Artificial Intelligence Technologies as Smart Solutions for Sustainable Protected Areas Management

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This manuscript explores the application of artificial intelligence (AI) in sustainable protected area management through qualitative interviews with stakeholders in Turkey, Lithuania, and Morocco. While the topic is timely and aligns with global efforts to integrate technology into environmental governance, the study suffers from methodological limitations, superficial technical analysis, and insufficient contextualization of AI tools. Substantial revisions are required to strengthen the technical foundation and analytical rigor.

1. The Introduction surveys AI in protected-area management in broad strokes (e.g. biodiversity monitoring, fire detection) but does not clearly articulate what this study adds beyond existing reviews.
2. The manuscript states a sample of “135 experts (45 per country)” but later refers to “30 experts” in the NVivo analysis.Purposive sampling of 135 experts introduces selection bias. Justify exclusion criteria and discuss how non-response rates might affect generalizability.
3. The paper discusses predictive modelling of threats (e.g. fires, invasive species) but omits key computational hydrology and dispersion models. Engage with recent advection–dispersion transport solutions (e.g. DOI:https://doi.org/10.1016/j.compgeo.2024.106944) to enrich the predictive‐modelling section.
4. Vague descriptions of "smart monitoring systems" and "predictive modeling" lack implementation details.
5. Overlooks sensor-network interoperability. Discuss middleware architectures or API standardization for heterogeneous AI tools.
6. How is the risk of false positives driven by artificial intelligence (such as misclassified poaching incidents) addressed?
7. Tables 2–8 succinctly summarize themes but offer little illustrative quotation or nuanced subtheme analysis.
8. Appropriate incorporation of graphical elements can enhance the aesthetic appeal and readability of academic articles.

Author Response

Dear Reviewer,

We sincerely thank you for the time and effort you dedicated to evaluating our study. Your observations and constructive criticisms have significantly contributed to the improvement of the manuscript. We are truly grateful for your valuable comments and insights.

All the points you raised in your report have been carefully and thoroughly reviewed, and corresponding revisions have been made accordingly.

You may find all the details regarding these revisions in the attached file.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

 The subject matter of the thesis is aligned with contemporary realities and current issues. A case study design in qualitative research methodology was employed, with the case being analysed in its entirety. It is challenging to provide a comprehensive commentary on this specific topic and the qualitative method employed in its analysis.

Nonetheless, there is some doubt as to the research basis of the article, and the experiment is recommended to be reconsidered.

The selection of experts from five sectors in three countries by the authors raises questions regarding the basis and criteria for this selection. In the context of ecologically protected areas, the focus is not exclusively on these five sectors. The emergence of artificial intelligence and its application in such areas necessitates public participation. The public is a pivotal subject, not only for experts but also for the general public. It is imperative to acknowledge that all of the aforementioned factors have the capacity to compromise the scientific validity of the research results. It is recommended that the study incorporate quantitative research, given that the article cited Creswell & Clark's "Designing and Conducting Mixed Methods Research."

Author Response

Dear Reviewer,

We sincerely thank you for the time and effort you dedicated to evaluating our study. Your observations and constructive criticisms have significantly contributed to the improvement of the manuscript. We are truly grateful for your valuable comments and insights. All the points you raised in your report have been carefully and thoroughly reviewed, and corresponding revisions have been made accordingly.

You may find all the details regarding these revisions in the attached file.

Author Response File: Author Response.docx

Reviewer 3 Report

Comments and Suggestions for Authors

This study addresses a timely and important topic: the application of Artificial Intelligence (AI) for the sustainable management of protected areas, with a comparative perspective across Turkey, Lithuania, and Morocco. The use of qualitative methods, including semi-structured interviews with diverse experts, is appropriate for exploring this complex issue. However, several areas require significant improvement to enhance the manuscript's rigor, clarity, and contribution.

