Introduction
Climate change poses urgent challenges across the planet: rising temperatures, shifting rainfall patterns, more intense wildfires, sea-level rise, biodiversity loss, and so on. At the same time, people are developing innovative, sometimes low-tech, sometimes high-tech, sometimes traditional-revived, responses to these challenges. In this essay I highlight 10 concrete ways that apps, goats (and related animal/grazing practices) and AI (artificial intelligence) are being deployed globally to reduce greenhouse gases, build resilience, protect ecosystems or simply help people live more sustainably. The examples are drawn from diverse geographies and cover both mitigation and adaptation.
1. Carbon-footprint tracking apps
One of the accessible tools for individuals and households is the mobile app. For example, the app Klima (launched by Climate Labs in Berlin) asks users about lifestyle habits (flights, diet, transport, etc.), estimates their annual carbon footprint and then offers a monthly subscription fee that goes to verified projects (tree-planting, solar, clean cook stoves).
Another example is Yayzy, a UK-based fintech/clean-tech app which assesses personal purchases and behaviour via machine learning, estimating the carbon emissions of those purchases, and enabling tracking and offsets.
These apps democratize climate action by empowering individuals with data and choices—changing behaviour, increasing awareness, and directing funds to climate-friendly projects.
2. Behaviour-change & gamified climate apps
Beyond simple footprint trackers, other apps focus on motivating behavioural shifts, habit changes, or community challenges. – Apps like Ant Forest (via Alipay in China) engage users in virtual forest-games where avoiding CO₂-emitting actions (e.g., less travel, recycling) earns “virtual energy” that translates into real trees planted. – More generic apps (e.g., “Greenie” or others) provide tips, track energy savings, reward users for sustainable actions.
By shifting climate action into the personal and social domain (rather than just large-scale policy), these apps help broaden participation.
3. Grazing goats (and sheep) for wildfire and vegetation-management
Here we shift to the “goats” part. A surprising but increasingly adopted practice: using goats, sometimes sheep, to graze combustible vegetation in fire-prone landscapes, thereby reducing fuel loads and lowering wildfire risk. For example:
– In southern Europe (Spain, Portugal) goats are used to clear under-brush in Mediterranean forests, creating natural fire-breaks. – In Norway, a study used “virtually fenced” goats to graze juniper on coastal wildland-urban interface zones, reducing the foliage of fire-prone plants by large amounts. – The Goatapelli Foundation is promoting “systematic goat grazing” as a nature‐based tool for wildfire mitigation, soil restoration and invasive species control.
This approach is appealing because it harnesses nature (animals) rather than only machines or chemicals, and it often costs less than heavy machinery for clearing vegetation. For example, Spain’s estimates show goats‐clearing cost about a quarter of mechanical crews.
In short: the goats are helping landscapes become more resilient to fire, a key climate adaptation challenge in many regions.
4. Grazing animals for ecosystem restoration and carbon-capture
The role of grazing animals can go beyond fire prevention. A literature review shows that in Mediterranean mountain areas, goat and sheep grazing contributes significantly to soil fertility, biodiversity conservation, and reduction of shrub biomass (which means less flammable material). So grazing supports both adaptation (fire/fuel risk) and mitigation (enhancing soil carbon through improved vegetation/land-use).
In effect, this revives traditional land-use practices (pastoralism) and integrates them into climate-smart strategies.
5. AI for climate-risk forecasting and early-warning
Now moving into the “AI” category: Artificial intelligence and machine-learning are being used to predict, monitor, and adapt to climate impacts. For example: – According to the International Telecommunication Union (ITU) and partners, recent competitions have produced AI solutions that detect climate extremes (floods, droughts), forecast water resource stress, and support adaptation. – A summary by the World Economic Forum highlights how AI is mapping deforestation via satellites, helping communities in Africa predict weather/climate change impacts, and improving waste management via computer vision systems.
Such AI systems make possible detection and response at scale (often in data-scarce regions), bridging information gaps and enabling smarter adaptation.
6. AI for urban/transport emissions and smart infrastructure
Another dimension: using AI to reduce emissions in urban settings. For instance: A report cites how Google’s partnership developed an AI/ satellite-based flood-forecasting system for data-scarce areas, and “Project Green Light” which uses AI to optimise traffic lights (reducing stops and emissions at intersections by up to ~10%).
This illustrates how AI isn’t just for agriculture or forests but also for everyday urban infrastructure and mobility—vital since cities are major emission sources and vulnerable to climate change.
7. Apps linking people to climate-smart agriculture/farming
Back to apps: in the agricultural sector, there are apps and platforms that support farmers to use resources (water, fertiliser) more efficiently, adopt climate-smart practices, or engage in carbon-friendly markets. For example, the review of AI start-ups indicates an Argentinian company called Kilimo that uses AI + satellite + weather + crop data to optimise irrigation, saving large volumes of water in Latin America. (Water use and agriculture are intrinsically linked to climate change.) While that example is more “AI” than “app”, the principle transfers: a digital tool helping real-world climate mitigation/adaptation in farming.
