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

Abstract

Lithium is a strategic resource due to its use in rechargeable batteries for electric vehicles and electronic devices, driving high demand for extraction. This study analyzes the lithium supply chain in Mexico, focusing on both the extraction of lithium carbonate for export and the potential for producing lithium–ion batteries and lithium grease, considering their environmental impact. The proposed mixed integer linear programming (MILP) model, solved using the GAMS modeling environment, suggests that lithium extraction in Mexico is viable, with Sonora having the greatest extraction capacity. Three solutions were evaluated: Solution A maximizes profits (USD 317.19 M) but has high greenhouse gas (GHG) emissions (1,119,808 tons), Solution B balances profits (USD 186.98 M) with lower emissions (559,904 tons), and Solution C prioritizes emission reduction (44,792 tons) at the cost of lower profits (USD 48.20 M). Solution C implies a scenario with severe environmental restrictions, which indirectly leads to lower investment costs by avoiding the production of lithium grease and batteries. This study highlights the potential impact of tariffs on U.S. lithium exports, with a 25% tariff making exports economically unviable. This underscores the need for Mexico to diversify its export markets. Decision-makers can use this model to explore alternative strategies, reduce dependence on a single market, and optimize the economic and environmental sustainability of the lithium sector.

1. Introduction

Lithium has become one of the most crucial elements in the global energy transition due to its fundamental role in energy storage solutions, particularly lithium–ion batteries [1,2]. These batteries power electric vehicles (EVs), renewable energy storage systems, and a vast range of consumer electronics, making lithium an essential component in global decarbonization efforts [3]. The rising demand for lithium has led to intensified global competition for resources focusing on South America’s “Lithium Triangle” (Argentina, Bolivia, Chile), home to over 50% of the world’s lithium reserves [4]. However, Mexico has recently gained prominence for its lithium deposits, especially in the state of Sonora, positioning the country as a potential strategic player in the global lithium supply chain [5].

The international lithium market is influenced by geopolitical factors, technological progress, and evolving energy policies [6]. With increasing efforts to reduce reliance on fossil fuels, nations seek stable and sustainable sources of lithium. The International Energy Agency (IEA) forecasts that, by 2040, electric vehicles and battery storage systems will account for 90% of the world’s lithium consumption, surpassing traditional uses such as consumer electronics [7]. This shift has driven countries to strengthen their lithium supply chains, develop local processing industries, and address environmental and social concerns associated with extraction. In Latin America, the lithium market is shaped by a mix of private and state-controlled enterprises. While Chile and Argentina have long been major lithium exporters, Bolivia has adopted a more nationalized approach, asserting state control over its lithium resources [8]. These differing governance models highlight the intricate balance between economic interests, environmental sustainability, and national sovereignty. Discussions surrounding lithium extend beyond resource extraction to include broader considerations of sustainable development, technological innovation, and equitable wealth distribution.

Mexico’s lithium deposits, particularly in Sonora, have attracted significant interest from investors and policymakers. However, robust strategic planning is essential to ensure that lithium extraction aligns with both economic feasibility and environmental sustainability. The extraction of lithium, especially from lithium-rich brines, demands considerable water resources [9], which is a critical concern in arid regions such as northern Mexico, where water scarcity already impacts agricultural and industrial activities. Water-intensive extraction processes have sparked conflicts over resource allocation, particularly in areas with annual precipitation below 500 mm [10]. Additionally, lithium extraction has notable environmental implications beyond water consumption, including carbon emissions associated with mining, refining, and transportation, which must be carefully assessed to develop sustainable production strategies [11]. It is worth noting that decision models play a critical role in evaluating solutions that optimize water usage within the lithium supply chain. By incorporating such models, decision-makers can identify strategies that balance environmental protection with economic feasibility, ensuring that water resources are utilized efficiently [12].

Multinational corporations, including Tesla and General Motors, have already explored investment opportunities in Mexico’s lithium sector [13,14]. Establishing lithium refining and battery manufacturing facilities within Mexico would not only contribute to regional energy security but also create economic opportunities, infrastructure development, and job creation [13]. However, significant challenges remain, including regulatory uncertainties, environmental concerns, and the need for compliance with international sustainability standards.

Lithium’s applications go far beyond batteries, playing a vital role in various industries. Among lithium-derived compounds, lithium carbonate (Li₂CO₃) and lithium hydroxide (LiOH) dominate, comprising approximately 71% and 22% of total lithium consumption, respectively [15]. Market projections suggest that LiOH demand will eventually exceed Li₂CO₃ due to its enhanced performance in next-generation battery technologies [16]. Additionally, lithium compounds serve critical functions in aluminum alloys, glass, ceramics, lubricants, greases, and air purification systems [17]. The pharmaceutical sector also relies on lithium for the production of medications treating psychiatric disorders [18]. In 2017, lithium consumption was distributed as follows: 35% for battery production, 32% for glass manufacturing, 9% for grease production, 5% for alloys, 5% for air treatment, 4% for polymer production, and 2% for pharmaceuticals [19]. However, by 2025, lithium consumption patterns had shifted dramatically, with nearly 60% of the global lithium supply directed toward battery manufacturing, underscoring the rapid transformation of the industry toward electrification and energy storage solutions [20].

As global demand for lithium grows, concerns regarding supply chain vulnerabilities have become increasingly pressing. The global lithium supply chain is led by Australia, which produces over 55% through hard-rock extraction, while South America contributes around 30% via lithium brine. The rest comes from clay deposits, including major projects like Kings Valley in the U.S. and Sonora Lithium in Mexico [21]. However, lithium refining and processing are heavily concentrated in China, which commands nearly 60% of global lithium processing capacity [22]. This geographical imbalance has raised concerns over geopolitical dependencies and the necessity of diversifying supply chains. Mexico’s strategic location and proximity to the United States present a valuable opportunity to develop a vertically integrated lithium supply chain that supports North American battery manufacturing and nearshoring initiatives.

