The New Geoeconomics of Geological Knowledge: How Control over Data Trumps Critical Raw Material Endowment

In Brief

• Economic networks do not distribute power evenly — they concentrate it at the hub. Whoever controls the infrastructure through which trade, finance, and data flow holds a structural advantage over everyone who uses it. This is not a new observation. It has, however, taken on a new urgency as the infrastructure in question has shifted from shipping lanes and telegraph cables to data centres and financial messaging systems.

• SWIFT — the financial messaging network that routes the vast majority of international bank transactions — is perhaps the cleanest example of weaponised network power in recent history. But the response to SWIFT as a sanctions tool tells a story that is at least as important as the instrument itself: states targeted by this mechanism did not accept their position. They built alternatives, diversified their financial architecture, and began the slow work of contesting the system from the outside.

• Data about natural resources — geological surveys, drill records, geochemical analyses — functions along the same structural logic as the financial system. The country that processes this information on foreign servers, under foreign jurisdiction, surrenders a negotiating advantage before negotiations begin. The solution may be the same in both cases: not comprehensive autarky, but deliberate, selective control of the nodes that matter most.

In 1945, a young German economist named Albert Hirschman — then working in wartime Washington, later one of the twentieth century's most original development thinkers — published a study of how Nazi Germany had engineered the economic dependence of its smaller neighbours as an instrument of political control. Germany had structured bilateral trade agreements to make its trading partners structurally dependent on German markets for exports and German suppliers for imports. The economic relationship looked, on the surface, like mutually beneficial trade. Underneath, it was leverage (cited in Mohr and Trebesch, 2025). The lesson Hirschman drew was precise: interdependence is not symmetrical. The party that can more easily exit the relationship holds the power.

Mohr and Trebesch (2025), in the Annual Review of Economics, identify this insight as the intellectual foundation of geoeconomics — the field that studies how states use economic instruments to achieve strategic objectives. The field has grown dramatically since 2018, when the US-China technology confrontation acquired institutional form. Five instruments dominate the contemporary literature: export controls and sanctions; state-directed investment and finance; technology and data standards governance; currency and payment system architecture; and the use of supply chain dependencies as coercive leverage.

All five are active simultaneously in the global critical minerals and AI landscape. Understanding how is thus a precondition for avoiding the exploitative extractive cycle that has characterised most of the history of resource development in low income countries. 

The Architecture of Network Power

How Infrastructure Becomes Leverage

In 2019, Henry Farrell of George Washington University and Abraham Newman of Georgetown published a paper in International Security that gave the policy community its sharpest conceptual tool for this moment. Global economic networks, Farrell and Newman argued, do not simply connect countries. They create chokepoints — nodes through which flows of money, data, or goods must pass — and the states that control those chokepoints can coerce the states that depend on them. They called this weaponised interdependence. The same network that ties countries together can be used as a weapon against any participant who has no alternative route (Farrell and Newman, 2019).

The cleanest empirical demonstration of the concept remains the SWIFT case. SWIFT — the Society for Worldwide Interbank Financial Telecommunication — is a Belgian-registered cooperative that provides the messaging infrastructure through which the world's banks communicate payment instructions. It connects over 11,500 financial institutions across more than 200 countries and processes more than 53 million financial messages per day, facilitating transactions that the network itself estimates at trillions of dollars in daily value (SWIFT, 2025).

SWIFT does not move money. It moves the instructions that tell banks to move money. But because virtually every significant cross-border financial transaction depends on those instructions, control of the network functions as control of the transaction.

SWIFT as a Weapon — and the Response That Followed

When the US and EU removed Iran from SWIFT in March 2012 — following an EU Council decision targeting Iranian banks identified as breaching sanctions — Iran lost access to a significant share of its oil export revenues almost immediately. A 2022 account in Foreign Policy noted that in 2012, Iran lost nearly half its oil revenues and experienced a 30 percent decline in foreign trade following the disconnection. 

