Analyzing the Feasibility of Lithium Extraction in Mexico: Supply Chain Modeling with Economic and Environmental Considerations

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This paper investigates the feasibility of lithium extraction in Mexico and develops a supply chain model that incorporates economic and environmental factors. The topic of the research holds significant practical relevance, the methodology is sound, and the analysis of results is thorough, thereby providing valuable insights for the development of Mexico’s lithium industry. The structure of the paper is clear, and the language is fluent, meeting the publication standards of academic journals. However, there are some areas that require improvement:

1. The literature review is somewhat superficial, failing to adequately present the academic background and originality of the research. It is recommended to substantially expand the literature review in the introduction, specifically concerning lithium supply chain optimization models and economic environmental assessment models.
2. In the methodology section, it is advised to provide a detailed enumeration of the sources for all key parameters used in the model.
3. A sensitivity analysis of the key parameters should be conducted to assess how variations in these parameters affect the model outcomes.
4. It is suggested to include a dedicated paragraph in either the methodology or discussion section that clearly outlines the model's key assumptions and limitations.
5. The presentation of results is somewhat monotonous, with insufficient graphical representation; thus, the level of result visualization could be improved.
6. The discussion section should delve deeper into the policy implications and practical applications of the results, offering more specific policy recommendations.
7. In the conclusion section, it would be beneficial to distill the key findings and policy suggestions more specifically and quantitatively.

Author Response

Dear Reviewer 1 

Please find attached the detailed response to your comments. 

Thank you very much for reviewing your manuscript.

 

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

In their Section 1. Introduction the authors approach the topic of deposits of lithium in the state of Sonora, Mexico, as a strategic material in the lithium supply chain (lines 32-41). Electric vehicles and battery storage systems need lithium consumption more than consumer electronics (lines 44-47). A mix of private and state-controlled enterprises control the lithium market in Latin America, with Bolivia representing a more nationalized control over lithium resources. The authors underline the two types of lithium: lithium carbonate (Li2CO3) and lithium hydroxide (LiOH), both accounting for a significant share of lithium consumption (lines 57-62). Notwithstanding the many sectors where lithium is employed, ranging from pharmaceutical to industrial employment, by 2025, lithium consumption has shifted dramatically, with a restriction mainly to battery manufacturing (lines 67-70). Among Countries, Australia is a leading controller of the lithium market, with Mexico, especially the Sonora region, as a strategic vendor near the United States market (lines 72-83).

The final part of the introduction does a great job of positioning Mexico’s role in the lithium market, linking economic feasibility with environmental concerns. In lines 84-94, the authors briefly describe lithium employment in Mexico. Other inputs related to the international supply chain are furnished in lines 102-119. The Mexican market has attracted multinational corporations such as Tesla and General Motors. This has increased the search for compliance with sustainability standards as environmental concerns grow.

Section 2. Problem Statement begins with the importance of Mexico in the lithium supply chain (lines 145-148), but it should more clearly emphasize the unique contribution of this research. It introduces the Mixed Integer Linear Programming (MILP) model proposed by the author and developed in Section 3. Methods. Its main target is to identify “sustainable development pathways that optimize resource utilization, minimize greenhouse gas emissions, and improve industrial competitiveness.” (lines 155-157) The superstructure of the lithium supply chain in Mexico is visually shown in Figure 1, line 170.

The authors then executed their calculation using a MILP model, a type of optimization problem. The authors specifically used GAMS (General Algebraic Modeling System), a high-level modeling system for mathematical programming problems. GAMS is the modeling layer. CPLEX is the computational solver engine. In Tables 1–5, along with subsection 3.1 Mathematical Model, the authors have provided the real-world inputs (costs, demands, GHG factors). After having calculated a potential extraction threshold of lithium in a given country, in Eq. (2), the authors formulate the application of extraction efficiency (η_j_ext); in Eq. (7), they show the production based on efficiency per product, and then they calculate cost and emissions equations. GAMS returns an optimal or near-optimal solution. Notably, in this paper, the model had 4,864 continuous variables (e.g., quantities, tons, km), 64 binary variables (e.g., yes/no for installing a plant), 749 equations, and it was solved in 0.36 seconds!