Strategic planning plays a crucial role in ensuring the resilience and sustainability of lithium supply chains by integrating economic, environmental, and geopolitical considerations.

In the recent literature, the crucial role of lithium supply chains in the transition to clean energy and electric mobility has been emphasized [23,24,25]. For example, the work presented by Popien et al. [26] addresses the fact that lithium–ion battery supply chains have significant environmental and socioeconomic impacts, requiring regulatory compliance and sustainability assessments using Life Cycle Sustainability Assessment (LCSA) and optimization models. This study integrates LCSA with decision-support models to design sustainable battery supply chains, considering activities and constraints such as cost, emissions, and social factors. Similarly, the work presented by Jones [27] demonstrates a mathematical optimization framework for analyzing the global lithium supply chain, assessing the trade-offs between minimizing costs and reducing CO₂ emissions to meet future lithium demand. In addition, other studies have explored the challenges of developing a sustainable lithium–ion battery supply chain within the framework of the circular economy. The work presented by Afroozi [28] applies Gray’s multi-criteria decision-making approach to identify and assess barriers to optimizing lithium battery recycling and reuse, revealing that a lack of supportive policies and standards is a key obstacle. On the other side, recent studies highlight the importance of strategic resilience in lithium supply chains, particularly in response to disruptions caused by shifts in energy policy, geopolitical risks, and increasing demand from the electric vehicle market. Shao and Jin [29] analyze the resilience of the lithium supply chain under supply interruptions and the rising demand for new energy vehicles, emphasizing the importance of recycling and reserve stockpiling to mitigate disruptions. Similarly, Jin et al. [30] assess the structural resilience of China’s lithium trade network, demonstrating the vulnerabilities of supply nodes and the need for diversified sourcing strategies. These insights are particularly relevant for Mexico as it positions itself within the global lithium supply chain. Poules and Bahno [31] further explore vulnerabilities in the European battery supply chain, emphasizing the role of resilience strategies in securing long-term resource availability, while Cervera Viza [32] examines risk management in lithium battery manufacturing, identifying key mitigation measures for geopolitical instability, environmental impacts, and technological failures. Applying these strategic frameworks to Mexico’s lithium sector can help ensure a robust and competitive supply chain, mitigating environmental and economic risks while enhancing industrial integration.

This study provides a groundbreaking assessment of Mexico’s lithium supply chain, moving beyond traditional extraction-focused analyses to evaluate its feasibility within a broader industrial and environmental context. Unlike previous research centered on lithium exports, this work introduces a novel optimization framework that systematically assesses economic viability and greenhouse gas emissions to identify the most sustainable development pathway. Employing a mixed-integer linear programming (MILP) model, it offers strategic insights into balancing profitability and environmental impact, guiding policymakers and industry leaders in decision-making. The MILP model is particularly well-suited for our objective of optimizing the lithium supply chain because it allows for the explicit consideration of multiple decision variables, such as extraction capacity, cost, and emissions, within a structured and quantifiable framework [33]. This study positions Mexico as a key emerging player in the global lithium economy, emphasizing its potential to develop a secure, efficient, and sustainable lithium industry while shaping responsible resource management strategies.

This article is structured as follows: The Problem Statement introduces this study’s relevance, emphasizing the growing demand for lithium and the strategic advantage of integrating Mexico into the global supply chain. The Methods Section outlines the development of the mathematical model and the case study. The Results and Discussion Section presents the optimization outcomes, assessing Mexico’s lithium supply chain from an economic and environmental perspective. Finally, the Conclusions summarize key findings and provide strategic recommendations for policymakers.

2. Problem Statement

The increasing demand for lithium, mainly associated with the electric vehicle market and the energy transition, has increased the existing concern in the current lithium supply chain. Although various studies have been carried out on lithium trade networks and supply disruption in various markets such as China, America, and Europe, very little has been explored regarding the integration of Mexico in these markets. From a supply chain perspective, integrating Mexico into the global lithium network presents a strategic advantage due to its geographical location and available lithium reserves. Positioned between the major North American markets, Mexico has the potential to play a key role in regional supply chain diversification, reducing dependence on dominant producers and enhancing supply chain resilience. However, the absence of comprehensive research on the lithium supply chain in Mexico creates uncertainty regarding its infrastructure, processing capacity, and logistical integration within existing global networks. Without a strategic framework, Mexico runs the risk of developing a vulnerable and unsustainable lithium supply chain.

To address this gap, this study presents an optimization-based framework to systematically evaluate the lithium supply chain of Mexico, incorporating economic, environmental, and policy dimensions. Therefore, this research identifies sustainable development pathways that optimize resource utilization, minimize greenhouse gas emissions, and improve industrial competitiveness, by using an MILP model, which integrates discrete decisions, such as facility openings or production discontinuations, with continuous flows, such as extraction quantities and emissions. Unlike previous models that focus solely on lithium trade or extraction, this framework integrates real-world policy constraints, such as tariffs and environmental restrictions, offering a unique perspective tailored to Mexico’s emerging lithium sector. The findings seek to support decision-makers in shaping a robust, safe, and sustainable lithium industry, positioning Mexico as a key player in the global lithium economy.

3. Methods

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).

3.1. Mathematical Model

The mathematical model is based on the lithium supply chain, as illustrated in Figure 1, where the parameters enable the selection of one type of lithium-derived product to be fabricated over another, as well as its distribution, either for domestic or international markets.

