Revised title: Atmospheric Emissions and Economic Impacts of Fishery Transport Operations: A Mode-Specific Assessment of Factory Freezer Trawlers
This roadmap sequences your revision by priority and dependency — structural/scope issues first (since they affect framing throughout), then methods, then results, then minor consistency fixes. Use it alongside a response-to-reviewers letter (template at the end).
Elsevier’s letter says JCLP has decided the paper “is not the right fit,” despite Reviewer #1 phrasing comments as “major revision.” This is effectively a rejection, not an R&R. Two implications for your plan:
| Decision | Consequence |
|---|---|
| Resubmit to JCLP as a new submission | You must address Reviewer #2’s core objection (see Step 1) head-on, or expect the same outcome |
| Submit revised paper to a different journal (e.g., Ocean Engineering, Marine Pollution Bulletin, Transportation Research Part D, JMSE) | You still need every fix below, but framing pressure around “novelty vs. JCLP scope” eases |
Reviewer #2 explicitly suggests a lesser/ESCI-indexed journal. Decide this before drafting, since it determines how hard you push the reframing in Step 1.
Comment: Pure emission/social-cost estimation is judged “outdated” (2010s-style) unless integrated with life-cycle assessment (LCA), financial/mitigation analysis, or alternative fuel/energy pathways.
This is not a wording fix — it requires new analytical content. Options, ranked by effort:
Action: Add a new subsection (suggest 4.5 “Emission Reduction and Cost Mitigation Scenarios”) before the Conclusion. This is the single highest-leverage change in the whole revision — it is also what justifies your new title’s “Economic Impacts” phrase (currently the paper is emissions + valuation, not “impacts/mitigation”).
| Item | Current state | Required change |
|---|---|---|
| Title | Old: “…A Mode-Specific Approach” | New: “Atmospheric Emissions and Economic Impacts… A Mode-Specific Assessment” — good, but only valid if Step 1’s mitigation/impact content is added. Otherwise “Economic Impacts” overclaims. |
| Abstract | Descriptive only (states methods + numeric results) | Reviewer #1, Comment 1: rewrite fully. Add: (a) explicit gap statement, (b) one sentence on the new mitigation/impact contribution, (c) keep top-down/bottom-up numeric contrast, (d) end with a policy-relevant closing sentence matching “Economic Impacts” framing. |
| Keywords | 4 terms, none reflect new angle | Add “emission mitigation” or “cost-effectiveness” if Step 1 content is added |
Reviewer #4 flagged “mode-specific approach” as unclear (is it top-down/bottom-up, or operational modes?). Your new title says “Mode-Specific Assessment” — still ambiguous on its own.
Action: In the Introduction (last paragraph, where you list contributions), add one explicit sentence: > “The term mode-specific refers to the six discrete operational modes (RSZ, sailing, trawling, standby, drifting, anchoring) across which emissions are disaggregated, distinct from the dual top-down/bottom-up estimation methods applied to each.”
This single sentence resolves the ambiguity without further title changes.
| # | Location | Error | Fix |
|---|---|---|---|
| 1 | Throughout (Intro, Abstract, wherever NOx/SOx/PM are grouped with GHGs) | NOx, SOx, PM mislabeled as GHGs | IMO recognizes only CO2, CH4, N2O as GHGs. Relabel NOx/SOx/PM/NMVOC as “air pollutants” or “exhaust emissions,” reserving “GHG” only for CO2 (your CO2 finding is the GHG-relevant one; the rest are local air-quality pollutants). This distinction should also sharpen your Introduction’s “environmental significance” argument (Reviewer #1, Comment 6) — GHGs drive climate impact, pollutants drive local health impact. |
| 2 | Section 2, Literature Review, opening line | “approximately 2.5% of total anthropogenic emissions” | Correct to ~2.89%, and cite as “Fourth IMO GHG Study (2020)” consistently (check your reference list entry currently reads “Fourth Greenhouse Gas Study 2020, 2020” — align in-text citation and reference list wording) |
| 3 | Abbreviations list + Eq. 1 text (line ~621) | “RSZ” defined as “Reduced speed zone” in abbreviations but glossed as “Restricted Speed Zone” beside Equation 1 | Pick one term for the entire document. Recommend “Reduced Speed Zone” since it’s used in ~90% of instances (abbreviations list, Eq. equation-1 variable definitions, Section 3.2, 3.5, Results). Do a document-wide find-and-replace on the other variant. |
| 4 | Section 3.1 (Figure 2 area) | Figure 2 (carbon footprint by mode) sits in Materials and Methods but presents results | Move Figure 2 (and its interpretive paragraph) to Results and Discussion (Section 4), ideally adjacent to Figure 6/10/11 discussion. Section 3.1 should retain only the study-area/vessel description, not computed emissions. |
Comment: HSD emission factors use an “old method”; reviewer asks you to consult USEPA methodology.
