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# Examining copper supply consistency in socioeconomic pathways: A mine-level dynamic approach

Gaël Parpan<sup>a, *, ID</sup>, Baptiste Andrieu<sup>b, ID</sup>, Olivier Vidal<sup>c</sup>, Louis Delannoy<sup>d, e</sup>, Hugo Le Boulzec<sup>f</sup>, Matthieu Gervais<sup>a</sup>, Yves Jégourel<sup>g</sup>, Stéphane Delalande<sup>a</sup>

## ARTICLE INFO

### Keywords:

Copper

Shared Socio-economic Pathways (SSPs)

Mine Supply

Decarbonization Pathways

Material Flow Analysis (MFA)

Critical Minerals

## ABSTRACT

Primary copper production capacity is crucial given future demand and social, environmental, technical, economic, and political constraints, often overlooked in decarbonization pathway models. To address this, we propose a methodology to examine the consistency of the basic drivers of Shared Socioeconomic Pathways (SSPs) for primary copper requirements using the DyMEMDS stock-flow model. Our approach involves projecting primary copper production capacities to 2050 on a mine-by-mine basis, integrating mining industry dynamics based on commercial data. Results indicate significant concerns regarding the consistency of SSPs' basic drivers for copper requirements, revealing potential gaps exceeding 40 Mt in worst-case scenarios. Such discrepancies could impact technology deployments necessary for socioeconomic and decarbonisation assumptions. We recommend that the decarbonization modeling community align scenarios with mining industry constraints. Considering resource efficiency and circular economy strategies is essential for proposing more consistent scenarios to decision-makers, thereby mitigating risks of copper supply shortages hindering climate action.

## 1. Introduction

Critical minerals are essential components to a wide spectrum of clean energy technologies (Hayes and McCullough, 2018; Reich and Simon, 2024). Driven by the need to decarbonize the economy, their global demand is projected to significantly increase in next decades (Calderon et al., 2024; Collins et al., 2024; IEA, 2024b). Resurgences in geopolitical tensions and volatility in price however pose supply risks that could result in failure to meet sustainable development goals. In response, policymakers have taken steps to secure supply chains, through bilateral agreements and strategic partnerships for importing countries such as USA, India and the EU (Berthet et al., 2024; Jain, 2024; McCaffrey et al., 2023), while, for exporting countries, policy efforts have focused on enhancing resource governance, increasing value addition through domestic processing, and leveraging mineral wealth (Dou et al., 2023; Srivastava, 2023).

Copper has received particular attention from states and international organizations due to its strategic importance in the global energy transition (Bauer et al., 2023; Grohol et al., 2023; IEA, 2021). This is because copper serves as a critical input for electromobility, renewable energy generation, and the transport and storage infrastructure required for low-carbon electricity (IEA, 2021, 2024b; Klose and Pauliuk, 2023; Schipper et al., 2018; Watari et al., 2020; Takuma, 2021). While decarbonization efforts contribute to increased demand, key drivers also include global population growth and economic development (ICSG, 2024; IEA, 2024b; Singer, 2024). However, copper could face a production deficit from primary sources, as current and planned projects are insufficient to cover resource requirements between now and 2035 (20 Mt primary supply versus 27 Mt primary demand in IEA Announced Pledges Scenario) (IEA, 2024b). Reason is that the expansion of primary production is increasingly constrained by a combination of social, environmental, technical, economic, and political factors (Guj and

<footnote><sup>a</sup> Laboratory PIMM, CNRS, Arts et Métiers Institute of Technology, Cnam, Paris, 75013, France</footnote>

<footnote><sup>b</sup> University of Cambridge, Engineering Department, Cambridge, CB2 1PL, United Kingdom</footnote>

<footnote><sup>c</sup> University Grenoble Alpes, University Savoie Mont Blanc, CNRS, IRD, Univ. Gustave Eiffel, ISTERe, Grenoble, 38000, France</footnote>

<footnote><sup>d</sup> Global Economic Dynamics and the Biosphere, Royal Swedish Academy of Science, Stockholm, Sweden</footnote>

<footnote><sup>e</sup> Stockholm Resilience Centre, Stockholm University, Stockholm, Sweden</footnote>

<footnote><sup>f</sup> University Grenoble Alpes, CNRS, INRAE, Grenoble INP, Grenoble Applied Economics Laboratory (GAEL), Grenoble, 38000, France</footnote>

<footnote><sup>g</sup> Laboratoire interdisciplinaire de recherche en sciences de l'action (EA 4603), Cnam, Paris, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: gael.parpan@lecnam.net (G. Parpan).</footnote>
Schodde, 2025; Jowitt et al., 2020; Mudd and Jowitt, 2018). These include the global decline in ore grade quality (Northey et al., 2014), a decrease in major deposit discoveries due to limited accessible resources and constrained exploration budgets (DeCoff, 2024; Schodde, 2017), growing socio-political resistance linked to the impacts on land-connected communities (Anguelovski, 2011; Dunlap, 2020; Valenta et al., 2019; Owen et al., 2022; Rorato et al., 2020), and the fact that over half of existing mining operations are situated in regions experiencing high water stress (IEA, 2024b). An emblematic recent example of the decisive influence of “non-geological” variables on primary copper supply was the closure of the Cobre Panama mine in 2023, following a decision by Panama’s Supreme Court that the mine was unconstitutional.

In light of these increasingly complex constraints on primary copper production, academic research has over the past decade turned its focus toward better understanding and anticipating future supply dynamics (Watari et al., 2020). Three distinct methodological approaches have emerged in this literature - top-down, bottom-up, and hybrid - each offering different insights into the structural and contingent factors shaping copper availability. The top-down approach consists in evaluating future copper production by assessing its relationship with development variables, and extrapolation from past trends (Schipper et al., 2018). It often relies on forecasting copper supply through a Hubbert linearization of production on ultimate recoverable resources estimates (Calvo et al., 2017; Sverdrup et al., 2017, 2019) or by using regression models built on exogenous socio-economic assumptions (Singer, 2024). Although conceptually elegant, top-down approaches are criticized for neglecting future production ramp-up due to the optimisation of extraction techniques, and hence the reduction in extraction costs, as a result of technological progress (Vidal et al., 2019). Therefore, bottom-up studies - mine or basin-level aggregation of production and consumption data - have enjoyed a significant boom (Liu et al., 2023; Northey et al., 2014, 2023; Ryter et al., 2025). Building on the emergence of Industrial Ecology (IE), bottom-up approaches have greater accuracy, and greater flexibility to address process-dependent aspects of mineral extraction, but require a significant amount of data (Schipper et al., 2018). Hybrid approaches have also been proposed as a way to combine advantages and drawbacks of top down and bottom-up approaches (Ali et al., 2017; Sverdrup et al., 2019; Vidal et al., 2019, 2021; Örtl, 2018).

