Round 1

Reviewer 1 Report

You presented data, findings and literature review in a good manner.  Introduction, material methods, and results part are well written and organised. However, discussion is the weak part of the manuscript and should be enhanced with main findings of the study. You should also add discussion about your direct results. Now you only discussed about known facts and literature findings. The Fourier-domain transfer entropy spectrum ,emprical results, time series.

What did your results tell us about the reltionship between climate parameters and geopolitical index? Discuss also these issues above. 

Please also see the pdf attachment for other comments.

  After major revision it can be accepted for publishing in the journal. 

 

Comments for author File: Comments.pdf

Please also see the pdf attachment for grammer and English comments. Please read the whole text again carrefully and make necessary grammer edits. 

Fix grammer edits

Add proper punctation

Correct spelling mistakes.

 

 

 

 

Author Response

Thank you so much for your comments.

Author Response File: Author Response.pdf

Reviewer 2 Report

Advantages:

1.      The choice of genre is novel. While previous articles on political risk have tended to be associated with economics, this paper takes a different perspective on the causal relationship between actions such as state conflicts and terrorist attacks and the environment and climate.

2.      Fourier-domain transfer entropy spectrum is a new causal analysis method, and the authors are creative in applying it to analyze the relationship between climate change and geopolitics.

3.      The results of this paper demonstrate a strong bidirectional causal relationship between political risk index and climate (five climate indicators), which can provide recommendations for the financial allocation of the environment by national leaders, as well as the feasibility of the newly proposed theoretical approach of the Fourier domain transfer entropy method.

Possible problems and suggestions:

4.      The title of the article may be ‘The nexus between Climate Change and Geopolitical Risk Index in Arabia Saudi based on Fourier-domain transfer entropy spectrum method’.

5.      The abstract of the paper lacks the research results and analysis.

6.      The literature analysis on the relationship between climate change and geopolitics in the second part of the paper is not deep enough. The author mainly lists the relevant literature, but as for the research methods adopted in these literatures, what achievements have been made and what shortcomings have not been deeply discussed.

7.      There is a problem between line 189 and line 190.

8.      Is the geopolitical risk index data source authoritative and reliable? ‘The geopolitical risk index is downloaded from the website https://www.matteoiacoviello.com/.’

9.      Some variables do not follow the normal distribution, does this affect the analysis results? Do the variables need to be normalized? If not, will the result be affected?

10.   The paper aims to discuss the causal relationship between climate change and geopolitics in Saudi Arabia, but fails to closely focus on the topic in literature review and discussion.

11.   The meteorological data of the paper used 480 months of data from 1982 to 2021. (’The sample spans monthly frequency from January 1982 to December 2021 as a total of 480 observations.’) Climate data can only be downloaded one point at a time from this website(https://power.larc.nasa.gov/data-access-viewer/), and the study area is a region, how does the author use a point to represent a study area?

 

12.   There are some problems in the English writing of the paper. Some sentences cannot be expressed clearly.

There are some problems in the English writing of the paper. Some sentences cannot be expressed clearly.

Author Response

Thank you so much for your comments

Author Response File: Author Response.pdf

Reviewer 3 Report

All data are clearly displaying some oscillations covered by noise. Provided spectra notice two possible spectral regions. Those two components must be separated for reconstruction of long-term component and other components. Ignoring of such step can lead to erroneous results in a linear models of variables covered by noise. Standard linear approaches to a noisy data can lead to the strongly compromised result without separation of different components. Direct filters of separation different scales (long, seasonal monthly, noisy synoptic) in time and space will provide incomparably more accurate results. The local entropy algorithm is specifying what % of total entropy is important for our consideration in a spectral domain. It can be found in the textbook  

 https://www.cambridgescholars.com/product/978-1-5275-6293-6

Different scales are run by different laws, so they cannot be mixed. Detail investigation of separated scales with included long term is necessary for noncompromising results.

Comments for author File: Comments.pdf

Generally is OK

Author Response

Thank you so much for your comments.

