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Page 6 of 23 Chaib et al. Carbon Footprints 2026, 5, 23

Table 1. Overview of the livelihood vulnerability data gathered from the community to determine the composite indices LVI-SF and
LVI-SF-IPCC

Category Livelihood vulnerability data
Household structure and dependency ratio
Education of household head and children
Migration and off-farm labour
Socio-demographics
Knowledge of climate change and weather forecasting
Health and access to healthcare
Safety
Land tenure and ownership
Crops, livestock, and other farm income sources
Production yields and losses
Livelihood strategies
Dependence on external inputs
Water sources and availability
Market access and intermediaries
Food security and self-sufficiency
Basic household needs Housing quality
Access to drinking water, potable water and energy
Exposure to extreme weather events and related damage
Natural disasters and climate variability
Climate variability
Access to capital
Financial stability Poverty level
Healthcare costs
Participation in agricultural groups, trainings and learning visits
Social networks
Access to telecommunication

The collected data were normalized to a 0-1 scale to generate two distinct community-level indices. First, the
Smallholder Farmer Livelihood Vulnerability Index (LVI-SF) was calculated by aggregating indicators across
the Sustainable Livelihoods Framework capitals, providing a synthetic measure of overall livelihood
vulnerability on a continuous scale from 0 (least vulnerable) to 1 (most vulnerable). Second, the IPCC
Livelihood Vulnerability Index (LVI-SF-IPCC) was derived by reorganizing the same set of indicators within
the IPCC’s conceptual framework of “exposure-sensitivity-adaptive capacity” , following the LVI-IPCC
[5]
formulation proposed by Hahn et al. , and synthesized in empirical studies as well . This formulation
[45]
[50]
applies equal weighting through simple arithmetic means across subcomponents and introduces the
inversion of some original items that belong to adaptive capacities only at the final aggregation stage. As a
result, the LVI-SF-IPCC yields values ranging from -1, indicating the lowest level of vulnerability, to +1,
representing the highest level of vulnerability. Eligibility for carbon finance is assessed using the operational
vulnerability screening thresholds, with at least moderate thresholds (LVI-SF > 0.30; LVI-SF-IPCC > -0.30)
used to identify contexts in which socio-economic constraints and climate-related exposure and sensitivity
are likely to outweigh adaptive capacity [42,43] . Additionally, both indexes could further inform the
prioritization of contexts in which socio-economic vulnerability and climate-related risks justify targeted
mitigation, adaptation and livelihood-protection interventions.

Remote-sensing and dynamic baseline data
The dynamic deforestation baseline was conducted in accordance with the standard methodological
requirements, including the definition of the reference region, class reclassification rules, accuracy
assessment and adjusted area estimation. Land-cover and land-use change analyses use the MapBiomas
10-metre land-cover product for Brazil (Collection 2, beta), derived from Sentinel-2 MSI Level 2A imagery at
10-metre resolution and processed according to the Algorithm Theoretical Basis Document for the
MapBiomas 10-metre project. QGIS (version 3.42) was used for spatial preprocessing, such as coordinate
harmonization, polygon preparation, area calculations, raster extraction and class transitions. Project and
reference boundaries were obtained from federal cadastral datasets. The Curiaú Quilombola territory was
sourced from the Cadastro Nacional de Territórios Quilombolas (CNTQ) maintained by the Instituto