While bottom-up and hybrid approaches are becoming increasingly mature, they have not yet been fully integrated into decarbonization pathway modeling. A key limitation is that dominant frameworks still lack adequate representation of the energy and material flows embedded in the provision of goods and services (Delannoy et al., 2024; Pauliuk et al., 2017; Wang et al., 2022). An additional constraint lies in the underutilization of sensitive market intelligence and detailed, field-scale bottom-up data sources, such as the S&P Global database (S&P Global, 2022), and commercial datasets used for energy production forecasts, like those from Rystad Energy (Mercure et al., 2021) or GlobalShift (Delannoy et al., 2021). Indeed, the few bottom-up studies on future primary metal production focus on deposits rather than on existing mines and future projects (McNeil et al., 2023; Northey et al., 2014, 2023; Liu et al., 2023). Addressing both gaps, methodological and empirical, has become a recognized priority for improving the robustness of decarbonization scenarios and is now a focal point of research efforts. Several initiatives have emerged to close the first gap, notably the MESSAGEix-Materials stock-and-flow accounting module (Ünlü et al., 2024) and the WILIAM model’s material availability constraints on technology deployment (Samsó et al., 2023), though these efforts remain focused primarily on methodological improvements rather than the integration of commercial data sources.

Our study aims to address these issues within the broader framework of the climate change mitigation–materials nexus, focusing specifically on the case of primary copper. Firstly, we propose a methodology for projecting primary copper supply based on mine-by-mine commercial

data from the International Copper Study Group (ICSG, 2022). Our objective is to incorporate constraints and delays within the copper mining industry to provide projections of primary copper production capacities from 2020 to 2050. Secondly, we develop scenarios for primary copper demand using the DyMEMDS stock and flow model, integrating the basic socioeconomic drivers of the Shared Socioeconomic Pathways (SSPs). Thirdly, we examine the consistency of these primary copper demand scenarios with the projections of production capacities through a mass-consistent supply-demand module.

The core innovation of our methodology lies in its utilization of mine-by-mine commercial data, integrating material requirement scenarios with production capacity projections. This analysis enables us to examine whether socioeconomic assumptions align with projections of primary copper production capacities. Consequently, we can provide recommendations for both the decarbonization pathway modeling community and policymakers.

## 2. Materials and methods

### 2.1. Model overview

Fig. 1 provides a general description of models and their interactions. The model aims to simulate the interaction between supply, corresponding to the annual capacities of operational copper mines, and demand for primary copper. The modeling of primary copper requirement ($R_t$) comes exogenously from the DyMEMDS model (Vidal et al., 2021) into supply-demand module. DyMEMDS integrates the socio-economic assumptions of the Shared Socio-economic Pathways and the resource intensities of technologies, modeling the evolution of technology stocks through logistic curves. The functioning of the DyMEMDS model is described in Section 2.3. Primary copper production capacities are projected from the mine-by-mine dataset of the International Copper Study Group (ICSG, 2022). The methodology is described in Section 2.2 and detailed in Section 2 of the Supplementary Materials. These projections are describing primary copper production capacities ($C_t$) on a mine-by-mine basis and integrate exogenously the supply-demand module. This module is stock-flow consistent and is described in Section 2.4. It allows for the interaction between the global operating production capacities from mine by mine capacity projections and requirements of primary copper from DyMEMDS. It thus determines the global primary copper production ($P_t$) for each year between 2020 and 2050.

### 2.2. Primary copper production capacities modeling

We base the modeling of copper extraction capacities on the ICSG dataset titled "Directory of Copper Mines, Smelters and Refineries - Capacities" (ICSG, 2022). As this dataset is private, we cannot provide a detailed distribution of production capacities. However, aggregated data by stage and by country are available in the Supplementary Data. A description of the dataset's contents is provided in Figures D & E and Tables B & C in the Supplementary Materials. Our modeling approach is grounded on Material Flow Analysis framework, detailing production capacities on a mine-by-mine basis. We model each mine as a copper stock (kt), comparable to reserves, characterized by an annual maximum production capacity flow (kt/year) and an operational lifespan (years).

This study is limited to the copper contained in productions exiting the mining site; constraints associated with the rest of the value chain (refining, semi-finished products, finished products) are not considered. Production capacities represent the maximum quantity of copper that a mining site can extract each year (expressed in kt/year) and are constrained by various factors, known as modifying factors (CRIRSCO, 2024). These factors include, among others, geological aspects (e.g., type of ore, grade, accessibility, etc.), technical and economic aspects (e.g., reserves, production costs, technologies), and the sizing of the production infrastructure (e.g., crushing, concentrator, vehicles, human
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Model overview. Primary copper requirements ($R_t$) are derived from the DyMEMDS model (orange), primary production capacities ($C_t$) are derived from analysis of the ICSG database (red), and the supply-demand module is used to obtain Primary copper production ($P_t$) (blue).</figcaption></figure>

resources, etc.) (CRIRSCO, 2024). Globally, the ICSG estimates that the utilization rate of mining capacities was approximately 82 % between 2020 and 2023, corresponding to the ratio of actual production to production capacity (ICSG, 2024).

Our methodology for projecting copper production capacities varies according to the stage of the mining sites, whether they are in operation, undergoing expansion, in development, in feasibility study, or in exploration (detailed information regarding the methodology, parameters, and equations is provided in Section 2 of the Supplementary Materials).

We project operating mining sites based on their closure date as provided by ICSG monitoring or through an analysis of the activity reports of mining companies (see Table E in Supplementary Materials). When a closure date is not available, we determine a mine's lifespan using the ratio of proven and probable reserves to the site's average capacity (see Figure H in Supplementary Materials). For mines for which reserve data is not available (25 % of global capacity in 2022), we adopt the global trend by aggregating characterized mining capacity and calculating the annual rate of change.

For mining projects in expansion and development, as previously mentioned, we use probable and proven reserves to determine the mine's lifespan if no closure date was found in the ICSG dataset or grey literature.

Regarding the commissioning and closure of mines, we apply a three-year constraint for ramp-up. This assumption is based on our analysis of the ICSG dataset and mining industry activity reports, and is slightly more optimistic than in the work of (Northey et al., 2014; Mohr, 2010), who applied a four-year ramp-up. The ramp-down is modeled more gradually (see Figure J and Table F in Supplementary Materials), based on our analysis of the production plans from several mines (Gomez Dominguez et al., 2021, 2023, 2024, Gray et al., 2019, 2020; OreWin, 2023; Issel et al., 2023; Young et al., 2024, 2023; Maureira et al., 2022).

