the revisions. We have rewritten the introduction section, adding the discussion of scientific questions, the necessity of this research, and the academic contributions, etc., as detailed in the manuscript.
First,the scientific questions are put forward，“In the context of "carbon peaks and carbon neutrality," for a country like China with many provinces and cities and unbalanced regional economic development, how to balance carbon emission reduction targets with economic development goals has raised important scientific questions: How high is the economic cost of carbon emission reduction? How can we scientifically and reasonably formulate emission reduction targets and allocate emission reduction tasks in provinces and cities with very different levels of economic development? Estimating the cost of CO2 emission reduction at all levels can provide a reference to solve these problems.
Then, the existing methods for calculating emission reduction costs are introduced, especially the principle and characteristics of CGE model in calculating emission reduction costs. “This feature of CGE gives it a unique advantage in estimating the cost of carbon reduction. First, under the assumption of a set emission reduction target (such as the reduction level of carbon intensity), it can simulate and calculate the carbon price that enterprises need to face to achieve the target, which is the cost of emission reduction that the enterprise must pay (that is, the actual emission reduction cost mentioned earlier). Second, it can simulate net changes in the output of enterprises in an economic network due to changes in supply and demand in the context of emission reductions. In this way, the opportunity emission reduction cost for the whole of society can be calculated more comprehensively. For example, the outputs of some low-carbon enterprises do not decrease but increase due to cost advantages in the context of carbon emission reduction, and the opportunity costs at the regional level are partially compensated for by these benefited enterprises. Third, the CGE model, as a laboratory for policy simulation, can simultaneously calculate the cost of emission reduction faced by enterprises and the socio-economic impact on the whole of society under various emission reduction schemes, which provides a good reference value for enterprises to choose emission reduction technologies and government decision-making departments to determine emission reduction targets, carbon taxes, or the price of pollutant discharge rights. “
At the same time, the reasons for shortage of similar research are analyzed. “at the municipal level, which is the direct administrative unit for the implementation of carbon emission reduction, relevant research is scarce. This is mainly because China only discloses input-output tables at the provincial level, and input-output tables at the municipal level are neither produced nor disclosed, so the municipal CGE model lacks data support, and carbon emission reduction policy research at the city level using the CGE model is scarce.”
Finally, the academic contributions of this study are presented. “In view of this, this paper explores the differences in the carbon emission reduction costs of cities with large economic differences under the scenario of industry-wide coverage by establishing a multi-regional CGE model in Guangdong, Hong Kong, and Macao to provide a scientific basis for the allocation of urban carbon quotas. This study can also provide reference ideas for other parts of the country and promote the early realization of national emission reduction targets” and “The contributions of this paper are as follows: 1) A method and idea for calculating the cost of carbon emission reduction using the CGE model is proposed, which provides a scientific basis for the formulation of carbon emission reduction targets and quota allocations; 2) This paper also proposes a method to decompose the provincial input-output table into a municipal interregional input-output table to provide a data basis for the construction of a multi-regional CGE model”.
Thank you again for your constructive comments which are very helpful to improve our work.
Best Regards,
Authors
Round 2
Reviewer 1 Report
Comments have been addressed satisfactorily.
Author Response
Dear Reviewer,
Thank you very much for your comments. We have also touched up our English.
Best Regards,
Authors
Reviewer 2 Report
The authors improved much but it will strengthen the quality of the study if discussion part is included along with quoting the related studies. So it will be good contribution that how this study is different from earlier studies so novelty of the study will be highlighted in this way also.
Author Response
Dear reviewer,
Thank you very much for your comments.
The paper has been carefully revised according to your opinions, as follows:
1 “it will strengthen the quality of the study if discussion part is included along with quoting the related studies.”
Response: In addition to comparing with the results calculated by CGE model method, it is also compared with other methods and the two methods are evaluated. “Some scholars have also calculated the cost of carbon reduction at the city level by using shadow price model. For example, Wang et al. [13] used a Data Envelopment Analysis (DEA) based method to evaluate the regional CO2 shadow prices of 30 Chinese major cities, and they found the average industrial CO2 emissions abatement cost is 45 US$/t during 2006–2010 and the existence of large gap on CO2 shadow prices between different Chinese regions, with the price of 170.70 US$/t in east coast area and 6.32 US$/t in middle yellow river area. Yang et al. [14] applied a parameterized directional distance function approach to estimate the regional CO2 marginal abatement costs in China based on provincial panel data covering the years 2003-2012, and they calculated the average shadow prices of CO2 is 717.27 Yuan/t for the whole country. The east coast and south coast areas have the highest shadow prices of CO2 (1143.48 Yuan/t and 1088.26 Yuan/t, respectively) and northwest area has the lowest shadow price (336.18 Yuan/t).
It should be noted that the cost of emission reduction calculated by the shadow price model is an opportunity cost, as described earlier, this method cannot be used to simulate policy changes, only historical emission reduction costs can be calculated. It also lacks sectoral dynamic interaction paths and technical implications. From the calculation results, most of the opportunity costs are much larger than the actual carbon market price, while the direct emission reduction cost calculated by CGE model is closer to the carbon market price.
Although CGE model can calculate the cost of carbon emission reduction more comprehensively, it should also be noted that it does not pay attention to the details of emission reduction technology, which is also the direction that the model should be improved in the future.”
Thank you very much.
Authors
