Effectively incentivising landscape level improvements means creating markets to tackle problems.

Programmable money helps to create more complex, conditional markets that can avoid the pitfalls of the measurement problem. Using machine learning enables objective analysis of habitats and avoiding corrupt data can be helped by diversifying the kinds of information the system takes into account.

Before we even get to creating a market for what we are tokenising we are first of all ensuring that the data is good. Remote sensing technology can certainly help in creating a clear and reliable picture of the ground truth. Taking measurements from the sky, soil and sound then cross referencing them with one another over time can create a reliable picture of improvements. The question is whether land stewards tackling invasive alien species for example will be happy to wait for payment as outcomes are verified and funds are locked.

We have the code and the equipment to accurately map a species such as rhododendron that is prolific in many areas of conservation around Ireland. Analysing aerial footage with machine learning we can pinpoint exactly where a species is prevalent. Cross referencing the footage with land ownership records we can allocate a funding pool to the relevant property that can be claimed by the owner when certain conditions are met.

Who funds the pool and the conditions that need to be fulfilled in order to claim the reward for clearing the property can be tuned to well informed conservation objectives and established practices. Further development of the AI behind the analysis will allow us to take a wider range of measurements into account, resulting in a more reliable outcome. For example a true AI could fetch all the images available from the area to create a composite of the space throughout time. We can do something similar with images from streetview or satellite images to show changes over time.

Analysing dMRV data with AI removes friction from the analysis process meaning it can scale and be a much more useful tool than it otherwise would be. For instance a property of 280 hectares (like the one photographed above) in the Wicklow Mountains which is covered in rhododendron will have certain characteristics associated with it. Besides being obvious from the air or ground, the soil will be acidic from all of the leaf litter, plant and wildlife diversity will also be limited due to the diminished quality of the habitat.

The initial assessment may include a very wide area of surveillance. Subsequently soil and water sampling takes place in target areas together with sound recordings which can be analysed to provide an appropriate baseline assessment of environmental conditions.

Rhododendron removal costs around €2500 per hectare with a professional company, factors such as the difficulty of the terrain and density influence that figure as well as the method of remediation prescribed. Variables such as whether the plants are in a gulley or riparian zone and likely to be spread further or not may also influence the value of tackling the problem.

Other factors such as whether the affected property falls within an area with existing statutory protections may also prioritise the focus and value of taking countermeasures. Where existing statutory protections exist it is less likely that the area will be subject to development pressures and the value of tackling invasive species will have more longevity.

There is also a legal obligation on the state within these areas to return the overall conservation status to one that is favourable. Seeding pools may happen with state funding sources such as NPWS and Coillte.

In short dMRV is the bonding curve for tokenising environmental assets.

Effectively incentivising landscape level improvements means creating markets to tackle problems.

Programmable money helps to create more complex, conditional markets that can avoid the pitfalls of the measurement problem. Using machine learning enables objective analysis of habitats and avoiding corrupt data can be helped by diversifying the kinds of information the system takes into account.

Before we even get to creating a market for what we are tokenising we are first of all ensuring that the data is good. Remote sensing technology can certainly help in creating a clear and reliable picture of the ground truth. Taking measurements from the sky, soil and sound then cross referencing them with one another over time can create a reliable picture of improvements. The question is whether land stewards tackling invasive alien species for example will be happy to wait for payment as outcomes are verified and funds are locked.

We have the code and the equipment to accurately map a species such as rhododendron that is prolific in many areas of conservation around Ireland. Analysing aerial footage with machine learning we can pinpoint exactly where a species is prevalent. Cross referencing the footage with land ownership records we can allocate a funding pool to the relevant property that can be claimed by the owner when certain conditions are met.

Who funds the pool and the conditions that need to be fulfilled in order to claim the reward for clearing the property can be tuned to well informed conservation objectives and established practices. Further development of the AI behind the analysis will allow us to take a wider range of measurements into account, resulting in a more reliable outcome. For example a true AI could fetch all the images available from the area to create a composite of the space throughout time. We can do something similar with images from streetview or satellite images to show changes over time.

Analysing dMRV data with AI removes friction from the analysis process meaning it can scale and be a much more useful tool than it otherwise would be. For instance a property of 280 hectares (like the one photographed above) in the Wicklow Mountains which is covered in rhododendron will have certain characteristics associated with it. Besides being obvious from the air or ground, the soil will be acidic from all of the leaf litter, plant and wildlife diversity will also be limited due to the diminished quality of the habitat.

The initial assessment may include a very wide area of surveillance. Subsequently soil and water sampling takes place in target areas together with sound recordings which can be analysed to provide an appropriate baseline assessment of environmental conditions.

Rhododendron removal costs around €2500 per hectare with a professional company, factors such as the difficulty of the terrain and density influence that figure as well as the method of remediation prescribed. Variables such as whether the plants are in a gulley or riparian zone and likely to be spread further or not may also influence the value of tackling the problem.

Other factors such as whether the affected property falls within an area with existing statutory protections may also prioritise the focus and value of taking countermeasures. Where existing statutory protections exist it is less likely that the area will be subject to development pressures and the value of tackling invasive species will have more longevity.

There is also a legal obligation on the state within these areas to return the overall conservation status to one that is favourable. Seeding pools may happen with state funding sources such as NPWS and Coillte.

In short dMRV is the bonding curve for tokenising environmental assets.