Furthermore, it is also necessary to consider that the initial lifespan of a mine evolves as its reserves are reevaluated over time. This reevaluation occurs when modifying factors, such as copper price and geological characteristics, enable the growth of reserves at mining sites and support a life-of-mine expansion. Schodde (2012) demonstrated that for 32 mines developed between 1970 and 1990; the conversion rate of reserves to exploitable ore exceeds 100 % for mines where initial reserves exceed 1 Mt. Nevertheless, there remains a lack of empirical data on the reevaluation of a mine's reserves over time. Guj and Schodde (2025) consider it likely that life-of-mine expansions for porphyry copper are typically 1.5 to 3 times greater than originally estimated in feasibility studies. Furthermore, our modeling cannot rely on this trend, as the ICSG database does not include the initial life-of-mine data for individual mines. Supplementary Figure P illustrates the evolution of proven and probable reserves over time for several mines, showing no particular trend. This observation suggests that each exploited deposit has its own characteristics influencing the evolution of reserves. However, on a global scale, reserves have continued to increase up to the present day (Schodde, 2010). Thus, the ratio of reserves to production has oscillated around 35 years of production between 1987 and 2022 (Jowitt et al., 2020). However, the constraints on the evolution of production capacities lie more in environmental, social, and governance issues, geopolitical risks and infrastructure constraints than in the evolution of reserves. (Guj and Schodde, 2025; Jowitt et al., 2020; Mudd and Jowitt, 2018).

Modeling this reevaluation of reserves would require characterizing each deposit in the ICSG database and assessing the evolution of reserves, taking into account the modifying factors typically considered in feasibility studies to develop the production plan (CRIRSCO, 2024). Therefore, we present a sensitivity modelling approach through low, medium, and high optimism cases for operation, expansion and development phases. Since we aim to explore a wide range of possible futures for production capacities, we assume no reserve reevaluation in the low case and full reserve reevaluation in the high case through 2050. These assumptions correspond to life-of-mine extensions of 0, 15, and 30 years over the 2020–2050 period. This reevaluation of reserves impacts the lifespan of mines with a closure date before 2050 and reflects varying levels of optimism regarding the socio-economic context for mining development. The assumptions considered in the projections are described in Table 1.

We also differentiate mining projects at the feasibility study stage into three cases (low optimism case, middle optimism case, high optimism case), reflecting varying levels of optimism regarding the commissioning of these sites. The low optimism case considers mining projects with a characterized operational lifespan and confirmed feasibility, as well as projects with an unknown lifespan modeled using the low-case parameters of the $C_r(x)$ function. The middle optimism case also includes projects with a defined lifespan for which the ICSG dataset highlights local issues (e.g., permits, disputes) that could cause delays, as well as projects with an unknown lifespan modeled using the middle-
<caption>**Table 1**</caption>

Key assumptions of the primary copper capacity modeling method for the low, medium and high optimism cases.

<table><caption>**Table 1 (continued)**</caption><thead><tr><th>Stage</th><th>Optimism Case</th><th>Assumptions</th></tr></thead><tbody><tr><td rowspan="3">Operating, Expansion, Development</td><td>Low</td><td>Life of mine based on declarations from the mining industry within the ICSG dataset or calculated using proved and probable reserves reported by mining companies. No additional reevaluation of mine reserves beyond their initial production plans is assumed.</td></tr><tr><td>Middle</td><td>Using the same basis as the Low Optimism Case, adding the reevaluation of reserves for operating mines without an indicated closure date results in a life-of-mine expansion equivalent to half of the projection period from 2020 to 2050 (i.e., 15 years)</td></tr><tr><td>High</td><td>Using the same basis as the Low Optimism Case, adding the reevaluation of reserves for operating mines without an indicated closure date results in a life-of-mine expansion equivalent to the entire projection period from 2020 to 2050 (i.e., 30 years).</td></tr><tr><td rowspan="3">Feasibility</td><td>Low</td><td>Feasibility-stage projects with a defined lifespan are considered, except for those facing local issues that could cause delays. For projects with an unknown lifespan, the following parameters are used: a maximum of 75 % of cumulative metal in discoveries that become mines, with the first project becoming operational in 2027, and half of the exploration projects operating by 2045. Average capacity (83 kt) of feasibility projects for which production capacity has not yet been characterized.</td></tr><tr><td>Middle</td><td>Feasibility-stage projects with a defined lifespan are considered. For projects with an unknown lifespan, the following parameters are used: a maximum of 85 % of cumulative metal in discoveries that become mines, with the first project becoming operational in 2027, and half of the exploration projects operating by 2040. Average capacity (83 kt) of feasibility projects for which production capacity has not yet been characterized.</td></tr><tr><td>High</td><td>Feasibility-stage projects with a defined lifespan are considered. For projects with an unknown lifespan, the following parameters are used: a maximum of 95 % of cumulative metal in discoveries that become mines, with the first project becoming operational in 2027, and half of the exploration projects operating by 2037. Average capacity (83 kt) of feasibility projects for which production capacity has not yet been characterized.</td></tr><tr><td rowspan="2">Exploration</td><td>Low</td><td>For low-case exploration projects, the following parameters are used: historical trend from 2000 to 2016 on cumulative metal in discoveries that become mines, with the first project becoming operational in 2031, half of the exploration projects operating by 2052, and a maximum 50 % discovery-to-production rate. The average capacity (81 kt) is calculated based on operating mines (&gt; 10 kt).</td></tr><tr><td>Middle</td><td>For middle-case exploration projects, the following parameters are used: historical trend from 1960 to 1969 and 1970 to 1979 on cumulative metal in discoveries that become mines, with the first project becoming operational in 2030, half of the exploration projects operating by 2049, and a maximum 65 % discovery-to-production rate. The average capacity (81 kt) is calculated based on operating mines (&gt; 10 kt).</td></tr></tbody></table>

<table><thead><tr><th>Stage</th><th>Optimism Case</th><th>Assumptions</th></tr></thead><tbody><tr><td></td><td></td><td>becoming operational in 2030, half of the exploration projects operating by 2049, and a maximum 65 % discovery-to-production rate. The average capacity (81 kt) is calculated based on operating mines (&gt; 10 kt).</td></tr><tr><td></td><td>High</td><td>For high-case exploration projects, the following parameters are used: historical trend from 1950 to 1959 on cumulative metal in discoveries that become mines, with the first project becoming operational in 2029, half of the exploration projects operating by 2041, and a maximum 80 % discovery-to-production rate. The average capacity (81 kt) is calculated based on operating mines (&gt; 10 kt).</td></tr></tbody></table>

case parameters of the $C_r(x)$ function. The high optimism case includes all feasibility-stage projects and uses the high-case parameters for the $C_r(x)$ function. The logistic function $C_r(x)$ is described in Eq. (1) (see also Figure K and Table J in Supplementary Materials) and accounts for commissioning with different levels of conversion to operational mines and a reduced time before commissioning, depending on the case's level of optimism. This function aims to reflect the delays between the feasibility study and the commissioning of mining sites, as well as the proportion of feasibility-stage projects that ultimately become operational mines. The assumptions presented in Table 1 are based on historical data on the delays between the discovery of deposits and production (Manalo, 2024; IEA, 2021; Schodde, 2017), taking into account the fact that projects under feasibility study are at a more advanced stage than exploration projects.

$$C_r(x) = \frac{H}{1 + e^{-k(x+a)-x_0}} \qquad (1)$$

$H$ is the maximum cumulative conversion rate, $k$ is the shape parameter that defines the conversion speed, $x_0$ defines the year $x$ in which $H/2$ is reached, and $a$ is an adjustment parameter.

