Spatial–Temporal Evolution of Interprovincial Ecological Efficiency and Its Determinants in China: A Super-Efficiency SBM Model Approach
Round 1
Reviewer 1 Report
This study aims : to investigate the spatial-temporal characteristics of interprovincial ecological efficiency in China and its influencing factors. Using the Super-Efficiency Data Envelopment Analysis (DEA) SBM model, this research calculated the ecological efficiency index for 31 Chinese provinces from 2005 to 2021.
We assessed this manuscript and there are changes ( in form and content) must be done to improve this study as follows:
1. Abstract : take consideration about
* Original value of the study
* Implication policy
* Keyword : add the region of the study ( China)
2. Introducation :
* Authors could provide 2 to 3 paragraphs illustrate the general context of the study and related similar studies made before.
3. Literature Review :
* Ecological Efficiency and Sustainable Development: Author can provide the most research paper in clair and descriptive table ( Authors's paper- Publication year - period of the study - research results)
* Methods of Ecological Efficiency Measurement : ( Idem.)
* Research on Interprovincial Ecological Efficiency in China : ( Idem.)
* Factors Influencing Interprovincial Ecological Efficiency in China : (Idem.)
* The Impact of Policies and Governance on Ecological Efficiency : ( Idem.)
** Authors invited to udate/ recent search sudies to be added (2021-2023) : Sustainability MDPI, Economies, Springer Nature, Wiley, etc. databases can provide more recent cited references to your theoritical section. We can help with these studies:
Sunkar, A.; Lakspriyanti, A.P.; Haryono, E.; Brahmi, M.; Setiawan, P.& Jaya, A.F.2022. Geotourism Hazards and Carrying Capacity in Geosites of Sangkulirang-Mangkalihat Karst, Indonesia, Sustainability,14(3),1704. https://doi.org/10.3390/su14031704
4. Research Methods and Data Sources:
* Table 1. Ecological Efficiency Evaluation Index System : Sources !!
* Research Hypotheses ( to support /no, your findings) ?
* Research Methods :
- ''Traditional Data Envelopment Analysis (DEA) model'' ; Provide usually cited reference - It means the authors who used this DEA model-?
- ''Super-Efficiency SBM model''; Reference/ Authors used this S-E-M?
* Spatial Autocorrelation :? Moran's Index & ?′ Moran's Index : What is the statistical significance ?
* Study Area and Data Sources :
- Figure 1. Study Area Overview and Provincial Distribution Map : revise the size to be more clair?
5. Temporal Analysis of Interprovincial Ecological Efficiency in China :
* ""From 2020 to 2021, China's ecological efficiency index experienced a slight decline. During this period, factors such as the COVID-19 pandemic"": We recommend this cited reference to be added as reference related to your study explaining the effect of the crisis as follows: (Erum, S.; Muhammad, N T.;Wali Muhammad, K.; Mohsen, B.; & Shahid, R., Chapter 16: The COVID-19 Pandemic Overlaps Entrepreneurial Activities and Triggered New Challenges: A Review Study, Book chapter: Managing Human Resources in SMEs and Start-ups, August 2022, 388 p. https://doi.org/10.1142/9789811239212_0008 )
* Line 253 : Beijing, Shanghai, Tianjin, Jiangsu, and Guangdong consistently exhibited higher ecological efficiency indices in most years : Explain why this higher index for thses region compared to the 35 Chinese regions?
* Global Spatial Autocorrelation Analysis : Moran's I index was observed in 2010 is 0.401, What is the statistical significance compared to the other study's period?
* Analysis of Factors Influencing China's Provincial Ecological Efficiency : ArcGIS software is utilized to classify the indicators for each group. Authors used the geographical detector is used to calculate the strength of each indicator's effect (q-value): Both of them generated some limites/ errors could you provide these issues in your study?
6. Limitation of your study?
7. References : All recent researcch papers you have added in the literature section of Empirical section as cited references, must be added in the Reference section. Do not miss to add the DOI for each reference in this section.
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Good revision
Moderate editing english language revison required.
Author Response
Thank you for your comments sincerely, which are very important for this study, and I will respond to your suggestions one-on-one below.
