Solutions - Real-world climate projects powered by community data
​Communities around the world are tackling climate change in their own unique ways. From protecting forests in Tanzania to monitoring clean cooking in rural India, each project needs different data, different approaches, and different ways to engage local people.
CitizenClimate adapts to what your project actually needs. Whether you're verifying carbon credits, tracking biodiversity, or measuring how well a new technology is working on the ground, our platform puts the tools directly in the hands of the people who know their environment best.
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Below, you'll find examples of how different projects use CitizenClimate. Each one shows what's possible when you design climate action with communities, not just for them.
Real Applications
The basic idea is simple: your project needs data, and the people in your community are the ones best placed to collect it. But here's what makes it actually work.
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First, education. Before anyone collects a single data point, they learn why it matters. What's a carbon credit? How does biodiversity monitoring help their community? Why does soil health affect their future? When people understand the bigger picture, the data they collect becomes meaningful—not just box-ticking.
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Then, flexibility. Your project in coastal Kenya won't look like one in rural Mexico. Different surveys, different languages, different kinds of evidence. CitizenClimate adapts to what you're measuring—whether that's tree survival rates, cookstove usage, or passenger satisfaction on electric buses.
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Finally, verification. The data communities collect meets the standards carbon markets and funders actually require. GPS timestamps, photo evidence, structured surveys that track change over time. It's rigorous enough for carbon credits, accessible enough for someone using it on a basic smartphone in an area with patchy internet.
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Below, you'll see how different types of projects use the platform. Find the one closest to what you're doing, or mix and match elements from several. The platform works for you, not the other way round.
Electric Buses & User Satisfaction
Electric Buses in Chilean Mining: Measuring What Workers Actually Think
Project Type: Clean Transport Transition | Electric Bus Fleet Replacement
Location: Atacama and Antofagasta Regions, Chile
Methodology: AMS-III.C
A bank funded an ESG program to support mining workers in northern Chile that switched from diesel to electric buses.
The emissions reductions looked good, but the bank funding it needed to know: do people actually like riding these buses? Community surveys captured what matters on a four-hour desert commute—air quality, noise, whether USB ports work, if workers feel less exhausted.
The feedback strengthened carbon verification, provided real ESG data, and caught practical problems before they became deal-breakers. Carbon credits measure tonnes of COâ‚‚. User satisfaction measures whether clean technology actually works for the people using it.
Biodiversity Monitoring & Land Protection in Degraded Mexico
Project Type: Ecological Restoration & Community-Led Forest Protection | AFOLU Carbon Credits
Location: Hidalgo and Tlaxcala States, Central Mexico
Methodology: VM0047
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The Plains of Apan had been stripped bare by decades of intensive monoculture, overgrazing, and logging. Five ejido communities faced 2,325 hectares of degraded land where soil washed away and forests had disappeared.
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Communities used offline AI to identify returning species, track vegetation health, and record wildlife—all in Yucatec Maya dialect alongside Spanish. But the critical piece was the illegal deforestation reporting system. When communities spotted tree cutting, land clearing, or logging equipment, they documented it with GPS-tagged photos and detailed reports. Type of activity, area affected, equipment seen, number of people involved. Evidence that led to actual enforcement.​
And communities weren't just data collectors—their reports drove where patrols focused, which areas needed different approaches, how ejido governance addressed internal violations versus external threats. They became guardians of their own land's recovery, equipped with tools that worked offline, spoke their language, and turned observations into action.
Indigenous Land Protection Mexico
Regenerative Agriculture in Ukraine
Regenerative Agriculture & Farmer-Led Monitoring in Ukraine
Project Type: Regenerative Agriculture Transition | Agricultural Land Management Carbon Credits
Location: Multiple regions across Ukraine
Methodology: VM0042
Regenerative Agriculture & Farmer-Led Monitoring in Ukraine
Five hundred thousand hectares of Ukrainian farmland are switching from industrial agriculture to regenerative practices that rebuild soil and sequester carbon. Farmers document the transition through their phones—which crops they grow, fertilisation methods (tracking the shift from chemical to organic), etc. Photos and videos provide GPS-tagged evidence of practices happening when and where reported.
AI crop health monitoring analyses farmer-taken photos for stress, disease, and nutrient status—replacing expensive satellites with accessible smartphone technology. Activity records track every compost application, cover crop establishment, reduced tillage event, creating continuous transparency for carbon verifiers and crop buyers between formal audits.