Russia's partial removal from SWIFT in February 2022, following the invasion of Ukraine, produced comparable initial shock — and a far more instructive response. Russia had anticipated this moment. As early as 2014, following Western sanctions over the annexation of Crimea, the Russian Central Bank began developing the System for Transfer of Financial Messages (SPFS) — a domestic alternative to SWIFT for ruble transactions. By the end of 2023, SPFS connected 557 banks and companies, including 159 non-resident institutions from 20 countries (Carnegie Endowment for International Peace, 2024). China's Cross-Border Interbank Payment System (CIPS), launched in 2015, provided a parallel yuan-denominated clearing infrastructure: by 2024, CIPS processed 8.2 million transactions worth approximately USD 24.5 trillion annually — a 43 percent increase in volume from the previous year, with both transaction count and value more than tripling since 2020 (FX C Intelligence, 2025). By 2024, over 92 percent of Russia-China bilateral trade — which reached USD 240 billion in 2023 — settled in yuan or rubles, compared to less than 5 percent before the war (Carnegie Endowment for International Peace, 2024).

This is not a story of SWIFT's defeat: It remains, by a considerable margin, the dominant global financial messaging system — 11,500 members in 200-plus countries versus CIPS's roughly 1,600 participants, with SWIFT processing more than 50 million messages daily against CIPS's approximately 26,000 transactions per day. The yuan accounts for just 3 percent of global SWIFT payments by value, against the US dollar's 48 percent and the euro's 24 percent (SWIFT RMB Tracker, November 2024). Critically, approximately 80 percent of CIPS's own cross-border messaging still runs over SWIFT infrastructure, meaning CIPS depends on the network it nominally competes with (FPRI, 2024). The weapon worked, in that it imposed real costs on the target.

But the story of what came after the weapon was deployed matters as much as the weapon itself. Russia's response — diversifying payment infrastructure, shifting bilateral trade into local currencies, piloting a digital ruble, encouraging BRICS partners to develop settlement alternatives — demonstrates something that orthodox sanctions theory tends to understate: targeted states are not passive recipients of coercive network power. They adapt. They invest in alternatives. They build redundancy. And in doing so, they progressively erode the coercive power of the original chokepoint, even if they cannot eliminate it. The US has achieved short-term political objectives through SWIFT sanctions while simultaneously accelerating the global trend toward payment system diversification — a dynamic that OANDA's analysis describes as the 'sanctions paradox': tactical success that generates its own strategic attrition (OANDA, 2025).

What This Architecture Means for Geological Data

The SWIFT case is useful not only as an illustration of weaponised interdependence but as a template for understanding a parallel and less-discussed form of the same structural dynamic: the geography of data infrastructure.

Africa generates data about its own economic activity — trade, finance, communications, and, most relevant here, geological information — and processes roughly 95 percent of it on servers located outside the continent (Bracewell, 2025, citing Africa Data Centres Association data). The entire continent hosts less than 1 percent of global data centre capacity, against its roughly 17 percent share of world population and approximately 30 percent share of critical mineral reserves (McKinsey, 2025).

For most data categories, this produces a privacy and economic efficiency problem. For geological data specifically, it produces a structural negotiating disadvantage. A comprehensive dataset of a country's mineral survey results — drill logs, geochemical analyses, geophysical readings, ore body models — gives whoever holds it a more accurate picture of that country's underground resources than the country's own institutions may possess. That picture determines the credible range for royalty negotiations. It shapes reserve characterisation disputes — the technical arguments about how much ore a deposit contains, which determine what a concession is worth. It informs decisions about where to explore next.

Four Models of Sovereignty — and the One That Works

Fratini et al. (2024), reviewing 271 peer-reviewed articles on digital sovereignty in Digital Society, find that comprehensive digital independence — owning and controlling the full stack of a country's digital infrastructure — is not achievable even for the most powerful states. The finding is important because it clears away a version of the argument that makes the whole project seem utopian. No one is proposing that Zimbabwe build its own global financial messaging system or its own data centre industry from scratch. The question is a narrower and more tractable one: which specific data assets matter enough to protect, and what is the most cost-effective way to protect them?

Fratini et al. (2024) identify four recognisable sovereignty models. The rights-based model centres on data protection law — the EU's GDPR approach. It works where regulatory enforcement is strong; where it is weak, it performs poorly.

The market-oriented model tries to build domestic digital industry through investment incentives and competition policy; it requires time and capital that most low-income states cannot spare.

The centralisation model — China's — gives the state direct control over digital infrastructure and the data flows through it; it achieves coherence but requires political conditions and fiscal capacity that are not transferable.

The fourth — the selective state-based model — concentrates sovereign control over specifically identified strategic assets while maintaining open conditions for everything else. Fratini et al. (2024) identify this as the most defensible approach for states with limited institutional capacity and fiscal space.