By enabling resource-efficient agriculture, these tools help reduce water stress, preserve ecosystems and reduce greenhouse-gas emissions.
8. Nature-based grazing + apps/AI: hybrid solutions
The convergence of the above categories is especially interesting. For example: goats grazing to reduce fire risk could be enhanced with digital tech: virtual fencing (GPS‐collars, sensors) to manage herds precisely in fire-prone terrain (as in the Norway study of goats with solar-powered GPS collars). This hybrid solution (animals + tech) showcases how combining simple nature-based practices with digital controls can yield scalable, climate-smart solutions.
It also illustrates that innovation doesn’t always mean “new gadget” but can mean “reviving an old practice with new support”.
9. Apps for citizen engagement & crowdsourced climate solutions
Another strand: apps that engage citizens in climate data, behaviour change, or collective action. For instance, while not always peer-reviewed, Reddit threads show developers building apps that let users explore regional climate data, compare emissions, receive actionable tips, challenge friends, track progress. Although such apps may not yet be large-scale, they reflect the potential of digital platforms for community-led climate action.
By enabling individuals to become aware, active and connected, these apps help build the social fabric needed for climate solutions.
10. AI in ecosystem monitoring, land-use change and carbon-management
Finally, AI is playing a critical role in ecosystem and land-use monitoring—tracking deforestation, soil degradation, carbon stocks and vegetation changes. e.g., a research paper on “AI for Earth: rainforest conservation by acoustic surveillance” uses machine learning to classify sounds in rainforests and monitor forest health. Similarly, studies using remote sensing and AI (deep learning) model drivers of extreme wildfires, helping predict fire risk based on vegetation, drought and temperature trends.
Such monitoring is foundational for climate-mitigation (knowing how much carbon is stored, where deforestation is happening) and adaptation (knowing where ecosystems are most vulnerable). AI thus becomes a backbone for global climate strategy.
Discussion & Implications
These 10 examples (and the three thematic threads: apps, goats/animals, AI) collectively highlight several important points for climate action:
- Multi-scale solutions: From personal behaviour (apps) to landscape management (goats) to global monitoring (AI), solutions operate across scales.
- Blending traditional + tech: Grazing goats is an old practice, but being revived with new purpose and sometimes tech‐enhancements. Apps and AI are newer, but their value increases when aligned with local practices.
- Cost-effectiveness & accessibility: Some of these innovations lower cost (goat grazing instead of heavy machinery), or make climate action accessible to individuals (apps).
- Data, information, empowerment: AI and apps provide data and decision-support; that empowers farmers, residents, municipalities to act with more precision and confidence.
- Cross-domain importance: Climate change affects many systems—fire, agriculture, urban transport, forests, water resources—and these solutions cut across those domains.
- Adaptation + mitigation: Some solutions are primarily adaptation (fire prevention, grazing), others mitigation (reducing emissions via apps/efficiency, monitoring carbon). Both are needed.
- Replication & scaling: Some initiatives are pilots (goat grazing in particular regions) and scaling them—and adapting to local conditions—is a key challenge.
- Equity & participation: Apps and AI can risk being accessible only in high-tech settings; ensuring rural/low-income communities benefit is key. Similarly, nature-based solutions must engage local stakeholders.
Challenges & Caveats
Of course, there are caveats:
- Many of these are still early stage or pilot projects; broad adoption will require funding, governance, policy support.
- Grazing animals alone cannot solve all wildfire risk—they must be part of integrated land‐management. Studies caution that grazing has limits (regrowth occurs, ecological trade-offs exist).
- Apps depend on user engagement and behaviour change – which is notoriously hard to sustain over time.
- AI solutions depend on data availability, computing infrastructure, and the right institutional frameworks; also risk biases or oversight.
- Monitoring carbon via AI is useful, but translating measurement into real-world emissions reductions still requires policy, incentives, markets.
- Local context matters: what works in Mediterranean grazing landscapes might not translate to other biomes; data challenges differ across regions.
- Equity and justice: climate solutions must ensure that benefits (and costs) are shared fairly, including local communities, indigenous groups, poorer regions.
Conclusion
In sum, the fight against climate change is not only about grand global treaties or massive engineering projects—it also involves everyday tools (apps), revived low-tech nature-based practices (goats grazing), and cutting-edge intelligence tools (AI). The ten examples showcased here illustrate that individuals, communities, regions and innovators are experimenting with new and re-imagined ways to reduce emissions, build resilience, restore ecosystems and empower citizens.
As we move forward, scaling up these solutions, ensuring they are contextually appropriate, inclusive and sustained will be crucial. But the diversity of approaches gives cause for hope: climate action is becoming more participatory, more technology-enabled, more rooted in nature, and more varied than ever.