1. Methodological Discrepancy in Sample Size: A critical issue is the inconsistency in reporting the sample size. The "Sample Group" section (line 83) states a total of 135 experts (45 from each country). However, the "Analysis of Interview Data" section (lines 141-142) mentions that "data obtained from interviews conducted with a sample group of 30 experts from both countries were processed and analyzed." This discrepancy must be urgently clarified and corrected throughout the manuscript. If only 30 experts' data were analyzed, the rationale for this subset, the selection process from the initial 135, and which two countries are represented, needs to be explicitly detailed. This profoundly impacts the study's scope and the generalizability of its findings.

2. Abstract Clarity on Methods: The abstract mentions "generalization and systematization methods" (line 22). This phrasing is somewhat vague. It would be beneficial to clarify what these methods entail, perhaps by linking them more directly to the qualitative data analysis techniques employed, such as thematic analysis or content analysis, to provide a clearer understanding of the methodological approach from the outset.

3. Enhancing Discussion on Inter-Organizational Cooperation: The abstract and discussion rightly point out the importance of cooperation for successful AI implementation. This point could be strengthened by connecting it to broader academic discussions on inter-organizational cooperation in technology adoption and sustainable practices. For instance, the role of collaboration in digital green supply chains offers a parallel, where cooperation moderates the relationship between DGSC implementation and eco-innovations. Consider discussing how such collaborative dynamics are essential in the context of AI for protected areas. Wang, S., & Zhang, H. (2024a). Inter-organizational cooperation in digital green supply chains: A catalyst for eco-innovations and sustainable business practices. Journal of Cleaner Production, 472, 143383. https://doi.org/10.1016/j.jclepro.2024.143383

4. Justification for Comparative Country Selection: While the study includes three countries, the introduction or methodology could more explicitly articulate the rationale for selecting Turkey, Lithuania, and Morocco. Highlighting their specific contrasting or comparable characteristics regarding environmental policy, technological adoption, or challenges in protected area management would better frame the comparative analysis and its potential to yield unique insights.

5. Detailing Purposive Sampling Rationale: The manuscript describes the purposive sampling method and the categories of experts. To further strengthen this, briefly elaborate on why these five specific groups (lawyers, academics, managers, government officials, and NGO representatives) were deemed the most crucial for providing comprehensive insights into the research questions. This would add another layer of justification to the sampling strategy.

6. Local Community Participation and AI: Table 2 and the subsequent discussion (lines 170, 177-182) identify the lack of local community participation as a significant challenge. The discussion could be deepened by exploring how AI technologies themselves might be leveraged to enhance community engagement, ensure their voices are heard, or facilitate benefit-sharing, thereby fostering greater buy-in for conservation efforts. Insights from how customer involvement enhances ESG performance in other sectors, like tourism SMEs using generative AI, might offer transferable ideas for engaging local communities through digital means. Wang, S., & Zhang, H. (2025). Promoting sustainable development goals through generative artificial intelligence in the digital supply chain: Insights from Chinese tourism SMEs. Sustainable Development, 33(1), 1231-1248. https://doi.org/10.1002/sd.3152

7. Specificity in Education and Local Involvement Needs: Table 3 points to "Education and Local Involvement" as vital for effective AI application. The manuscript should specify the nature of this education and involvement. For instance, does it refer to developing AI literacy among local stakeholders, fostering understanding of data privacy, co-designing AI solutions to ensure local needs are met, or training in using AI-driven monitoring tools?

8. Deepening Analysis of Legal and Ethical Barriers: Tables 5 and 6 present findings on legal and ethical challenges. The discussion could be more impactful by providing more concrete examples from the interviews illustrating how these specific legal gaps or ethical dilemmas (e.g., data ownership, algorithmic bias in wildlife monitoring, accountability for AI errors) practically hinder AI adoption or could lead to adverse outcomes in protected area management.