The Legal Instrument

Classifying Geological Data as a National Asset — and Why the Sequence Matters

The mechanism is straightforward. An amendment to Zimbabwe's Mines and Minerals Act classifies primary geological data — all data generated through drilling, geophysical measurement, geochemical analysis, or survey work within Zimbabwe's territory — as a national strategic asset. A corresponding amendment to the Cyber and Data Protection Act (2021) creates a mandatory localisation requirement for that category. Together, they create the legal foundation for four concession conditions that enforce the classification in practice.

The first condition: mandatory data deposition — all primary geological data deposited with the Zimbabwe Geological Survey within 90 days of generation, in standardised machine-readable formats. The second: on-site initial processing — the conversion of raw measurements into geological interpretations performed on hardware within Zimbabwe's legal jurisdiction, not on foreign cloud platforms. The third: a geological data levy — a per-tonne contribution to the Survey's data management unit and to the integrated into university curriculum programmes structured as an export clearance condition rather than a royalty. The fourth: model licensing reciprocity — any AI or machine learning model trained on Zimbabwean geological data licensed back to national institutions with source code access, as a condition of operating licence.

None of this is novel. Ghana's Minerals Commission has required geological data deposition for mining operations since revisions to the Mining Act in 2012. Botswana's Debswana joint venture has since 1969 included information-sharing provisions that give the Botswana Geoscience Institute genuine access to resource characterisation data — an arrangement credited with enabling Botswana to renegotiate the joint venture terms more favourably in 2023 than virtually any comparable resource economy has achieved with a major mining partner (Lewin, 2011). The point is not that these instruments are exotic. It is that they require a prior legal decision: the state must first decide that the data belongs to it.

The most immediate leverage point sits in the concession renewal timelines for the sector's two largest operations. Sinomine's acquisition of Bikita in 2022 and Huayou Cobalt's acquisition of Arcadia in 2023 were structured under Zimbabwe's standard concession framework. Both will require renegotiation within the next several years. That renegotiation is the moment when data conditions attach as a standard feature of the concession, comparable to environmental management requirements, local employment provisions, and the royalty structures already in place.

Indonesia's Nickel Export Ban as a Data Sovereignty Move

When Indonesia banned the export of unprocessed nickel ore in 2020, the reasoning was publicly about value-addition: keep the smelting and processing onshore, rather than shipping the raw material abroad. The ban produced foreign smelter investment and a shift in export revenue composition toward higher-value products — outcomes well-documented in the trade literature. Less discussed is what Indonesia did with the information side of the equation: it progressively required that geological and metallurgical data generated by mining operations be deposited with the national geological survey. The combination of processing requirements and data localisation meant that Indonesian institutions developed genuine independent capacity to understand and value their own nickel resource base.

Knowledge as Geoeconomic Power

Geological data is, in the most direct sense, knowledge of what a country owns underground. A government that does not hold that knowledge cannot independently verify what mining companies tell it about its own reserves, cannot negotiate royalties from an informed position, and cannot build the domestic research and educational infrastructure needed to generate long term, sustained value from geological resources.  The legal instruments to change this are available The lesson from SWIFT — from Russia's eventual capacity to route around a system that had been weaponised against it — is not that resistance to network power is futile. It is that the state must build its own architectural capacity before the moment of crisis, not after. The geological data question is, structurally, the same problem. The question is whether it gets addressed before or after the leverage has been permanently ceded.REFERENCES

Bracewell LLP (2025) Powering Africa's Digital Future: The Challenge of Energy for Data Center Development. Available at: https://www.bracewell.com/resources/powering-africas-digital-future-the-challenge-of-energy-for-data-center-development/ (Accessed: 21 March 2026). [Industry analysis citing Africa Data Centres Association data.]

Carnegie Endowment for International Peace (2024) What Are the Limits to Russia's "Yuanization"? [Alexandra Prokopenko analysis, May 2024]. Available at: https://carnegieendowment.org/russia-eurasia/politika/2024/05/china-russia-yuan?lang=en (Accessed: 21 March 2026). [Non-peer-reviewed policy analysis; cited for Russia-China bilateral trade and yuan share statistics derived from Chinese customs data.]

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SWIFT (2024) RMB Tracker, November 2024. Available at: https://www.swift.com/swift-resource/252355/download (Accessed: 21 March 2026). [Official SWIFT monthly tracker: yuan 3% of global SWIFT payments by value; USD 48%; EUR 24%.]