Review Reports

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Thank you for the opportunity to review this study. This study draws on the context of self-driving tourism to investigate factors beyond the traditional 6A framework that shape tourists’ environmentally responsible behavior. The topic is really interesting. However several challenges are required:

 

1. Introduction: (1)the introduction fails to provide a rationale for employing grounded theory as opposed to other qualitative research approaches. (2) it does not sufficiently articulate the potential academic significance and practical implications of the study’s findings.
2. Materials and Methods: (1) The sources of the information in the table should be clearly indicated. (2) Line 162-164,What is the rationale for selecting government officials, academics and researchers, tourism industry professionals, local community representative as interviewees, and how are they connected to the focus of this study? Why were tourists not selected as interviewees? (3) Line 176, The 20–30 minute interview may be too brief to elicit meaningful insights from participants, raising concerns about whether the researcher was able to gather sufficiently rich and detailed primary data. (4) I will recommended to include a “Research Design” section in this part, accompanied by a research flowchart, which would help readers gain a clearer and deeper understanding of the study.
3. Results: The Results section only contains the researcher’s summary of the grounded theory analysis, without including any direct quotes from the original data.
4. Discussion: The Discussion section tends to repeat the findings presented in the Results and lacks deeper analysis or elaboration, particularly regarding the study’s theoretical contributions and practical significance.
5. Conclusion: The Conclusion section doesn’t clearly tie back to the research objectives or questions. It would be stronger if it explicitly showed how the findings address the study’s original goals.

Author Response

1. Comment:Novelty / Contribution

Builds on the popular 6A tourism model and adds three new measures: governance capacity, green innovation and community-based empathy.

Provides the multi-layered environmentally responsible behaviour (ERB) model of self-driving tourism.

Timely and relevant, however, theoretical integration can be more robust.

Response

• We agree. In the revised manuscript, we strengthened theoretical integration by explicitly linking the three newly identified constructs to established theories. Specifically, governance capacity (C1) is aligned with perceived behavioral control in the Theory of Planned Behavior (Hagger et al., 2022), green innovation practices (C2) resonate with the theory of transaction cost reduction and incentive mechanisms (Damberg et al., 2019), and community-based environmental empathy (C3) corresponds to the sociological theory of collective consciousness (Morgenroth et al., 2015; Pizarro et al., 2022). These revisions highlight the theoretical robustness of our multi-layered ERB model (see Section 5, Discussion).

2. Methodology

2.1 Relies on grounded theory where 20 interviews with experts are conducted though with more interviews aiding to ensure that saturation is achieved.

Response

• We conducted five additional interviews to test theoretical saturation, bringing the total to 25. No new codes emerged, confirming that saturation was achieved. The details are reported in Table 8 (see Section 4.4).

2.2 Gives methodical codes (open, axial, selective) but does not give details on intercoder reliability, balance in the sample and use of long quotes.

Response

• We added details on intercoder reliability. Coding was repeatedly cross-checked and refined until consensus was reached, following established practices for ensuring consistency (Cascio et al., 2019). This improves the transparency of our methodology (see Section 3.4).

2.3 Omits real tourists, thereby constraining the view.

Response

• We appreciate the reviewer’s insightful question. The rationale for selecting these four categories of interviewees lies in the purpose of this qualitative phase. The study adopts grounded theory to explore destination-side drivers that influence the environmentally responsible behavior (ERB) of self-driving tourists. Since the focus is on identifying and theorizing destination-level factors beyond the 6A framework, it was essential to recruit informants with deep professional knowledge, policy experience, and first-hand involvement in the governance, operation, and community engagement of the tourism destination.
• Government officials were included because they design and implement regulatory frameworks, infrastructure planning, and environmental policies that directly shape the destination’s governance capacity.
• Academics and researchers were selected for their analytical expertise and ability to provide theoretical and evidence-based interpretations of destination management and sustainability.
• Tourism industry professionals (e.g., practitioners and managers) were included as they operate tourism products and services, and can reveal how green innovation and management practices are applied in daily operations.
• Local community representatives were crucial because they embody cultural values, community norms, and grassroots initiatives that underpin environmental empathy and social participation.
• Tourists were not selected because they are the subjects of influence in this model rather than the sources of destination governance, innovation, or cultural drivers. In other words, their perspectives are essential in the quantitative phase (through the large-scale questionnaire survey), but they are not suited for the exploratory stage where expert insights into structural, managerial, and cultural mechanisms were required. This aligns with the grounded theory principle of theoretical sampling (Corbin & Strauss, 2015), which prioritizes participants most capable of providing rich information relevant to conceptual development.
• On the other hand,we acknowledged this limitation and specified that the study only included expert participants. We suggest that future studies should incorporate real and professionally knowledgeable tourist data to complement and validate the framework (see Section 5).