9. Expanding on Government Support Mechanisms for AI Adoption: The role of government support is touched upon, particularly in Table 7 regarding strategies like public-private partnerships and financial incentives. This section could be enriched by discussing how government policies can more effectively orchestrate resources to foster AI adoption in environmental sectors, drawing parallels with how government support amplifies the impact of GAI on green entrepreneurship through knowledge management and innovation pathways. Wang, S., & Zhang, H. (2024b). Green entrepreneurship success in the age of generative artificial intelligence: The interplay of technology adoption, knowledge management, and government support. Technology in Society, 79, 102744. https://doi.org/10.1016/j.techsoc.2024.102744

10. Theoretical Grounding of the Discussion: While the study is empirically driven, the Discussion section could be strengthened by more explicitly linking the findings to established theories in technology adoption (e.g., Technology Acceptance Model, Unified Theory of Acceptance and Use of Technology), institutional theory, or collaborative governance. This would help to position the empirical findings within broader academic debates and enhance the study's theoretical contribution.

11. Specificity in Policy Recommendations: The conclusion (lines 590-594) suggests governments should support and incentivize managers. To make this more actionable, consider outlining specific types of incentives or support mechanisms derived from your findings (e.g., funding for pilot projects, development of national AI strategies for conservation, capacity-building programs for protected area staff).

12. Data Presentation and Confidentiality: Given the potential sensitivity of expert opinions on legal and governmental inadequacies, ensure the manuscript clearly states how anonymity and confidentiality were maintained beyond the general statement in lines 88-90, especially if the analyzed sample is indeed only 30 individuals, making them potentially more identifiable.

13. Consistency in Referencing: Please conduct a thorough review of the reference list for consistency and adherence to a standard academic citation style (e.g., APA 7th edition, which the provided example comments appear to use). Several references, such as those for legal documents (e.g., references 46, 47, 48, 49), are very brief.

14. Elaboration on "Smart Solutions": The title and keywords use "smart solutions." While AI is the focus, briefly defining what constitutes a "smart solution" in the context of protected area management in the introduction would be beneficial, linking it clearly to the AI technologies discussed.

 

Addressing these points will significantly strengthen the manuscript and its potential contribution to the field of sustainable management of protected areas.

Comments on the Quality of English Language

While the manuscript is generally understandable. Several sentences are overly long and complex, which occasionally obscures meaning (e.g., lines 33-38, 61-64). There are also instances of awkward phrasing, repetition, and minor grammatical errors that could be rectified through professional copyediting. For example, the repeated use of "it is understood that" (e.g., lines 249-250, 261-263) could be varied. Improving conciseness and idiomatic expression will enhance the clarity and impact of the research for a global audience. 

Author Response

Dear Reviewer,

We sincerely thank you for the time and effort you dedicated to evaluating our study. Your observations and constructive criticisms have significantly contributed to the improvement of the manuscript. We are truly grateful for your valuable comments and insights. All the points you raised in your report have been carefully and thoroughly reviewed, and corresponding revisions have been made accordingly.

You may find all the details regarding these revisions in the attached file.

Author Response File: Author Response.docx

Reviewer 4 Report

Comments and Suggestions for Authors

The abstract introduces the significance of artificial intelligence (AI) in protected area management and outlines the research aim and methodology succinctly. The emphasis on predictive capabilities, strategic planning, and collaboration adds value. However, the abstract remains overly general, lacking quantitative highlights or clearly articulated research contributions. A brief mention of the number of participants or a unique insight from the study would enhance its informativeness.

The introduction is comprehensive, covering the relevance of AI in environmental sustainability and contextualizing it with climate change, biodiversity loss, and resource limitations. It successfully conveys the urgency of the problem and the potential of AI solutions. Still, the introduction is verbose and somewhat repetitive—phrases describing AI’s predictive and real-time capabilities are reiterated multiple times. Moreover, the research gap and originality of this work could be more explicitly stated to distinguish it from existing literature.

The materials and methods section effectively explains the qualitative design, purposive sampling, and use of semi-structured interviews. The inclusion of coding strategies and the professional diversity of the sample (spanning Turkey, Lithuania, and Morocco) enhance credibility. However, the section does not clarify how saturation was achieved or whether any steps were taken to ensure inter-coder reliability or reduce bias during analysis. The rationale for choosing 135 participants is also unclear, and while the interview questions are well-framed, further justification on tool validation would strengthen this section.