In Section 4. Results and Discussion when reviewing the three proposed solutions, it is evident that the state of Sonora consistently emerges as the primary lithium extraction site (lines 350-353 and Figure 4). This is due to its large reserves, relatively lower extraction costs, and existing infrastructure that could support further development in the lithium industry. Other states, including Zacatecas, Baja California Sur, San Luis Potosí, and Chihuahua, maintain consistent extraction levels across all scenarios, indicating that their role in the supply chain remains stable under different economic and environmental constraints (lines 422-426). However, in Solution C, the model excludes lithium extraction from Puebla's rock deposits due to the high extraction costs associated with this deposit type.

In Section 5. General Analysis, the comparison of the three solutions clearly highlights the trade-offs between economic profitability and environmental impact. Solution A prioritizes profit, generating $317.19 million annually, but results in high greenhouse gas (GHG) emissions of 1,119.81 thousand tons of CO₂. In contrast, Solution B represents a more balanced approach, achieving $186.98 million in profit while reducing emissions to 559.90 thousand tons of CO₂—positioning it closest to the so-called “utopian point” where economic and environmental concerns are optimized. Finally, Solution C places environmental sustainability at the forefront, reducing emissions to just 44.79 thousand tons of CO₂, though at the expense of significantly lower profits ($48.20 million). This is accomplished by limiting processing and exporting lithium primarily as a precursor rather than converting it into batteries or lubricants. A key insight from this study is that trade policies significantly affect profitability. The increase in U.S. tariffs (25%) on lithium-related exports would render certain trade routes unviable. However, the model identifies alternative strategies to maintain profits, such as shifting exports to other markets (e.g., Europe and Asia), optimizing extraction levels, and diversifying trade partnerships. This underscores the importance of flexibility in Mexico's lithium supply chain strategy to remain competitive globally.

In conclusion, by developing a MILP model, this study evaluates the trade-offs between profitability, environmental sustainability, and geopolitical constraints. The results show that Mexico's lithium extraction is economically viable, with Sonora being the most strategic location for large-scale production. However, the balance between economic gains and environmental impact depends on which industry pathways are prioritized:

• Prioritizing economic gains (Solution A) results in high profits but substantial GHG emissions.
• Seeking a balance (Solution B) offers a compromise between financial viability and sustainability.
• Prioritizing environmental impact (Solution C) significantly reduces CO2 emissions but generates lower profits.

A major takeaway from this study is the importance of trade policies and strategic diversification. The findings suggest that Mexico should reduce reliance on U.S. exports and develop trade agreements with other major lithium markets, such as the EU, China, and South Korea. Additionally, policy frameworks should encourage investment in domestic lithium-ion battery production to add value to the supply chain.

AMENDMENTS:

• The abstract effectively presents the MILP model and its economic/environmental trade-offs. However, clarify why these results matter for policy and industry (e.g., how might decision-makers use this model?).
• Instead of just stating, "Solution C prioritizes emission reduction (44,792 tons) with lower profits ($48.20M) and avoids lithium grease and battery production," consider explaining what that means for sustainability policies or investment decisions.
• The paper correctly highlights geopolitical aspects (Chile, Bolivia, Argentina, and China), but the Mexico-specific context comes a bit late.
• The text includes strong industry statistics (e.g., IEA's lithium forecast and distribution across industries), but the connection between statistics and the research aim could be stronger.
• Section 1. Introduction correctly highlights environmental challenges, particularly water scarcity in arid regions like Sonora. However, mentioning potential mitigation strategies early on would be helpful.
• In Section 2. Problem Statement: the introduction of the MILP model is well-placed, but the reasoning behind choosing this approach could be more straightforward. Briefly state why a MILP model is the best choice compared to other methodologies (e.g., agent-based models, LCA approaches). This could help reviewers who may question why this specific method was chosen.

• I suggest clarity in explaining the model structure in Section 3. Methods. The superstructure approach is a great choice, but the explanation should explicitly state what elements are included in the framework (e.g., extraction, transportation, processing, export decisions).
• Suggestion: Instead of just stating in lines 161-163

"This section presents a model for the lithium supply chain, considering a superstructure approach..." Consider adding:
"This model integrates all key stages of lithium extraction, processing, and distribution. It evaluates trade-offs between selling raw lithium, producing lithium-ion batteries or greases, and different processing locations. The framework also incorporates economic parameters (cost, revenue, profits) and environmental considerations (GHG emissions, water use)."