3.1.1. Lithium Extraction

The total annual extraction of lithium ($f_{i,j}^{dep}$) from a given state i must not exceed the available lithium for the different types of deposits j ($D_{i,j}^{dep}$) (see Equation (1)). The description and values of the parameters used can be found in

Table 1. Amount of potentially extractable lithium $\left(D_{i,j}^{dep}\right)$ in each state of Mexico i [34,35,36] and the national demand for lithium $D_{k,o}^{dem,nac}$ for use in lithium–ion batteries and lithium greases [35].

States	${\mathit{D}}_{\mathit{i},\mathit{j}}^{\mathit{d}\mathit{e}\mathit{p}}$ j = Clay	${\mathit{D}}_{\mathit{i},\mathit{j}}^{\mathit{d}\mathit{e}\mathit{p}}$ j = Brine	${\mathit{D}}_{\mathit{i},\mathit{j}}^{\mathit{d}\mathit{e}\mathit{p}}$ j = Rock	${\mathit{D}}_{\mathit{k},\mathit{o}}^{\mathit{d}\mathit{e}\mathit{m},\mathit{n}\mathit{a}\mathit{c}}$ k = Lithium-Ion Battery	${\mathit{D}}_{\mathit{k},\mathit{o}}^{\mathit{d}\mathit{e}\mathit{m},\mathit{n}\mathit{a}\mathit{c}}$ k = Lithium Grease	Units
Aguascalientes	0.0000	0.0000	0.0000	52.0634	2.1107	tons Li
Baja California	0.0000	0.0000	0.0000	184.1658	7.4662	tons Li
Baja California Sur	0.0000	2.5980	0.0000	0.0124	0.0005	tons Li
Campeche	0.0000	0.0000	0.0000	0.1103	0.0045	tons Li
Chihuahua	0.8413	0.7303	0.0000	373.7020	15.1501	tons Li
Ciudad de México	0.0000	0.0000	0.0000	139.3059	5.6475	tons Li
Coahuila	0.0000	0.0000	0.0000	1.4072	0.0570	tons Li
Colima	0.0000	0.0000	0.0000	0.2376	0.0096	tons Li
Durango	0.0000	0.0000	0.0000	0.1049	0.0043	tons Li
Guanajuato	0.0000	0.0000	0.0000	0.1979	0.0080	tons Li
Jalisco	0.0000	0.0000	0.0000	101.5318	4.1162	tons Li
México	0.0000	0.0000	0.0000	9.5839	0.3885	tons Li
Michoacán	0.0000	0.0000	0.0000	0.0123	0.0005	tons Li
Morelos	0.0000	0.0000	0.0000	0.1508	0.0061	tons Li
Nuevo León	0.0000	0.0000	0.0000	12.4727	0.5056	tons Li
Puebla	4.5561	0.0000	4.0465	2.9264	0.1186	tons Li
Querétaro	0.0000	0.0000	0.0000	2.7002	0.1095	tons Li
Quintana Roo	0.0000	0.0000	0.0000	0.0946	0.0038	tons Li
San Luis Potosí	2.0207	0.0000	0.0000	2.7501	0.1115	tons Li
Sinaloa	0.0000	0.0000	0.0000	0.1702	0.0069	tons Li
Sonora	42,885.4400	0.0000	0.0000	105.7297	4.2863	tons Li
Tabasco	0.0000	0.0000	0.0000	19.1392	0.7759	tons Li
Tamaulipas	0.0000	0.0000	0.0000	20.5447	0.8329	tons Li
Tlaxcala	0.0000	0.0000	0.0000	0.0024	0.0001	tons Li
Veracruz	0.0000	0.0000	0.0000	0.8082	0.0328	tons Li
Yucatán	0.0000	0.0000	0.0000	0.1835	0.0074	tons Li
Zacatecas	4.6968	0.0000	0.0000	0.0415	0.0017	tons Li

.

$$D_{i,j}^{dep}\ge f_{i,j}^{dep},\forall i\in I,j\in J$$

(1)

Lithium processed either in the local state i ($f_{i,j}^{loc}$) or in an external state ia ($f_{i,ia,j}^{ext}$) will account for an extraction efficiency (${\eta }_j^{ext}$) (see

Table 2. Parameters for each type of lithium deposit j including extraction efficiency (${\eta }_j^{ext}$) [37,38], extraction cost ($C_j^{ext}$) [39], and the GHG emission factor for lithium extraction ($E{F}_j^{ext}$) [40,41,42,43].

Parameter	Clay	Brine	Rock	Units
${\eta }_j^{ext}$	92.37	90.43	91.35	%
${\eta }_{j,k}^{prod},$ k = Lithium-ion Battery	91.40	92.40	90.40	%
${\eta }_{j,k}^{prod},$ k = Lithium grease	59.10	59.70	58.50	%
$C_j^{ext}$	0.0037	0.0011	0.0030	$M
$E{F}_j^{ext}$	12.7500	2.9000	15.0000	Ton CO2/Ton Li

) specific to each type of deposit j (see Equation (2)). Note that both i and ia take values from the set I, but they must be different (I ≠ ia). This ensures that exchanges between states account for the import or export of resources from all external states.

$$f_{i,j}^{dep}\cdot {\eta }_j^{ext}={{\displaystyle \sum }}_{\underset{\forall i\ne ia}{ia}}^I{f}_{i,ia,j}^{ext}+f_{i,j}^{loc},\forall i\in I,j\in J$$

(2)

Equation (3) details the amount of lithium sent from a particular type of deposit j in a state i ($f_{ia,j}^{imp}$) that will be processed in the external state ia ($f_{i,ia,j}^{exp}$), which ensures the conservation of lithium flows between exporting and importing states.

$${{\displaystyle \sum }}_{\underset{\forall i\ne ia}{i}}^I{f}_{i,ia,j}^{exp}=f_{ia,j}^{imp},\forall ia\in I,j\in J$$

(3)