Author Response File: Author Response.pdf

Reviewer 4 Report

There is no mention or implication about sustainability in the text of the article, but it can be considered partially within the sustainability content. It has several defects that should be rectified before a final decision made. The following points are among the major comments that the authors should take care of their rectification, if they are prepared to provide revised version with complete responses to each comment then a re-review process can be started. The article needs extensive revision along the following points.

Author Response

Thank you so much for your comments.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

It can be accepted for publication. 

Author Response

We sincerely appreciate the time and effort you dedicated to reviewing our manuscript. Your positive feedback and acceptance of our paper for publication are greatly valued.

Reviewer 2 Report

• Please carefully check formatting, spelling, etc., to improve the paper.

Author Response

We sincerely thank you for your comments and constructive suggestions. We have taken the time to correct all the formatting, spelling, and presentation issues mentioned in our manuscript.

We believe that the adjustments we have made will contribute to improved readability and a more professional presentation of our manuscript. We hope that our efforts meet your expectations. All corrections are made in the red color of our new version of the article.

Reviewer 3 Report

All data are clearly displaying some oscillations covered by noise. Provided spectra notice two possible spectral regions. Those two components must be separated for reconstruction of long-term component and other components. Ignoring of such step can lead to erroneous results in a linear models of variables covered by noise. Standard linear approaches to a noisy data can lead to the strongly compromised result without separation of different components. Direct filters of separation different scales (long, seasonal monthly, noisy synoptic) in time and space will provide incomparably more accurate results. The local entropy algorithm is specifying what % of total entropy is important for our consideration in a spectral domain. It can be found in the textbook  

 https://www.cambridgescholars.com/product/978-1-5275-6293-6

Different scales are run by different laws, so they cannot be mixed. Detail investigation of separated scales with included long term is necessary for noncompromising results.

All data are clearly displaying some oscillations covered by noise. Provided spectra notice two possible spectral regions. Those two components must be separated for reconstruction of long-term component and other components. Ignoring of such step can lead to erroneous results in a linear models of variables covered by noise. Standard linear approaches to a noisy data can lead to the strongly compromised result without separation of different components. Direct filters of separation different scales (long, seasonal monthly, noisy synoptic) in time and space will provide incomparably more accurate results. The local entropy algorithm is specifying what % of total entropy is important for our consideration in a spectral domain. It can be found in the textbook  

 https://www.cambridgescholars.com/product/978-1-5275-6293-6

Different scales are run by different laws, so they cannot be mixed. Detail investigation of separated scales with included long term is necessary for noncompromising results.

All data are clearly displaying some oscillations covered by noise. Provided spectra notice two possible spectral regions. Those two components must be separated for reconstruction of long-term component and other components. Ignoring of such step can lead to erroneous results in a linear models of variables covered by noise. Standard linear approaches to a noisy data can lead to the strongly compromised result without separation of different components. Direct filters of separation different scales (long, seasonal monthly, noisy synoptic) in time and space will provide incomparably more accurate results. The local entropy algorithm is specifying what % of total entropy is important for our consideration in a spectral domain. It can be found in the textbook  

 https://www.cambridgescholars.com/product/978-1-5275-6293-6

Different scales are run by different laws, so they cannot be mixed. Detail investigation of separated scales with included long term is necessary for noncompromising results.

All data are clearly displaying some oscillations covered by noise. Provided spectra notice two possible spectral regions. Those two components must be separated for reconstruction of long-term component and other components. Ignoring of such step can lead to erroneous results in a linear models of variables covered by noise. Standard linear approaches to a noisy data can lead to the strongly compromised result without separation of different components. Direct filters of separation different scales (long, seasonal monthly, noisy synoptic) in time and space will provide incomparably more accurate results. The local entropy algorithm is specifying what % of total entropy is important for our consideration in a spectral domain. It can be found in the textbook  

 https://www.cambridgescholars.com/product/978-1-5275-6293-6

Different scales are run by different laws, so they cannot be mixed. Detail investigation of separated scales with included long term is necessary for noncompromising results.

