Unequal Paths to Decarbonization in an Aging Society: A Multi-Scale Assessment of Japan’s Household Carbon Footprints

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The study analyzes how Japan’s aging population, income inequality, and regional economic disparities influence unequal household carbon footprints (HCF). It assesses the impact of demographic factors on HCF, examines income- and spatial-based emission inequalities using Gini coefficients, and projects HCF trajectories at the prefectural level from 2020 to 2050. The topic is highly relevant for readers of the journal Sustainability, offering important insights into the decarbonization challenges faced by aging societies. The study fills a gap in the literature by integrating age, income, and regional conditions within a multi-scale framework, which represents a novel approach. It provides practical guidance for equitable climate policy in aging societies. Previous research typically examined these factors in isolation; this holistic perspective enables a more comprehensive understanding of the drivers of carbon inequality.

The paper is well-structured and follows academic writing standards. I do not perceive any ethical problems. Data sources are properly cited, and the study adheres to ethical research standards.

Most of the cited references are appropriate, covering topics such as carbon inequality, aging populations, and input-output methodologies. However, the number of recent studies (post-2022) is limited. Comparative context is also lacking—adding more examples from other aging societies would strengthen the article’s generalizability and broaden its impact.

The methodology section uses MRIO data from 2005 for the prefectures, which creates a temporal mismatch when compared with the 2020 national-level data. This discrepancy may introduce bias. Conducting sensitivity analyses or applying temporal adjustments could mitigate this issue.

The rationale for assuming static consumption patterns and technologies is not clearly explained. A more transparent justification would enhance methodological clarity.

The conclusions are consistent with the presented evidence.

Figures A1–A2 (geographic and projected trends) are critical but insufficiently visible; the authors should ensure they are clearly presented in the final version.

For Table A1 (regional GDP data), normalization (e.g., per capita) would improve comparability.

Author Response

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Reviewer 2 Report

Comments and Suggestions for Authors

Thank you for an interesting manuscript. I have a few suggestions, large and small — and please address my questions in the text of the manuscript: 

• The manuscript would benefit by being copy edited by a native-English speaker for clauses — such as "aged 40–44s" — that are awkwardly phrased. 
• The map of Japan probably needs a credit or who created it. 
• The conclusion would benefit from evidence and citations justifying the suggestions. 
• Why ignore the carbon emissions from food consumption? 
• What is the context of consumer choice for energy in Japan? Can consumers choose their energy suppliers (potentially choosing between green energy and fossil energy) or is the energy market a monopoly? To what extent does Japan's density — with a large percentage of the public living in apartment buildings — affect consumers' ability to decarbonize as they are unable to install solar panels and affect building infrastructure? (In other words, how much can one one assign responsibility for carbon emissions to individuals?) 

Author Response

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Reviewer 3 Report

Comments and Suggestions for Authors

In my opinion, the paper is interesting, but requires changes and additions.

1. The paper lacks a clearly defined research purpose (or research hypothesis and detailed objectives). The authors have separated subchapter 1.3 Research purpose, but have not defined the research purpose in it.
2. Please supplement the paper with the characteristics of the households studied (the structure of households in Japan depending on age, income, number of people in the household, etc.). This will introduce the reader to the topic, and is especially important for non-Japanese readers. In my opinion, it is also necessary to present a general characteristic regarding the differentiation of households within prefectures.
3. To what extent can the number of people in the households affect the carbon footprint in individual types of households. The authors omit this aspect in the paper. In the compared types of households, was the average number of people per household similar (not statistically different)?
4. The paper lacks information on the methods of dividing households into age and income groups. Why were the groups and range widths chosen in this way?

In addition, what does it mean, for example, that a household is in the age group of 30-34? In this case, is the average age of people in this household in the range of 30-34? Please explain.

Similarly, in the case of income groups, is it income per household or per person in the household? Monthly or annual income? Please explain.