However, within the lithium supply chain, the sale of lithium as a precursor is contemplated. Therefore, the total lithium processed across all states takes account for lithium sold as a lithium carbonate ($f_{i,j,int,m}^{precur}$) and used for lithium–ion batteries and lithium greases ($f_{i,j}^{process}$) (see Equation (4)).

$$f_{i,j}^{imp}+f_{i,j}^{loc}={{\displaystyle \sum }}_m^M{f}_{i,j,l,m}^{precur}+f_{i,j}^{process},\forall i\in I,j\in J l=int$$

(4)

3.1.2. Lithium Processing

Equation (5) determines the total production capacity ($p{c}_{i,k}^{tot})$, which must be equal to or less than the established minimum production capacity parameter ${\mathrm{PC}}_{i,k}^E$ multiplied by $z_{i,k}^{new}$, which is a binary decision variable used to determine the state i in which lithium-derived product types k will be fabricated. This variable can take two values:

0: Indicates that products of type k will not be fabricated in state i

1: Indicates that products of type k will be fabricated in state i

$$p{c}_{i,k}^{tot}={\mathrm{PC}}_{i,k}^E\cdot z_{i,k}^{new},\forall i\in I,k\in K$$

(5)

Equation (6) will also correspond to the amount of lithium processed from a specific deposit type j determined for the types of lithium-derived products k ($f_{i,k,l}^{prod})$, batteries, or greases

$${{\displaystyle \sum }}_l^L{f}_{i,k,l}^{prod}\le p{c}_{i,k}^{tot},\forall i\in I,k\in K$$

(6)

The total production of batteries or greases ($f_{i,k,l}^{prod}$) is determined by the amount of processed lithium allocated for these activities ($f_{i,j}^{process}$) multiplied by the production efficiency (${\eta }_{j,k}^{prod}$) specific to each product k (see Equation (7)). Details and values for the parameters used are provided in Table 2.

$${{\displaystyle \sum }}_l^L{f}_{i,k,l}^{prod}={{\displaystyle \sum }}_j^J\left[f_{i,j}^{process}\cdot {\eta }_{j,k}^{prod}\right],\forall i\in I,k\in K$$

(7)

The parameters related to the ${\mathrm{PC}}_{i,k}^E$ are based on the worst-case scenario and are multiplied by a decision variable that represents whether or not this capacity is reached, fixed costs ($A_k^{fix}$) correspond to expenses that do not change with the level of production, while variable costs ($B_k^{var}$) depend directly on the quantity produced [44,45], and GHG emissions are associated with the production of lithium-derived products for each ton of lithium used. The description and values of the parameters used can be found in

Table 3. Parameters associated with production capacities and production costs [44,45,46,47] and GHG emissions [48,49,50].

Parameter	Lithium–Ion Battery	Lithium Grease	Units
${\mathrm{PC}}_{i,k}^E$	600.000	300.000	tons Li
$A_k^{fix}$	0.085	0.002	$M
$B_k^{var}$	0.012	0.141	$M/ton Li
$E{F}_k^{prod}$	108.931	101.111	Ton CO2/ ton Li

.

The distribution of lithium requires knowledge of both national and international demand, depending on the specific needs of each market, whether they require lithium carbonate, lithium–ion batteries, or lithium greases; therefore, to allow the model to determine which product to sell and in what quantity for the international market, two equations are considered: the quantity of lithium, as lithium carbonate ($f_{i,j,l,m}^{precur}$), must not exceed the international demand for it ($D_{j,m}^{precur}$) (see Equation (8)).

$$D_{j,m}^{precur}\ge {{\displaystyle \sum }}_i^I{f}_{i,j,l,m}^{precur},\forall j\in J,m\in M l=int$$

(8)

Likewise, the quantity of production of lithium–ion batteries or lithium greases for the international market ($f_{k,m}^{int}$) must not exceed the international demand for it ($D_{k,m}^{dem,int}$) (see Equation (9)). These parameters can be found in

Table 4. Lithium demand and transportation costs for export from Mexico to international markets [45,51,52,53].

International Market m	${\mathit{D}}_{\mathit{j},\mathit{m}}^{\mathit{p}\mathit{r}\mathit{e}\mathit{c}\mathit{u}\mathit{r}}$ j = Brine	${\mathit{D}}_{\mathit{j},\mathit{m}}^{\mathit{p}\mathit{r}\mathit{e}\mathit{c}\mathit{u}\mathit{r}}$ j = Rock	${\mathit{D}}_{\mathit{j},\mathit{m}}^{\mathit{p}\mathit{r}\mathit{e}\mathit{c}\mathit{u}\mathit{r}}$ j = Clay	${\mathit{D}}_{\mathit{k},\mathit{m}}^{\mathit{d}\mathit{e}\mathit{m},\mathit{i}\mathit{n}\mathit{t}}$ k = Lithium–Ion Battery	${\mathit{D}}_{\mathit{k},\mathit{m}}^{\mathit{d}\mathit{e}\mathit{m},\mathit{i}\mathit{n}\mathit{t}}$ k = Lithium Grease	${\mathit{C}}_{\mathit{m}}^{\mathit{t}\mathit{r}\mathit{a}\mathit{n}\mathit{s},\mathit{i}\mathit{n}\mathit{t}}$	$\mathit{D}\mathit{I}{\mathit{S}}_{\mathit{m}}^{\mathit{i}\mathit{n}\mathit{t}}$
China	13,980.5419	28,863.0542	2254.9260	33,372.9060	1352.9560	0.0828	13,247.0000
Korea	3357.8868	6932.4114	541.5946	8015.6010	324.9570	0.0735	11,761.5600
Japan	2341.6919	4834.4607	377.6922	5589.8450	226.6150	0.0683	10,929.7000
USA	387.6011	800.2087	62.5163	925.2410	37.5100	0.0000	2362.6400
Russia	300.8942	621.2009	48.5313	718.2640	29.1190	0.0684	10,946.2500
United Kingdom	300.1917	619.7507	48.4180	716.5870	29.0510	0.0563	9000.5000
Belgium	255.5592	527.6061	41.2192	610.0450	24.7320	0.0601	9621.6200
Netherlands	213.3238	440.4104	34.4071	509.2250	20.6440	0.0601	9616.9100
Germany	169.0860	349.0808	27.2719	403.6250	16.3630	0.0622	9948.9800
France	126.7501	261.6777	20.4436	302.5650	12.2660	0.0605	9683.1100
Turkey	80.9469	167.1161	13.0559	193.2280	7.8340	0.0768	12,292.0400
Spain	49.2741	101.7273	7.9474	117.6220	4.7680	0.0593	9493.6800