• In Section 3. Methods, The equations are well-structured, but the explanations of variables and parameters could be clearer. The text often jumps directly into equations, assuming readers are familiar with variable definitions. It would help to briefly define symbols before presenting equations.
• Example improvement:
• Before Eq. 1: Instead of jumping directly into mathematical notation, consider adding: "The total lithium extracted in each Mexican state (fi,j,dep​) is constrained by the available lithium reserves (Di,j,dep​), as shown in Eq. 1:"

• You need clarifying ‘Decision Variables’: The model includes binary decision variables (e.g., selecting whether a facility should operate at full capacity). However, this is not explicitly stated.
• Recommendation: Clarify whether zi,k,new​ is a binary variable (1 = facility operates at full capacity, 0 = facility remains idle).
• Example clarification:
  "The decision variable zi,k,new represents whether a production facility is established and operates at full capacity (1) or remains idle (0). This ensures that new investments align with production requirements."
• The equations and parameter tables are excellent, but the text between them is quite dense.

• Suggestion:
• Use shorter paragraphs to make it easier for readers to follow the logic behind each equation.
• Add brief transitional sentences between equations to explain their purpose.
• Example before Eq. 17 (GHG emissions calculation):

• Instead of:
  "Eq. 17 quantifies the total greenhouse gas (GHG) emissions associated with each phase of the lithium supply chain..."
• More structured version:
  "To assess the environmental impact of different supply chain scenarios, we calculate the total GHG emissions from extraction, processing, and transportation. Eq. 17 aggregates emissions from each supply chain stage using emission factors for extraction (EFj,ext​), processing (EFk,prod​), and transport (EFmin,EFgro​)."

• Strengthening the Justification for Economic & Environmental Trade-offs: The paper presents two competing objectives:
• Maximizing profit (Eq. 18)
• Minimizing GHG emissions (Eq. 19)
  However, the justification for these trade-offs is somewhat implicit.
• Suggestion: Add a sentence explaining why multi-objective optimization is not used (i.e., why a single-objective approach was chosen instead).
• Example addition:
  "Since decision-makers may prioritize either profitability or sustainability, we present two alternative optimization objectives: maximizing net profit (Eq. 18) and minimizing GHG emissions (Eq. 19). A multi-objective optimization approach was not used to maintain computational tractability and provide clear policy insights for different industrial strategies."

• In Section 4. Results and Discussion: I would add clarity in Model Complexity and Execution. The model formulation description (number of variables, equations, and solver details) is clear but could benefit from a shorter, more structured explanation.

• Suggestion:
  Instead of:

"The mathematical model formulation related to the selected case study consists of 4,864 continuous variables, 64 binary variables, and 749 equations..." Consider simplifying to: "The formulated MILP model consists of 4,864 continuous variables, 64 binary variables, and 749 equations. It was solved using the CPLEX solver in GAMS, with a computational time of 0.36 seconds on an Intel i5-8350U processor (1.70 GHz, 8GB RAM)."

• The description of Solution A, B, and C in the Explanation of the Pareto Curve (Figure 3) is clear, but the role of the Pareto Curve could be better emphasized.

• Suggestion: Briefly explain how the Pareto Curve is used in decision-making and why Solution B is considered the "utopian point."

• Example improvement: "The Pareto Curve in Figure 3 visualizes the trade-off between economic profitability and environmental impact. Solution A prioritizes economic gain, while Solution C minimizes GHG emissions. Solution B, positioned closest to the 'utopian point,' offers a balanced compromise between these two objectives."

• Clarifying the Impact of Tariffs: The discussion on U.S. tariff increases (25%) under Trump's policy is relevant, but it needs a stronger connection to the study’s objective.

• Suggestion:
• Instead of just stating:

"The increase in tariffs negatively impacted profits when exporting products to the United States..."

• Consider explaining the implications: "The findings suggest that tariff policies can significantly alter Mexico’s lithium export strategy. A 25% tariff renders lithium exports to the U.S. unprofitable, highlighting the need for diversified export markets."