5. When analyzing the carbon footprint of households by prefecture, it is worth presenting the general differentiation of the data at the beginning, in my opinion.
6. Please consider the possibility of dividing the carbon footprint of households by type of expenditure, dividing it into necessary expenditure (e.g. medical expenses) and at discretion.

It seems impossible to assume that decarbonization will proceed in the same way in all types of households.

 

Detailed comments:

- Please explain all abbreviations in the paper (when they are used for the first time in the paper);

- Line 241 - "in year" is written twice;

- figure 1 – it should be explained that apart from the figure entitled: Overall HCP, the other figures on the X axis present income groups;

- the note in figure 2 refers to figure A1 – it should be indicated that it refers to the appendix; similarly in the case of Figure A2 (line 372).

In my opinion, introducing the proposed changes and additions will significantly improve the quality of the paper and will result in better reception by readers.

Author Response

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Reviewer 4 Report

Comments and Suggestions for Authors

1. The abstract is rich in detail but may be overly dense for the typical journal reader. Could the authors simplify some of the technical language (e.g., "CF-Gini coefficient") and focus more on key takeaways and policy implications to improve accessibility?
2. The authors mention projections up to 2050, but it’s unclear in the abstract what variables were held constant in these projections. Including this limitation upfront would enhance transparency.
3. The introduction does a good job linking HCF to aging. However, the novelty of combining demographic projections with input-output modeling could be emphasized more clearly. How does this work go beyond existing studies like Wang et al. (2024) or Long et al. (2019)?
4. While the paper aligns itself with Japan’s 2050 climate goals, there is little discussion on how this research interfaces with actual policy instruments (e.g., carbon tax, regional subsidies). Could this context be introduced earlier?
5. The section introduces many overlapping concepts (e.g., demographic aging, carbon inequality, spatial disparity), but lacks a conceptual diagram or framework to relate them together. I suggest a visual summary that help guide readers.
6. While the research purpose is comprehensive, it lacks clearly defined research questions or hypotheses. Can the authors state 2–3 guiding questions or hypotheses to better frame the analysis?
7. A key limitation is the use of 2005 MRIO data for prefectures versus 2020 IO data for national analysis. Could the authors elaborate on the potential impact this mismatch may have on the validity of comparisons, and whether any statistical adjustment was attempted?
8. The assumption that household consumption structures remain static from 2020–2050 is a strong one. How might changes in technology, policy, or behavior affect these projections? A sensitivity analysis could be informative. Consider this if in the scope of current research Or must be considered in future research.
9. The use of Dagum Gini decomposition is methodologically sound, but the explanation is mathematically dense. Could the authors provide a simple example or schematic to make the decomposition logic clearer for non-econometric readers?
10. The paper identifies an inverted U-shaped HCF pattern across age groups. This is well-explained, but it may be useful to explore behavioral reasons (e.g., lifestyle shifts, mobility) more deeply. What drives the HCF plateau in middle-income elderly households?
11. The relationship between income and HCF becomes nonlinear in older age groups. Could the authors clarify why high-income elderly groups show lower HCF, how much is this due to changes in consumption types (e.g., more services, less travel)?
12. Given the age of MRIO data (2005), are the spatial patterns in prefectural HCF still likely to hold in 2025 or 2050? Could a robustness check using a different regional proxy or updated demographic trend help here? Or is it in your future exploration?
13. The paper highlights a "late-life rebound" in HCF in some regions. I am wondering ! Are these patterns driven more by structural infrastructure (e.g., lack of efficient eldercare services) or household behavior?
14. The finding that the middle–low gap increases again in the 85+ group is crucial. How might heterogeneity in eldercare access across regions contribute to this? Is it linked to health expenditure differences?
15. The conclusion outlines thoughtful, multi-tiered policy suggestions. However, some are abstract. Could the authors suggest concrete policy tools (e.g., carbon vouchers, age-specific pricing) that operationalize their findings?
16. Some phrases such as “ultra-high density” (Section 2.2.2) or “life-cycle-specific inequality” could be more clearly defined. Consider simplifying complex phrasing where possible. This will keep the interest up for the readers.
17. The authors mention that NSFIE data are at purchaser prices while the IO and MRIO tables are at producer prices, and that they used an “optimization method” to reconcile these (line 254–255). However, the exact method used for this reconciliation is not explained. Since this step is critical to ensuring the accuracy of consumption estimates, a short description or reference to a prior application of this method would make the methodology more transparent.
18. In the policy section, the authors suggest pooling long-term care insurance funds across regions (line 532). This is a strong idea, but I noticed that no mention is made of the administrative or legal feasibility of this in the Japanese policy context. Even a brief sentence acknowledging potential governance solution would make the policy discussion more supported. For example, you may include  pollution control initiative (https://doi.org/10.3390/en18102413 ) and cross sector coupling of carbon neutral industries (https://doi.org/10.1016/j.esd.2025.101728).
19. .