.

$$D_{k,m}^{dem,int}\ge f_{k,m}^{int},\forall k\in K,m\in M$$

(9)

For the case of the domestic market, only the demand for lithium technologies within the country (see Table 1) is considered ($D_{k,o}^{dem,nac}$), which will be greater than the rate of lithium-derived products fabricated for this specific market ($f_{k,o}^{nac}$) (see Equation (10)).

$$D_{k,o}^{dem,nac}\ge f_{k,o}^{nac},\forall k\in K,o\in O$$

(10)

3.1.3. Costs and Sales

The lithium supply chain requires different levels of investment for each of its stages, depending on the type of extraction deposit ($cos{t}^{ext}$) and the type of technology to be processed ($cos{t}^{proc}$), and transportation costs ($cos{t}^{trans}$) are also included. Total costs ($tac)$ are calculated as follows in Equation (11):

$$tac=cos{t}^{trans}+cos{t}^{proc}+cos{t}^{ext}$$

(11)

Equation (12) offers a way to calculate the total cost of transporting lithium ($cos{t}^{trans}$), considering the different destinations and modes of transport both within the international (see Table 4) and domestic market (see

Table 5. Transportation costs.

Parameter	Value	Units
National Transport Cost	0.075	USD ton/km
$S_k^{int}, k=Lithium–ion Battery$	0.154	USD M
$S_k^{int}, k=Lithium Grease$	0.162	USD M
$S_k^{nac}, k=Lithium–ion Battery$	0.154	USD M
$S_k^{nac}, k=Lithium Grease$	0.162	USD M
$S_j^{precur}$	0.01	USD M
$E{F}^{gro}$	0.00010112	ton CO2/ton Li per Km
$E{F}_m^{int}$	0.00001614	ton CO2/ton Li per Km

) where the parameters $C_{i,ia}^{trans,exp}, C_m^{trans,int}, C_o^{trans,nac}, C_{i,l}^{trans,precur}$ mean transport cost of state i to state ia, international transport cost to market m, national transport cost to market o, and transport cost for carbonate lithium of state i to hub l, respectively.

$$\begin{array}{c}cos{t}^{trans}={\displaystyle {\displaystyle \sum }_i^I}{\displaystyle {\displaystyle \sum }_{ia}^I}{\displaystyle {\displaystyle \sum }_j^J}\left(f_{i,ia,j}^{exp}\cdot C_{i,ia}^{trans,exp}\right)+{\displaystyle {\displaystyle \sum }_i^I}{\displaystyle {\displaystyle \sum }_k^K}{\displaystyle {\displaystyle \sum }_l^L}\left(f_{i,k,l}^{prod}\cdot C_{i,l}^{trans,hub}\right)\\ +{\displaystyle {\displaystyle \sum }_k^K}{\displaystyle {\displaystyle \sum }_m^M}\left(f_{k,m}^{int}\cdot C_m^{trans,int}\right)\\ +{{\displaystyle \sum }}_k^K{{\displaystyle \sum }}_o^O\left(f_{k,o}^{nac}\cdot C_o^{trans,nac}\right)+{{\displaystyle \sum }}_i^I{{\displaystyle \sum }}_j^J{{\displaystyle \sum }}_m^M{{\displaystyle \sum }}_{l=int}^L\left(f_{i,j,l,m}^{precur}\cdot C_{i,l}^{trans,precur}\cdot C_m^{trans,int}\right)\end{array}$$

(12)

To calculate the total cost of lithium processing ($cos{t}^{proc}$), the fixed ($A_k^{fix}$) and variable costs ($B_k^{var}$) are taken into account, as well as the production and processing capacity in different locations and with different lithium-derived products (see Equation (13)). The parameters used in this equation are presented in Table 3.

$$cos{t}^{proc}={{\displaystyle \sum }}_i^I{{\displaystyle \sum }}_k^K\left(p{c}_{i,k}^{tot}\cdot A_k^{fix}\right)+{{\displaystyle \sum }}_i^I{{\displaystyle \sum }}_k^K{{\displaystyle \sum }}_l^L\left(f_{i,k,l}^{prod}\cdot B_k^{var}\right)$$

(13)

To obtain the total cost of lithium extraction ($cos{t}^{ext}$), the different types of deposits located in a specific state are considered, together with the specific costs associated (see Table 2) with the extraction of lithium from each type of deposit ($C_j^{ext}$) (see Equation (14)).

$$cos{t}^{ext}={{\displaystyle \sum }}_i^I{{\displaystyle \sum }}_j^J\left(f_{i,j}^{dep}\cdot C_j^{ext}\right)$$

(14)

The parameters related to the demand for lithium carbonate and lithium-derived products in international markets are described in Table 4, along with the cost of transportation, considering that it is by barge and the export distance from Mexico to these destinations.