• The breakdown of lithium extraction across Mexican states is well-detailed, but it could be better connected to Mexico's industrial capacity.
• Suggestion:
• Instead of:

"Sonora is the state in which the largest amount of lithium is extracted, with 42,885.44 tons per year..."

• Consider providing insight into why Sonora is prioritized: "Sonora emerges as the primary extraction site due to its vast lithium reserves and lower extraction costs, making it central to Mexico’s lithium supply chain development."

• The adjustments made in response to tax increases (e.g., shifting exports from the U.S. to Belgium, Netherlands, and Spain) are important but should be linked to global trade strategy.
• Suggestion: Instead of just listing numerical changes, briefly interpret the shifts in trade strategy:
• "As U.S. tariffs increase, the model reallocates lithium exports to European markets, particularly Belgium and the Netherlands, suggesting a strategic pivot towards less tariff-restrictive trade agreements."

• Strengthening the Justification for Solution B: The text states that Solution B is "closest to the utopian point," but this needs a stronger justification in layman’s terms.

• Suggestion:
• Instead of:

"Solution B represents a more balanced solution between both objectives and happens to be the closest to the utopian point..."

• Consider: "Solution B achieves a near-optimal balance between economic profitability ($186.98M) and environmental sustainability (559,903.94 tons of CO₂ emissions). It significantly reduces lithium extraction in Sonora while maintaining viable export levels."

• Stronger Framing of the Sustainability Argument: Solution C is clearly described as the most environmentally sustainable, but the rationale for why batteries and lubricants were excluded should be clearer.

• Suggestion:
• Instead of:

"The model chooses to stop producing batteries and lubricants, exporting all lithium as a precursor..."

• Consider explaining: "To minimize environmental impact, Solution C prioritizes raw lithium exports, eliminating energy-intensive battery and lubricant production. This results in the lowest GHG emissions (44,792 tons CO₂) but significantly reduced profitability ($48.19M)."

• In Section 5. General Analysis: The conclusions summarize the findings well but could more explicitly connect them to real-world applications.

• The conclusions briefly mention decision-making but lack a direct recommendation for policymakers or industry stakeholders.
• Suggestion:
• Add a final paragraph with clear policy implications: "Policymakers should consider incentives for sustainable lithium extraction, strategic trade agreements, and investment in domestic lithium processing to enhance Mexico’s position in the global supply chain. Further research could explore dynamic pricing models and policy-driven trade simulations."

• This section simply repeats the numerical differences between Solutions A, B, and C, but it lacks deeper insights into:
• Why Sonora is the leading lithium extraction site (geological? industrial feasibility?)
• How the model determines trade adjustments when tariffs rise
• What strategic decisions policymakers or industry leaders can take based on these results

• Suggestions:
  • Instead of just saying, “Sonora has the highest lithium extraction in all solutions”, add context:

"Sonora remains the dominant extraction hub across all solutions due to its large lithium reserves and favorable extraction conditions. This aligns with Mexico’s potential role in the North American lithium supply chain, particularly as nearshoring initiatives gain traction."

• Expand the discussion on trade strategy shifts under tariffs:

"Despite the impact of tariff increases between the U.S. and Mexico, the model shows that strategic realignment of exports toward Europe and Asia can sustain profits, reducing dependency on U.S. markets."

• Highlight the practical implications of different strategies:

"These findings suggest that Mexico's lithium industry should diversify its trade partners, optimize extraction processes, and explore sustainable production pathways to remain competitive in a rapidly evolving global market."

• The conclusion feels like a rushed summary instead of a well-structured closing discussion that offers insights, implications, and future recommendations.
• The paper presents a sophisticated model, but the conclusion does not reflect this sophistication.

• The importance of Mexico’s lithium industry is stated but not analyzed.
• Policy implications are missing: The authors mention "future decision-making" but do not specify who benefits from these findings (government? investors? companies?).
• Future research directions are absent: A complex MILP model requires validation, additional constraints, or scenario testing—none of this is addressed.

MINORS:

• Solve the GHG acronym on line 201, the first time greenhouse gas appears in the main body. The acronyms must be solved and/or added the first time they appear in every single part of the paper (abstract, main body, Tables, Figures, Appendices, if any)
• Figure 3 on line 326 is somewhat blurred. Please provide a better figure and distribute the text along graphs and Tables.
• Figure 5 on lines 383-384 is also not really appealing. Please provide a better Figure or remove it from your paper.
• Keep Table 6 on line 330 nearer to its mention on line 396 for easier navigation of readers along your text.