3. Clarity & Presentation

In most cases well structured, however, there are redundant and long parts (coding tables).

Response

• Thank you for this helpful observation. We have streamlined the presentation as follows. We relocated all detailed coding tables—open, axial, and selective—from the main text to Online Appendices A–
• In addition, we carefully revised the entire manuscript to improve readability and conciseness. Specifically, we condensed overlapping explanations, integrated repetitive passages into single, more focused statements, and replaced lengthy descriptions of coding steps with concise summaries supported by representative examples. These adjustments ensure that the main text highlights the analytical logic and theoretical contributions, while technical details remain accessible in the appendices. We believe this restructuring makes the argument clearer, reduces redundancy, and enhances the overall flow of the paper.

Response

• In the revision, we added three figures to improve clarity: Figure 7 (Three-Stage Coding Process), Figure 8 (Word Cloud of Open Coding Results), and Figure 9 (Comparative Framework of Destination Influence on ERB: Traditional 6A vs. Extended Model). These visualizations make the methodological process and conceptual framework more transparent and present the model in a plain and accessible manner.

Language is clear yet it needs editing in terms of grammar and brevity.

Response

• The manuscript has undergone a thorough language review. Sentences were shortened, redundancies removed, and grammar corrected to enhance readability.

4. Significance of Results

The results may be of interest in terms of theoretical application (empowerment, behavioural economics, and collective consciousness) as well as empirical governance of tourism destinations.

Findings should be compared more closely with the available literature on ERB and tourism governance.

Response

• We expanded the discussion to compare our findings with existing literature. For example, Cheng and Wu (2015) emphasize the role of knowledge and attachment in ERB, Su et al. (2020) highlight the shift from recreation to responsibility, and Wang et al. (2024) demonstrate the importance of cultural and ecological contexts. Our study extends these insights by positioning governance, innovation, and community empathy as distinct destination-level drivers within a progressive model (see Section 5).

The practical implications are to be put in Lijiang/China in greater context.

Response

• We revised the implications section to situate the findings in the Chinese context. For example, the Implementation Rules for the Lijiang Tourism Regulations (2025) and the Yunnan “Green Shield” initiative are discussed in relation to our model. Furthermore, the framework’s applicability to other Chinese self-driving destinations such as Xinjiang and Sichuan is emphasized (see Section 5).

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Novelty / Contribution

1. Builds on the popular 6A tourism model and adds three new measures: governance capacity, green innovation and community-based empathy.
2. Provides the multi-layered environmentally responsible behaviour (ERB) model of self-driving tourism.
3. Timely and relevant, however, theoretical integration can be more robust.

Methodology

1. Relies on grounded theory where 20 interviews with experts are conducted though with more interviews aiding to ensure that saturation is achieved.
2. Gives methodical codes (open, axial, selective) but does not give details on intercoder reliability, balance in the sample and use of long quotes.
3. Omits real tourists, thereby constraining the view.

Clarity & Presentation

1. In most cases well structured, however, there are redundant and long parts (coding tables).
2. Lack of figures/visualisations (conceptual model must be presented plainly).
3. Language is clear yet it needs editing in terms of grammar and brevity.

Significance of Results

1. The results may be of interest in terms of theoretical application (empowerment, behavioural economics, and collective consciousness) as well as empirical governance of tourism destinations.
2. Findings should be compared more closely with the available literature on ERB and tourism governance.
3. The practical implications are to be put in Lijiang/China in greater context.

