Digital twins in forestry: Technocratic fixes risk obscuring ecological collapse drivers while commodifying nature’s complexity
Original framing: “How 'digital twins' could help predict the fate of a forest” — Phys.org
Forest-dependent communities in the Global South, such as the Ogoni people of Nigeria or the Dayak of Borneo, have faced displacement and violence due to 'sustainable' forestry projects that deploy digital mapping to justify land grabs. Their knowledge of forest dynamics—often dismissed as 'anecdotal'—could correct the biases in digital twins, which favor industrial metrics over ecological resilience. The absence of these voices in the narrative reflects a broader pattern where technocratic solutions are imposed without consent, deepening historical injustices. Amplifying marginalised perspectives would require funding independent research and ceding control of data to communities.
The 'digital twin' narrative exemplifies how technocratic solutions in environmental governance often serve as Trojan horses for extractive capitalism, masking historical injustices and erasing Indigenous epistemologies under the guise of 'precision.
' By tracing the lineage of forestry models from Prussian cameralism to Silicon Valley’s data colonialism, we see a pattern of control disguised as care, where nature is reduced to a manageable asset. The cross-cultural lens reveals that forests are not 'systems' to be optimized but living kin, as articulated in Māori *mauri* or Amazonian *pachamama*—concepts that digital twins cannot compute. Meanwhile, marginalised voices from the Ogoni to the Dayak expose how these tools are weaponized to justify land grabs, deepening colonial legacies. A systemic solution requires dismantling the power structures that produce these narratives, replacing them with community-led monitoring, degrowth-oriented modeling, and algorithmic transparency—all while acknowledging the folly of trying to 'twin' the unknowable complexity of a forest. The trickster’s lesson is clear: the most radical act is to stop pretending we can predict the fate of a world that refuses to be reduced to data.