 

Overall Suggestions:

✅ Maintain focus on the study’s originality – The optimization framework is a key contribution, so emphasizing it earlier would be beneficial.
✅ Improve readability – Some paragraphs are quite dense; breaking them up slightly will help.
✅ Strengthen transitions – Adding smoother connections between statistics, economic aspects, and environmental concerns would enhance flow.

✅ Clarify decision variables, particularly whether some are binary.
✅ Explain why economic and environmental trade-offs are modeled separately.

✅ Clarify why Solution B is the optimal balance and justify it more clearly.
✅ Enhance the discussion of the Pareto Curve and how it helps in decision-making.
✅ Better explain the impact of tariffs on Mexico’s trade strategy (U.S. vs. Europe).
✅ Explicitly connect geographic lithium extraction to Mexico’s industrial development.
✅ Frame Solution C in terms of long-term sustainability trade-offs.
✅ Strengthen the Conclusion with clear policy and industry recommendations.

The paper appears imbalanced in its structure, with overly detailed mathematical formulations in Section 3, while Sections 5 and 6 are too brief, given their importance in interpreting results and drawing conclusions. Notably, Section 5 needs more interpretation, connecting the model’s outputs to real-world decisions for policymakers, industry, and investors.
Section 6 needs a more substantial structure with explicit takeaways, trade strategy implications, and policy recommendations.
Future research should be addressed, ensuring that the study's insights are not static but adaptable to evolving market conditions.

Comments for author File: Comments.pdf

Comments on the Quality of English Language

The paper would benefit from a revision to enhance its flow, clarity, and overall aesthetic. Reorganizing sections for better thematic coherence, trimming redundant details, and refining the language for grammar and tone would improve readability. Additionally, incorporating visual elements like subheadings or lists where appropriate can make the content more engaging and accessible. A focused effort on these aspects will ensure the paper achieves its full potential.

Author Response

Dear Reviewer 2

Please find attached the detailed response to your comments. 

Thank you very much for reviewing your manuscript.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Here are some minor comments that the authors should consider.

In Section 5, titled "General Analysis," it would be beneficial to transform this section into a discussion. The authors should provide more in-depth analysis and recommendations, as well as reorganize the structure for clarity.

Some sentences are slightly cumbersome. For instance, the phrase "heightened international competition for resources, with significant attention directed toward the 'Lithium Triangle' in South America—comprising Argentina, Bolivia, and Chile—where over 50% of the world's lithium reserves are found" could be streamlined for conciseness and clarity.

Author Response

Dear Reviewer

Please find attached the detailed response to your comments.

Thank you very much for reviewing our manuscript.

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The abstract has been tightened and made more policy-relevant, which aligns with my earlier feedback. Also, the Introduction now includes a more cohesive and literature-informed rationale for the MILP model and Mexico’s strategic positioning, which is a strong improvement.

Section 2.

It remains solid in emphasizing Mexico’s strategic geographical advantage.

• It clearly defines the research gap and positions the paper as addressing it through an MILP model.
• The objectives (optimization, sustainability, competitiveness) are clearly stated.

🔁 What Was Suggested Earlier (and still needs attention):

1. Unique Contribution Not Highlighted Enough
• The paragraph currently states: "To address this gap, this study presents an optimization-based framework…"
• ➤ It would be stronger if it explicitly mentioned what sets this MILP model apart from previous work (e.g., integration of tariffs, trade reallocation under constraints, detailed environmental-emission coupling).
• Suggested sentence to add/insert:

“Unlike previous models focused on lithium trade or extraction alone, this MILP framework incorporates real-world policy constraints such as tariffs and environmental restrictions, offering a unique perspective tailored to Mexico’s emerging lithium sector.”

1. Justification for Using MILP Could Be Clearer
• The section says the MILP “identifies sustainable development pathways,” which is good — but why MILP over other models is not stated here.
• Earlier we suggested a brief rationale:

“A MILP model is particularly suitable because it enables the integration of discrete decisions—such as facility openings or production halts—with continuous flows like extraction quantities and emissions.”

