Review Reports - Exploring Optimal Regional Energy-Related Green Low-Carbon Socioeconomic Development Policies by an Extended System Planning Model

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

How Clear and Presentable the Model Is (Sections 2.1 and 2.2):

The explanation of the model is too complicated and hard to understand. It's not clear what the objective function (Eq. 1) means. The notation is introduced in an inconsistent way, like when de_{k,i} is used before j is defined as the energy source index. The constraints are presented with very little explanation, which makes it very hard for the reader to comprehend how the model works and what it is.

Suggestion: The authors need to change the way this part is set up. It is important to have a summary table that lists all the decision variables together with their units and meanings. A flow chart or a more full story that explains the reasoning and connections between constraint groups (for example, how the Leontief constraints affect the energy-output nexus) would make it much easier to read. There should be a short paragraph before each collection of constraints that explains what they are.

Section 2.2: Justification and Explanation of the FCCA Method

The factorial Copula-based chance-credibility constrained analysis (FCCA) is introduced as a significant innovation. But the reasons for include it in the model and the need for it in real life are not very clear.

The explanation of the 250 cases (10 groups x 5 q_joint x 5 λ) is hard to understand. It is not clear how these 10 groups were picked from the 5 random variables. C(5,2)=10 is given, but this is a combination, not a formula. The results (Fig. 2) show the CEI for all 250 situations, but it's hard to make sense of this "cloud" of results. The selection of correlations to model with Copulas appears random in the absence of robust rationale grounded in the unique context of the study topic.

The authors need to explain further why this level of uncertainty complexity is necessary compared to more standard interval or two-stage stochastic approaches. A concise table that shows the 10 correlated pairings and why they were chosen (for example, "energy demand and available water are correlated because drought can limit hydropower and cooling water for thermal plants") is very important. The results should focus on a few key cases that clearly show what happened, not all 250 of them.

Results and Discussion (Section 4):

This part tells you what happened, but it doesn't give you a critical critique. The figures are mentioned, but they weren't included in the text that was examined, thus it was difficult to judge the results. The text delineates "selected scenarios" (e.g., S5, S26) yet fails to elucidate the rationale for their selection, rendering the analysis appear arbitrary.

The commentary inadequately contextualises the results within the framework of previous literature or the particular socio-economic circumstances of Fujian province. For example, what do the results mean for Fujian's current policies? How do the results stack up against the paths laid forth in government programs, like the Fujian Energy Development Plan?

Advice: The authors need to make sure that all of the figures are included and are of good quality. The discussion needs to be stronger to show how important the main findings are (for example, the continued use of fossil fuels, the important role of certain sectors like OSI and CON, and the growing need for water for CCS/CSI) and what they mean for policymakers in Fujian and other places like it.

Policy Implications (Section 4.6):

This part is pretty weak. It is not clear why S150 was chosen as the "most suitable" policy. The criteria for "most suitable" are not clear. Is it just because carbon neutrality is reached and economic production is high? What about fairness, cost, or stress on water?

Suggestion: The policy portion should be turned into a separate topic. The writers should make it explicit how they will judge policy scenarios. The consequences need to be clear and easy to follow. For example, "Policy should prioritise investment in WIP and PHP capacity," or "Water management plans must account for a 17x increase in water for mitigation by 2060, requiring desalination investments in coastal areas like Fujian."

Minor Comments: The abstract employs acronyms (CEI, FIE) too much before explaining what they mean. It ought to be easier for a wider range of people to use.

The introduction is well-organised, but it should be more focused on explicitly saying what research need this work covers.

Data (Section 3.2): The use of the 2017 input-output table is a major constraint because China's economy has changed a lot since then. The writers should emphasise that this is a big problem with the study.

Language: A native English speaker or a professional editing service needs to examine the text carefully to find and fix grammar mistakes and make the sentences flow better (for example, "tease out" should be changed to "analyse" or "elucidate," and "pay vital roles" should be changed to "play vital roles").

Author Response

Manuscript Number: sustainability-3888994

RESPONSES TO REVIEWER ONE’S COMMENTS

We are grateful to Reviewer #1 for his/her insightful review. The provided comments have contributed substantially to improving the paper. According to them, we have made significant effects to revise the manuscript, with the details explained as follows.