Action: 1. Check your Table 4/5 sources — currently cited as Cooper (2004) and your own Mohiuddin et al. (2024b). Cooper (2004) is a legitimate, still-cited EF source, but if USEPA (e.g., EPA’s Exhaust Emission Factors for Marine Vessels, or AP-42) is not cross-referenced, add it as a validation/comparison layer, not necessarily a wholesale replacement: - Pull USEPA EFs for the same engine categories (ME/AE/WE) and pollutants. - Add a comparison table (this also satisfies Reviewer #1, Comment 5 — see Step 6) showing Cooper/EEA-Tier-3 EFs vs. USEPA EFs side by side, with a short paragraph explaining any discrepancy (fuel sulfur content assumptions, engine age/tier, test-cycle differences are the usual drivers). 2. If your EF values already fall within USEPA’s published ranges, state this explicitly as validation rather than silently omitting the comparison — this is a very low-cost way to preempt the “old method” critique.
Two things are needed, not one: 1. EF validation table (from Step 5) — Cooper/EEA vs. USEPA. 2. Result-level validation — compare your total emissions/ESC figures against at least 2–3 prior fishing-vessel or small-craft emission studies (e.g., McKuin & Campbell 2016, Zhang et al. 2023) to show your top-down (67,239 t) and bottom-up (86,923 t) estimates are of plausible order of magnitude for a 20-vessel, 350–500 GRT fleet.
Suggested placement: new Table (validation) in Section 4.1, right after you introduce your own emissions totals.
| # | Location | Issue | Fix |
|---|---|---|---|
| 3 | Tables 4 & 5 | No units on EF columns | Add units to column headers: CO2/CO/SOx/NOx/PM/NMVOC in g/kWh (bottom-up, Table 5) and kg/ton fuel (top-down, Table 4) — confirm against your Methods text (line ~651, 719) which already states these units; just propagate them into the table headers. |
| 4 | Equation 2 (Section 3.4) | Reviewer asks whether LHS should be indexed by j | Your current LHS is \(E_{trip,i,j}\) summed over \(p\) — check that this matches your definition list (i = engine, j = pollutant, p = mode). If the equation is meant to give emissions for one specific pollutant across all engines for a trip, the sum over \(p\) is correct but the LHS should not carry \(i\) if \(i\) is being summed too — clarify whether the sum is over \(p\) only or over both \(i\) and \(p\). Rewrite the summation explicitly as \(\sum_{p}\sum_{i}\) if both are aggregated, and drop \(i\) from the LHS if so. |
| 5 | Figure 2 | No unit specified on emissions axis; unclear if it’s total pollutant sum | Label the y-axis explicitly (tons, all pollutants summed) and add one sentence clarifying whether the bars represent aggregate mass across all seven pollutants or CO2 only — then briefly note the point Reviewer #4 raises: absolute tonnage is CO2-dominated, but health/environmental significance is not proportional to mass (PM2.5 has outsized health impact despite low mass). This should be an explicit caveat near Figure 2. |
| 7 | Figures 10 & 11 | Y-axis units on the top-down/bottom-up discrepancy not stated | Confirm and label as tons; if the discrepancy axis uses a different scale than Figures 2/6, standardize or explain why. |
Comment: Why is the top-down/bottom-up gap especially large for VSL-3 in standby mode? What structurally drives top-down vs. bottom-up divergence generally?