Total lithium annual sales ($sls$) consider both sales in international markets ($S_k^{int}$) and sales in the domestic market ($S_k^{nac}$), taking into account processed lithium for international markets ($S_j^{precur}$) (see Equation (15)), and the selling price is considered in Table 5.

$$sls={{\displaystyle \sum }}_k^K{{\displaystyle \sum }}_m^M\left(f_{k,m}^{int}\cdot S_k^{int}\right)+{{\displaystyle \sum }}_k^K{{\displaystyle \sum }}_o^O(f_{k,o}^{nac}\cdot S_k^{nac})+{{\displaystyle \sum }}_i^I{{\displaystyle \sum }}_j^J{{\displaystyle \sum }}_m^M(f_{i,j,l,m}^{precur}\cdot S_j^{precur}), l=int$$

(15)

Net profits or total benefits ($prof$) are calculated from the production and sale of lithium, taking into account both sales revenues and costs associated with production and transportation (see Equation (16)).

$$prof=sls-tac$$

(16)

3.1.4. Emissions

Equation (17) quantifies the total greenhouse gas (GHG) emissions generated across of the lithium supply chain ($ghg)$, including production ($E{F}_k^{prod}$) and transportation process ($E{F}^{gro}$), considering the distance between the state and the state ia ($DI{S}_{i,ia}^{exp}$), the distance to the hub ($DI{S}_i^{hub}$), the distance to the domestic ($DI{S}_o^{nac}$) and international markets ($DI{S}_m^{int}$). It also considers the GHG emissions associated with the extraction of lithium ($E{F}_j^{ext}$). The parameters can be consulted in Table 2, Table 3 and Table 5.

$$\begin{array}{c}hg={{\displaystyle \sum }}_i^I{{\displaystyle \sum }}_{ia}^I{{\displaystyle \sum }}_j^J(f_{i,ia,j}^{exp}·E{F}^{gro}·DI{S}_{i,ia}^{exp})+{{\displaystyle \sum }}_i^I{{\displaystyle \sum }}_k^K{{\displaystyle \sum }}_l^L(f_{i,k,l}^{prod}·E{F}^{gro}·DI{S}_i^{hub})+\\ {{\displaystyle \sum }}_k^K{{\displaystyle \sum }}_o^O(f_{k,o}^{nac}·E{F}^{gro}·DI{S}_o^{nac})+{{\displaystyle \sum }}_k^K{{\displaystyle \sum }}_m^M(f_{k,m}^{int}·E{F}_m^{int}·DI{S}_m^{int})+{{\displaystyle \sum }}_i^I{{\displaystyle \sum }}_j^J(f_{i,j}^{dep}·E{F}_j^{ext})+\\ {{\displaystyle \sum }}_i^I{{\displaystyle \sum }}_k^K{{\displaystyle \sum }}_l^L(f_{i,k,l}^{prod}·E{F}_k^{prod})+{{\displaystyle \sum }}_i^I{{\displaystyle \sum }}_j^J{{\displaystyle \sum }}_m^M((f_{k,m}^{int}+f_{i,j,l,m,\left\{l=int\right\}}^{precur})·E{F}_m^{int}·DI{S}_m^{int})\end{array}$$

(17)

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

$$\mathit{max} prof$$

(18)

$$\mathit{min} ghg$$

(19)

The ε-constraint method is used to simultaneously consider both objectives: maximizing profit (Equation (18)) and minimizing GHG emissions (Equation (19). This approach allows for prioritizing one objective while treating the other as a constraint, enabling the exploration of trade-offs between these competing goals.

The parameters presented in Table 5 show the national transport cost by humans driving diesel trucks [52], sales prices for the international market of lithium carbonate ($S_j^{precur}$) [54], lithium–ion batteries and lithium greases ($S_k^{int}$), as well as the price for the domestic market ($S_k^{nac}$) [46,55,56,57,58] and GHG emissions from transport to domestic ($E{F}^{gro}$) [59] and international markets ($E{F}_m^{int}$) [47].

3.1.5. Assumptions and Parameters

It is important to consider that the proposed model includes assumptions such as fixed tax rates for lithium exports, constant extraction capacity, and predefined export destinations and volumes. Furthermore, the model assumes that lithium and lithium derivative prices remain stable throughout the analyzed period. The model also has limitations, particularly regarding data availability and the simplification of certain economic factors, such as the potential impact of geopolitical events or fluctuations in global demand. The industry statistics presented in this paper highlight the growing global demand for lithium, especially in sectors such as electric vehicles and energy storage. This information is directly related to the research objective, which seeks to evaluate the lithium supply chain in Mexico, assessing both its economic viability and its environmental impact.

To improve clarity and avoid redundancy, we included references for each key parameter directly in the table titles. This approach allowed us to provide proper documentation without overburdening the tables with repetitive citations.

3.2. Case Study

Despite having multiple lithium reserves, Mexico currently lacks active exploitation of its deposits [60,61]. However, the Mexican government has expressed interest in the mineral’s exploitation with the creation of the public organization “LitioMX”, which is tasked with the exploration, extraction, processing, and utilization of lithium found within Mexican territory, as well as overseeing the management and control of its economic value chains [62]. This paper focuses on Mexico’s vast lithium deposits and their potential implications for the global supply chain, considering key factors such as transportation logistics, processing capacity, and market integration within the country’s economic, environmental, and geopolitical context. Lithium is found in various types of deposits, with brine deposits located in large salt flats, especially in the northern and central regions of Mexico [34,44,45]. Lithium is extracted from the brine solution, which is rich in lithium salts. This method is widely used in countries such as Bolivia and Argentina and is generally more cost-effective and environmentally friendly than hard rock mining, as it requires less energy and water. However, challenges such as evaporation rates, water scarcity, and environmental concerns related to brine disposal must be addressed [57]. Additionally, Mexico contains significant hard rock lithium deposits, particularly in the form of spodumene [63]. This extraction method involves traditional mining techniques, where the rock is mined, crushed, and heated to extract lithium. While energy-intensive and more costly than brine extraction, this method offers a more stable and predictable supply, particularly for long-term projects [64]. Figure 2 illustrates the estimated tons of lithium in Mexico (Li tons), with color intensity representing concentration levels. Darker shades of blue indicate higher lithium reserves, with Sonora showing the highest concentration.