Author Response

1. Introduction:
   • the introduction fails to provide a rationale for employing grounded theory as opposed to other qualitative research approaches.

Response

• Added a sentence clarifying why grounded theory was chosen over other qualitative methods, stressing its suitability for theory building(see Section 1 Introduction)
  • it does not sufficiently articulate the potential academic significance and practical implications of the study’s findings.

Response

• Added a short paragraph on academic contribution (extending 6A) and practical value for destination governance. (see Section 1 Introduction)

2. Materials and Methods:

• The sources of the information in the table should be clearly indicated.

Response

• We specified the data sources below Tables(fieldwork transcripts and coding results).
• Line 162-164,What is the rationale for selecting government officials, academics and researchers, tourism industry professionals, local community representative as interviewees, and how are they connected to the focus of this study? Why were tourists not selected as interviewees?

Respons

• We appreciate the reviewer’s insightful question. The rationale for selecting these four categories of interviewees lies in the purpose of this qualitative phase. The study adopts grounded theory to explore destination-side drivers that influence the environmentally responsible behavior (ERB) of self-driving tourists. Since the focus is on identifying and theorizing destination-level factors beyond the 6A framework, it was essential to recruit informants with deep professional knowledge, policy experience, and first-hand involvement in the governance, operation, and community engagement of the tourism destination.
• Government officials were included because they design and implement regulatory frameworks, infrastructure planning, and environmental policies that directly shape the destination’s governance capacity.
• Academics and researchers were selected for their analytical expertise and ability to provide theoretical and evidence-based interpretations of destination management and sustainability.
• Tourism industry professionals (e.g., practitioners and managers) were included as they operate tourism products and services, and can reveal how green innovation and management practices are applied in daily operations.

 

• Local community representatives were crucial because they embody cultural values, community norms, and grassroots initiatives that underpin environmental empathy and social participation.
• Tourists were not selected because they are the subjects of influence in this model rather than the sources of destination governance, innovation, or cultural drivers. In other words, their perspectives are essential in the quantitative phase (through the large-scale questionnaire survey), but they are not suited for the exploratory stage where expert insights into structural, managerial, and cultural mechanisms were required. This aligns with the grounded theory principle of theoretical sampling (Corbin & Strauss, 2015), which prioritizes participants most capable of providing rich information relevant to conceptual development.
• Line 176, The 20–30 minute interview may be too brief to elicit meaningful insights from participants, raising concerns about whether the researcher was able to gather sufficiently rich and detailed primary data.

Response

• We acknowledge the reviewer’s concern. Although each interview lasted 20–30 minutes, several measures ensured data richness. Prior to the interviews, participants received a briefing document and guiding questions, and the researcher engaged in pre-interview communication to clarify the study’s scope. These preparations allowed interviewees—who possessed strong professional expertise—to respond concisely and substantively. These preparatory tasks were carried out with great rigor and were sustained over an extended period.
• The semi-structured design focused on well-defined themes directly linked to the research questions, which allowed participants to provide concise yet detailed accounts. All interviewees were professionals or community representatives with substantial expertise, enabling them to articulate insights efficiently without extended prompting.
• In addition, data richness was further ensured by triangulating perspectives across four stakeholder groups and by conducting five additional interviews to test for theoretical saturation. The saturation test confirmed that no new categories emerged, suggesting that the data were sufficiently comprehensive for theory development (Corbin & Strauss, 2015).
• I will recommended to include a “Research Design” section in this part, accompanied by a research flowchart, which would help readers gain a clearer and deeper understanding of the study.

Response

• We appreciate this suggestion. In the revised manuscript, we created a separate Research Design subsection at the beginning of Materials and Methods. This subsection outlines the overall logic of the study, from problem framing and expert sampling to data collection, coding, and model construction. We also added a flowchart (Figure 6) to visualize these steps and show the iterative nature of grounded theory. This improves transparency and allows readers to clearly distinguish the research process from the subsequent results.

3. Results:

The Results section only contains the researcher’s summary of the grounded theory analysis, without including any direct quotes from the original data.