Point 1: Abstract : take consideration about * Original value of the study * Implication policy * Keyword : add the region of the study ( China)
Response 1: Yes, we have restructured the abstract to provide a detailed exposition of the factors influencing provincial ecological efficiency, aiming to offer insights for diverse policy formulation. Additionally, we have included "China" in the keywords section. (Lines 11-31)
Point 2: Authors could provide 2 to 3 paragraphs illustrate the general context of the study and related similar studies made before.
Response 2: Thank you for your suggestion. We have incorporated the research background into the introduction section.(Lines 50-60)
Point 3: Ecological Efficiency and Sustainable Development: Author can provide the most research paper in clair and descriptive table ( Authors's paper- Publication year - period of the study - research results)
Response 3: Thank you for your suggestion. We have reorganized the content of the literature review and included the references you recommended.。(Lines:620+631+643+659+667+671)
Point 4: Research Methods and Data Sources:
* Table 1. Ecological Efficiency Evaluation Index System : Sources !!
* Research Hypotheses ( to support /no, your findings) ?
* Research Methods :
- ''Traditional Data Envelopment Analysis (DEA) model'' ; Provide usually cited reference - It means the authors who used this DEA model-?
- ''Super-Efficiency SBM model''; Reference/ Authors used this S-E-M?
* Spatial Autocorrelation :? Moran's Index & ?′ Moran's Index : What is the statistical significance ?
* Study Area and Data Sources :
- Figure 1. Study Area Overview and Provincial Distribution Map : revise the size to be more clair?
Response 4: Thank you for your suggestions. Based on your feedback, we have made the following modifications in this section:
We have included the references for constructing the ecological efficiency evaluation index system.
We have revised and enlarged the font size in the newly redrawn regional study area map (Figure 1).
Additionally, we would like to clarify that the "Super-Efficiency SBM model" mentioned in the paper is a type of Data Envelopment Analysis (DEA) model. The evolution of the model and relevant research literature are elaborated in detail in the second section of the literature review. In this section, we will focus on introducing the formulas.. (Lines 160-170&229)
Furthermore, regarding the spatial autocorrelation Moran's Index that you mentioned, we provide the following explanation:
â‘ In spatial statistics, Moran's Index and standardized Moran's Index are crucial tools used to assess spatial autocorrelation. Moran's Index measures whether similar values in geographic space tend to cluster together or disperse. Its values range between -1 and 1, where positive values indicate positive spatial autocorrelation (similar values cluster together), negative values indicate negative spatial autocorrelation (similar values disperse), and values close to 0 suggest spatial randomness.
â‘¡Subsequently, the standardized Moran's Index standardizes the Moran's Index to enable comparability across different datasets. This aids in determining whether observed spatial autocorrelation holds statistical significance. The standardized Moran's Index can be used to obtain p-values through simulation methods or by comparing it with the expected values and variances of a spatial random distribution, thereby establishing its statistical significance.
Point 5: Temporal Analysis of Interprovincial Ecological Efficiency in China
Response 5: Thank you very much for your suggestions. First, Beijing, Shanghai, Tianjin, Jiangsu, and Guangdong consistently exhibited higher ecological efficiency indices in most years. This section primarily describes the state presented by the indices. The specific reasons behind this are analyzed in detail later in the paper. Overall, the increase in production efficiency is attributed to economic factors, and these regions have superior policy implementation and technological advancement compared to other provinces, leading to higher ecological efficiency indices in these areas. (Lines 659-678)
Second, the Moran's I index observed in 2010 is 0.401. How does its statistical significance compare with other observation periods? This aspect is elaborated upon in the paper. In the beginning of Section 4.3.1 on global spatial autocorrelation analysis, it is mentioned that the ecological efficiency index of 31 Chinese provinces from 2005 to 2018 exhibits significant spatial autocorrelation. From Table 4, it can be observed that the peak value of Moran's I index reaches 0.401 in 2010, indicating the most prominent spatial dispersion. The significance value for 2010, as indicated in Table 4, is 0.00, indicating strong statistical significance. (Lines 335-355)
Third, we acknowledge your point regarding the limitations of ArcGIS classification and the Geographical Detector. Although the Geographical Detector may not match the precision of econometric empirical analyses, it possesses certain advantages in revealing spatial heterogeneity, conducting multifactor analysis of influencing factors, quantitatively assessing impact degrees, and analyzing spatial distribution patterns. Furthermore, the Geographical Detector analysis takes spatial characteristics and correlations into full consideration when studying influencing factors, offering more comprehensive and accurate analytical outcomes, contributing to a deeper understanding of geographic phenomena and issues. Hence, we opted for the Geographical Detector analysis to examine the impact of ecological efficiency across five key dimensions: economic factors, efficiency factors, environmental governance, pollution factors, input factors, and natural factors encompassing 30 specific indicators. Additionally, we have addressed these points in Section 6.2 "Limitations and Prospects." (Lines: 557-577)
Point 6: Limitation of your study?
