 The project focuses on the problem of evaluation of impact in climate actions, government sustainability projects and agriculture-oriented initiatives. It is often assumed that public good projects should be funded retroactively, when the communities or activists through action show the impact to society and can convince the peers about the contribution of the results. However, scientific validation of the results is often omitted and the lack of scientific evidence in decision making and especially in climate actions becomes a big problem. Relying solely on voting schemes or the number of supporters on Twitter cannot accurately measure the true impact of these initiatives. It is crucial to incorporate scientific validation and evidence-based approaches to ensure effective decision making and meaningful outcomes.

As an example, a new work published in the journal ‘Science’ shows that most of the carbon offset projects exaggerate their benefits and only a mere 6% of the credits granted with this objective are linked to real carbon reductions. More than 90% of rainforest carbon offsets by biggest certifier are worthless.

Voting and number of followers can be an important indicator, but may also be misleading. One of the issues within the system is that addressing climate projects tend to be located in areas and specific regions where conservation is more likely to succeed, regardless of any intervention. Furthermore, there are concerns that the methods used to evaluate impact can be manipulated to maximize revenue from credit sales. 

The initiative aims to address this problem by providing a solid foundation for the scientific evaluation of the impact and positive change resulting from the validation of regenerative practices. The methodology employed can also be applied to other public goods.

https://vin-q.com/principles-of-distributed-experiments-in-vin-q-ecosystem/

How this works: Through a dedicated database that integrates real data from fields and open data available from different sources, it is possible to assess the positive change of specific regenerative practices that are used by farmers and machine learning methods can validate the practices and certify the actual positive impact on the climate.