4. Results and Discussion

4.1. General Results

The formulated MILP model consists of 4864 continuous variables, 64 binary variables, and 749 equations. It was solved using the CPLEX solver in GAMS, with a computational time of 0.36 s on an Intel i5-8350U processor (1.70 GHz, 8GB RAM).

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. The selected solutions are based on three different criteria. Solution A represents the optimal result obtained according to the mathematical model developed in this work, whose objective is to maximize profits (see Equation (18)). Solution B was chosen as the solution that most closely approximates the utopian point where the greatest profits are obtained with the lowest GHG emissions (see Figure 3). Finally, in solution C, the model chooses not to produce lithium–ion batteries or lithium greases, limiting itself to the extraction and processing of lithium for sale abroad (see

Table 6. Exports of lithium in precursors, batteries, and greases for Solutions A, B, and C.

	Solution A	Solution B	Solution C	Units
Export of lithium precursor	3485.22	3485.22	3040.10	Ton of Lithium
Export of lithium-derived products	3406.52	1126.60	0.00	Ton of Lithium

).

Some key differences between the scenarios lie in the profits and the amount of lithium extracted. Solution A presents the highest annual profits, with a total of USD 317.19 M because lithium extraction reaches 42,904.93 tons per year, almost double that in Solution B, where 23,004.78 tons are extracted.

In contrast, Solution C focuses solely on meeting the international demand for lithium as a precursor, so the model reduces extraction to 3327.93 tons of lithium per year, an amount 12 times smaller than in Solution A, which includes the production of batteries and greases.

The findings suggest that tariff policies can significantly alter Mexico’s lithium export strategy. Specifically, a 25% tariff proposed by President Donald Trump [65] makes lithium exports to the United States unprofitable, highlighting the crucial need for Mexico to diversify its export markets. This situation underscores the importance of exploring alternative markets, both geographically and through product diversification, to reduce dependence on a single market. Considering these results, we recommend that policymakers focus on establishing new international partnerships, particularly with countries that offer favorable trade conditions, and consider the development of value-added lithium products. Such strategies would help mitigate the risks associated with trade policy fluctuations and strengthen Mexico’s position in the global lithium market.

4.2. Solution A

Solution A prioritizes sales maximization, resulting in higher GHG emissions, with annual profits of USD 317.19 M. This solution implies the largest amount of lithium extracted since it is the scenario that has the highest production of batteries and greases to generate greater exports, as seen in Table 6. The model in Solution A extracts lithium from the states of Sonora, Puebla, Zacatecas, Baja California Sur, San Luis Potosí, and Chihuahua. 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 (see Figure 4).

Thereby, this solution implies the largest international export of lithium carbonate, lithium–ion batteries, or lithium grease, being China, Korea, and Japan. It is worth noting that countries such as the United States, for example, receive a smaller quantity of products compared to China. However, this does not imply that countries further away from the extraction and production site are being prioritized, but that the model satisfies the demand of the closest countries, even if this is small compared to other countries.

Considering taxes, in solution A, the export of lithium–ion batteries and lithium grease to the United States is stopped with only a 1% increase in taxes. However, to minimize costs and maximize profits, lithium extraction in Sonora is reduced from 42,885 to 42,123 tons per year. At the same time, battery exports are increased, with 11,338 units being distributed to various low-tariff countries, 3811 units to Belgium, 6619 units to the Netherlands, and 10 units to Spain. As for greases, China was the only country that showed an increase in its imports, reaching a total of 10 tons. 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. Additionally, the rise in grease exports to China indicates an adaptive strategy to diversify markets and mitigate the impact of shifting trade policies. This flexibility in supply chain management ensures continued profitability, resulting in a total annual profit of USD 306.56 M.

To evaluate how variations in key parameters affect the model outcomes, a sensitivity analysis was conducted. This analysis was crucial for understanding the robustness of the model and identifying the parameters with the greatest impact on the results. Through this analysis, we determined the amount of taxes that the supply chain can tolerate before halting lithium shipments to the United States.

By the time the tax increase has reached 6%, the model decides not to export lithium carbonate. On the other hand, it minimizes the losses that this could cause, reducing lithium extraction in Sonora by 42,085 tons per year. Increasing exports to Belgium with 27 more battery units and, in China, 10 more tons of lithium grease. This results in a total annual profit of USD 305.90 M.

4.3. Solution B

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

Compared to Solution A, this solution shows a slight increase in the export of lithium precursors; however, concerning the export of batteries and greases, it shows a significant reduction, exporting approximately 33% in relation to Solution A. This is because it is a solution aimed at mediating GHG emissions, given that lithium extraction within the country is lower, specifically in the state of Sonora, which was reduced to 22,985.29 lithium tons per year (see Table 6 and Figure 4).

As for taxes, Solution B shows that, with a 1% increase, the model chooses not to export batteries or greases to the United States. However, the export of batteries to Great Britain rises from 0 to 3727 units, while grease exports to China increase from 278 to 1474 tons. These were the only countries that showed significant variations. As U.S. tariffs increase, the model reallocates lithium exports to alternative markets, particularly Great Britain and China, suggesting a strategic shift towards less tariff-restrictive trade agreements. Regarding lithium precursors, exports remain viable up to a 5% increase in tariffs, allowing shipment of 62.52 tons of lithium. However, when tariffs reach 6%, the model ceases precursor exports and reduces lithium extraction in Sonora, lowering production costs while still meeting projected demand in other markets before the tariff adjustments take effect.