Response

• We agree. In the revised manuscript, we inserted short verbatim quotations from government officials, academics, industry practitioners, and community representatives under each axial and selective category. For example, section 4.2 (Axial Coding): added one representative quotation at the end of each paragraph for B1, B2, and B3 (labeled ID-15,1,2).Three-Layer Model paragraphs (Layer 1/2/3): added 1–2 short illustrative quotations at the end of each layer paragraph (labeled ID-1,2,3).
• This addition provides direct evidence for the categories, in line with qualitative reporting guidelines.

4. Discussion:

The Discussion section tends to repeat the findings presented in the Results and lacks deeper analysis or elaboration, particularly regarding the study’s theoretical contributions and practical significance.

Response

• We thank the reviewer for noting that the Discussion partly repeated Results and lacked deeper analysis of theoretical contributions and practical significance. We have substantially revised Section 5 as follows:
• Refocused opening to theorize cross-level mechanisms (governance → technological enablement → community-based empathy) rather than restating findings.
• Added a new subsection “Theoretical Contributions” that (i) extends the 6A lens into a layered, progressive process, (ii) articulates mechanisms and sequencing, and (iii) specifies boundary conditions.
• Rewrote “Practical Implications” into an actionable three-part agenda (governance, digital nudging, community empathy), each supported by recent peer-reviewed evidence (e.g., Qin & Hsu, 2022; Chen et al., 2024; Huo et al., 2025; Ni et al., 2025; Majid & Tussyadiah, 2025; Liu et al., 2024).
• Removed/condensed passages that duplicated Results and trimmed policy lists into concise, evidence-based recommendations.

We believe these changes improve conceptual depth, clarify theoretical value, and offer clear, implementable guidance for destination management.

5. Conclusion:

The Conclusion section doesn’t clearly tie back to the research objectives or questions. It would be stronger if it explicitly showed how the findings address the study’s original goals.

Response

• We appreciate the reviewer’s comment that the Conclusions did not clearly tie back to the study’s objectives and research questions. To address this, we have restructured the entire Conclusions section to explicitly map findings to the original aims. The revised text now (i) states the three objectives/questions (RQ1–RQ3) at the outset, (ii) presents one-to-one answers that show how the results address each objective, and (iii) draws out the overall significance without repeating the Results. In addition, we inserted a short “Research objectives and questions” sentence at the end of the Introduction so that the paper now provides a clear thread from aims → findings → conclusions. We believe these revisions improve clarity, coherence, and the perceived contribution of the study.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Daer All,

This article represents a significant contribution to the field of sustainable tourism by advancing theoretical frameworks and providing practical strategies for enhancing environmental responsibility among self-driving tourists. The layered model offers a comprehensive view that bridges immediate tourist experiences with broader socio-cultural and institutional influences, paving the way for more sustainable tourism practices. However, some minor remarks should be deemed as follows:

1. Generalizability:

   • The findings are primarily based on interviews conducted in Lijiang, which may not accurately represent self-driving tourism behaviors in different cultural or geographical contexts. This limitation raises concerns about the applicability of the proposed model to other destinations.

2. Bias in Qualitative Methodology :

   • Please report how you avoid  biases and subjective interpretations while interpreting your prescreptions?. The results may not fully capture the diversity of tourist behaviors and motivations across broader populations.

3.  Validation:

   • The article does not include any answer abour validation that  support the proposed model. It  is encouraged to employ methods to validate the findings, which would enhance the credibility and reliability of yourmodel.

4. Focus on Self-Driving Tourists!!!!

   • The emphasis on self-driving tourists may overlook other important segments of the tourism market, such as public transport users or those engaged in eco-tourism. This narrow focus may limit the model's effectiveness in addressing environmental responsibility across the entire tourism spectrum.

5. Potential Overemphasis on Emotional Factors:

   • While the article highlights emotional connection and cultural empathy as significant drivers of environmentally responsible behavior, there may be a risk of overemphasizing these factors at the expense of more pragmatic or economic motivations that also influence tourist behavior. Please fix this issue..