Point #1

COMMENT: The explanation of the model is too complicated and hard to understand. It's not clear what the objective function (Eq. 1) means. The notation is introduced in an inconsistent way, like when de_{k,i} is used before j is defined as the energy source index. The constraints are presented with very little explanation, which makes it very hard for the reader to comprehend how the model works and what it is.

RESPONSES: We are very grateful for the reviewer’s helpful suggestions. We have (i) provided Appendix C to clarify nomenclature for variables and parameters (including their units and meanings) (on pages 40 to 42); (ii) added a short paragraph before each collection of constraints to explain the reasons and connections between constraint groups as (on page 4):

“2.1.2 Constraints

These constraints present relationships among decision variables and parameters. For decision variables, their connects can be summarized as:

Constraint (1) restricts economic output ();

Constraint (2) connects , direct energy consumption () and indirect energy consumption by energy-output consumption coefficients;

Constraint (3) connects and water consumption by energy-water consumption coefficients;

Constraint (4) restricts carbon emissions (caused by and indirect energy consumption) by emission coefficients and emission allowances;

Constraint (5) restricts by energy supply and energy demand;

Constraint (6) connects electricity consumption (), heat consumption (), electricity generation () and energy conversion capacity (and );

Constraint (7) restricts related carbon emission mitigation (,,,,,) by each measure’s mitigation ability;

Constraint (8) limits related water utilization by water availability;

Constraint (9) adjusts the percentages of secondary and tertiary industries’ economic outputs in to be reasonable;

Constraint (10) controls related pollutant discharges;

Constraint (11) emphasizes all decision variables’ non-negative feature.”

Point #2

COMMENT: Section 2.2: Justification and Explanation of the FCCA Method

(a) The factorial Copula-based chance-credibility constrained analysis (FCCA) is introduced as a significant innovation. But the reasons for include it in the model and the need for it in real life are not very clear.

(b) The explanation of the 250 cases (10 groups x 5 q_joint x 5 λ) is hard to understand. It is not clear how these 10 groups were picked from the 5 random variables. C(5,2)=10 is given, but this is a combination, not a formula. The results (Fig. 2) show the CEI for all 250 situations, but it's hard to make sense of this "cloud" of results. The selection of correlations to model with Copulas appears random in the absence of robust rationale grounded in the unique context of the study topic.

(c) The authors need to explain further why this level of uncertainty complexity is necessary compared to more standard interval or two-stage stochastic approaches. A concise table that shows the 10 correlated pairings and why they were chosen (for example, "energy demand and available water are correlated because drought can limit hydropower and cooling water for thermal plants") is very important. The results should focus on a few key cases that clearly show what happened, not all 250 of them.

RESPONSES: We are very grateful for the reviewer’s insightful comments. (a) In real-world system planning problem, it is hard to catch precise information for determining some parameters’ value. Some parameters can be expressed as different probability distributions, while their correlation between each other is unknown; some parameters may be expressed as possibilistic distributions. Neglecting these uncertainties and complex relationships may lead to higher decision bias risk. The CHA can quantify random parameters’ joint effects, and the CRA method can quantify fuzzy parameters’ effects on the system performance. Introducing them into the FEOWC model for handling these uncertainties is essential. To clarify this, we have added a paragraph in the revised manuscript as follows (on pages 9 to 10):

“Parameters in the above model are deterministic; thus, results of objective function and decision variables are also deterministic. In real-world system planning problem, it is hard to catch precise information for determining some parameters’ value. For instance, parameters of energy demand and available water often have random features due to the effects of economic development, population growth, political change and etc [43]. They can be expressed as different probability distributions, while their correlation between each other is unknown [44]. For another example, parameter of energy conversion capacity expansion ability may have fuzzy characteristic since it is often estimated based on existing facilities, technique improvements, investment cost and etc [45]. It can be expressed as possibilistic distributions. Neglecting these relationships may lead to higher decision bias risk. The CHA can quantify random parameters’ joint effects, and the CRA method can quantify fuzzy parameters’ effects on the system performance [46]. CHA and CRA methods are appropriate to introduced into the FEOWC model for handling these uncertainties.”

