Uncertainty and Lithium Mining Research

A rocky relationship: lithium, risk and social uncertainty

by George Katito*

December 27, 2025

Open‑pit, hard‑rock lithium mining is significantly more carbon‑intensive than many people assume, especially when compared with brine operations in South America’s salt flats. Recent analyses estimate that producing lithium chemicals from spodumene ore can emit roughly three times the greenhouse gases of brine‑based production, once crushing, grinding, flotation, high‑temperature conversion and chemical refining are taken into account. This has become a familiar talking point in both academic life‑cycle assessment work and mainstream outlets that contrast “green batteries” with the  physical scars of open pits.

That familiarity has allowed a neat moral narrative to take hold: companies as villains, communities as passive victims, landscapes as sacrificial zones. Communities appear as static characters who step onto the stage during the Environmental and Social Impact Assessment, give a quote or a photograph for the sustainability report, and then fade into the background. For social science researchers, blunt survey instruments often reproduce this script, yielding true but tediously predictable findings about disrupted livelihoods, water stress and contested benefit sharing in lithium districts from the Atacama to emerging African hard‑rock camps. The storyline writes itself: the environment is damaged, companies are bad, people are sad and good.

Rethinking how we pose the questions

Part of the problem lies less in the rocks than in the research design. Simple binary choices—approve or oppose the mine, satisfied or dissatisfied with compensation—struggle to capture the inherently uncertain, long‑duration nature of lithium exploration and investment. Hard‑rock projects may move from grassroots exploration to decommissioning over decades, with changing ownership, regulatory regimes and community expectations along the way. Treating “social risk” as something that appears only in early discovery or permitting windows misses the evolving relationship between geology, infrastructure, prices and social life over the full project cycle.

This suggests that methods, not just morals, need an upgrade. The question is less “are communities for or against lithium?” and more “how do people’s risk perceptions, forms of organisation and everyday stress change as a function of exploration intensity, employment cycles and environmental signals, and how uncertain are those relationships?” Answering that requires approaches that can register gradations, feedback and ambiguity, rather than forcing messy realities into yes/no boxes.

Quantitative data still matters—just not only in binaries

Quantitative information still matters, especially early on. Basic demographic and livelihood data establish the scale and distribution of potential impacts and benefits, and allow some comparability across regions and projects. Time‑series indicators—on migration, local employment shares, water availability or household income sources—help track how a lithium project re‑orders local economies, for better or worse. In Chile’s Salar de Atacama, for example, lithium and other mining activities have been associated with a more than twofold increase in labour influx, even as the share of local employment in mining fell sharply, a pattern that fed into rising social activism.

The question is how to extend this quantitative backbone so that it can register uncertainty and lived experience more sensitively. That is where more experimental “next‑generation” instruments come in—not to replace surveys, but to complicate and enrich them.

Beyond check‑boxes: visual, sensory and wearable methods

One way to escape familiar binaries is to give communities more direct control over what counts as data. Photovoice and other participatory visual methods let residents document, in images and captions, how mining shows up in their health, environment and sense of place. Recent work with adolescents living near mining sites, for instance, used photovoice within focus groups to surface nuanced perceptions of dust, noise, safety and aspirations that would have been hard to elicit through questionnaires alone. Those photographs can then be coded—for themes, sentiment or frequency of particular concerns—and linked to more conventional indicators.

Wearable technologies offer a different but complementary route. Advances in body‑worn sensors and mobile devices make it possible to track heart rate, skin conductance, movement and even environmental noise exposure throughout the day. In mental health research, such devices have been used to distinguish social from non‑social contexts and to detect stress or anxiety patterns around specific situations with reasonable accuracy. Translated (carefully and ethically) into mining contexts, similar approaches could be used on an opt‑in basis to explore how physiological stress responds to blasting days, community meetings, shift changes or water shortages, generating high‑frequency data that map “social impact” as a fluctuating process rather than a single survey response. 

Non‑traditional information streams can further deepen this picture. Sensory ethnography, for example, systematically records ambient soundscapes, smells and textures, offering a grounded way to document how mining reshapes the sensory environment of a valley or town. Coupled with self‑reported well‑being, short audio diaries and migration records, such datasets could support more dynamic “social impact maps” linking geological operations, infrastructure and everyday life.