Although the revised introduction has improved the rationale for using MILP, the Problem Statement section (Section 2) could benefit from a brief mention of the unique contribution of this particular model and its suitability compared to other approaches.

Section 3.

Positive Observations:

1. Clear Structuring of Model Sections
   The subsections 3.1.1, 3.1.2, and 3.1.3 now each begin with a brief description before introducing the equations — this is a good improvement for reader comprehension and aligns with our earlier suggestion for transitional framing between formulas.
2. Improved Definitions of Decision Variables
   Equation 5 finally clarifies that zi,k,new is a binary decision variable, and its two possible values are explained clearly. This fixes a key ambiguity from the previous version. ✔️
3. Updated Table 2 (Parameter Values)
   The newly presented Table 2 includes extraction efficiency and GHG emissions, production efficiencies per product type, and costs per deposit type. This table is now much more informative and supports the mathematical formulations better.
4. Flow and Equation Purpose More Explicit
   The authors have explained what each equation calculates (e.g., Eq. 6 and Eq. 7 relate to lithium processing limits and efficiency). This helps to guide readers through the modeling logic — a clear improvement.
5. Transitional Sentences Added Before Some Equations
   Especially noticeable before Eq. 8–10, the transitions describing demand constraints are well-phrased and easier to follow.

✏️ Minor Issues / Suggestions for Clarification:

1. Rewritten Eq. 2
   The new version of Eq. 2 now correctly balances the amount extracted with processing destinations. Still, there is no longer an index of summation for Iia in the equation (as it was in the original):
   Σ𝑓𝑖,𝑖𝑎,𝑗𝑒𝑥𝑡𝐼𝑖𝑎
   – Suggestion: clarify whether the summation still ranges over all states ia ≠ i. Otherwise, readers may question the summation bounds.
2. Rewritten Eq. 3

It remains logically sound (indicating that exports from i to ia are imported by ia), but:

– Suggestion: add a clarifying sentence similar to:
“This ensures conservation of lithium flows between exporting and importing states.”

1. Consistency in Units in Table 2
   Table 2 now includes multiple efficiency rates and cost values for each deposit type, but the units for extraction efficiency and production efficiency are listed as %, while the actual values are decimal (e.g., 0.9237).
   – Suggestion: Write them as percentages (e.g., 92.37%), correct the unit to “fraction” or leave them blank for clarity.
2. Minor Language Improvement: In the line:

“The parameters related to the PC𝑖,𝑘𝐸 is based on the worst-case scenario…”
➤ Should be:
“The parameter PC𝑖,𝑘𝐸 is based on the worst-case scenario…”

The improvements to Section 3 — particularly the more explicit variable definitions, updated parameter tables, and revised equations — greatly enhance the transparency of the model. Minor suggestions concern consistency in units (e.g., in Table 2) and clarifying summation indices in Eq. 2 and Eq. 3.

I mainly have appreciated the following:

1. Better Framing of the Equation
   The authors now provide a more structured description before Eq. 17, clearly stating what each emission source represents (extraction, production, transport – domestic and international). This makes the complex aggregation easier to interpret.
2. Expanded GHG Coverage
   The equation now explicitly includes:
• GHGs from extraction (𝐸𝐹𝑗𝑒𝑥𝑡),
• GHGs from transport to domestic (𝐷𝐼𝑆𝑜𝑛𝑎𝑐) and international markets (𝐷𝐼𝑆𝑚𝑖𝑛𝑡),
• GHGs from intermediate product movements (e.g., lithium to hubs),
• And from the production of batteries or grease (𝐸𝐹𝑘𝑝𝑟𝑜𝑑).

This enhanced scope better aligns with life-cycle accounting and sustainability assessments.

1. Reference to Tables (Good Practice)
   The sentence “The parameters can be consulted in Table 2, Table 3, and Table 5” is very helpful and anticipates the reader’s needs.
2. ε-Constraint Method Introduced
   This is a valuable addition! The previous version did not explain how the authors navigated the competing goals (profit vs. GHG). The trade-off logic is now better grounded by explicitly stating the use of the ε-constraint method.