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Coffee Farmers Build Credit Through Data in Tanzania
Project Type: Coffee Value Chain Climate Resilience | Agricultural Financial De-risking
Location: Tanzania (pilot), scalable across East Africa
Methodology: Community-based monitoring integrated with AI crop health assessment and satellite Earth Observation for parametric insurance and creditworthiness enhancement
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Banks won't lend to Tanzanian coffee farmers—no collateral, no documented history. Farmers can't buy inputs or invest in improvements.
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Solution: smartphone documentation becomes creditworthiness. Photograph coffee plants, AI analyses health and predicts yields. Document practices and climate adaptation. Data creates risk scores that determine loan terms. Good documentation = 3-5% lower interest rates, larger loans, cheaper insurance.
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Layered de-risking: farmer data + AI forecasting + satellite verification + parametric insurance + credit guarantees. Banks see real-time crop health, intervene before problems destroy harvests. Two years of performance builds credit history. The data is the collateral.
Financial De-Risking of Coffee in Tanzia
Smallholder Agroforestry in India
Smallholder Agroforestry Verification in Telangana, India
Project Type: Agroforestry & Community Reforestation | ARR Carbon Credits
Location: Telangana State, India
Methodology: VM0047
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Six million trees on 10,000 hectares of degraded Telangana land. Smallholder farmers planted mixed agroforestry—fruit for near-term income, timber long-term, carbon credits throughout. They document species, quantities, survival rates, growth through smartphone surveys. Photos show plantation development. Audio recordings track bird diversity returning.
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Farmer data combines with satellite verification and statistical field sampling for VCS VM0047 carbon credits. This approach cuts verification costs 50-80% versus traditional methods, making smallholder carbon projects economically viable. Communities own both trees and monitoring—documenting their own success rather than compliance burden.
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MicroEnergy Credits: Affordable LEDs, Quantified Poverty Reduction
Project Type: Energy Access | Microfinance LED Distribution with Pro Metrics SDG Monitoring
Location: Rural households across India
Methodology: AMS-II.C | Replacing incandescent bulbs with LED inverter bulbs via microfinance
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MicroEnergy Credits distributes LEDs via microfinance in rural India, measuring impact through weighted SDG surveys. Results: SDG 7 (energy) score 5.0 → 35.0. 68% electricity access, 73% clean lighting, 62% efficiency adoption. Energy savings reduce poverty: SDG 1 scores +25 points as households redirect kerosene spending to food, education, sanitation (43% increase).
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Weighted methodology: electricity access weight 3.1 (highest metric), 100 reward points for energy surveys. Anonymous community data from 500+ households aggregates to dashboard metrics. VCS carbon credits (43,079 tCO2e annually) with verified co-benefits for defensible ESG evidence.
Microfinance Energy Credits in India
Adopt Amazon Forest in Bolivia
Adopt Your Own Amazon Forest With Live Community Health Monitoring
Project Type: Adoptable Forest Conservation with Live Community Monitoring
Location: Bolivia Amazon region
Methodology: Corporate/individual forest adoption with community-verified protection via real-time digital MRV dashboard including SDG health outcomes
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Conservation has an opacity problem. Funders donate, then trust—but rarely see—whether forests stay protected or communities genuinely benefit. Real-time monitoring by indigenous communities documenting everything—forest health, biodiversity, AND how conservation affects their water, sanitation, and health.
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Communities protect adoptable forest parcels using smartphones. Seventeen-question SDG health surveys ask directly: has your water quality improved? Has sanitation access changed? Anonymous responses mean honest feedback. The 6-month pilot proves adoptable parcels with SDG monitoring create transparent conservation. Not promises—documented evidence: water improving, sanitation being built, health tracked, biodiversity returning. All visible monthly, anonymously reported, GPS-verified.
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MicroEnergy Credits: Affordable LEDs, Quantified Poverty Reduction
Project Type: Agroforestry Carbon Credits | Reforestation | Agriculture Forestry and Other Land Use (AFOLU) — ALM, ARR, IFM
Location: Andhra Pradesh, Telangana, and Orissa, India
Methodology: VCS AR-ACM0003 (Afforestation and Reforestation of Degraded Land)
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Tree planting carbon projects need to verify more than biomass. This case study covers a 13,500-hectare VCS agroforestry project in India that used three digital surveys — tree planting verification, sustainable agriculture monitoring, and SDG 1 household poverty assessment — to document both carbon and community outcomes across smallholder farms in Andhra Pradesh, Telangana, and Orissa.
GPS-tagged, farmer-reported, and structured around AR-ACM0003 methodology requirements. For project developers running similar initiatives, this is what complete agroforestry dMRV looks like in practice.