(b) In this study, official reports, academic articles, industrial standards are the data sources of energy, water, carbon, and pollutant related parameters. All parameters are divided into crisp parameters, random and fuzzy parameters. We tested and debugged the model prior to its formal operation. Crisp parameters are those that seem do not significantly affect the objective function values. Random and fuzzy parameters are determined based on the sufficiency of data and the characteristics of parameters. Given 5 kinds of random parameters, the total number of pairwise parameter tests required is 10 under a given joint probability (their interconnections were first identified). Since there are 5 joint probabilities (q_joint = 0.01, 0.05, 0.10, 0.15, and 0.20), the number of chance-constrained scenarios is 50. The fuzzy parameters are quantified as triangular membership function with 5 credibility degrees (λ = 0.5, 0.6, 0.7, 0.8, 0.9, and 1.0). All fuzzy parameters share identical λ degrees, leading to 5 credibility-constrained scenarios. The total number of chance-credibility-constrained scenario is 250. We have clarified this in the revised manuscript as follows:

“The model is tested and debugged prior to its formal operation. Crisp parameters are those that seem do not significantly affect the objective function values. Random and fuzzy parameters are determined based on the sufficiency of data and the characteristics of parameters.” (on page 14)

AND

“In the IFEOWC model, based on data processing, it is summarized that there are five kinds of random parameters: energy demand (, , , , , and ), available water (), carbon emission allowance (), electricity demand ( and ), and maximum final use (). 10 groups of potential copula-based chance constraints are deigned based on combination: C(5, 2) = 5*4/2 = 10, with each group has 5 joint probabilities (q_joint = 0.01, 0.05, 0.10, 0.15, and 0.20). When considering one correlation (e.g., energy demand-available water, energy demand-carbon emission allowance and etc.), other three types of parameters correspond to the values under marginal probability of 0.01. Thus, there are 50 chance-constrained scenarios. Parameters related to abilities such as , , , , , , , , , , , , as well as allowances such as , , , ,, and are featured with vagueness, and corresponding credibility constraints are assumed to be satisfied under 5 credibility degrees (λ = 0.5, 0.6, 0.7, 0.8, 0.9, and 1.0). All fuzzy parameters share identical λ levels, leading to 5 credibility-constrained scenarios. Overall, the multiple uncertainties are quantified and divided into 250 scenarios. Appendix D presents the scenario design framework. Solutions of the IFEOWC model are listed in Appendix E.” (on page 18)

(c1) This study focuses on addressing randomness and vagueness through the proposed method. A comparative analysis can be conducted between the proposed method and both interval parameter programming and two-stage stochastic methods. We have added a paragraph to discuss it as future work (on page 38). (c2) We have added Appendix D to present the scenario design (on page 44). (c3) We also analyze a few key cases that clearly show. This is clarified in points 4 and 5.

(c1) “Several limitations require targeted interventions in subsequent research phases. First, alternative uncertainty analysis approaches, such as interval parameter programming and two-stage programming can be introduced into the system. They can convert deterministic parameters into bounded intervals to quantify uncertainty, and decompose decision-making into sequential phases (initial target and recourse decisions) to systematically mitigate risk exposure. Second, prediction models can be employed to forecast key parameter values (e.g., electricity demand), thereby enhancing the accuracy of system objective values. Third, additional carbon emission mitigation measures can be explored to identify diverse pathways for reduction.”

(c2) A table in Appendix D

Point #3

COMMENT: Results and Discussion (Section 4):

This part tells you what happened, but it doesn't give you a critical critique. The figures are mentioned, but they weren't included in the text that was examined, thus it was difficult to judge the results. The text delineates "selected scenarios" (e.g., S5, S26) yet fails to elucidate the rationale for their selection, rendering the analysis appear arbitrary.

RESPONSES: We are very grateful for the reviewer’s insightful advice. We have added more discussions to show the importance of the main findings, which can be found in the Section 4 in the revised manuscript.

Point #4

COMMENT: Policy Implications (Section 4.6):

RESPONSES: We are very thankful for the reviewer’s helpful suggestion. We have rewritten the “Policy Implications” sub-section as follows:

“4.6 Policy implications

Policy implemented under S150 is identified as the most suitable one (i.e., available water-electricity demand, λ = 1, and q_joint= 0.20), since there is a balanced outcome is attainable here: carbon neutrality, high economic development, as well as restrained water utilization and energy consumption. Figure 13 and 14 presents the energy, output, water, and carbon flows in 2030 and 2060. Corresponding to the results, main policy implications are summarized as follows:

