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Peer-Review Record

A Study of Forest Carbon Sink Increment from the Perspective of Efficiency Evaluation Based on an Inverse DEA Model

Forests 2022, 13(10), 1563; https://doi.org/10.3390/f13101563
by Xiao He 1,2, Liye Chen 2,3 and Yan Huang 2,3,*
Reviewer 1:
Reviewer 2: Anonymous
Forests 2022, 13(10), 1563; https://doi.org/10.3390/f13101563
Submission received: 31 August 2022 / Revised: 19 September 2022 / Accepted: 21 September 2022 / Published: 25 September 2022
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)

Round 1

Reviewer 1 Report

This article proposes to explore the development of China's forest carbon sink through the inverse DEA model from the perspective of constant efficiency, which is innovative. The article is well organized, and its quality is standard. However, there are several issues that the authors should take into consideration.

1. The authors omitted some studies in the literature review section. In addition to the research content mentioned in the text, there are lack of some articles focusing on the development of forest carbon sinks, and the references used lack the latest and most cutting-edge research results.

2. The author's description of the input-output indicators for the study of forest carbon sink efficiency is not complete and clear, and the indicators need to be further explained. Why is it suitable for the study of forest carbon sink efficiency?

3. The author uses the output-based inverse DEA model to study the increase of forest carbon sinks from the perspective of constant efficiency, and gives the changes of input indicators. Whether it is possible to use the input-type inverse DEA model to study the increment of input indicators through the changes of output indicators to further illustrate the significance of the study.

4. The author uses the gray prediction model to predict the input indicators, which is used as the premise of the inverse DEA model to predict the forest carbon sink increase. Then the accuracy of the gray prediction has a great impact on the inverse DEA prediction results. How to improve the accuracy of the gray prediction? ?

5. The suggestions given in the policy recommendations are too broad, and should be combined with the actual situation of the relevant provinces to give more suitable suggestions.

6. When analyzing the results at the conclusion, the results should be analyzed in depth in combination with factors such as geographical conditions and natural resource status, which is more profound and meaningful.

Author Response

Please see the attachment

Author Response File: Author Response.docx

Reviewer 2 Report

This paper introduces the application of inverse DEA model in forest carbon sequestration, and explains the two problems raised. The thesis has a clear theme and is well organized, but there are also several problems that need to be considered by the author

1. The literature should be up to date considering the recent papers on the topic.Moreover, there is a lack of references for existing studies on forest carbon sequestration, which need to be supplemented.

2. Are the weights in Eq. 2 objectively assigned or subjectively assigned? Explain the reasons.

3.When constructing the indicator system, the indicator description is not clear enough and needs to be further improved. And when there is reference to others, it should be marked at the index, not generalized.

4.The explanation after Table 2 in Section 4.1 is too abrupt. What is the point of writing this paragraph? A statement of intent is needed.

5.It is necessary to further explain the advantages of inverse DEA method in this field.

6.As a scientific paper, the abstract and conclusion lack numerical support, and the conclusion should echo the preceding text, whether it addresses the question raised.

7.The paper should be examined throughout for punctuations and grammar.

Author Response

Please see the attachment

Author Response File: Author Response.docx

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