For mining sites in the exploration phase, the ICSG dataset does not provide information on capacities or on start and end dates of operation. We replicate the conversion trends from a discovered deposit to an operational mine (see Fig. L in Supplementary Materials). We base our modeling on historical data from MineX Consulting (Schodde, 2017). The parameters of the logistic function $C_r(x)$ for commissioning an exploration-phase site also vary by case to reproduce historical dynamics as presented in Table 1.

Table 1 summarises the main assumptions used to model primary copper production capacity for each phase of mine site development and the low, middle and high optimism cases.

## 2.3. Copper requirement modeling

DyMEMDS (Dynamic Modeling of Energy and Material Demand and Supply) is a bottom-up, stock and flow model linking the evolution of energy demand and production, the development of technological infrastructures, CO₂ emissions and raw material demand from 1950 to 2060. The model used in this study is an extension of the sectoral modules used in Le Boulzec (2022); Le Boulzec et al. (2022) and Andrieu (2023). The model is built in the Vensim environment. DyMEMDS operates at the global, world regions and country scales for a wide range of technologies from the end-use sector (transportation, building, appliances and electronic devices) to the agriculture, industry and the energy supply chain (supply, transportation and distribution). The methodology implemented to estimate the total requirements for copper
lies in successive steps.

In the initial stage, logistic curves are used to represent the evolution of technology stocks as a function of gross domestic product (GDP) and population (Vidal et al., 2021). This approach aims to reproduce a diverse array of historical data observed across different countries, utilizing a uniform set of equations and a limited number of input parameters, with calibration based on technology levels in 2015. In the subsequent stage, the estimated technological stocks serve as inputs to a material flow analysis which ultimately facilitates the estimation of material stocks for each technology using material intensities - measured in kilograms of copper per technological unit. Material intensities vary by technology, being static, for example, in household appliances and electronics, and dynamic in technologies such as automobiles, airplanes, and trains. The main output of DyMEMDS consists of total copper requirements (primary + secondary), representing copper contained in end-use products. However, the output utilized in our study is the net primary copper requirement, which represents copper sourced from mining production, obtained by subtracting the contribution of secondary copper from End-of-Life recycling. The recycling indicator employed in DyMEMDS is the End-of-Life Recycling Input Rate (EoL RIR), representing the proportion of material from end-of-life products that is recycled and reincorporated into new products of the same type. This metric is material-specific and varies dynamically over time, reflecting the utilization of secondary scrap. All recycled material is utilized, with no stockpiling of recycled material, and thus takes priority over primary material.

In the final stage, the model also estimates the embodied energy and CO₂ emissions associated with these infrastructures, although these aspects are not part of the present study. The DyMEMDS model is further described in Andrieu (2023); Le Boulzec (2022); Le Boulzec et al. (2022); Vidal et al. (2022). A description of the technologies and sectors modeled in our study is provided in Table A of the Supplementary Materials.

We used the Shared Socioeconomic Pathways (O'Neill et al., 2014, 2017) projections of basic drivers (population and GDP, by country) to run DyMEMDS. These data are provided by IIASA (https://data.ece.iiasa.ac.at/ssp/) in collaboration with the work of the Wittgenstein Center (KC et al., 2024) and Vienna University of Economics and Business (Crespo Cuaresma, 2017). The SSP's global population and GDP projections are illustrated in Figs. A, B & C of the Supplementary Materials.

We used International Wrought Copper Council (IWCC) data on end-use sectors (IWCC, 2024) to compare global copper requirements (primary and recycling) from DyMEMDS for the 2012–2023 period. Following Klose and Pauliuk (2023), we have aggregated the technologies and sub-sectors corresponding to the IWCC end-use dataset (building, transport, other equipment, industrial, infrastructure). For our study, the material intensities (IM) for the building aggregated sector was adjusted to align with the sector shares from the IWCC. Consequently, the IWCC data were utilized for calibrating the aggregated sectoral material intensities. The results of this comparison are presented in Fig. T of the Supplementary Materials.

## 2.4. Supply-demand interactions module

The model discretizes copper flows and stocks on an annual basis (t). As illustrated in Fig. 1, each year, the DyMEMDS model exogenously provides a global primary copper requirement ($R_t$). This requirement is compared to the global primary copper capacities ($C_t$) for that year within the supply-demand module, depending on the case considered (low optimism case, middle optimism case, high optimism case). Global primary copper production capacities represent the total capacity of all operational mines in a given year. Then, two situations can occur: one in which the primary copper requirement exceeds the global primary production capacities ($R_t > C_t$), and one in which demand is less than or equal to production capacities ($R_t \le C_t$). In the first situation, part of the requirements is not met, and the entire primary copper production

capacity is used. In the second situation, the overall copper production capacities ($C_t$) are not fully utilized during a year (t). For each mine, the unused portion of the production capacity ($Cu_t$) is reintegrated into the mine stock. To do this, the overall Capacity Utilization Rate ($UR_t$) is calculated as follows using Eq. (2):

$$UR_t = \frac{R_t}{C_t} \qquad (2)$$

Thus, Eq. (3) describes the calculation of the unused production capacities to be reintegrated for an individual mine, as follows:

$$Cu_t = (1 - UR_t) \cdot M_t \qquad (3)$$

With $Cu_t$ is the unused portion of mine capacity and $M_t$ is the mine capacity, at year t. For the purposes of our modeling, this mechanism relies on the assumption that the overall Capacity Utilization Rate ($UR_t$) is equal to the utilization rate of each individual mine, which may not necessarily reflect the reality of the mine site level. Then, unused capacities ($Cu_t$) are redistributed to the first year in which its production capacities are lower than its maximum operating capacity; generally corresponding to the first year of ramp-down before closure.

This approach preserves production capacities and stocks using a mass-consistent logic similar to that applied in Material Flow Analysis (Brunner and Rechberger, 2016), thereby extending the duration of the maximum production capacity plateau, and extending the overall life-span of each mine. All these data are then visualized through graphs displaying the initial primary copper requirement, the initial primary copper production capacity, and the evolution of available primary copper production capacities between 2020 and 2050.

# 3. Results and discussion

## 3.1. Primary copper production capacities to 2050

Our initial estimates of global copper mine capacities indicate peak capacities of 34.2 Mt in 2031 and 44.4 Mt in 2045 for the low, middle cases, respectively, while the high optimism case shows a plateau between 48.1 and 48.7 Mt starting from 2042, as illustrated in Fig. 2.