Response 6: Thank you for your feedback. We have included the section "6.2 Limitations and Prospects" as suggested. (Lines: 557-577)
Point 7: References : All recent researcch papers you have added in the literature section of Empirical section as cited references, must be added in the Reference section. Do not miss to add the DOI for each reference in this section.
Response 7: Thank you for your suggestions. We will further communicate with the editor to carefully review and refine the references section of the paper, ensuring it adheres to academic standards and meets the requirements of the journal.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
1. The innovation of the study was insufficient and needed to be better articulated in the introduction.
2.The research methods are more conventional, and the applicability of the methods needs to be emphasized.
3.The map is not clearly drawn.
4.What is the logical relationship between spatial autocorrelation analysis and Geographical Detector?
5.Since it is a national provincial sample, Figure 1 does not need to exist.
In total, the manuscript is recommended for major revision.
The quality of the English language needs to be further improved, with long sentences reduced and concise.
Author Response
Thank you for your comments sincerely, which are very important for this study, and I will respond to your suggestions one-on-one below.
Point 1: The innovation of the study was insufficient and needed to be better articulated in the introduction.
Response 1: Thank you for your suggestions. We have revised and supplemented the abstract and introduction sections of the paper accordingly. (Lines: 10-28) (Lines: 50-60)
Point 2: The research methods are more conventional, and the applicability of the methods needs to be emphasized.
Response 2: Thank you for your efforts. We have incorporated the provided additions into the paper. Firstly, we have provided a more detailed explanation of the research background in the introduction section. Secondly, we have included additional references in the methods and model construction part.
It's important to note that the paper primarily focuses on the spatiotemporal distribution of ecological efficiency among Chinese provinces and their influencing factors. Although the research methodology employs conventional approaches, the innovation lies in our in-depth analysis of influencing factors. We utilized the Geographical Detector to thoroughly analyze the impact of 30 specific indicators across five key dimensions: economic factors, efficiency factors, environmental governance, pollution factors, input factors, and natural factors on ecological efficiency. This approach of indicator selection holds a certain degree of originality. (Lines 50-60) (Lines 401-415)
Point 3: The map is not clearly drawn.
Response 3: Thank you for your feedback. Firstly, we have redrawn Figure 1, the overview map of the study area, by increasing the resolution and enlarging the font size of the text within the figure. Additionally, I'd like to note that we used ArcGIS software for figure creation. We will further communicate with the editor to ensure that all figures can be re-exported to enhance their resolution.
Point 4: What is the logical relationship between spatial autocorrelation analysis and Geographical Detector?
Response 4: The logical relationship between spatial autocorrelation analysis and the Geographical Detector method lies in their shared objective of understanding spatial patterns, relationships, and influences within geographic data. However, they approach this goal from distinct perspectives and with different methodologies.
â‘ Spatial Autocorrelation Analysis:
Spatial autocorrelation analysis focuses on identifying and quantifying the degree to which similar values are clustered or dispersed in geographic space. It helps uncover patterns of spatial dependence, indicating whether neighboring locations have similar or dissimilar attribute values. This analysis doesn't necessarily explore the underlying causes of these patterns but aims to describe the overall spatial structure.
â‘¡Geographical Detector Method:
Geographical Detector goes beyond spatial autocorrelation analysis by not only identifying spatial patterns but also examining the potential causal factors contributing to these patterns. It is a powerful tool for exploring the complex interactions between multiple factors and their joint effects on a specific phenomenon. Geographical Detector assesses the influence strength and interaction effects of various factors, revealing how they collectively shape the observed spatial patterns.