The adoption of advanced technologies for green low-carbon fossil fuel production is critical, given planned consumption would surge to ‌185.72×10⁶ tce‌ by 2060.
The investment of low-carbon energy conversion capacities for green low-carbon electricity generation, e.g., WIP, PHP, and NUP would expand to 40.12 GW, 36.35 GW, and 31.78 GW by 2060;
The conduction of end-removal actions for reducing carbon in the air is essential, the contributions would plan to be CTR 18.73%, CCS 6.64%, CSI 3.14%, and DAC 0.27% in 2035, and CSI 40.40 %, CCS 35.82%, and DAC 23.78% in 2060;
The strategic advancement of key sectors (e.g., CON, OSI and OTI) is significant to obtain great total economic outputs;
The reduction of freshwater allocation to the system is helpful for relieving water shortage, e.g., decreasing to 362.96 × 10⁶ m³ in 2030, and to 26 × 10⁶ m³ in 2060;
The seawater extraction volume should maintain at a stable level, e.g., 258.34 × 10⁶ m³ 2030 and 248.44 × 10⁶ m³ in 2060.” (on page 33)

Point #5

COMMENT: Minor Comments: (a) The abstract employs acronyms (CEI, FIE) too much before explaining what they mean. It ought to be easier for a wider range of people to use.

(b) The introduction is well-organised, but it should be more focused on explicitly saying what research need this work covers.

(c) Data (Section 3.2): The use of the 2017 input-output table is a major constraint because China's economy has changed a lot since then. The writers should emphasise that this is a big problem with the study.

(d) Language: A native English speaker or a professional editing service needs to examine the text carefully to find and fix grammar mistakes and make the sentences flow better (for example, "tease out" should be changed to "analyse" or "elucidate," and "pay vital roles" should be changed to "play vital roles").

RESPONSES: We are very grateful for the reviewer’s helpful suggestions. (a) We have added the explanation of CEI and removed explanation of FIE according to the number of occurrences of in the Abstract. The details are listed below (on Page 1):

“This study develops an inexact fractional energy-output-water-carbon nexus system planning model to minimize carbon emission intensity (CEI, total carbon emissions / total economic outputs) under a set of nexus constraints.”

AND

“Eight mitigation measures would be adopted to reduce the final carbon emission into the air to 0 in 2060.”

(b) We have improved the Introduction section as much as possible.

(c) The table is often published every five years, while the 2022 table has not been published. The latest table (e.g., 2017) is collected from National Bureau of Statistics. Recent statistical data indicate that Fujian's economic structure has remained relatively stable. Thus, it can be adopted as data source. We have clarified this in the revised manuscript as follows (on page 14):

“The table is often published every 5 years, while the 2022 table has not been published. The latest table (e.g., 2017) is collected from National Bureau of Statistics [52]. According to Fujian provincial Statistical Bulletin, Fujian's economic structure has remained relatively stable [53]. The 2017 table is acceptable data source.”

(d) The manuscript has been meticulously checked and all discrepancies have been addressed. All revisions are clearly highlighted in yellow within the updated version.

Generally, we are deeply grateful to the reviewer’s insight and careful review. His/her comments have greatly helped improve the paper. We also expressed our gratitude in the “Acknowledgments” of the revised manuscript.

Author Response File: Author Response.doc

Reviewer 2 Report

Comments and Suggestions for Authors

see the attachment

Comments for author File: Comments.pdf

Author Response

Manuscript Number: sustainability-3888994

RESPONSES TO REVIEWER TWO’S COMMENTS

We are grateful to Reviewer #2 for his/her insightful review. The provided comments have contributed substantially to improving the paper. According to them, we have made significant effects to revise the manuscript, with the details explained as follows.

Point #1

COMMENT: Please clarify the novelty, significance and necessity of this study. What is the scientific question and research gap? How did the question and research gap solve in this study?