Our projections indicate that the continuous growth of production capacities is unsustainable, even under highly optimistic assumptions regarding their deployment or life-of-mine expansion. This issue is particularly pronounced in the low optimism case, which shows a decline in production capacities. The reevaluation of reserves, and thus the life-of-mine expansion, has a significant influence on currently operating mines, showing 15 Mt in 2050 for the low optimism case versus 28 Mt for the high optimism case. Capacities actually under development or expansion have only a minimal impact on overall capacity trends. The assumptions regarding the dynamics of bringing feasibility study projects into production also have a significant impact on the projections, showing a variation between the low and high optimism cases of 5 Mt by 2050. Projects in the exploration phase have a minimal influence on the 2020–2050 period due to the delays between discoveries and production, contributing between 2 Mt and 5 Mt by 2050.

We compared cumulative primary copper capacities with USGS data, assuming 100 % utilization rate. The USGS reports that copper reserves and identified resources amount to 1 billion tons and 2.1 (as of 2015) billion tons, respectively (USGS, 2024). The cumulative primary production capacities between 2020 and 2050 totalize 926 Mt (low), 1117 Mt (middle), and 1242 Mt (high). These results correspond to 93 %, 117 %, and 124 % of the reserves, 44 %, 56 %, and 59 % of the identified resources reported by USGS.

We compared our projections of primary copper production capacity by applying an 82 % capacity utilization rate. These production projections appear consistent with the analyses presented in Fig. 3. The low-case scenario follows a trend similar to the analyses by the IEA Critical
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Primary copper production capacities projections (low optimism case, middle optimism case, high optimism case) from 2020 to 2050. The dotted lines represent a production Capacity Utilization Rate (UR) of 82 %.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Comparison of primary copper production with previous studies. Historical data on copper mining is sourced from the ICSG Copper Factbook 2024 (ICSG, 2024). The BHP scenarios are derived from the report titled "BHP Insights: How Copper Will Shape Our Future" (BHP, 2024). Projections for announced projects are obtained from the IEA Critical Minerals Outlook, 2024 (IEA, 2024a; 2024b). The S&P scenarios are derived from the report titled "The Future of Copper" (S&P Global, 2022). The production scenario labeled "Production (82 % UR)" represents 82 % of the production capacities across low, middle, and high cases. Additionally, the trend from 1960 to 2023 is projected with an approximate annual growth rate of 3 %.</figcaption></figure>

Minerals Outlook 2024 (IEA, 2024a; 2024b) and the Possible Scenario of the BHP Report 2024 on copper (BHP, 2024). In line with these analysis, currently operational capacities decrease over time. The medium and high cases have trends close to the analyses in the S&P Global report (S&P Global, 2022) and the Probable Scenario of BHP (2024) report on copper. However, by 2040, we observe a plateau that deviates from the historical trend, which is equivalent to a growth rate of around 3 % per year between 1960 and 2023. The reasons for this change in trend lie in the combination of a shortage of mining projects in the pipeline and the development delays captured in our projections, even under a socio-economic context of full reserve reevaluation that would maintain existing capacities.

## 3.2. Primary copper requirement scenarios to 2050

Primary copper requirements for the Shared Socioeconomic Pathways (SSPs), as estimated from the DyMEMDS model, are illustrated in Fig. 4, which shows demand for each aggregate sector described in the IWCC dataset (IWCC, 2024). The scenarios are globally aligned with the

historical copper mining data provided by ICSG Copper Factbook 2024 (ICSG, 2024). Prior to 1990, the scenarios slightly underestimate the primary copper requirement. The SSP1 scenario shows an overestimation of the primary copper extracted between 2000 and 2023, followed by a more significant upward trend from 2030 to reach 77 Mt/year in 2050. The SSP2 and SSP4 scenarios closely follow the historical trend until 2035, after which demand growth increases to reach approximately 51 Mt/year (SSP2) and 41 Mt/year (SSP4) by 2050. The SSP3 scenario slightly underestimates historical demand for primary copper for the period 2015–2023 and follows a slightly lower trend thereafter to reach 28 Mt/year in 2050. The SSP5 scenario forecasts rapid and sustained growth in demand for primary copper, which overestimates demand for primary copper over the period 2000–2023, reaching 88 Mt/year in 2050. These slight over- and underestimations of primary copper reflect the evolution of technology stocks tied to GDP per capita for specific SSPs, which at the global level show a more pronounced trend change for SSP1, SSP3, and SSP5 (see Figure C in Supplementary Materials). The figures concerning copper requirements from recycling and overall requirements (primary and recycled) are shown in the Supplementary Materials (Figs. R & S).

We have compared the results of our scenarios with two previous studies (Klose and Pauliuk, 2023; Schipper et al., 2018) that focus on primary copper requirements and Shared Socio-economic Pathways, as shown in Table 2. The copper requirements (primary + recycling) are significantly higher in 2050 than these studies for the SSP1, SSP2 and SSP5 scenarios. In contrast, SSP3 is lower than in Schipper et al. (2018). It appears that DyMEMDS effectively captures the distinctions between SSPs as GDP per capita is employed to recalculate all infrastructure stocks using logistic equations (see Figure C in Supplementary Materials).

The recycling rate has a major impact on the total amount of primary copper required to meet the demand for copper needed to deploy technologies. As the stock of copper increases in the global economy, copper from recycling rises in all five SSPs, from 7 Mt in 2020 to 33 Mt for SSP1, 25 Mt for SSP2, 19 Mt for SSP3, 22 Mt for SSP4 and 36 Mt for SSP5 in 2050. To stay consistent with historical primary copper mining production, we use a more conservative estimate than Schipper et al. (2018), who assumed a speculative 90 % recycling rate. As a result, in all five SSPs, demand for primary copper is higher than in these two studies, even for SSP3. For SSP2, our result is quite similar to the Klose and Pauliuk (2023) study, but diverges to reach 51 Mt compared to 30 Mt in 2050 for this previous study.

The results of aggregate sector calibration on the basis of IWCC end-use copper requirement works well for building construction, transport and infrastructure, as illustrated by Figure T in Supplementary Materials. The sum of industrial and other equipment aligns with the IWCC distribution, but it is distributed differently in the DyMEMDS
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Primary copper requirements in the Shared Socioeconomic Pathways resulting from the DyMEMDS model. The areas correspond to the aggregated IWCC sectors (IWCC, 2024). Historic Copper Mined comes from the ICSG Copper Factbook 2024 (ICSG, 2024).</figcaption></figure>

**Table 2**

Comparison of the DyMEMDS results (2020 - 2050) with previous studies on the primary copper requirements of Shared Socioeconomic Pathways (SSPs). Schipper et al. (2018) is characterized by a stock dynamics method, driven by SSPs GDP and population assumptions, with disaggregated products. Klose and Pauliuk (2023) is characterized by a dynamic material stock and flow model, driven by SSP2 GDP and population assumptions, with an energy supply system compatible with RCP2.6, and disaggregated products. This study is characterized by a dynamic material stock and flow model, driven by SSPs GDP and population assumptions, with disaggregated products.