6. Insufficient Exploration of Community Dynamics:

   • Although community-based environmental empathy is identified as a key factor, the article may benefit from a deeper exploration of how community dynamics, including conflicts and differing stakeholder interests, can impact the effectiveness of the proposed model.

7. Need for Clear Implementation Strategies:

   • I hope to see your next draft any actionable strategies for destination managers to implement the findings into practice. Clear guidelines for applying the model in real-world settings would enhance its practical relevance.

All in all, i see a merit in this valuable effort.

With my best regards

Author Response

1. Generalizability:

The findings are primarily based on interviews conducted in Lijiang, which may not accurately represent self-driving tourism behaviors in different cultural or geographical contexts. This limitation raises concerns about the applicability of the proposed model to other destinations.

Response: 

• We appreciate the reviewer’s concern that interviews were conducted in Lijiang and may not represent other cultural or geographical contexts. We respectfully note that, as a grounded-theory study, our aim is analytical generalization rather than statistical inference.
• To make this intent explicit, we added clarifying language in the case selection paragraph (3.2 Research site) and revised the opening sentence of Section 5.3 Limitations. The text now states the scope conditions (governance maturity, digital readiness, community cohesion) under which transferability is expected and cites recent methodological work on transferability and theoretical replication.
• We also inserted a one-line statement in the Conclusions to signal that applicability should be examined via multi-site replication across destination types. These focused changes preserve the logic of a theory-building design while offering a concrete path to evaluate portability beyond Lijiang (Drisko, 2025; Stalmeijer et al., 2024; Goertz & Haggard, 2023).

2. Bias in Qualitative Methodology :

Please report how you avoid  biases and subjective interpretations while interpreting your prescreptions? The results may not fully capture the diversity of tourist behaviors and motivations across broader populations.

Response: 

• Thank you for raising this concern. We have clarified our bias-mitigation and interpretive safeguards and acknowledged limits on diversity coverage. We added details on intercoder reliability. Coding was repeatedly cross-checked and refined until consensus was reached, following established practices for ensuring consistency (Cascio et al., 2019). This improves the transparency of our methodology (see Section 3.3).
• Diversity limitation. In Limitations, we explicitly note that an expert-focused, single-destination design may not fully reflect the diversity of tourist behaviors/motivations and propose multi-site replication plus a large-sample tourist survey.  
• We hope these targeted revisions make our procedures and boundaries explicit.

3. Validation:

The article does not include any answer abour validation that  support the proposed model. It  is encouraged to employ methods to validate the findings, which would enhance the credibility and reliability of yourmodel.

Response: 

• In Section 4.4, we now state that the five additional interviews produced no new codes and stable inter-category relations, which we frame as a pragmatic internal validation of the proposed model.
• Validation via literature triangulation. Beyond the internal check provided by our saturation test, we now explicitly frame parts of the Discussion as literature-based corroboration of the proposed pathways. Specifically, we added a short paragraph after the mechanism summary to show convergence with recent empirical studies: (i) governance and policies supporting perceived behavioral control and ERB (Qin & Hsu, 2022; Huo et al., 2025); (ii) digital nudges and flashback prompts lowering participation costs and sustaining behaviors (Ni et al., 2025; Majid & Tussyadiah, 2025); and (iii) resident modeling and empathy generating contagion effects on tourists’ ERB (Liu et al., 2024; Chen et al., 2024).
• We also add a one-sentence note linking our three-step sequencing to these studies. We present this as theory-consistent triangulation that enhances credibility while acknowledging that formal statistical validation remains an agenda for future research.

4. Focus on Self-Driving Tourists!!!!

The emphasis on self-driving tourists may overlook other important segments of the tourism market, such as public transport users or those engaged in eco-tourism. This narrow focus may limit the model's effectiveness in addressing environmental responsibility across the entire tourism spectrum.