RESPONSES: We are very grateful for the reviewer’s insightful comments. The research gaps exist in: (i) the conventional ratio optimization models cannot reflect material and economic flows embodied in energy-related productions and service transactions; (ii) the traditional energy-output-carbon nexus system planning models have difficulty in optimizing direct energy supply chain (from primary energy to final use), as well as controlling water utilization in the processes of energy consumption and carbon emission mitigation; (iii) system uncertainties that jointly affect the solution generation processes need to be handled. To fill the research gap, this study proposes an inexact fractional energy-output-water-carbon nexus system planning (IFEOWC) model through embedding the FCCA method in the FEOWC model. Compared with the previous studies, main contributions are: (i) proposing the CEI from a new perspective of connecting total carbon emissions and total economic outputs (including direct and indirect); (ii) quantifying the dependencies among energy, output, water, and carbon; (iii) restricting water utilization for energy consumption and carbon emission mitigation; (iv) adopting diverse mitigation measures to achieve carbon neutrality; (v) handling correlative chance-constraints and crisp credibility-constraints. These can be found in “Introduction” section.

Point #2

COMMENT: In the Copula-based chance constraints, the joint violation probability takes values of 0.01, 0.10, and 0.20. What is the basis for setting this value range?

RESPONSES: We are very thankful for the reviewer’s insightful comments. The joint violation probability (q) presents the probability level that the inequalities cannot be satisfied; the confidence level (1-q) denotes the probability level that the inequalities can be satisfied. It is assumed that the confidence level is not be lower than 0.80 (i.e., 1-0.20) and higher than 0.99 (i.e., 1-0.01). Some medium confidence levels (i.e., 0.95, 0.90 and 0.85) are chosen for detecting other probable results. We have added descriptions in the revised manuscript as follows:

“It is assumed that the confidence level is not be lower than 0.80 (i.e., 1-q_joint = 1-0.20) and higher than 0.01 (i.e., 1-q_joint = 1-0.01). Some medium confidence levels (i.e., 0.95, 0.90 and 0.85) are chosen for detecting other probable results.” (on page 14)

Point #3

COMMENT: In the credibility constraints, what method is used to determine the three characteristic parameters (lower bound a, core value b, upper bound c) of triangular fuzzy numbers?

RESPONSES: We much appreciate the reviewer’s insightful comments. The lower bound (a) and upper bound (c) are the most possible value and least possible value, which are determined based on previous data and future assessment; the core value (b) is the middle value of a and c. To clarify this, we have added descriptions in the revised manuscript as follows:

“The lower bound (a) and upper bound (c) are the most possible value and least possible value, which are determined based on previous data and future assessment; the core value (b) is the middle value of a and c.” (on page 14)

Point #4

COMMENT: The literature divides parameters into random variables and fuzzy variables. Please clarify the specific basis for classifying each uncertain variable.

RESPONSES: We are very grateful for the reviewer’s insightful comments. In this study, official reports, academic articles, industrial standards are the data sources of energy, water, carbon, and pollutant related parameters. All parameters are divided into crisp parameters, random and fuzzy parameters. We tested and debugged the model prior to its formal operation. Crisp parameters are those that seem do not significantly affect the objective function values. Random and fuzzy parameters are determined based on the sufficiency of data and the characteristics of parameters. We have added descriptions in the revised manuscript as follows:

Point #5

COMMENT: How are the "low-level (L)" and "high-level (H)" parameters defined in mixed factor analysis? Is it subjectively defined?

RESPONSES: We are very thankful for the reviewer’s insightful comments. The purpose of conducting mixed factorial analysis is to quantify key parameters’ single and joint effects on the objective function variations. The low level and high level of selected parameters would be processed based on the parameter value in the model. For crisp parameter, the high-level value increases by 10%, while the low-level value decreases by 10%.; for random and fuzzy parameters, the high-level value and the low-level value correspond to values under different λ and q levels.

Point #6

COMMENT: Is the method of mixed factor analysis the same for analyzing variables that contain both uncertain and deterministic variables, or only uncertain or deterministic variables? If they are different, please explain the differences.

RESPONSES: We much appreciate the editor’s insightful comments. The model operation analysis and mixed factorial analysis are different. The model operation analysis aims to obtain executable planning policies under probable-possible scenarios; the mixed factorial analysis aims to quantify key parameters’ single and joint effects on the objective function variations. The former establishes the parameter selection criteria for the latter.

Author Response File: Author Response.doc

Reviewer 3 Report

Comments and Suggestions for Authors

Where, when and how would this model find real application?

Will these optimal regional policies for ecological and low-carbon socio-economic development have an effect on larger territories?

Are there any legislative acts that would mandate the implementation of such policies?

Please add answers of the questions from 1 to 3 in the text of the article. Maybe in the conclusion.

The conclusion is too long. Perhaps part of it should be placed in the results section of the article.