<table><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="2">SSP1</th><th colspan="2">SSP2</th><th colspan="2">SSP3</th><th colspan="2">SSP4</th><th colspan="2">SSP5</th></tr><tr><th>2035</th><th>2050</th><th>2035</th><th>2050</th><th>2035</th><th>2050</th><th>2035</th><th>2050</th><th>2035</th><th>2050</th></tr></thead><tbody><tr><td rowspan="2">Copper requirements (primary + recycling) (Mt/year)</td><td>Schipper et al. (2018)</td><td>50</td><td>67</td><td>47</td><td>64</td><td>45</td><td>55</td><td>47</td><td>62</td><td>50</td><td>67</td></tr><tr><td>Klose and Pauliuk (2023)</td><td>-</td><td>-</td><td>35</td><td>44</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="3">Primary copper requirements (Mt/year)</td><td>This study</td><td>53</td><td>109</td><td>42</td><td>76</td><td>34</td><td>47</td><td>38</td><td>63</td><td>58</td><td>124</td></tr><tr><td>Schipper et al. (2018)</td><td>22</td><td>31</td><td>19</td><td>25</td><td>18</td><td>19</td><td>19</td><td>25</td><td>22</td><td>29</td></tr><tr><td>Klose and Pauliuk (2023)</td><td>-</td><td>-</td><td>26</td><td>30</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td></td><td>This study</td><td>38</td><td>77</td><td>29</td><td>51</td><td>22</td><td>28</td><td>26</td><td>41</td><td>43</td><td>88</td></tr></tbody></table>

**Table 3**

Main technologies and sub-sectors by copper requirements for DyMEMDS SSP results, for 2020 and 2050.

<table><thead><tr><th rowspan="3">Technologies or subsectors</th><th colspan="8">Copper requirements (Mt/year)</th></tr><tr><th colspan="2">SSP1</th><th colspan="2">SSP2</th><th colspan="2">SSP3</th><th colspan="2">SSP4</th><th colspan="2">SSP5</th></tr><tr><th>2020</th><th>2050</th><th>2020</th><th>2050</th><th>2020</th><th>2050</th><th>2020</th><th>2050</th><th>2020</th><th>2050</th></tr></thead><tbody><tr><td>Building</td><td>9.4</td><td>30.7</td><td>8.4</td><td>18.9</td><td>7.7</td><td>10.0</td><td>8.1</td><td>14.8</td><td>9.8</td><td>36.6</td></tr><tr><td>Diverse equipment</td><td>5.6</td><td>17.8</td><td>5.1</td><td>12.4</td><td>4.7</td><td>7.6</td><td>4.9</td><td>10.3</td><td>5.8</td><td>20.2</td></tr><tr><td>Light Battery Electric Vehicle</td><td>0.28</td><td>12.7</td><td>0.24</td><td>9.4</td><td>0.23</td><td>6.4</td><td>0.23</td><td>8.0</td><td>0.26</td><td>14.0</td></tr><tr><td>Other Industrial sectors</td><td>3.5</td><td>12.3</td><td>3.2</td><td>8.4</td><td>2.9</td><td>5.0</td><td>3.1</td><td>6.9</td><td>3.6</td><td>14.0</td></tr><tr><td>Electricity Transport</td><td>3.3</td><td>6.9</td><td>3.0</td><td>5.2</td><td>2.8</td><td>3.0</td><td>2.9</td><td>4.4</td><td>3.5</td><td>7.5</td></tr><tr><td>Light Internal Combustion Vehicle</td><td>2.7</td><td>5.1</td><td>2.4</td><td>3.8</td><td>2.3</td><td>2.5</td><td>2.4</td><td>3.2</td><td>2.6</td><td>5.7</td></tr><tr><td>Battery for Electric Vehicle</td><td>0.54</td><td>3.6</td><td>0.51</td><td>2.4</td><td>0.48</td><td>1.3</td><td>0.49</td><td>2.0</td><td>0.55</td><td>4.0</td></tr><tr><td><strong>Total</strong></td><td><strong>25.3</strong></td><td><strong>89.1</strong></td><td><strong>22.9</strong></td><td><strong>60.5</strong></td><td><strong>21.1</strong></td><td><strong>35.8</strong></td><td><strong>22.1</strong></td><td><strong>49.6</strong></td><td><strong>26.1</strong></td><td><strong>102.0</strong></td></tr><tr><td><strong>Rate compared with all sectors</strong></td><td><strong>77 %</strong></td><td><strong>82 %</strong></td><td><strong>77 %</strong></td><td><strong>80 %</strong></td><td><strong>77 %</strong></td><td><strong>76 %</strong></td><td><strong>77 %</strong></td><td><strong>79 %</strong></td><td><strong>77 %</strong></td><td><strong>82 %</strong></td></tr><tr><td><strong>Total (all sectors)</strong></td><td><strong>32.7</strong></td><td><strong>109</strong></td><td><strong>29.6</strong></td><td><strong>76</strong></td><td><strong>27.5</strong></td><td><strong>47</strong></td><td><strong>28.6</strong></td><td><strong>63</strong></td><td><strong>34.0</strong></td><td><strong>124</strong></td></tr></tbody></table>
aggregation. This is due to the three sub-sectors: mechanical engineer-
ing, military, chemical production (see Table A in Supplementary Ma-
terials) are integrated into industry, though part of which could be
attributed to Other Equipment.

To identify which technologies or sub-sectors will have the highest
copper demand, or experience significant increases between 2020 and
2050, we have summarized in Table 3 the main copper-consuming
sectors (representing more or less 80 % of copper requirement). We
observe that historical sectors such as construction, other equipment,
other industrial sectors, electricity transport and light internal com-
bustion vehicles are predominant. This is partly due to the development
of low-income and middle-income regions, where GDP per capita is
rising and basic needs are developing for access to decent housing,
electricity and a mobility network, as well as the maintenance of existing
facilities in high-income countries. Copper requirements for clean
technologies are mainly driven by electromobility such as light battery
electric vehicles and batteries and also electricity transport, in line with
Klose & Pauliuk’s findings on SSP2 RCP2.6 and the IEA’s analysis of

copper requirements for clean technologies (IEA, 2021, 2024b; Klose and Pauliuk, 2023).

