Artificial Intelligence and Blockchain-Driven Circular Platforms: Fostering Green Innovation and Sustainable Consumer Behavior in High-Value Resale

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The paper presents an interesting and timely exploration of how artificial intelligence and blockchain technologies can support circular economy practices and sustainable consumer behavior in high-value resale markets. The proposed TRUCE framework is conceptually rich and aligned with current sustainability goals (notably SDG 12), offering an innovative synthesis of trust, circularity, and governance in digital commerce.

However, the manuscript would benefit from stronger clarity, concision, and methodological transparency. Specific comments:

1. Structure and clarity
• The text is dense and often repetitive, especially in the Introduction and Literature Review. Please condense sections and emphasize the main research gap and objectives more explicitly.
• Consider reducing the number of references cited per sentence; this would improve readability and focus.
2. Methodology
• While the Design Science Research (DSR) approach is appropriate, the description of data collection and analysis (CHVDC and benchmarking datasets) remains somewhat abstract. Clarify the empirical nature of these datasets, were they based on interviews, simulations, or secondary data?
• Add more detail on validation procedures and sampling logic for the expert panels used in the Delphi evaluation.
3. Results and Evidence
• Reported performance metrics (e.g., “25% increase in product lifespan,” “20% reduction in authentication disputes”) appear to be derived from simulations. Please indicate clearly how these percentages were obtained and what baseline data were used.
• Figures (e.g., Figures 2–4) are well-conceptualized but need more quantitative evidence or empirical grounding.
4. Discussion and Contribution
• The discussion is conceptually strong but sometimes overlaps with the introduction. Consider separating theoretical implications from practical applications more clearly.
• While the integration of AI, blockchain, and circularity is novel, ensure that the contribution beyond existing frameworks (e.g., other blockchain-enabled circular systems) is clearly articulated.
5. Language and Style
• The English is generally understandable but verbose. A professional language revision is recommended to improve fluency, punctuation, and sentence economy.
• Simplify overly long sentences and ensure consistent tense and terminology (e.g., “resale logic,” “governance architecture,” etc.).
6. Conclusions
• Strengthen the link between findings and SDG 12 objectives with clearer takeaways for practitioners.
• Consider including a short paragraph on limitations and future research directions earlier in the Discussion, rather than only at the end.

Overall, this is a promising and conceptually rich study, but it requires major structural and stylistic revision before it can be considered for publication.

Author Response

Please see the attachment

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The paper examines how the core technologies of the digital economy-specifically artificial intelligence and blockchain enable green innovation and sustainable consumption through circular platform design in high-value resale markets.

It is a current paper, but it needs to clarify its objective, what gap it fills in practical and theoretical contexts, and what it is for.

The article is well structured and uses the Delphi methodology, which is bold.

Author Response

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Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

This paper explores the potential of Artificial Intelligence (AI) and blockchain technologies in promoting green innovation and sustainable consumption within the high-value resale market. The topic is cutting-edge and of considerable relevance; however, the paper still exhibits significant shortcomings in research design, data support, and logical rigor. The main comments are as follows:

 

1.The innovation points of this paper need to be further refined and articulated.

 

2.The citations are overly dense. Some indirect references can be removed to retain only the core and most relevant literature.

 

3.Although Figure 1 illustrates the DSR cycle, the correspondence between the described “relevance cycle–rigor cycle–design cycle” and the results is not fully consistent. It is recommended to clarify the input, output, and evaluation metrics of each design iteration in the methodology section to enhance reproducibility and transparency.

 

4.The five modules (T, R, U, C, E) are defined too broadly and lack distinctive innovation compared with the existing literature.

 

5.The discussion in Section 5 combines theoretical reflection and practical implications without clear distinction. It is suggested to divide it into two subsections: “Theoretical Contributions” and “Practical Implications.”

 

6.The paper lacks a discussion of its limitations and future research directions.

Author Response

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Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

Major Revision is appropriate and necessary. While the paper presents a theoretically sound framework, it critically lacks empirical validation required for publication in a peer-reviewed journal. For a journal like Sustainability that emphasizes practical impact, conceptual frameworks alone are insufficient—actual effectiveness verification is essential.

【MAJOR ISSUE 1】Critical Absence of Empirical Validation and Methodological Weakness

Problems Identified

1. Complete Absence of Operational Data
   • Lines 438-443 explicitly state TRUCE remains in "pre-operational stage" with no actual market deployment
   • Claimed metrics such as "20% reduction in authentication disputes" and "25% extension of product lifespan" (lines 507, 521, 805) are entirely simulation-based and do not reflect actual consumer behavior or market effects
   • The "over 500 stakeholder interactions" (line 252) are merely design process records, not actual user data from operational platform usage
2. Severe Limitations in Validation Methods
   • Delphi evaluation conducted with only 5 experts (Table A1), yielding statistically insufficient validity
   • Authors acknowledge sample size inadequacy (lines 915-916) but defer resolution to "future validations will expand to 10-15 experts"—an unacceptable postponement
   • "Sandbox environment" testing fails to replicate real market conditions
   • Simulations (lines 804-806) represent controlled scenarios, not ecological validity
3. Circular Reasoning Problem
   • CHVDC (Circular High-Value Design Corpus) is self-generated by the author (line 1041), with zero independent verification
   • Benchmarking relies solely on publicly available reports without actual operational comparison
   • No third-party validation or external replication of findings

Required Improvements

Minimum Essential Requirements:

• Implement Pilot Deployment: Conduct minimum 3-6 months operational testing with actual consumers (n≥100) and collect real usage data including:
  • Transaction completion rates
  • Authentication dispute rates
  • User satisfaction scores (validated scales)
  • Actual product lifecycle extension (tracked items)
• Design Controlled Experiments: Employ quasi-experimental or randomized controlled design comparing TRUCE vs. non-TRUCE conditions on:
  • Authentication accuracy
  • Transaction completion rates
  • Customer trust metrics
  • Actual product lifespan extension
• Add Statistical Validation: For all claimed effects (20% reduction, 25% increase, etc.), provide:
  • Confidence intervals (95% CI)
  • P-values with appropriate corrections for multiple comparisons
  • Effect sizes (Cohen's d or equivalent)
  • Statistical power analysis
• Incorporate Independent Evaluation: Conduct Delphi evaluation with minimum 15-20 independent experts, stratified by:
  • Blockchain technology specialists
  • Circular economy practitioners
  • E-commerce platform operators
  • Consumer behavior researchers

Proper Application of DSR Methodology:

• While citing Peffers et al. (2008) DSR framework, the "Demonstration" and "Evaluation" phases remain critically underdeveloped
• Evidence beyond proof-of-concept to demonstrate actual problem-solving effectiveness is required
• Current state represents a "research proposal" or "conceptual framework," not completed research

In its current form, this manuscript is at the level of a research prospectus, not a complete research article suitable for publication.

 

【MAJOR ISSUE 2】Unsubstantiated Claims of Sustainability Impact

Problems Identified

1. Unmeasured Environmental Impact
   • Repeated claims of contributing to SDG 12.5 (lines 19, 36, 170, 237, etc.) without any measurement of actual waste reduction, CO2 reduction, or resource conservation
   • No quantitative assessment of how "25% product lifespan extension" translates to environmental load reduction
   • Missing analysis of blockchain energy consumption trade-offs (line 855 mentions "energy-efficient blockchain scaling" without specifics)
   • No comparison of environmental footprint: blockchain operation vs. environmental benefits
2. Undemonstrated Consumer Behavior Change
   • Claims to "foster sustainable consumer behavior" (lines 61-63, 188-190) without evidence that consumers actually shifted to ethical consumption
   • No examination of rebound effects (whether affordable resale purchases induce additional new purchases)
   • References Gen Z preferences (lines 185-187) without actual generational behavioral analysis
   • Missing longitudinal tracking of purchasing pattern changes
3. Unclear Circular Economy Effects
   • No verification that repair integration actually functions in practice
   • No evidence that Digital Product Passport (DPP) data is actually used in repair/reuse decisions
   • No tracking to confirm closed-loop circularity across supply chain
   • Missing data on product retention rates, repair rates, and multiple resale cycles

Required Improvements

Quantification of Environmental Effects:

• Conduct Life Cycle Assessment (LCA): Perform LCA before and after TRUCE platform introduction for target product categories, calculating:
  • Carbon footprint reduction (kg CO2-eq)
  • Raw material conservation (kg materials)
  • Water usage reduction
  • Energy consumption differential
  • Waste diversion from landfills (kg, units)
• Analyze Environmental Costs of Blockchain: Provide net environmental effect analysis:
  • Platform operational energy consumption (kWh)
  • Associated carbon emissions
  • Net environmental benefit after subtracting platform costs
  • Comparison with alternative technologies (e.g., centralized databases)
• Measure Actual Waste Reduction: Track and report:
  • Weight/volume of products diverted from disposal
  • Number of products entering second, third, nth lifecycle
  • Landfill diversion rates with statistical significance testing

Empirical Evidence of Consumer Behavior:

• Add Behavioral Economics Experiments: Verify how TRUCE's information transparency affects actual purchasing decisions using quasi-experimental design:
  • Randomized controlled trial comparing TRUCE vs. control platforms
  • A/B testing of transparency features
  • Willingness-to-pay studies for authenticated vs. non-authenticated items
• Conduct Longitudinal Tracking: Follow resale purchasers for minimum 6 months to measure:
  • New product purchase reduction (paired t-tests)
  • Total consumption patterns (increase/decrease)
  • Attitude changes toward sustainability (validated scales)
• Perform Demographic Analysis: Statistical verification of effect differences:
  • Gen Z vs. other generations (ANOVA)
  • Europe vs. other regions (regional comparisons)
  • Income levels and education effects (regression analysis)

Demonstration of Circularity:

• Track Repair and Reuse Rates: Measure for DPP-enabled products:
  • Actual repair completion rates (%)
  • Second, third, fourth resale rates (%)
  • Average number of ownership cycles per product
  • Survival analysis of product lifecycles
• Material Flow Analysis: Track where/when products exit circular system:
  • Identify bottlenecks in circular flow
  • Quantify leakage points
  • Analyze reasons for disposal vs. continued circulation

Currently, "sustainability" functions merely as a keyword without demonstrated environmental and social value creation. Meeting Sustainability journal's review standards requires quantitative proof of sustainability effects.

Author Response

Please see the attachment

Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

Comments and Suggestions for Authors

The paper has been well revised and is recommended for publication.

Author Response

Dear Reviewer 3,

Thank you very much for your positive evaluation of our revised manuscript and for recommending it for publication. We sincerely appreciate your time, constructive engagement, and supportive comments. As we approach the holiday season, we extend our warmest wishes to you and your loved ones.

With kind regards,
The Authors

Reviewer 4 Report

Comments and Suggestions for Authors

I am happy to approve the revised manuscript. Well done to the author! 
Just a minor thing, please insert a clearer figure 1, if you can.

Author Response

Dear Reviewer 4,

Thank you sincerely for your encouraging feedback and for approving our revised manuscript. We appreciate your attention to detail and your helpful suggestion regarding Figure 1. In response, we have enlarged the figure to improve its visibility, and we trust that the MDPI production team will further enhance clarity during the final typesetting and formatting stages.

With gratitude for your thoughtful review, we wish you a pleasant and restful holiday season.

Warm regards,
The Authors