Response: 

• Thank you for this helpful comment. We acknowledged this limitation. We have revised Limitations & Future Research (Point 2) to call for cross-segment replications—beyond self-driving tourists to public-transport users and ecotourists—and tests across diverse destination types and development stages, in order to assess the model’s applicability, generalizability, and boundary conditions.
• On the other hand, Our study intentionally focuses on self-driving tourists for reasons already documented in the manuscript.
  First, self-driving constitutes a major share of China’s tourism market (38.4 billion person-times in 2019, 64%), with high autonomy over routing and activities, which makes destination-level levers salient.
  Second, destination-side environmental pressures attributed to self-driving are concrete and urgent in our setting—for instance, waste loads along famous self-drive routes (Duku Highway; Sichuan–Tibet) and peak-season vehicle emissions in Lijiang.
  Third, Lijiang combines strong self-driving appeal with pronounced low-carbon constraints, offering a context where waste management, congestion, and emissions are observable and policy-relevant.
  Fourth, our model’s levers align closely with self-driving touchpoints: 6A elements such as accessibility and amenities (road/parking and on-site sorting) and the added layers (governance, digital nudges in navigation/booking, and community empathy) directly map to self-driving behaviors at the destination.
• Our study is intentionally scoped to this segment, recent research identifies road transport—and private-car travel in particular—as a principal hotspot of tourism-related emissions (Sun et al., 2024; Zhou et al., 2024; Zientara et al., 2024). To make this rationale explicit in the manuscript, we have added a sentence in the Introduction linking our empirical motivation to current evidence (after the paragraph describing destination environmental issues).
• To avoid any misunderstanding, we added a short scope and delimitation note in the Introduction clarifying that the model is domain-specific. _____【Car-based trips often entail higher per-capita emissions and distinct decision constraints compared with public transport users (Zhou et al., 2024; Blättler et al., 2024; Dolnicar, 2025). Second, car reliance remains salient in many destinations, making this segment both high-impact and managerially actionable (Zhou et al., 2024). This study intentionally focuses on self-driving tourists, whose high autonomy over routing, parking, and in-vehicle practices creates distinct intervention points at the destination. Other segments (e.g., public-transport users, eco-tourists) operate under different mobility constraints and norms and thus require re-anchored levers rather than a different theory (Zhou et al., 2024; Zientara et al., 2024; Cao et al., 2022)】

5. Potential Overemphasis on Emotional Factors:

While the article highlights emotional connection and cultural empathy as significant drivers of environmentally responsible behavior, there may be a risk of overemphasizing these factors at the expense of more pragmatic or economic motivations that also influence tourist behavior. Please fix this issue.

Response: 

• We sincerely appreciate this comment. Our intention is not to overemphasize cultural empathy at the expense of pragmatic motivations. In fact, the model embeds instrumental/economic drivers in C1–C2 (governance policy mixes and green innovation that reduce cost/friction and employ light incentives), while C3 consolidates the normative–affective route. To make this balanced view more explicit, we have: (i) added a short paragraph in the Discussion titled “Instrumental–economic alignment,” clarifying how cost/time/hassle reduction and small incentives work alongside empathy-based mechanisms; (ii) inserted one sentence in Theoretical Contributions (2) to describe the two complementary routes (instrumental vs. normative–affective); and (iii) added an Implications item “(2a) Incentive alignment,” listing practical tools (deposit–refund, differential parking fees, micro-rewards). We also cite recent evidence showing that economic incentives and low-friction design effectively support sustainable mobility and recycling in tourism-related contexts, and that flashback/app-based nudges help sustain behaviors post-visit (Bissel, 2025; Huo et al., 2025; Aravind et al., 2024; Majid & Tussyadiah, 2025; Ni et al., 2025).
• We hope these focused edits address the concern while preserving the clarity of the multi-layered model.

6. Insufficient Exploration of Community Dynamics:

Although community-based environmental empathy is identified as a key factor, the article may benefit from a deeper exploration of how community dynamics, including conflicts and differing stakeholder interests, can impact the effectiveness of the proposed model.

