Quantifying GHG Emissions of Korean Domestic Tourism: Spend-Based Multiregional EEIO Approach to Category 6 of Scope 3

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Dear authors

Your manuscript, "Spend-Based Multiregional EEIO Approach to Category 6 of Scope 3,” has valuable ideas. Still, I prefer to provide some recommendations that are not mandatory to be applied, but they can help improve the quality of your manuscript. These are as follows:

1. The abstract seems good. Please use CO_2eq instead of CO2e,
2. The introduction seems reasonable, but it needs more info. I believe you can add some more information about the background of the sissue. Additionally, you need to provide an explanation of the research gap and the novelty of your research, comparing it with other issues in the field after the paragraph on line 56.
3. In Section 2, please clarify the difference between the emission and carbon footprint, and how you calculate the print based on which activities? In this regard, you can utilise some good ideas from the research “J. Mar. Sci. Eng. 2023, 11(5), 1007,” which addresses these issues and classifies and scopes the emissions.
4. I believe you can revise Section 3 into a section of methods and materials, then you can add the materials in subsection 3.1. In addition, plz extend the width of the tables to reduce the length of the manuscript.
5. If figures need to be with references, plz provide them.
6. While the spend-based EEIO approach is well justified, the paper would benefit from a more precise sensitivity analysis on how variations in emission factors, in this regard, you can use some information from research of “ Mar. Sci. Eng. 2024, 12(8), 1290” which is precisely about sensitivity analyses on carbon footprint calculation.
7. The comparison with the MIT study is valid. Still, the paper could strengthen its contribution by explicitly discussing boundary differences (e.g., inclusion/exclusion of construction, imported goods, or infrastructure emissions) to improve transparency and facilitate replicability across contexts.
8. All figures must be revised. Some statistics are minimal and the text cannot be read, such as Fig. 5, and others are abnormally large, like Fig. 4.
9. The manuscript needs a separate discussion to analyse the current outcome with the expectation of good scenarios.
10. Plz revise the conclusion and add some explanations about the limitations of the study and recommendations for future research.

Thanks for your attention.

Author Response

Please refer to the attached file. Thank you. 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The article quantifies GHG emissions from domestic tourism in Korea using expenditure data from the 2023 National Travel Survey, mapped to a provincial multiregional input–output (EEIO) model. Expenditures (transportation, accommodation, food & beverage) are linked to 33 sectors; total emissions are 2,623 tCO₂e, disaggregated by region, expenditure type, and industry. Transportation dominates; Jeju exhibits disproportionately high emissions due to aviation dependence. Validation compares category shares with MIT’s Scope-3 Category-6 benchmark.

This is a timely, policy-relevant contribution that brings subnational granularity to tourism-related emissions in Korea. The spend-based MR-EEIO pipeline is appropriate given data availability, the mapping is transparent, and the regional read-outs are informative for targeted mitigation. To meet Sustainability’s standards for methodological transparency and robustness, the manuscript would benefit from clearer boundary choices, explicit uncertainty treatment, and sharper positioning vis-à-vis alternative (activity-based and hybrid) approaches.

Literature review
The background sections correctly situate global/national evidence and the Scope-3/Category-6 context, citing recent work (e.g., G20, interprovincial studies in China, Korea cases). The review could be strengthened by:

• A comparative discussion of spend- vs. activity- vs. hybrid approaches to tourism carbon accounting, highlighting known biases and where MR-EEIO excels or falls short at the provincial scale. (The paper already lists the four approaches; expanding the critical comparison would help justify the choice.)
• A brief note on emission-factor heterogeneity and its implications for inter-regional comparability in Korea (energy mixes, supply chains).

Adequacy and currency: Overall adequate and up to date (citations through 2024–2025). Maintain a clear link between each literature strand and implications for model choice, validation, and policy interpretation.

Data, sampling, and mapping
Data come from the 2023 National Travel Survey with 24,282 domestic tourists (out of 52,111 respondents), mapped to 17 provinces and three expenditure categories; regional allocation distinguishes lodging and visiting regions. Table 4 presents the regional spend breakdown. Please clarify: (i) derivation and use of the exclusion multiplier 2.7 beyond the explanatory note; (ii) treatment of business trips with leisure; and (iii) any deduplication of multi-region itineraries in the allocation.

The bridge from expenditures to the 33 sectors (Table 5) is clear, including the split of Food & Beverage between Food services & accommodation and Wholesale & retail—good practice.

Methodology
The EEIO formulation (industry emission factors , Leontief inverse, purchases vector ) is standard and well presented (Eqs. 1–3). The ME2IO2023 database (17 regions × 33 sectors) is described, with sector-specific emission factors and outputs in Appendix A1. It is important that the authors:

• Indicate the price base/deflation used to align survey expenditures with the IO table and emission factors; note any inflation adjustments.
• Clarify temporal alignment (tables updated via FlexIO; survey year 2023) and whether imports/leakages are handled consistently across regions.
• Add a concise flowchart or pseudo-code of the pipeline (collection → allocation → bridging → Leontief → disaggregation by type).

