Achieving restoration, conservation and poverty alleviation goals through Indonesia’s community forests

Technical Summary: Achieving restoration, conservation and poverty alleviation goals through Indonesia's community forests

Problem Statement
Global area-based restoration targets, such as those under the Global Biodiversity Framework and the Bonn Challenge, frequently fail to scale effectively because they overlook critical trade-offs between climate mitigation, biodiversity conservation, and poverty alleviation. In the absence of spatially explicit guidance, restoration initiatives risk becoming interventions that marginalize local populations or strategies that erode carbon stocks and biodiversity. Indonesia presents a critical case study: while it has pioneered an ambitious social forestry programme aiming to reallocate 14.1 million hectares of forest to community-led management, national implementation has been uneven. By 2022, only approximately 5 million hectares had been allocated, largely through reactive, administrative processes that lack a systematic framework to optimize spatial distribution for simultaneous socio-ecological benefits. Consequently, the programme risks meeting area-based targets on paper while failing to deliver the intended synergies for climate, nature, and people.

Methodology
The authors developed a multi-objective spatial optimisation framework to identify priority areas for new social forestry licenses across 10.89 million hectares of eligible land within Indonesia's 81,912 village administration units. The study utilized the official Indicative Social Forestry Map (PIAPS) as the decision space.

The framework integrated three high-resolution spatial layers representing core objectives:

1. Carbon Sequestration and Restoration Feasibility: Derived from PALSAR and Sentinel satellite data (2018), this layer targeted "intermediate disturbance" landscapes to maximize potential forest biomass gain. It excluded hyper-degraded areas (below a 74 t ha⁻¹ biomass threshold) to ensure ecological viability and economic efficiency.
2. Biodiversity Conservation and Ecological Representation: This layer combined distribution data for 311 threatened terrestrial vertebrate species (from IUCN and BirdLife International) with Indonesia's land systems map. It prioritized critical habitats and ensured broad representation across diverse soil, lithology, and ecosystem profiles.
3. Multidimensional Poverty Alleviation: Utilizing the 2018 Village Potential (PODES) census, this layer mapped socio-economic deprivation using a 15-indicator index covering health, education, living standards, infrastructure, and social cohesion.

The authors employed a minimum shortfall spatial optimisation approach (using the prioritizr package in R and Gurobi solver) to resolve tensions between these objectives. They formulated a maximum coverage problem to maximize the protection of spatial features subject to fixed area-based budgets. Analyses were conducted at the provincial level and aggregated to regional and national scales. The study explored the Pareto front by varying the weightings of restoration, biodiversity, and poverty objectives to identify how priority areas shift under different policy preferences. A boundary penalty was applied to prevent highly fragmented selection of planning units.

Key Results

• Optimisation Dividend: Strategic targeting of the top-ranked 10% of eligible land yields an additional 23% of combined restoration, conservation, and social benefits compared to random allocation. This performance is double that of random selection.
• Provincial Variation: The potential for social forestry to deliver outcomes varies significantly by region. For instance, in South Sumatra, allocating licenses to the top 10% of priority land secures an additional 3.7 Mt of aboveground biomass and covers 8% more species ranges than random allocation, while simultaneously benefiting the most socio-economically deprived communities.
• Regional Archetypes: Aggregating results reveals distinct strategic profiles across the archipelago:
  • Western Indonesia (Sumatra and Kalimantan): These regions function as primary engines for simultaneous gains in restoration, biodiversity, and poverty alleviation. Here, targeted licensing delivers outsized returns for climate, nature, and people.
  • Eastern Indonesia (Papua and Maluku): In these conservation strongholds with largely intact primary forests, the marginal contribution of social forestry to new biodiversity protection is lower. Instead, the strategic value here lies in tenure security, reparative justice, and pre-emptive protection against future industrial pressures.
• Current vs. Optimised Allocation: The current reactive approach to licensing fails to capture significant potential. In South Sumatra, a random selection of 10% of eligible land captures only 41% of forest restoration potential, 71% of poverty alleviation benefits, and 45% of biodiversity value compared to the optimised solution.

Significance and Claims
The paper argues that achieving synergistic social and environmental outcomes depends fundamentally on deliberate spatial design rather than merely increasing the volume of land allocated. The authors claim that their framework provides a scalable roadmap to transform ambitious restoration pledges into socially legitimate and ecologically impactful land-use policies.

Key contributions include:

• Demonstrating the "Optimisation Dividend": Showing that a small fraction of land can capture disproportionate returns if selected strategically, thereby de-risking investments in community-led conservation.
• Context-Appropriate Strategy: Moving beyond a "one-size-fits-all" approach by identifying regional archetypes where the role of social forestry shifts from restorative (in the fragmented west) to tenure-securing (in the intact east).
• Policy Relevance: Providing evidence to support the transition from administrative, convenience-based licensing to data-driven prioritisation. This is critical for meeting Indonesia's 2029 target and the global "30x30" biodiversity target by positioning social forestry as an effective Other Effective Area-Based Conservation Measure (OECM).

The authors conclude that while ambitious targets are necessary, they are insufficient without spatial discipline. By clarifying where interventions matter most, this framework enables a shift toward a forest management paradigm that aligns local and Indigenous rights with climate and biodiversity objectives, ensuring that licenses granted today remain ecologically viable and socially legitimate in the future.