Response: 

• We appreciate the reviewer’s suggestion to examine how community dynamics and potential conflicts may shape the effectiveness of C3. In the revised manuscript, we added a concise discussion on community heterogeneity, power asymmetries, and implicit conflicts in the Discussion section and clarified in Theoretical Contributions that stakeholder alignment and perceived fairness may moderate C3’s effects. We also introduced a new practical implication—“conflict-aware community engagement”—that outlines participatory, benefit-sharing, and grievance-handling measures to stabilize empathy-based mechanisms. These additions are supported by recent evidence on tourism conflicts and collaborative/meta-governance (Li, 2024; Rastegar & Ruhanen, 2022; Huber et al., 2023; El Alam et al., 2024).
• We hope these focused changes address the concern while keeping the paper concise

7. Need for Clear Implementation Strategies:

I hope to see your next draft any actionable strategies for destination managers to implement the findings into practice. Clear guidelines for applying the model in real-world settings would enhance its practical relevance.

Response: 

• Thank you very much for encouraging us to strengthen the paper’s practical relevance. In the revised manuscript we now provide clear, actionable guidance for destination managers in Section 5.2 – Practical Implications. The section is organized into three implementable levers that map directly onto the model:
• Governance & capacity management. Pair clear rules with graduated on-site enforcement and adopt capacity limits / time-phased reservations for sensitive areas. This policy mix is presented as a practical way to narrow the intention–behavior gap and stabilize ERB.
• Technological enablement (“design for low-effort green”). Implement digital nudges in navigation and booking flows (e.g., default green routes/parking, refill prompts, waste-sorting cues) and use post-trip flashback nudges to sustain spillover behaviors at home. These tools explicitly target friction and attentional limits.
• Community-anchored empathy. Run resident-led modeling (e.g., attendant-led sorting, volunteer crews) and micro-narratives that humanize ecosystem impacts; ethical guardrails (privacy, transparency, opt-out) are specified for all digital interventions.
• Section 5.2 begins by situating these actions in the current governance context and then translates mechanisms into management steps for routine use by destination teams.
• We hope this makes the model readily applicable in day-to-day management and demonstrates how the three layers (governance, technology, community) can be implemented as a coordinated package in real destinations.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

I have no further comment on this manuscript. 

Author Response

Thanks for your comments, we already try our best to do a native speaker English proofreading.

Sincerely.

Reviewer 2 Report

Comments and Suggestions for Authors

The paper is clearly outlined and presents a valuable input into the field by supplementing the 6A model with governance, innovation, and community attributes to describe the environmentally responsible behavior of tourists. I would suggest it to be accepted with minor changes:

1. In the *Introduction*, the third paragraph would benefit from additional supporting references to strengthen the arguments presented.
2. Some terms (e.g., *United Nations Sustainable Development Goals*, *Global Sustainable Tourism Council*) are introduced with abbreviations (e.g., SDGs, GSTC) but are not used consistently throughout the manuscript. In such cases, abbreviations are unnecessary. Please review and ensure consistency across the entire manuscript.
3. For key constructs such as *environmental responsibility behavior*, it is recommended to introduce the full term once and then consistently use the abbreviation (ERB) throughout the manuscript. Please check for similar consistency issues with other variables as well.
4. In particular, the authors can better justify the conclusion part by including three subsections (i) a more articulate explanation of what theoretical contributions can be made, (ii) practical implications to the destination managers, (iii) study limitations and future research directions.
5. Moreover, it would be enhanced by making few adjustments to the clarity of figures, the consistency of terminology and synthesis of literature that will enhance readability.

Comments on the Quality of English Language

A professional editor should review the manuscript to confirm that it adheres to academic writing style. This will assist in enhancing readability, grammar and phrasing as well as overall clarity of manuscript.

Author Response

Responce to Reviewers

 

In the Introduction, the third paragraph would benefit from additional supporting references to strengthen the arguments presented.

Response

• We appreciate the reviewer’s suggestion. In the revised manuscript, we have fully rewritten the third paragraph of the Introduction to anchor our claims in recent, high-quality sources. Specifically, we now cite empirical and review studies on (i) managed-access tools such as timed-entry systems and daily caps for peak-flow dispersion and resource protection (Creany et al., 2024; Rogowski et al., 2024), (ii) destination-level waste governance through multi-actor networks and circular practices (Xu et al., 2024).(iii) We have added authoritative policy sources to document recent regulatory efforts at national, municipal, and provincial levels. Specifically, we now cite the Ministry of Culture and Tourism’s 2022 Guidelines for Civilized Guidance in Tourist Attractions, Lijiang’s 2025 Implementation Rules for the Lijiang Tourism Regulations, and Yunnan’s 2025 joint Notice on Regulating Tourism Activities in Nature Reserve. These additions clarify the institutional context. No other textual changes were made in the Introduction.

