Decoupling Analysis and Scenario Prediction of Port Carbon Emissions: A Case Study of Shanghai Port, China

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The structure of the paper is reasonable, and the topic  is also quite meaningful. However, there are still some issues that require the authors to further consider or improve.

(1) Page11, The error term in equation (18) should be removed.

(2) The paper involves multiple methods, such as LSTM, which involve the selection of many parameters. How to achieve the optimal parameter selection? Is it subjective to try or is there an objective selection method?

(3) How are the prediction intervals in Figures 4 and 5 calculated, and why are they so wide?

(4) How can you achieve good prediction results with only over ten years of data? You can refer to the indicator values in Tables 8 and 9.

(5) Suggest adding some latest references, especially those from the past three years.

Author Response

Please find attached the PDF file containing the authors' point-by-point responses to the reviewer' comments.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This study examines carbon emission trends, driving factors, and peak carbon timing projections for the Port of Shanghai, offering valuable theoretical and practical insights for sustainable port development. While the research provides a solid foundation, the following enhancements would strengthen its scholarly contribution:

1. The analysis relies on 2009–2023 data—a relatively short timeframe for robust long-term carbon trajectory modeling. Extending the historical dataset (e.g., to 20–30 years) would better capture cyclical fluctuations, policy lag effects, and structural shifts in port operations, thereby improving prediction reliability.
2. The LMDI decomposition and Tapio decoupling models presuppose factor independence (e.g., treating economic activity, energy structure, and technological progress as isolated variables). In reality, these elements exhibit dynamic interdependencies (e.g., tech innovation reduces energy intensity while enabling economic growth).
3. While strategic proposals (e.g., "clean energy adoption," "smart port development") are well-intentioned, they lack actionable technical pathways. Concrete measures should be detailed.
4. the prediction results of the paper are mainly based on model simulations and scenario analyses, without sufficient validation using actual data. As port carbon emissions are influenced by various factors, there may be differences between the model predictions and the actual situation. In follow-up research, it will be necessary to calibrate and verify the prediction models using more actual data to enhance the credibility of the prediction results.

Author Response

Please find attached the PDF file containing the authors' point-by-point responses to the reviewer' comments.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Due to economic growth and the increase in maritime cargo transportation, as well as the loading of port capacities, the consumption of fuel and electricity increases, and therefore, carbon dioxide emissions also increase. The study Port Carbon Emissions on the example of the port of Shanghai will allow us to assess the prospects for reducing CO2 emissions, as well as their decoupling from economic growth. The results of the authors' work can be useful in studies on reducing emissions in other ports.

In the introduction, the authors briefly review the statistical models used to estimate emissions based on a number of key parameters. Section 1 describes the research methods in sufficient detail, but in some cases minor clarifications are required:

l.208. It should probably be noted that ln(I), not I, is directly proportional to b%, c%, and d%.

l.217. It is not clear from the model description at which stage the logarithm is applied.

l.241. RNN should be deciphered.

l.255,259,261,265,276. The text should describe the parameters included in equations 5-12, similar to equations 1-4.

Section 2 identifies the main energy resources that produce the largest emissions and analyses the key factors related to port activities that influence emissions. However, further clarification is required.

l.287. The authors should explain why they include electricity consumption to account for port emissions equally to fossil fuel emissions. Aren't emissions already accounted for in electricity production?

l.360. What data was used to conduct the regression and how were the regression coefficients determined?

l.376. The notation of eq.19 is unclear. Following mathematical logic, most parameters in the numerator and denominator will be reduced. It is necessary to explain what this choice of parameter notation means.

l.380. It is also not entirely clear why the port throughput is singled out as a separate parameter, although it is obvious that this parameter is directly taken into account in the operating income of the port. Moreover, it was stated earlier in the text that in STIPART the TG parameter, related to Tt, has the least statistical significance. It is necessary to justify why this parameter should be considered separately from the port income.

l.391. “(1) significantly lower sum of squared errors” - The value 10.08 is less than 12.47 by only 20%, it cannot be "significantly lower". The sentence should be rewritten more correctly.

l.418. Due to the weak justification for the choice of parameter Tt, effects (4) and (5) are consequences of the same process – the transition to low-carbon technologies. Which again raises the question of why port capacity should be separated from the port economy?

l.438. Perhaps in the text and Table 7 it would be clearer to designate the elasticity eps_1, eps_2, eps_3 as eps_T, eps_G, eps_A.

l.443. In Table 7, the corresponding elasticities were assigned different values ​​of the parameter "status". However, there is no discussion of these results in the text. A brief description of the significance of these statuses in the corresponding years should be added to the manuscript. Otherwise, the mention of the current statuses of the ports in the "Conclusion" section is inconsistent with the results discussed.

