These barriers do not invalidate the framework. Rather, they highlight the need for phased implementation and institutional coordination. 7.4 Enablers At the same time, several trends favor TLRI adoption: ● growing digitalization of mining and registry databases ● stronger anti-money laundering frameworks ● increasing ESG disclosure pressure from investors ● wider use of GIS dashboards in extractive operations ● stronger demand for anticipatory risk systems This creates an increasingly favorable environment for governance intelligence tools. 7.5 Limitations This study should be interpreted as a pilot application. Territorial Aggregation The current version is estimated primarily at regional level. District-level hotspots may be partially masked. Proxy Measurement Some variables capture manifestations of power rather than latent territorial control directly. Weighting Judgments Weights are theory-driven and analytically justified, but future versions may test alternative calibrations. Political Economy Complexity Elite protection networks, corruption channels, electoral brokerage, and informal authority systems are difficult to quantify fully through administrative data. Temporal Constraints The model combines sources from different periods, although harmonized for analytical consistency. 7.6 Future Research Future versions of the TLRI should: ● move to district-level geospatial estimation ● incorporate panel data and yearly trend analysis ● test predictive power against conflict events ● include electoral and governance variables ● refine social legitimacy indicators using surveys or perception data ● integrate satellite and environmental monitoring layers 7.7 Transferability Although developed using Peru’s gold sector, the framework is adaptable to other extractive contexts: ● Colombia (informal gold districts) ● Ghana (artisanal gold clusters) ● Democratic Republic of Congo (ASM cobalt zones) 41
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