3.3. Consistency of scenarios

In our study, a scenario is considered consistent if the primary copper
requirements are fully met by primary production capacities. The low,
middle, and high scenarios offer insights into the mining industry dy-
namics necessary to fulfill these requirements. The results of the
assessment of SSPs’ primary copper requirement from DyMEMDS,
regarding primary copper production capacity projections (low opti-
mism case, middle optimism case, high optimism case), are illustrated in
Fig. 5. This figure presents results for a 100 % Capacity Utilization Rate
limit, which could be considered optimistic but provides insight into
how the UR is evolving over the period from 2020 to 2050. For SSP1, we
observe a Capacity Utilization Rate exceeding 95 % by 2020, followed
by a rapid divergence between primary copper requirements and pro-
duction capacities around 2035 across all three cases. The primary

<figure><img src="image_7.png" /><figcaption>Fig. 5. Primary Copper Consistency Assessment Based on Shared Socioeconomic Pathways (SSPs) Basic Drivers with a 100 % Capacity Utilization Rate. Primary copper capacities are shown for low, middle, and high case projections. Primary copper requirements are derived from the DyMEMDS model. Primary copper production represents the output of the supply-demand module.</figcaption></figure>
copper production remains very close to the initial production capacities, as the demand does not allow for the redistribution of unused capacities. For SSP2, the demand for primary copper follows a trend that allows production capacities to supply sufficiently across all three cases between 2020 and 2035. The rapid growth in requirements after 2035 leads to a divergence with primary copper capacities for low optimism case. For the middle and high optimism cases, primary copper requirements exceed primary copper production capacities after 2045. For SSP3, the demand is fully met by primary copper capacities. Nevertheless, in the low optimism case by the 2050 horizon, requirements exceed initial production capacities and the divergence in trends between requirements and production capacities likely implies a bottleneck post-2050. For SSP4, the bottleneck shifts over time depending on the production capacity projections, with requirements for primary copper exceeding production capacities around 2040. For SSP5, the Capacity Utilization Rate slightly exceeds 100% starting from 2020, with a rapid divergence occurring towards 2030. In cumulative terms for 100% UR, SSP1 requirements face a deficit ranging from 215 Mt (high case) to 502 Mt (low case), and SSP5 requirements face a deficit ranging from 357 Mt (high case) to 667 Mt (low case), corresponding respectively to 15-35% of SSP1 cumulative requirements and 22-42% of SSP5 cumulative requirements.

A second assessment incorporating an 82 % utilization rate constraint, is presented in Figure U of the Supplementary Materials. This assessment shows a further amplification of the divergence between SSP1, SSP2, and SSP5 primary copper requirements and production capacities. SSP3 remains generally consistent, except under the low-optimism scenario. It is important to note that the 82 % utilization rate constraint reflects historical trends, but under conditions of strong supply-demand tension and resulting price increases, this value is expected to rise, notably due to the profitability of existing mines currently in care and maintenance.

In summary, the consistency of primary copper requirement scenarios derived from GDP per capita in the Shared Socioeconomic Pathways in DyMEMDS is only assured for SSP3 between 2020 and 2050, with major concerns arising post 2050 for the low optimism case.

## 4. Discussion

The results of our study reveal, with a novel approach, the potential gaps between SSP's socioeconomic assumptions and the mining industry's capacity to supply primary copper. The discussion of our results focuses on highlighting the methodological limitations of our study and the opportunities for improvement in future research work.

### 4.1. Discussing projections of primary copper production capacities

The scope of our study is limited to the copper content in the output products from a mining site (concentrate, cathodes for SX-EW units). Constraints related to the downstream value chain are beyond our scope. In the case of copper, our analysis led us to focus on mining extraction; however, this is not necessarily the primary area of concern for all critical minerals. For instance, in the nickel EV supply chain and NMC Li-ion battery chemistry, long-term bottlenecks are more related to nickel sulfate and intermediate products such as mixed hydroxide precipitate (MHP) rather than laterite ores.

One limitation of our study concerns the data provided by the ICSG. These data appear robust for operational, expansion, and developing sites, as they accurately reproduce production capacities between 2020 and 2024 when compared with USGS or BGS production data, considering the capacity utilization rate. However, feasibility study projects and particularly those in the exploration phase may be incomplete. This represents a significant methodological limitation in constructing our projections, as future capacities directly depend on the number of projects in development, even though the dynamics of discovering new large deposits remain low (Schodde, 2017; Guj and Schodde, 2025),

despite rising copper market prices and increased exploration budgets since 2020 (see Figures N & O in Supplementary Materials). However, these budgets remain well below the peak levels recorded in the early 2010s. It appears that it is rather the known deposits that are reclassified as resources and reserves that drive the growth of global reserves and resources (Guj and Schodde, 2025). This leads to the extension of the lifespan of existing mines rather than the development of new sites, as illustrated by the trend of aging mines (BHP, 2024).

At this stage of our research, we assume a sensitivity modeling of the influence of modifying factors, including copper market price, on production capacities. Alongside with treatment and refining charges (TC/RC, i.e. the amount paid by miners to smelters when they sell concentrates - lower TC/RCs indicate higher constraints on ore availability-), this price reflects the balance between the supply and demand for copper. In the short term, this balance will influence the utilization rate (UR) of production capacities. Structurally, the copper price influences the exploitable reserves at an active mining site. As a result, a mined deposit sees its proven and probable reserves either increase or decrease. For this reason, we have introduced a reevaluation of reserves for the medium and high optimism cases, reflecting modifying factors more or less favourable to this life-of-mine expansion. This approach also partially accounts for future projects not yet listed in the ICSG database, as additional production capacities will more likely come from the requalification of reserves at existing deposits (brownfield) rather than from the commissioning of new significant deposits (greenfield) (Guj and Schodde, 2025).

However, this overall approach remains simplified, as its disaggregation at the level of each mining site would require a huge amount of detailed data on the modifying factors (e.g. cut-off, reserves, resources, extraction costs). All these parameters would need to be adjusted each year as a function of an exogenous market price, and more broadly, of modifying factors. Our modeling approach of primary copper production capacities focuses instead on illustrating a wide range of possible futures taking into account the mining industry constraints, particularly the lead times for commissioning new capacity and renewing existing capacity. This methodology appears more suited to our aim of examining the consistency of primary copper demand scenarios in light of our projections of production capacities. Indeed, it enables us to observe the necessary changes for achieving these scenarios and provides the opportunity to incorporate modifying factors, such as socio-environmental considerations, that would be obscured by solely considering an exogenous evolution of the copper price. As a result, using three level of optimism cases allows us to bypass modeling future production capacities endogenous to the market price for copper.

We consider that a mine-by-mine description provides an opportunity to explore new research on the historical dynamics of mining project development. Two aspects are particularly important: analyzing reserve reevaluation in relation to changes in modifying factors, and examining the historical progression of mining projects by stage, thereby complementing previous work on the delays between deposit discovery and mine commissioning.

Finally, our projections do not account for the possibility that some mining sites may reduce or temporarily cease production when market prices are low, or more permanently during "care and maintenance" periods. In our future production capacity projections, no "care and maintenance" phase is modeled, which could result in an overestimation of available capacities each year. Future research on this topic could integrate the modifying factors of each mine along with copper prices, so that under low-price conditions, some mines could be placed on care and maintenance. In this way, the Capacity Utilization Rate could be made endogenous to copper prices.