Some terms (e.g., *United Nations Sustainable Development Goals*, *Global Sustainable Tourism Council*) are introduced with abbreviations (e.g., SDGs, GSTC) but are not used consistently throughout the manuscript. In such cases, abbreviations are unnecessary. Please review and ensure consistency across the entire manuscript.

Response

• Thank you for pointing out the inconsistent and—in some cases—unnecessary use of abbreviations. We have implemented a manuscript-wide policy: abbreviations are retained only when they appear frequently; otherwise, full terms are used throughout. Concretely, we now (i) write “United Nations Sustainable Development Goals” and “Global Sustainable Tourism Council” in full when they occur only once or twice, and (ii) where an abbreviation is warranted, we define it at first mention (e.g., “Environmental responsibility behavior(ERB)” and use the abbreviation consistently thereafter. We also harmonized common field terms (ERB, 6A, SEM) by ensuring a single first-mention definition and uniform usage across text, tables, and captions. These changes have been applied across the entire manuscript and appendices..

For key constructs such as *environmental responsibility behavior*, it is recommended to introduce the full term once and then consistently use the abbreviation (ERB) throughout the manuscript. Please check for similar consistency issues with other variables as well.

Response

• We thank the reviewer for highlighting the need for abbreviation consistency. We have standardized the manuscript to use Environmental Responsibility Behavior (ERB) at first occurrence and ERB thereafter.For constructs that appear only once, we follow the journal’s common practice and keep the full term without introducing a new abbreviation. We hope these changes satisfactorily address the reviewer’s concern.

In particular, the authors can better justify the conclusion part by including three subsections (i) a more articulate explanation of what theoretical contributions can be made, (ii) practical implications to the destination managers, (iii) study limitations and future research directions.

Response

• Thank you for this helpful suggestion. In the revised manuscript, we strengthened the Conclusions by appending three concise subsections that summarize previously established points without adding new material. (i) Conclusions—Theoretical Contributions restates that the destination 6A is reframed as a multi-layered, progressive mechanism, specifying the cross-level sequence C1→C2→C3that converts experiential triggers into durable ERB.
• (ii) Conclusions—Practical Implications for Destination Managers distills the takeaway for practice: align capacity-based rules (C1), low-friction digital defaults (C2), and visible community participation (C3) to “green” daily 6A operations and sustain ERB.
• (iii) Conclusions—Limitations and Future Research reiterates the single-case qualitative scope and calls for multi-site surveys and field experiments to test the sequence and boundary conditions.

Moreover, it would be enhanced by making few adjustments to the clarity of figures, the consistency of terminology and synthesis of literature that will enhance readability.

Response

• Thank you for pointing this out. We have implemented three targeted revisions to improve readability without altering our findings.Figure clarity. We corrected numbering and cross-references and standardized captions: Figure 8 (three-stage coding), Figure 9 (word cloud), Figure 10 (comparative framework: traditional 6A vs. extended model), and Figure 11 (multi-layered model). We also fixed label spellings and harmonized terminology within figures (e.g., “Community-Based Environmental Empathy (C3)”). (Edits at Section 4 and 5; figure captions and in-text references updated.)
• Terminology consistency. We aligned figure labels with the manuscript’s constructs: Basic Experience Layer (6A); Destination Governance Capacity for Sustainability (C1); Green Innovation Practices at the Destination (C2); Community-Based Environmental Empathy (C3). We also removed minor inconsistencies and typos in figure text.
• To strengthen the synthesis without adding a new subsection, we inserted a brief bridging paragraph at the end of Section 2.3 (immediately before Materials and Methods) that links Sections 2.1–2.3 and naturally introduces our research question and method. No new citations or results were added.