4.4. Solution C

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

The extraction that is chosen to be performed within the country is only to meet international demands; unlike Solutions A and B, this solution decreases lithium extraction in Sonora and rules out the extraction of lithium within the state of Puebla for the rock deposit, as its extraction capacity is low and the associated costs and greenhouse gas emissions are higher compared to other sources (see Figure 4).

In Solution C, as in Solution A, a 1% increase in taxes eliminates the export of lithium precursors to the United States. To avoid an increase in GHG emissions, lithium extraction in Sonora is reduced from 3313 to 3009 tons. This reduction allows exports to other countries to remain unchanged, as the model prioritizes supplying demand in countries where costs are lower and GHG emissions are minimized, optimizing the balance between production and environmental sustainability. As U.S. tariffs increase, the model reallocates lithium exports to alternative markets, prioritizing trade agreements with less restrictive tariffs. This strategic adjustment ensures stability in international supply while maintaining environmental considerations. Figure 5 shows the percentages allocated to lithium derivatives or lithium carbonate for the three selected solutions.

5. General Discussion

When reviewing the three proposed solutions, it can be observed that the state of Sonora is the state with the highest extraction of lithium in the three solutions. In the states of Zacatecas, Baja California Sur, San Luis Potosí, and Chihuahua, the amount of lithium extracted remains the same. Only in the state of Puebla does the model decide not to extract lithium from the rock deposit for Solution C.

Furthermore, within each of the solutions, the model chose to give a different priority to each variable. In Solution A, profit is prioritized, with a profit of USD 317.19 M, however, this solution results in higher GHG emissions of 1119.81 thousand tons of GHG. In Solution B, a balance was found between the profits generated of USD 186.98 M and greenhouse gas emissions, with a total of 559.90 thousand tons of CO₂. Finally, in Solution C, GHG emissions were prioritized, which caused the model to choose not to produce greases and batteries, exporting internationally all the lithium extracted in the country, obtaining a profit of USD 48.20 M, with emissions of 44.79 thousand tons of CO₂. These results underscore the critical role of trade-offs in decision-making for lithium production. While prioritizing profit maximization leads to substantial economic benefits, it also results in significantly higher environmental costs. Conversely, prioritizing sustainability significantly reduces emissions but at the cost of reduced economic returns. Solution B represents a compromise, demonstrating that balanced approaches can achieve reasonable profitability while mitigating environmental impact.

Additionally, the impact of trade policies is evident in the model’s response to tax increases in taxes between the United States and Mexico. It was found to significantly affect the lithium supply chain. However, it is also possible to identify solutions that allow profits to remain almost intact. This is achieved through strategic adjustments in the amount of lithium extraction and the ability to redirect trade to other countries outside the Americas, which helps to offset the losses resulting from the changes in taxes. These findings provide valuable insights for policymakers and industry stakeholders by demonstrating how trade policies and environmental considerations shape the economic and operational feasibility of lithium production. The results offer a strategic framework for optimizing lithium extraction and international trade, guiding real-world decision-making in the face of evolving regulatory and market conditions.

6. Conclusions

Lithium plays a crucial role in the manufacturing of electric vehicles and energy transition due to its various uses in storage systems. It is also essential in other industries, such as the production of greases and certain medications. For this reason, this study analyzes the lithium supply chain in Mexico, a country with significant deposits of this mineral. Beyond the extraction of lithium for export as lithium carbonate, this work also explores the possibility of producing lithium–ion batteries and lithium grease while considering GHG emissions to identify the most viable and environmentally friendly option. The proposed model is an MILP problem, solved using the GAMS modeling environment. Unlike previous models that focus solely on lithium trade or extraction, this framework integrates real-world policy constraints, including tariffs, trade reallocation, and environmental restrictions. This allows for a more comprehensive analysis of how different policy scenarios impact Mexico’s lithium supply chain. The analysis of the three proposed solutions shows that 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. Each solution prioritizes different variables: Solution A focuses on maximizing profits, reaching USD 317.19 M, but results in higher GHG emissions (1119.81 thousand tons). Solution B strikes a balance with profits of USD 186.98 M and emissions of 559.90 thousand tons of CO₂. In contrast, Solution C prioritizes emission reduction, achieving a significant decrease to 44.79 thousand tons, though it sacrifices total profit (USD 48.20 M).

6.1. Lithium Trade Diversification

This study also highlights how the model determines trade adjustments in response to tariff changes. Despite the impact of increased tariffs between the U.S. and Mexico, the results suggest that strategic realignment of exports towards Europe and Asia can sustain profits while reducing dependence on the U.S. market. By prioritizing exports to regions where supply costs are lower and emissions are minimized, the model identifies economically and environmentally optimal trade flows. A 25% tariff makes lithium exports to the U.S. economically unfeasible, reinforcing the need for Mexico to diversify its trade partners and explore alternative markets.

6.2. Strategic Insight

From a strategic perspective, these findings highlight the importance of optimizing lithium extraction processes, expanding trade agreements, and investing in sustainable production pathways. To remain competitive in a rapidly evolving global market, Mexico must not only enhance domestic lithium processing capabilities but also adapt to shifting trade policies through proactive economic strategies. Based on these insights, policymakers should consider incentives for sustainable lithium extraction, strategic trade agreements, and investment in domestic lithium processing to strengthen Mexico’s position in the global supply chain.

6.3. Future Directions

Future research could explore dynamic pricing models to evaluate how fluctuations in global lithium prices impact trade decisions, policy-driven trade simulations to analyze the effects of shifting international regulations, and optimization under uncertainty to incorporate demand fluctuations and geopolitical risks, for decision-making in the lithium sector.