Results and interpretation
Headline findings are transparent: total 2,623 tCO₂e; transport dominates; Jeju high due to aviation; Gangwon/Gyeongnam/Gyeongbuk high due to volume and infrastructure. Figures 2–3 are informative and consistent with Table 6 (provincial breakdown). At the industry level, Transportation, Electricity & gas, Agriculture, and Waste management stand out (Table 7). It is recommended to add uncertainty/stability indicators (e.g., simple ±10–20% EF sensitivity or a bootstrap with sampling weights) for shares and totals.

Validation
Comparing category shares with MIT’s Category-6 study is a sensible plausibility check; the manuscript correctly notes that absolute levels differ by context, focusing on proportions. It would be useful to quantify this comparison (e.g., a table with Korea vs. MIT shares and percentage-point differences) rather than relying solely on visual inspection.

Policy relevance
The paper derives practical implications for transport decarbonization (rail electrification, modal shift), accommodation efficiency, and food-system measures—consistent with the evidence. The authors should include a short paragraph on regional prioritization (e.g., aviation in Jeju, food in Jeonju), pointing directly to Table 6/Figure 2 for target setting.

Presentation, English quality, and research artefacts
The manuscript is readable; minor issues remain (acronym consistency, a few long sentences). Figures/tables are captioned and cited in text; the regional map (Fig. 2) and stacked bars (Fig. 3) aid comprehension. Data availability is currently “upon request”; for reproducibility, consider sharing an anonymized regional-by-type expenditure matrix and a working script, even if ME2IO2023 is proprietary.

 

The manuscript offers useful provincial-level insight and a defensible MR-EEIO pipeline. Addressing uncertainty, price alignment, and validation presentation will consolidate robustness and enhance policy relevance.

 

CORRECTIONS REQUIRED BEFORE PUBLICATION

Literature review – Expand the method comparison and the discussion of EF heterogeneity; include a summary table contrasting spend/activity/hybrid approaches (it may build on Table 3), with pros/cons, data requirements, typical biases, and suitability at the provincial scale.

Prices, deflators, and boundaries –

• Indicate the price base/deflators applied to expenditures and confirm temporal consistency with the IO year and emission factors.
• Clarify the role (if any) of the exclusion multiplier (2.7) beyond descriptive context.
• Specify system boundaries: what is included/excluded (e.g., island/international trips, upstream energy emissions, duty-free purchases) to avoid double counting/omissions.

Uncertainty and sensitivity – Provide at least a simple sensitivity of totals/shares to emission factors and mapping assumptions (e.g., ±10–20% EF; alternative F&B split) or a bootstrap with sampling weights; where feasible, report the impacts in the key figures/tables (see next item).

Validation and communication of results –

• Add a compact Korea vs. MIT table with category shares and percentage-point differences, discussing drivers of divergence (aviation vs. private vehicles; electricity intensity; regional mixes).
• Include uncertainty/stability indicators in Figures 2–3 and Tables 6–7 (e.g., error bars/bands or a stability note: “rankings remain under ±10% EF changes”).

Treatment of imports and interregional flows – Briefly explain how ME2IO2023 handles imports and interprovincial trade when assigning emissions to provinces (allocation, leakage, origin assumptions for EFs).

Methodological transparency and software – Indicate the tools used for data wrangling/graphics (e.g., R/Python and specific packages) and add a concise flowchart/pseudo-code of the pipeline (collection → allocation → bridging → Leontief → aggregations).

Data availability and replication – Strengthen the Data Availability by sharing an aggregated regional expenditure file (transport/accommodation/food) and a script/pseudo-code to replicate Tables 6–7 and Figures 2–3 (repository with DOI).

Editorial polish and consistency – Unify acronyms (USEEIO/ME2IO/MR-EEIO), ensure consistent units/labels (tCO₂e; million KRW), shorten long sentences, and check consistency of figure/table captions and in-text calls.

Author Response

Please refer to the attached file. Thank you. 

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Thank you for an interesting manuscript. I think it would benefit from answering the following questions in a revision:

• Why isolate domestic tourism from the tourism of foreigners within Korea? And how does the carbon footprint of one compare to the other? 

This work could use more context as to the relative importance of the carbon emissions from domestic tourism in Korea. How important is this compared to other issues on which policymakers could concentrate?

Author Response

Please refer to the attached file. Thank you. 

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Dear authors. thanks for your revisions. I do not have issues with the current manuscript. Just plz merge sections 5 and 6 and name them the conclusion. thanks 

Author Response

Dear Reviewer 1:

Thank you. It merged as Section 5. Conclusion and Discussions. 

Sincerely,

Jiyoung Park