Agroforestry dMRV in India
Biochar dMRV: Puro.Earth in Brazil
Biochar dMRV: Supply Chain Carbon Monitoring | Puro.Earth
Project Type: Biochar Carbon Removal | Soil Amendment | Nature-based Carbon Removal Credits
Location: Capelinha, northeast Minas Gerais, Brazil
Methodology: Puro.Earth Biochar Methodology
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Biochar's carbon claims depend on what happens at ground level — what feedstock was collected, how it was stored, where it was applied, and in what quantities. That data has typically relied on paper records or periodic spot-checks, neither of which is robust enough for Puro.Earth verification or credible LCA carbon footprint calculation.
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This case study covers a Brazilian biochar operation — approximately 40,000 tonnes of carbon removal per year, Puro.Earth verified, based in Minas Gerais — that moved its MRV fully digital. Farmers complete smartphone surveys at each stage: feedstock collection records waste type, weight, moisture and contamination; application surveys capture crop type, soil type, application rate and method. Transport is tracked via drivers' phones to feed real journey data into LCA calculations.
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The platform makes the whole supply chain visible, timestamped, and auditable from a single dashboard.​​
Peatland dMRV: REDD+ Conservation & SDG Monitoring | Indonesia
Project Type: REDD+ Peatland and Dryland Forest Conservation | Wetland Conservation (WRC) | AFOLU — REDD, WRC
Location: Barito Selatan and Kapuas Regencies, Central Kalimantan, Indonesia
Methodology: VM0007 REDD+
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REDD+ peatland projects need to verify more than avoided deforestation. Canal blocks need to be documented. Water levels need to be monitored. Biodiversity co-benefits need evidence beyond species lists in project design documents. And SDG claims — gender equality, reduced inequalities, life on land — need scored, auditable data rather than narrative assertions.
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This case study covers a 39,853-hectare VCS VM0007 REDD+ project in Central Kalimantan using five surveys to cover all of it. Peat Swamp Rewetting tracks canal blocking, water levels, peat moisture, wildlife activity, and plant growth. Biodiversity surveys capture endangered species sightings and community-observed threats. Three Pro Metrics weighted SDG surveys produce quantified impact scores for SDG 5, 10, and 15 — each question mapped to a specific SDG indicator.
Peatland Conservation in Indonesia
Solar PV dMRV in South Africa
Solar PV dMRV: Community Electricity & SDG 7 Monitoring
Project Type: Renewable Energy | Solar PV | Energy Industries (Renewable Sources)
Location: Gauteng, Republic of South Africa
Methodology: AMS-I.F. (Renewable Electricity Generation for Captive Use and Mini-Grid)
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South Africa's load shedding crisis makes the case for community solar better than any project document can. But verified carbon credits under AMS-I.F. need documented proof that systems are working, communities are benefiting, and energy access is genuinely improving.
This case study covers a grouped small-scale solar PV project in Gauteng using three surveys: community electricity usage and outage reporting, monthly PV performance and maintenance tracking, and SDG 7 affordable clean energy access monitoring. The full dMRV picture for a solar carbon project.​​
AI Plant Identification | Citizen Science Biodiversity Monitoring | Education-First Species Data Collection
Feature Type: AI Plant Identification
Demonstrated in: Northern Norway (Lofoten archipelago, 68°N) — subarctic coastal ecosystem
Key capabilities shown: AI-powered species suggestions with probability scores, full taxonomy data, species description, edible parts identification, GPS coordinate capture, step-by-step guided survey flow.
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CitizenClimate's AI plant identification doesn't just collect biodiversity data — it teaches the communities collecting it. Photograph a plant, get ranked species suggestions with probability scores, taxonomy data, species descriptions and edible parts information. Select your identification.
The GPS-tagged record is logged. Over time, communities build genuine ecological literacy alongside the biodiversity dataset conservation projects need. Demonstrated here identifying Cornus suecica — dwarf cornel — at 68°N in the Lofoten archipelago of northern Norway.
AI Plant Identification
Your project is different. That's the point.
Every place has its own climate challenges, its own resources, and its own community knowledge. CitizenClimate doesn't force your project into a template—it adapts to what you're actually trying to achieve.
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If you're working on something we haven't listed here, let's talk. The platform is designed to be flexible, because real climate action doesn't come in one-size-fits-all packages.
Get Started:
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Get the app – Available on iOS, Android, and Web. Works offline wherever you are.
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Access the dashboards – Drop us a line for API keys and dashboard access for your project.
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Join the community – Connect with citizen scientists around the world to share what you've learned and make a bigger impact together.