In essence, while both spatial autocorrelation analysis and Geographical Detector aim to understand spatial relationships, Geographical Detector takes a step further by dissecting the underlying causal mechanisms. It helps researchers identify which factors contribute significantly to observed spatial patterns and how their interactions impact those patterns. By integrating spatial relationships with causal analysis, Geographical Detector offers a more comprehensive understanding of geographic phenomena and their influencing factors.
Point 5: Since it is a national provincial sample, Figure 1 does not need to exist.
Response 5: Thank you for your explanation. Figure 1 serves as the overview map of the study area, considering that the readers of the paper may come from various countries worldwide. As some readers might not be familiar with the distribution of Chinese provinces, we have created this figure to illustrate the distribution of different provinces.
Author Response File: Author Response.pdf
Reviewer 3 Report
Comments for author File: Comments.pdf
Author Response
Thank you for your comments sincerely, which are very important for this study, and I will respond to your suggestions one-on-one below.
Point 1: I think a more substantial bibliography is needed.
Response 1: Thank you for your efforts. We have reviewed and restructured the content and structure of the paper according to your suggestions. Specifically, we have added the research background and research scope in the introduction section. We have included additional references in the methods and model section and incorporated a new section addressing limitations and prospects. (Lines: 10-28) (Lines: 50-60)
Point 2: 3.1. Cological Efficiency Evaluation System – to correct.
Response 2: Thank you for your explanation. Indeed, ecological efficiency research encompasses various perspectives, and different scholars construct models with differing content. The non-desired output evaluation index system established in this paper is a result of referencing other scholars' work, taking into account data availability and other factors. It's a comprehensive model designed for provincial ecological efficiency assessment. It's important to note that this model is just one of the explorations presented in this paper.
In line with your suggestion, we have added references in this section to provide clear sources for the development of the model. (Lines 160-170)
Point 3: citation review: [2,3]; [5,6]; [7,8]; [10-12]; [43,44]; [45,46];
Response 3: Thank you for your feedback. We will meticulously review the entire list of references to ensure that it aligns with academic standards and meets the requirements of the journal.
Author Response File: Author Response.pdf
Round 2
Reviewer 2 Report
Accepted after minor revision.
My main comment on this manuscript is the Geodetector, a model that needs to be elaborated in more detail by the authors to reflect adaptability, and the analysis of the results needs to be clearer.
1. The authors' understanding of GeoDetector needs to be deeper. Explanatory power is not the same as influence strength.
2. According to Wang and Xu (2017), Geographical Detector can be modified to GeoDetector.
Wang J, Xu C. Geodetector: Principle and prospective. Acta Geographica Sinica, 2017,72(01):116-134.
Author Response
Thank you for your comments sincerely, which are very important for this study, and I will respond to your suggestions one-on-one below.
Point 1: The authors' understanding of GeoDetector needs to be deeper. Explanatory power is not the same as influence strength.
Response 1: Thank you for your valuable suggestions and comments on our article. We have carefully considered your feedback, especially regarding a deeper understanding of the Geodetector model and the clarity of its internal concepts.
In Wang Jinfeng's 2017 paper, it is explicitly stated in Section 2 that 'If stratification is generated by the independent variable X, then a higher q-value indicates a stronger explanatory power of the independent variable X on attribute Y, and vice versa.' Furthermore, in the case study provided in Section 4.1, the results indicate that hydrological variables (X3) have the highest q-value, suggesting that among these variables, rivers are the most significant environmental factors determining the spatial pattern of NTDs.
In this paper, with the assistance of Geodetector, we use ecological efficiency as the dependent variable (Y) and various indicators (X1~X30) as independent variables to calculate the impact strength (q-value) of each indicator on ecological efficiency. It can be considered that a higher q-value corresponds to a stronger influence of the respective indicator on Y.
To highlight the applicability of Geodetector to this paper, we have rewritten Section 3.2.3 and made adjustments to the content in Section 5 (Lines: 204-230) (Lines: 421-433).
Point 2: According to Wang and Xu (2017), Geographical Detector can be modified to GeoDetector..
Response 2: Yes, we have replaced 'Geographical Detector' with 'GeoDetector' in the article.
Author Response File: Author Response.pdf