Section 3 discusses several emission forecasting models based on different machine learning implementations. It is shown that the presented models reproduce the actual emission data for the studied period well, with the hybrid GRU-LSTM model having one of the smallest errors and quite high performance. The authors then consider three development scenarios (baseline, low-carbon and enhanced emission reduction) to predict the peak carbon emissions trajectory of Shanghai Port using the optimized hybrid GRU-LSTM model. The results presented in Table 11 and Figure 6 will be of interest to many researchers and policymakers. However, the text of the manuscript in Section 3 does not refer to Table 10 or discuss the data in this table, raising the question: Why did the authors present this table? A brief discussion should be added to Section 3.2.1. It is also not entirely clear why 1) under the BL scenario the volume of emissions reaches a constant value by 2035, and does not continue to grow, as is typical for historical data at the stage of 2010-2023; 2) under the ER scenario there is no inertial growth of emissions in the first 2 years. It would be desirable if the authors provided an explanation for these features of the projected scenarios.

Overall, the conclusion is consistent with the presented discussion of the results, but it would be useful if the authors provided a probabilistic assessment of the implementation of certain scenarios in their proposals.

The text of the manuscript is written competently, there are no repetitions of text. The results are presented clearly. The manuscript can be published in its current form. However, if the authors want to improve the perception of their research by readers, they can take into account the comments indicated in the review for each section.

Author Response

Please find attached the PDF file containing the authors' point-by-point responses to the reviewer' comments.

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

This manuscript, “Decoupling Analysis and Scenario Prediction of Port Carbon Emissions: A Case Study of Shanghai Port, China”, conducts a thorough analysis of carbon emission at Shanghai Ports and what kind of factors are driving the emission. Employing the LMDI model, carbon emission intensity, energy structure, energy efficiency, economic intensity, and operational revenue have been decomposed as the factors influencing Shanghai Port’s carbon emission. The article forecast the carbon peak for different scenarios in terms of carbon reductions, providing forecast for the future emission reduction.  In conclusion, the article matches with Sustainability’s standard and theme, I recommend this paper to be published with minor but necessary modifications. Detailed comments as follow:

1. I recommend authors to switch the expression when mentioning the references. Instead of using references as subjective in the sentence, authors could mention the references’ author names. E.g. line 119 to line 121, line 242 in section 1.4 (Hochreiter et al’ proposed the LSTM neural network, instead of “[9] proposed the LSTM neural network”).
2. Equation 15: I don’t see "i" appears in the equation, how come there is a summation for it?
3. After line 496, in 3.1.1 – mean square error (MSE) paragraph. The whole paragraph is not understandable to me. Please rephrase and proofread. There is also misspelling in the sentence.

Author Response

Please find attached the PDF file containing the authors' point-by-point responses to the reviewer' comments.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The author has carefully addressed all points raised in the initial review，The manuscript now requires only professional language editing prior to publication.I therefore recommend acceptance contingent on this minor revision.

Comments on the Quality of English Language

The author has carefully addressed all points raised in the initial review，The manuscript now requires only professional language editing prior to publication.I therefore recommend acceptance contingent on this minor revision.

Author Response

Dear Reviewer,
Thank you for your valuable feedback and for acknowledging the scientific quality of our manuscript. We fully agree with your suggestion regarding professional language editing. To address this, we have engaged a native English-speaking co-author to thoroughly polish the English language and grammar. The revised manuscript has been carefully checked for clarity, coherence, and academic style.
We hope the current version meets the journal’s standards. Please let us know if further adjustments are needed.