Comments on the Quality of English Language

Consider paraphrasing the difficult phrases for ease of reading.

Author Response

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Reviewer 5 Report

Comments and Suggestions for Authors

This study investigates the impact of demographic shifts on household carbon footprints, and further improvements are essential in the following aspects:

(1) The literature review section should be expanded to include a comprehensive discussion of household carbon footprint calculation methods, with particular attention to approaches beyond the emission factor method adopted in this manuscript. In this regard, the method selection basis of this manuscript can be clarified.

(2) Line 248, "carbon intensity calculations drew on 2025 energy consumption statistics from the agency for natural resources and energy". As the year 2025 is still ongoing, a precise temporal scope (e.g., specific months) for the data collection period must be clearly delineated.

(3) Regarding the carbon footprint projections in Section 3.2.2, the use of projected population data is acceptable. However, the authors should explicitly clarify whether projected values were also adopted for carbon emission intensity.

If constant historical values were adopted for carbon emission intensity, this would substantially undermine the validity of the research findings. Under the background of low-carbon development, carbon intensity is subject to dynamic changes that cannot be disregarded. Otherwise, the carbon footprint projections only depends on the change of population size, while neglecting variations in residents' quality of life.

(4) It is acceptable to calculate the carbon footprint based on the carbon emission factor method, which is also the current mainstream method. However, the validity of the research results significantly depends on the rationality of the emission factors. Therefore, the manuscript must give specific values of carbon emission intensity from 2020 to 2050, rather than omitting them. In addition, it is necessary to supplement the prediction method for carbon emission intensity along with empirical analysis results.

Author Response

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Round 2

Reviewer 3 Report

Comments and Suggestions for Authors

Thank you for the opportunity to read the paper corrected by the Authors. In my opinion, the paper is now much better than before the reviews. The Authors professionally and in great detail addressed the comments in my review, indicated changes in the text of the paper, explained all my doubts and answered my questions.
I have no more comments and I believe that the paper can be published.

Author Response

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Reviewer 4 Report

Comments and Suggestions for Authors

Authors have responded to all my comments and have made changes in the manuscript accordingly. I have no more comments.

Author Response

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Reviewer 5 Report

Comments and Suggestions for Authors

(1) Although the authors explained the reasons for adopting constant 2005 data, which is generally acceptable. It is important to note that the projection period extends to 2050. The temporal span of over four decades would inevitably create significant disparities in socio-economic development. Therefore, in the Section 3.3, the authors should provide a more detailed analysis of how the use of 2005 data might influence several key findings of this manuscript (such as carbon footprint calculations and regional heterogeneity assessments). This is crucial for ensuring the scientific robustness of the research.

(2) Line 336, "The energy statistics used to calculate the national‐level carbon intensity for 2020 (𝐾N) and the prefectural‐level carbon intensity for 2005 (𝐾P ) were obtained from Japan’s Agency for Natural Resources and Energy, incorporating the latest update as of 25 April 2025". Why did the authors use newly released 2025 data to calculate parameters for earlier years such as 2005 and 2020? This approach appears logically inconsistent and requires a clearer explanation. The methodological rationale for applying future-projected data to historical periods should be explicitly justified to avoid confusion.

Author Response

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