✏️ Minor Recommendations

1. Language Clarity: Sentence Tweaks This sentence is dense:

“Eq. 17 provides a way for quantifying the total GHG associated with each phase of the lithium supply chain…”

✅ Suggested rewrite:

“Eq. 17 quantifies the total greenhouse gas (GHG) emissions generated across each stage of the lithium supply chain, including extraction, processing, and transportation activities.”

1. Index Clarity in Eq. 17 The summation in the last term:

ΣΣΣΣ((𝑓𝑘,𝑚𝑖𝑛𝑡 + 𝑓𝑖,𝑗,𝑙,𝑚𝑝𝑟𝑒𝑐𝑢𝑟) ∙ 𝐸𝐹𝑚𝑖𝑛𝑡 ∙ 𝐷𝐼𝑆𝑚𝑖𝑛𝑡)𝐿𝑙=𝑖𝑛𝑡 𝑀𝑚 𝐽𝑗 𝐼𝑖

The l = int part is a bit cryptic and might confuse some readers. The authors are advised to clarify the range for l (e.g., l ∈ L, where l = international hubs). Otherwise, it is unclear if l is being summed or fixed.

1. GHG Units Look Very Small
   Table 5 shows:
• 𝐸𝐹𝑔𝑟𝑜 = 0.00010112 ton CO₂ / ton Li
• 𝐸𝐹𝑚𝑖𝑛𝑡 = 0.00001614 ton CO₂ / ton Li

➤ Are these conversion factors per km, or are they cumulative? If per km, it might help to clarify that they should be multiplied by distance. Otherwise, they seem too small to reflect realistic emissions.

1. Redundant Sentence
   This line is a bit repetitive after Eq. 17:

“For the superstructure shown above, it is possible to set different objectives depending on the case...”

✅ Suggested rewrite:

“The model supports both economic and environmental optimization objectives, allowing the user to prioritize profit (Eq. 18) or emissions reduction (Eq. 19) or explore trade-offs between the two.”

Section 3.1.4 has been notably improved with a more structured emissions formula (Eq. 17) that comprehensively integrates extraction, production, and transportation impacts. The introduction of the ε-constraint method (lines 318–322) enhances the clarity of the modeling approach and supports real-world trade-off analysis. A few aspects may still benefit from refinements, such as clearer variable indexing in summations and an explanation of very small GHG coefficients in Table 5.

While the mathematical structure and MILP optimization approach are solid, the graphical presentation of the results remains a significant weakness. Figures 2, 3, and 4 are still blurred, visually unappealing, and sometimes contain errors that should have been corrected in this revised version. Specifically:

• Figure 2 lacks a legend and is challenging to interpret without prior context.
• Figure 3, intended to show the Pareto Curve, contains typographical errors ("soltions", "whit", "lubricants") that compromise its credibility. Additionally, the overall design is unclear.
• Figure 4 mixes languages ("SOLUCION C" in Spanish, while the rest of the paper is in English), and the font size in bubbles is so small that it is barely legible. These graphical inconsistencies can confuse readers and reduce the professionalism of the presentation.
• Figure 5, although formally correct in content, is not visually appealing and does not enhance the narrative meaningfully. The axis scales, labels, or legends are minimal, and the layout needs redesign.

A graphical revision is essential because these figures convey key model outputs. Clear legends, consistent language, corrected spelling, and improved formatting would greatly enhance readability and impact. This should be addressed before the manuscript is considered for publication.

The updated narrative in Sections 4.3 and 4.4 improves clarity regarding Solutions B and C, particularly in the explanation of market adjustments under tariff changes. Still, a few sections feel repetitive, and the discussion could benefit from tighter phrasing and more nuanced interpretation of why certain decisions emerge from the model (e.g., why Puebla is excluded, or why certain exports are prioritized).

The new Conclusion section is notably clearer than in the earlier version and now includes practical policy implications. However, it still reads like an extended summary. A more structured closing would elevate the contribution: for example, by clearly distinguishing methodological value, strategic insight, and future directions. The mention of potential extensions—such as policy-driven trade simulations and optimization under uncertainty—is welcome but deserves more specificity.

Author Response

Dear Reviewer

Please find attached the detailed response to your comments.

Thank you very much.

Author Response File: Author Response.pdf