This building block highlights the importance of aligning economic incentives with ecosystem conservation. NFTree increases the opportunity cost of deforestation by providing financial rewards for preserving forests. By connecting private-sector funding to biodiversity-rich carbon sinks, NFTree ensures that conservation becomes a competitive and sustainable land-use option. The integration of digital tools and financial mechanisms creates a model where ecological preservation supports both environmental and economic resilience.

This building block utilizes advanced technology to monitor, report, and verify (MRV) conservation outcomes. The MRV system integrates satellite imagery, artificial intelligence, and on-ground data to measure carbon stocks, forest productivity, and biodiversity in the Andean Chocó. This robust system provides high-resolution data to validate the ecological impact of conservation efforts, ensuring transparency and attracting high-integrity climate financing.

This building block introduces the tokenization of conservation efforts using blockchain technology. NFTree tokenizes 10x10 meter plots of conserved or restored forest land, creating unique digital assets that represent carbon sequestration and biodiversity credits. These tokens are purchased by individuals and companies to offset their carbon footprints, with funds directly channeled to local conservation initiatives. The blockchain ledger ensures transparency, traceability, and accountability in transactions, making conservation efforts more attractive to investors and contributors.

This building block establishes decentralized governance structures using a DAO (Decentralized Autonomous Organization). It ensures transparent and equitable distribution of conservation funds to local land stewards while empowering them with decision-making authority. The DAO model integrates community members into governance, enabling scalable and bottom-up landscape management that complements top-down conservation initiatives

Previously these have been donated by Cisco Inc

The network server connects sensors, gateways and end-user applications and ensures reliable and secure data routing all along the LoRaWAN network. Along with the Operation Support System (or OSS), they are the brain that controls the complete LoRaWAN network

https://www.actility.com/lorawan-network-server/

To address human-carnivore conflict, a range of protective measures were implemented in three villages of Limi Valley, Humla, Nepal, with the active involvement of local communities. These measures were designed to protect livestock from depredation by wolves and snow leopards, ensuring the safety of both local livelihoods and wildlife conservation efforts. Key actions include:

• Commitment to Zero Poaching Policy: Local villagers were engaged to establish a strong commitment to a zero-poaching policy, fostering a collective responsibility for wildlife protection.
• Construction of Communal Predator-Proof Night Corrals: Villagers collaborated to build sturdy, predator-proof corrals where livestock could be safely kept at night, reducing the risk of depredation.
• Distribution of Predator-Deterring Foxlights: Foxlights, an effective visual deterrent for predators, were distributed to livestock herders, helping to prevent attacks on livestock by mimicking a human patrolling with lights at night.
• Co-funding and Maintenance Responsibilities: Villagers took on responsibilities for co-funding the construction of the corrals and ensuring their proper maintenance over time, strengthening their investment in the success of the conservation efforts.

These actions were designed not only to protect livestock but also to build a sense of ownership and responsibility within the local communities, ensuring the sustainability of the efforts.

The creation of Community Conservation Groups (CCG) has been fundamental in building local capacity for effective conservation of the Himalayan wolf. These groups are composed entirely of local residents who are trained to participate actively in monitoring and conservation activities. Periodic workshops have been conducted to provide training in species monitoring, data collection, and camera trap management. Through these workshops, local participants gain valuable skills that enable them to contribute to the long-term success of the conservation project.

The research on Himalayan wolf ecological requirements to face the climate crisis is based on camera trap data collected in the field in Limi valley of upper Humla (Nepal) from 2021 and 2023. 61 Cameras were powered by batteries and information was stored in SD cards. SD cards were collected by local collaborators who were trained specifically on these management tasks. After delivery, data was deposited in a cloud storage and camera trap data was processed using Traptagger, Wild eye AI technology. Local collaborators were equipped with GPS devices to register events and handle camera trap management.

Programs like eco-classrooms, guided tours, and school collaborations raised awareness, while immersive experiences, such as observing salmon in their natural habitats, fostered public engagement.