All data are clearly displaying some oscillations covered by noise. Provided spectra notice two possible spectral regions. Those two components must be separated for reconstruction of long-term component and other components. Ignoring of such step can lead to erroneous results in a linear models of variables covered by noise. Standard linear approaches to a noisy data can lead to the strongly compromised result without separation of different components. Direct filters of separation different scales (long, seasonal monthly, noisy synoptic) in time and space will provide incomparably more accurate results. The local entropy algorithm is specifying what % of total entropy is important for our consideration in a spectral domain. It can be found in the textbook  

 https://www.cambridgescholars.com/product/978-1-5275-6293-6

Different scales are run by different laws, so they cannot be mixed. Detail investigation of separated scales with included long term is necessary for noncompromising results.

All data are clearly displaying some oscillations covered by noise. Provided spectra notice two possible spectral regions. Those two components must be separated for reconstruction of long-term component and other components. Ignoring of such step can lead to erroneous results in a linear models of variables covered by noise. Standard linear approaches to a noisy data can lead to the strongly compromised result without separation of different components. Direct filters of separation different scales (long, seasonal monthly, noisy synoptic) in time and space will provide incomparably more accurate results. The local entropy algorithm is specifying what % of total entropy is important for our consideration in a spectral domain. It can be found in the textbook  

 https://www.cambridgescholars.com/product/978-1-5275-6293-6

Different scales are run by different laws, so they cannot be mixed. Detail investigation of separated scales with included long term is necessary for noncompromising results.

All data are clearly displaying some oscillations covered by noise. Provided spectra notice two possible spectral regions. Those two components must be separated for reconstruction of long-term component and other components. Ignoring of such step can lead to erroneous results in a linear models of variables covered by noise. Standard linear approaches to a noisy data can lead to the strongly compromised result without separation of different components. Direct filters of separation different scales (long, seasonal monthly, noisy synoptic) in time and space will provide incomparably more accurate results. The local entropy algorithm is specifying what % of total entropy is important for our consideration in a spectral domain. It can be found in the textbook  

 https://www.cambridgescholars.com/product/978-1-5275-6293-6

Different scales are run by different laws, so they cannot be mixed. Detail investigation of separated scales with included long term is necessary for noncompromising results.

All data are clearly displaying some oscillations covered by noise. Provided spectra notice two possible spectral regions. Those two components must be separated for reconstruction of long-term component and other components. Ignoring of such step can lead to erroneous results in a linear models of variables covered by noise. Standard linear approaches to a noisy data can lead to the strongly compromised result without separation of different components. Direct filters of separation different scales (long, seasonal monthly, noisy synoptic) in time and space will provide incomparably more accurate results. The local entropy algorithm is specifying what % of total entropy is important for our consideration in a spectral domain. It can be found in the textbook  

 https://www.cambridgescholars.com/product/978-1-5275-6293-6

Different scales are run by different laws, so they cannot be mixed. Detail investigation of separated scales with included long term is necessary for noncompromising results.

All data are clearly displaying some oscillations covered by noise. Provided spectra notice two possible spectral regions. Those two components must be separated for reconstruction of long-term component and other components. Ignoring of such step can lead to erroneous results in a linear models of variables covered by noise. Standard linear approaches to a noisy data can lead to the strongly compromised result without separation of different components. Direct filters of separation different scales (long, seasonal monthly, noisy synoptic) in time and space will provide incomparably more accurate results. The local entropy algorithm is specifying what % of total entropy is important for our consideration in a spectral domain. It can be found in the textbook  

 https://www.cambridgescholars.com/product/978-1-5275-6293-6

Different scales are run by different laws, so they cannot be mixed. Detail investigation of separated scales with included long term is necessary for noncompromising results.