In the results, the study presents a well-structured, comparative analysis of challenges, current AI applications, and future potential across the three countries. Tables summarizing responses by category are a valuable addition, and the integration of expert quotations adds authenticity. However, the narrative tends to mirror the tables too closely, often repeating content without deeper interpretation. Furthermore, some terms used—such as “advanced” or “emerging” AI development—require clearer definitions to avoid subjective interpretation. A more technical discussion of AI tools (e.g., how PAWS AI or TrailGuard AI operate) would benefit readers unfamiliar with these applications.

The discussion effectively connects findings to legal, infrastructural, and ethical themes, and emphasizes the importance of public-private-academic collaboration. It appropriately reflects on broader frameworks like the European Union’s AI Act. However, it occasionally restates results without deeper synthesis. It would benefit from restructuring into clearer subsections (e.g., technological barriers, legal challenges, collaboration strategies). The discussion could also engage more critically with existing literature by comparing the study's findings to similar international research in AI-driven environmental management.

In the conclusion, the authors summarize their key findings across countries and emphasize the need for legal reforms, collaboration, and infrastructural investment. This is coherent and policy-relevant. Nonetheless, the conclusion misses an opportunity to reflect on the study’s limitations, such as the qualitative focus, geographic scope, or lack of technical detail in AI implementation. Future research directions are not explicitly suggested, which could help guide subsequent studies in this area.

Author Response

Dear Reviewer,

We sincerely thank you for the time and effort you dedicated to evaluating our study. Your observations and constructive criticisms have significantly contributed to the improvement of the manuscript. We are truly grateful for your valuable comments and insights. All the points you raised in your report have been carefully and thoroughly reviewed, and corresponding revisions have been made accordingly.

You may find all the details regarding these revisions in the attached file.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

All the problems were solved.

Author Response

We sincerely thank you for taking the time to contribute to this research and for your valuable feedback. We would like to emphasize that your esteemed contributions have significantly improved the quality of this study. We are grateful for the support you have provided throughout this process.

With all due respect.

Reviewer 2 Report

Comments and Suggestions for Authors

The authors have enriched the study from the original, but the use of qualitative research to study AI techniques in the field of sustainable conservation is rare in my cognitive field, and it remains to be hoped that experiments will be added to subsequent studies as the authors promise.

Author Response

Thank you very much for taking the time to contribute to this research and for your valuable feedback. The improvements made in response to your second review, along with the authors’ responses, are provided below.

• Firstly, we would like to express our sincere gratitude for your valuable contributions and thorough evaluation. To clarify the use of qualitative research methods in examining artificial intelligence techniques within the field of sustainable conservation, the introduction section has been expanded with straightforward additions, highlighted in red. Furthermore, improvements have been made through supplementary explanations in the methodology section.
• All the enhancements requested in the previous review have been addressed and documented in the referee report. The presentation and discussion of the findings have been evaluated with reference to the international literature, with particular attention paid to the use of sources and references directly relevant to the research topic, especially in the discussion section. Prior to submitting the revised manuscript to the journal system, language checks were also conducted.
• To facilitate the identification of the revisions, the "track changes" function has been activated and changes are clearly indicated within the text. Future research aims to adopt a more comprehensive approach by incorporating both qualitative and quantitative data. We sincerely thank you for your valuable comments and critiques and respectfully submit our regards.

With all due respect.

Reviewer 3 Report

Comments and Suggestions for Authors

After a thorough review of the revised manuscript, I am satisfied that the authors have effectively addressed all the feedback provided. 
In addition, considering that most readers are not native English speakers, it is recommended that language polishing will be more conducive to the dissemination of the study.

Author Response

We sincerely thank you for taking the time to contribute to this research and for your valuable feedback. We would like to emphasize that your esteemed contributions have significantly improved the quality of this study. We are grateful for the support you have provided throughout this process.

The study was reviewed one final time by the researchers and language experts before being uploaded to the journal system.

With all due respect.

Reviewer 4 Report

Comments and Suggestions for Authors

Aptly updated document.

Author Response

We sincerely thank you for taking the time to contribute to this research and for your valuable feedback. We would like to emphasize that your esteemed contributions have significantly improved the quality of this study. We are grateful for the support you have provided throughout this process.

With all due respect.