Projected socioeconomic benefits of ThinkingEarth use cases

What is the main purpose of assessing the socioeconomic value of ThinkingEarth use cases?

The main purpose is to estimate the potential economic, environmental and societal benefits of combining Earth Observation (EO) data with artificial intelligence (AI). The assessment focuses on four applications: solar energy forecasting, urban green space monitoring, forest carbon assessment and food security.

By examining projected cost savings, potential efficiency gains and improved decision-making, the assessment aims to demonstrate the socioeconomic value that these solutions could generate when implemented at scale.

What are the projected economic benefits of improved solar forecasting for energy communities?

ThinkingEarth’s first use case (UC1) develops AI-based solar forecasting solutions to help energy communities better predict electricity generation and manage consumption.

An assessment of four energy communities in Lower Austria estimates that a 10% improvement in forecast accuracy could avoid approximately €100,000 in balancing costs annually.

These projected savings result from reducing discrepancies between predicted and actual electricity generation, limiting costly adjustments in the balancing market.

More accurate forecasts could also help communities increase their consumption of locally generated renewable electricity. In one community, shifting water pumping activities from nighttime to daytime increased solar self-consumption from 23% to 34%.

What additional benefits could improved monitoring of solar energy production provide?

The assessment identified a significant gap in the monitoring of rooftop solar generation. Estimates suggest that up to 50% of the solar electricity generated in the communities studied may not appear in official records.

Improved monitoring could therefore support more accurate energy planning, better accounting of renewable electricity generation and more reliable estimates of avoided CO₂ emissions.

In Athens, rooftop mapping by the National Observatory of Athens (NOA) suggests that solar installations on the city’s rooftops could potentially cover almost half of its energy needs.

What cost savings could municipalities achieve through satellite-based urban green space monitoring?

ThinkingEarth’s urban biodiversity monitoring use case (UC2a) combines Copernicus satellite imagery and AI to identify vegetation stress and support the management of parks and other urban green spaces.

Working with AQUATEC in Spain, the project assessed the potential economic benefits of replacing some routine inspections and drone surveys with satellite-based monitoring.

The assessment projects that a 25% reduction in routine field visits and drone flights could save approximately €355 per hectare annually.

For a municipality managing 100 hectares of green space, this would represent potential annual savings of around €35,000.

These estimates assume that the service is integrated into routine operations. Satellite monitoring would not replace activities requiring on-site inspections, such as soil testing, individual tree assessments and species surveys.

What other socioeconomic benefits could satellite-based vegetation monitoring deliver?

Beyond financial savings, satellite-based monitoring could help municipalities identify areas where vegetation is under stress and prioritise maintenance activities accordingly.

This could improve the allocation of staff and technical resources, reduce unnecessary inspections and support more timely interventions.

By providing regular information on vegetation conditions, the tool could also contribute to better urban biodiversity management and climate adaptation planning.

What is the estimated economic value of forest carbon stocks in the Amazon Basin?

ThinkingEarth’s forest carbon monitoring use case (UC2b) combines satellite observations and AI to estimate above-ground biomass and forest carbon stocks.

For 2024, the model estimated that the Amazon Basin contained approximately 280 billion tonnes of CO₂ equivalent in forest carbon stocks.

Applying representative prices for high-quality forest carbon credits suggests a theoretical economic valuation of several trillion dollars, illustrating the scale of the carbon stocks at risk.

However, this valuation is not equivalent to potential carbon credit revenue. It is highly sensitive to the carbon price applied and should be understood as an illustration of the economic significance of preserving forest carbon stocks.

How much could satellite-based monitoring reduce the costs associated with forest carbon projects?

Traditional forest carbon monitoring requires field measurements, periodic verification and substantial technical resources.

By automating biomass estimation using satellite imagery and AI, UC2b could provide a more scalable and cost-efficient monitoring solution.

The socioeconomic assessment projects that monitoring large forest carbon projects could cost only a few cents per hectare annually, equivalent to approximately 1–3% of the measurement, monitoring and other transaction costs associated with such projects.

The potential benefits include more affordable monitoring, greater transparency in carbon stock assessments and improved evidence for forest management and carbon project evaluation.

How could AI-based causal analysis improve food security interventions?

ThinkingEarth’s food security use case (UC3) combines satellite observations, food prices and household survey data with causal inference techniques to identify the factors driving food insecurity.

The tool aims to improve the targeting of humanitarian assistance and nutrition programmes by identifying where interventions are likely to have the greatest impact.

The analysis produced several findings with important potential socioeconomic implications:

• Somalia: Districts receiving sufficient cash transfers experienced an estimated 650,000 fewer people in food crisis annually.
• Climate-related food price shocks: Identifying regions where El Niño and La Niña-related vegetation changes increase vulnerability to rising food prices could support earlier and better-targeted assistance.
• Nutrition: Closing gaps in fruit consumption could help nearly one million households in Ethiopia and Nigeria achieve adequate folate intake.

These results illustrate the potential for causal analysis to support more effective interventions, although their wider impact depends on how the findings are used in practice.

What is the projected economic benefit of improving the targeting of food security interventions?

The socioeconomic assessment estimated the potential value of using UC3 to improve the targeting of assistance ahead of food price shocks.

To avoid overstating the project’s contribution, less than 1% of the estimated benefits of improved interventions was attributed directly to UC3.

Even under this conservative assumption, better-targeted interventions could generate socioeconomic benefits approaching €1 million annually.

This estimate represents the potential value of improved decision-making rather than direct financial savings generated by the tool.

What are the main projected socioeconomic benefits across the four use cases?

The assessments identify several potential benefits:

• Solar forecasting (UC1): Around €100,000 in annual avoided balancing costs across four energy communities, assuming a 10% improvement in forecast accuracy.
• Urban green space monitoring (UC2a): Approximately €355 in annual savings per hectare, or €35,000 for 100 hectares, assuming a 25% reduction in routine inspections and drone flights.
• Forest carbon monitoring (UC2b): Monitoring costs of only a few cents per hectare annually, corresponding to 1–3% of the measurement, monitoring and transaction costs associated with large forest carbon projects.
• Food security (UC3): Potential socioeconomic benefits approaching €1 million annually from better-targeted interventions ahead of food price shocks.

These figures represent scenario-based estimates, modelled outcomes or projected benefits rather than financial savings already achieved through the routine deployment of all four tools.

What are the main limitations and uncertainties associated with these projections?

The projected benefits depend on several factors, including data availability, model performance, local operating conditions, implementation costs and the extent to which organisations adopt the proposed solutions.

For example, savings from solar forecasting depend on balancing market conditions, while those from urban vegetation monitoring depend on the number of inspections that can realistically be avoided.

Forest carbon valuations are particularly sensitive to carbon prices, and the benefits of food security analysis depend on whether the resulting evidence leads to effective interventions.

Further operational deployment and validation would therefore help determine how closely the projected benefits correspond to realised outcomes.

What is the broader socioeconomic potential of ThinkingEarth?

The four use cases demonstrate how Earth Observation and AI could deliver measurable socioeconomic benefits across energy, urban management, forestry and food security.

Their projected impacts range from reduced operating and monitoring costs to improved resource allocation, more transparent environmental assessments and better-targeted humanitarian assistance.

Beyond their individual applications, the use cases highlight the potential for scalable, satellite-based solutions to support more efficient decision-making and generate economic, environmental and societal value.

As these tools are further developed and adopted, their practical implementation will provide opportunities to validate the projected benefits and assess their wider socioeconomic impact.