Quantifying uncertainty, not just impact

If lithium supply chains are to be treated as critical infrastructure, the social dimensions of risk need to be modelled as rigorously as ore grades or price scenarios. Here there are useful lessons from fields such as linguistics, land‑use planning and engineering risk analysis that have embraced fuzzy logic and hierarchical decision methods. Fuzzy Analytic Hierarchy Process, for example, combines fuzzy mathematics with the classic Analytic Hierarchy Process to evaluate complex, multi‑factor risks where inputs are imprecise or expressed in linguistic terms. Instead of forcing experts or community representatives to give a single crisp score for “project legitimacy” or “livelihood impact,” fuzzy numbers allow degrees of membership—capturing, for instance, that a compensation scheme is somewhere between “acceptable” and “unacceptable” depending on employment prospects, cultural attachment to land and perceived procedural fairness.

Such methods have already been applied to issues ranging from earthquake triggers and mega‑city infrastructure to land conflict risk, where they help structure indicators into object, rule and factor layers and then derive weighted risk scores under uncertainty. Translating that architecture to lithium means building hierarchical models that link upstream geological uncertainty (prospectivity, resource classification), midstream investment and processing choices (brine versus hard rock, energy mix, technology) and downstream social factors (water stress, employment quality, cultural heritage, trust in institutions). Factor analysis and related multivariate techniques can then be used to identify latent social factors—such as “perceived procedural justice” or “future security”—that are not directly observed but strongly shape overall risk profiles.

In other words, the aim is not just to measure impact but to quantify uncertainty itself: to model how incomplete information, contested narratives and shifting power relations propagate through the supply chain, and to make those uncertainties explicit rather than hiding them behind nominal categories.

Towards a more honest theory of social risk

Treating the social dimension of lithium as a serious site of uncertainty, volatility and complexity is not a luxury add‑on to the “real” work of geoscience and finance; it is part of understanding how projects succeed or fail over time. Methodologically, this calls for a willingness to combine conventional statistics with participatory visual methods, sensory ethnography, wearable‑sensor datasets and fuzzy, hierarchical models that can cope with ambiguity rather than erasing it.

Rather than imposing tidy narratives on messy realities, the goal is to use quantitative and qualitative tools that remain open to emerging patterns, register unpredictability and move beyond clean binaries. That kind of methodological pluralism will not eliminate conflict around lithium, but it can produce a more honest map of where the risks lie, who bears them and how they might evolve as the energy transition accelerates.

Zimbabwe as a case in point

Zimbabwe exemplifies this need for nuance, where fears around lithium investments often arise from binary catastrophism and tired tropes of hyperinflation, poverty and dysfunction—real challenges that cannot be ignored, yet ones that obscure a far fuzzier, non-static reality suspended between "risky" and "not risky," ungradable by simple labels like moderate or high. A complex web of social actors has evolved gradually over time: distinct urban-rural divides (with Harare's policy elites contrasting but at times aligning with rural smallholders), ethnic and language cleavages (intricate webs of familial, clan, dialectal group and other cultural affiliations), generational gaps (youth unemployment  fueling urban migration and the reverse, versus older rural resilience), and a vast diaspora (over 3 million strong, remitting $1.4 billion annually to shape family ties, land claims and investment views). Politically, ZANU-PF has provided post-independence continuity as the dominant force, yet its internal dynamics blend stability with factional fluidity, as seen in recent succession maneuvers. Economically, interests shift with commodity booms, while geopolitics adds layers—Chinese firms dominate mining stakes amid Western sanctions, fostering a policy environment that is dynamic yet resilient, with GDP growth rebounding to 6% in 2024 despite external pressures. Dynamic data streams, from real-time remittance flows to sentiment tracking across these divides, are essential; Zimbabwe offers textbook ground to show why fuzzy, hierarchical models are indispensable for mapping such evolving contexts, which defy what outsiders typically assume.

George Katito is CEO and founder of Geostratagem.