Action: Add a dedicated paragraph (or short subsection, e.g., 4.3.1 “Sources of Method Discrepancy”) that: - Checks VSL-3’s standby fuel consumption/engine data for outliers (data entry error, unusually long standby hours, or an EF mismatch for that vessel’s engine model) - Explains generally that top-down (fuel-based, Cooper/EEA Tier-3) vs. bottom-up (activity + load factor, EEA Tier-3/SME) diverge because bottom-up is sensitive to load factor assumptions per mode, while top-down assumes a constant fuel-to-emission conversion regardless of load — standby/low-load modes are where this assumption breaks down most, which is consistent with your data pattern.
Comment: If SCF is fixed, ESC is just emissions × constant — why present it as a separate analysis?
Action: Add a short justification paragraph (Section 3.4/3.5 or early Results) making explicit that: - ESC’s value-add is translating heterogeneous pollutant masses into a single comparable monetary metric — CO2, NOx, and PM2.5 have very different per-ton social costs (Table 6), so ranking by ESC vs. ranking by raw mass can reorder which mode/pollutant matters most. If your data shows this reordering (e.g., a mode that isn’t the top mass emitter but is the top-cost contributor due to PM2.5’s high SCF), highlight that explicitly — it’s a legitimate, defensible answer to this comment and adds interpretive value.
Action: Restructure your Conclusion/Recommendations to explicitly separate: 1. Policy-solvable interventions (e.g., mandatory RSZ compliance near coast, fuel-quality regulation, port-state emission reporting requirements for the fishing fleet) 2. Technology-solvable interventions (e.g., engine retrofits, hybrid AE, exhaust scrubbers for PM/SOx) 3. Operational/behavioral interventions (e.g., trawling-duration optimization, speed management — links back to your Step 1 mitigation-scenario addition)
Tie each recommendation to a specific quantitative finding from your results (e.g., “since trawling accounts for X% of total ESC, technology retrofits targeting main-engine load during trawling offer the largest single-mode cost-reduction potential”).
This section should also explicitly state the environmental significance (Reviewer #1, Comment 6): connect your CO2/pollutant findings to Bangladesh coastal air-quality and ecosystem health literature, closing the loop the reviewer asked about.
| Reviewer | Comment # | Your response | Manuscript location (revised) |
|---|---|---|---|
| R1 | 1 (Abstract) | ||
| R1 | 2 (old EF method) | ||
| R1 | 3 (health/ecosystem threat levels) | ||
| R1 | 4 (USEPA method) | ||
| R1 | 5 (validation/comparison table) | ||
| R1 | 6 (environmental significance) | ||
| R2 | Core (novelty/LCA/mitigation) | ||
| R2 | Minor 1 (GHG classification) | ||
| R2 | Minor 2 (2.89%, IMO citation) | ||
| R2 | Minor 3 (Figure 2 placement) | ||
| R2 | Minor 4 (RSZ consistency) | ||
| R4 | 1 (title clarity) | ||
| R4 | 2 (fuel type in emissions) | ||
| R4 | 3 (units in Tables 4/5) | ||
| R4 | 4 (Eq. 2 notation) | ||
| R4 | 5 (Figure 2 units/pollutant differences) | ||
| R4 | 6 (VSL-3 discrepancy) | ||
| R4 | 7 (Figures 10/11 units) | ||
| R4 | 8 (discrepancy discussion depth) | ||
| R4 | 9 (ESC significance) | ||
| R4 | 10 (focused recommendations) |
Note: Reviewer #3 is not present in the comments you shared — confirm with the editor whether a Reviewer #3 report exists, since Elsevier’s numbering skips from #1 to #2 to #4.