All data are clearly displaying some oscillations covered by noise. Provided spectra notice two possible spectral regions. Those two components must be separated for reconstruction of long-term component and other components. Ignoring of such step can lead to erroneous results in a linear models of variables covered by noise. Standard linear approaches to a noisy data can lead to the strongly compromised result without separation of different components. Direct filters of separation different scales (long, seasonal monthly, noisy synoptic) in time and space will provide incomparably more accurate results. The local entropy algorithm is specifying what % of total entropy is important for our consideration in a spectral domain. It can be found in the textbook  

 https://www.cambridgescholars.com/product/978-1-5275-6293-6

Different scales are run by different laws, so they cannot be mixed. Detail investigation of separated scales with included long term is necessary for noncompromising results.

All data are clearly displaying some oscillations covered by noise. Provided spectra notice two possible spectral regions. Those two components must be separated for reconstruction of long-term component and other components. Ignoring of such step can lead to erroneous results in a linear models of variables covered by noise. Standard linear approaches to a noisy data can lead to the strongly compromised result without separation of different components. Direct filters of separation different scales (long, seasonal monthly, noisy synoptic) in time and space will provide incomparably more accurate results. The local entropy algorithm is specifying what % of total entropy is important for our consideration in a spectral domain. It can be found in the textbook  

 https://www.cambridgescholars.com/product/978-1-5275-6293-6

Different scales are run by different laws, so they cannot be mixed. Detail investigation of separated scales with included long term is necessary for noncompromising results.

All data are clearly displaying some oscillations covered by noise. Provided spectra notice two possible spectral regions. Those two components must be separated for reconstruction of long-term component and other components. Ignoring of such step can lead to erroneous results in a linear models of variables covered by noise. Standard linear approaches to a noisy data can lead to the strongly compromised result without separation of different components. Direct filters of separation different scales (long, seasonal monthly, noisy synoptic) in time and space will provide incomparably more accurate results. The local entropy algorithm is specifying what % of total entropy is important for our consideration in a spectral domain. It can be found in the textbook  

 https://www.cambridgescholars.com/product/978-1-5275-6293-6

Different scales are run by different laws, so they cannot be mixed. Detail investigation of separated scales with included long term is necessary for noncompromising results.

Comments for author File: Comments.pdf

Author Response

The authors express their sincere reviewer for gratitude for his valuable comments.

Author Response File: Author Response.docx

Reviewer 4 Report

  The authors have implemented my comments in the revişsed version.

Author Response

We sincerely thank you for your meticulous review and insightful comments on our manuscript. Your insightful observations and suggestions have helped refine our work.

Round 3

Reviewer 3 Report

All data are clearly displaying some oscillations covered by noise. It contains long term component, periodic midterm, and noisy fluctuations. Provided spectra notice two possible spectral regions. Those three components must be separated for reconstruction of long-term component and other components. Separation of scales filters are widely known and must be used for such analysis. Ignoring of such step can lead to erroneous results in a linear models of variables covered by noise. Standard linear approaches to a noisy data can lead to the strongly compromised result without separation of different components. Direct filters of separation different scales (long, seasonal monthly, noisy synoptic) in time and space will provide incomparably more accurate results. The local entropy algorithm is specifying what % of total entropy is important for our consideration in a spectral domain. It can be found in the textbook  

 https://www.cambridgescholars.com/product/978-1-5275-6293-6

Different scales are run by different laws, so they cannot be mixed, otherwise they strongly obscure each other and provide some average irrelevant to each scale. Detail investigation of separated scales with included long term is necessary for noncompromising results.

Author Response

Response to Reviewer 3 Comments:

Thank you very much for  your invaluable feedback.

We have taken great care to attentively address your comments by resorting to alternative methods that are firmly rooted in theoretical reliability. Notably, we opted for the EMD method to facilitate the decomposition process and turned to the VLTE method to accurately compute transfer entropy percentages. However, it is with regret that we acknowledge the impracticality of directly integrating your suggestions within our present framework. Please accept our sincere apologies for any inconvenience this may have caused. We enthusiastically anticipate the opportunity to incorporate your proposed techniques into our forthcoming endeavors. Should we encounter projects that harmonize with your methods, we are committed to their integration, coupled with the development of requisite software tools.