### 4.2. Improving the modelling of primary copper requirements

Our results provide an overview of future copper requirements based on different GDP per capita evolutions across the five Shared
Socioeconomic Pathways (SSPs). However, to describe these scenarios more accurately, several improvements should be considered. Firstly, the building and other equipment sectors need to be disaggregated to better understand the origin of copper requirements. Nevertheless, we lack disaggregated data on material intensity for the building and other equipment sectors. Thereafter, we could integrate the shares of technologies associated with the future trajectories proposed by the IAM community. This would allow us to better align with the narratives of the SSPs and to understand the impact of the technologies required to achieve decarbonization pathways on copper requirements.

### 4.3. Analyzing the consistency of scenarios

If we consider that the low, middle and high optimism cases reflect an increasingly favorable socio-economic context and modifying factors, then the SSP2, SSP3, and SSP4 scenarios for primary copper demand could prove consistent with the dynamics of the high optimism case.

However, caution should be exercised regarding the conclusions of inconsistency between the exogenous GDP per capita assumptions integrated into DyMEMDS and our projections of production capacities. Indeed, our study aims to underscore the challenges of primary copper supply within socioeconomic pathways. Given that demand invariably meets supply, the risks associated with the inconsistency of copper requirements include price increases, which can eliminate a portion of demand and potentially hinder the deployment of technologies essential for climate change mitigation. Conversely, over the longer term, these dynamics could encourage the reevaluation of reserves in existing mining sites or spur investments in new copper mining capacities. This underscores the importance of considering the material footprint of decarbonization pathways, particularly since most models proposing these pathways do not account for the numerous constraints associated with primary copper supply. Nevertheless, further research will require a more detailed consideration of end-of-life recycling integration, resource efficiency strategies, and the substitution of copper with aluminum for electrical cables or with plastics in the plumbing sector, for example.

### 4.4. Future development

Our future work will focus on evaluating additional primary copper requirement scenarios to assess their consistency in relation to our primary copper production projections. This review would aim to emphasize the importance of assessing the consistency of future scenarios to provide decision-makers with a more consistent set of options. It also seeks to engage the decarbonization pathways modeling community to integrate material requirements modules into their models and prioritize decarbonization strategies that align with the industrial constraints of metal mining. This approach could help align these strategies with material sufficiency, efficiency, substitution, and circularity.

Regarding the modeling of copper requirement, we will particularly focus on the reduction in requirements for primary copper resulting from the scenarios of the DyMEMDS model. We aim to address this reduction by estimating the potential of material sufficiency measures, material efficiency of technologies, and the substitution potential of copper in various industrial sectors (Klose and Pauliuk, 2021, 2023; Watari et al., 2022). This study will enable us to evaluate the consistency of our scenarios in light of strategies to reduce primary copper consumption.

Regarding future production capacities, we aim to capitalise on the mine-by-mine description of our projections. Indeed, we plan to estimate the impacts on production capacities of the social, ecological, and geopolitical components of copper availability. With the help of the EJ Atlas' work on social disputes related to mining projects, we would be able to estimate a reduction in production capacities based on the risk of slowdown or abandonment of mining projects (Scheidel et al., 2020; Temper et al., 2018; 2015). We also plan to investigate the vulnerability

of mining sites to climate change, with a particular focus on water stress. This analysis could be conducted at both global and regional scales, with specific attention to areas such as Peru, Chile, and the USA–Mexico border region, as highlighted in a previous study (Islam et al., 2025). This would enable us to evaluate the criticality of copper in a comprehensive manner within prospective scenarios, allowing us to detail and estimate the potential reduction in production capacities based on the characterized risks. This would provide a new perspective on the criticality of copper, counter to the methodologies currently used to evaluate the criticality of minerals.

Finally, we would be able to expand our study's scope by regionally allocating the copper content output from mining sites derived from the projections. This would involve utilising ICSG data on refining sites and data on global copper exchanges. We could design projections in line with the description of Shared Socioeconomic Pathways narratives, particularly the degree of cooperation (O'Neill et al., 2014, 2017).

All these future developments require large amounts of data. Since most mining industry data are proprietary, further efforts are needed to build an open-access database on copper mining, facilitating open assessment of the consistency of decarbonization pathways.

## 5. Conclusion

Copper is a critical mineral for decarbonization of the economy. Yet, it faces supply risks due to surging demand and geopolitical trade concerns. To assess the consistency of copper supply under climate mitigation scenarios, we provide the first mine-level dynamic comparison between projected copper production capacities and demand trajectories across the Shared Socioeconomic Pathways (SSPs). Our results reveal that copper supply will struggle to keep pace with future demand. Even under optimistic assumptions, global production capacity peaks below 50 Mt/year by the early 2040s, while demand in high-growth pathways like SSP5 and SSP1 climbs toward 88 and 77 Mt/year, respectively, by 2050. These trajectories imply significant supply gaps up to 40 Mt/year comparable to today's entire global copper output (e. g., SSP5 vs. Low-optimism scenario). Severe mismatches emerge well before mid-century, with SSP1 diverging by 2035 and SSP2 facing deficits by 2045. Only SSP3, with its slower growth narrative, remains within the limits of projected supply through 2050. In cumulative terms, 15–42 % of global primary copper requirements could remain unmet in the highest-demand scenarios (SSP1 and SSP5).

Our findings underscore a critical blind spot in many decarbonization models: the assumption that material inputs like copper will be available as needed. In reality, constraints within the mining industry on future primary copper production capacities challenge this assumption. We call for urgent integration of material availability into climate scenario modeling and for stronger emphasis on strategies such as material efficiency, substitution, sufficiency, and circularity. These strategies provide key pathways to mitigate supply-demand gaps and reduce the mining industry's socio-environmental impacts. Without addressing these constraints, the energy transition risks stalling not from lack of will, but from lack of copper.

### Financial support

This work was supported by the Conservatoire national des arts et métiers, Paris. L.D. thanks the Erling-Persson Family Foundation for funding the research.

### Declaration of AI use

We have used AI-assisted technologies for spellchecking and as inspiration for rewording individual sentences. After using these tools, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.
CRediT authorship contribution statement

**Gaël Parpan:** Writing – original draft, Visualization, Methodology, Formal analysis, Conceptualization. **Baptiste Andrieu:** Writing – review & editing, Methodology. **Olivier Vidal:** Writing – review & editing, Software. **Louis Delannoy:** Writing – review & editing, Supervision. **Hugo Le Boulzec:** Writing – review & editing. **Matthieu Gervais:** Writing – review & editing, Supervision, Methodology. **Yves Jégourel:** Writing – review & editing, Supervision, Methodology, Funding acquisition. **Stéphane Delalande:** Writing – review & editing, Supervision, Methodology, Funding acquisition.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Louis DELANNOY reports financial support was provided by Erling Persson Family Foundation. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The authors would like to thank Giorgio Parlato for his contribution to the SSPs modeling in the DyMEMDS model.

Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.resconrec.2025.108633.

Data availability

Original ICSG data are private and confidential. Aggregated data are provided in the supplementary data file.

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