Author Response

Manuscript Number: sustainability-3888994

RESPONSES TO REVIEWER THREE’S COMMENTS

We are grateful to Reviewer #3 for his/her insightful review. The provided comments have contributed substantially to improving the paper. According to them, we have made significant effects to revise the manuscript, with the details explained as follows.

Point #1

COMMENT: (1) Where, when and how would this model find real application? (2) Will these optimal regional policies for ecological and low-carbon socio-economic development have an effect on larger territories? (3) Are there any legislative acts that would mandate the implementation of such policies? Please add answers of the questions from 1 to 3 in the text of the article. Maybe in the conclusion.

RESPONSES: We much appreciate the reviewer’s helpful suggestion. The optimal regional policies can also provide ecological and low-carbon socio-economic development directions to larger territories. Such polices are often enforced under the oversight of competent authorities. We have added answers of the questions from (1) to (3) in the Conclusion section in the revised manuscript as follows:

“The optimal regional policies can also provide ecological and low-carbon socio-economic development directions to larger territories. Such polices are often enforced under the oversight of competent authorities.” (on page 37)

Point #2

COMMENT: The conclusion is too long. Perhaps part of it should be placed in the results section of the article.

RESPONSES: We are very thankful for the reviewer’s helpful suggestion. We have removed some results in the revised manuscript as follows:

“5. Conclusions

This study proposes an inexact fractional energy-output-water-carbon nexus system planning (IFEOWC) model to detect low-carbon development pathways considering the four elements’ nexus relationships under randomness and fuzziness information. The objective is to minimize the carbon emission intensity (CEI), subjecting to the Leontief production, energy-output nexus, energy-water nexus, energy-carbon nexus, direct energy consumption, direct electricity supply, direct carbon emission, direct water utilization, industrial structure, pollution control etc constraints. The model is applied in the Fujian province, China. 10 groups of potential copula-based chance-constraints with 5 joint probabilities (q_joint), and 5 credibility degrees (λ) for each credibility-constraint are considered, resulting in 250 scenarios. Optimal policies relating to direct (by production processes) and indirect (by products/services transactions) energy consumption, economic growth, water utilization, and carbon emissions are obtained. Some major findings of the case study are summarized:

(1) The effects of uncertainties on the CEI are q_joint> combinations (especially energy demand-available water) > λ, and the CEI would fluctuate between 45.05 g/RMB¥ and 47.67 g/RMB¥; (2) The annual shares of fossil fuels would maintain at a high level ([72.99, 79.58] %); low-carbon energy conversion capacities such as WIP, PHP, and NUP would expand to [39.49, 42.01] GW, [33.48, 38.76] GW, [31.23, 33.23] GW for fulfilling the increased energy demand, the limited water resources, and the restricted carbon emission; (3) SEW would always be the main direct water source (annually occupying [51.15, 58.34]%); (4) OTI, OSI, CON would be the major final demand supply sectors (annually occupying [87.79, 88.42]%); (5) Approximately, the ability of mitigating direct carbon emission would be CTR (19.11%) > FOC (1.07%) > PCS (0.84%) > SCS (0.35%) > others (0.12%) in 2025, and gradually become DAC (22.98%) > FOC (22.11%) > SCS (15.73%) > PCS (13.01%) > OCC (12.90%) > FAC (7.12%) > OCS (6.14%) in 2060.

The developed model can be utilized by decision makers when formulating provincial development plans (e.g., Special Planning for Energy Development, National economic development plan and etc.). The optimal regional policies can also provide ecological and low-carbon socio-economic development directions to larger territories. Such polices are often enforced under the oversight of competent authorities.

Several limitations require targeted interventions in subsequent research phases. First, alternative uncertainty analysis approaches, such as interval parameter programming and two-stage programming can be introduced into the system. They can convert deterministic parameters into bounded intervals to quantify uncertainty, and decompose decision-making into sequential phases (initial target and recourse decisions) to systematically mitigate risk exposure. Second, prediction models‌ can be employed to forecast key parameter values (e.g., electricity demand), thereby enhancing the accuracy of system objective values. Third, additional carbon emission mitigation measures can be explored to identify diverse pathways for reduction.”

Author Response File: Author Response.doc

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The authors have addressed my comments and made significant improvements compared to the previous version. I recommend acceptance.