dMeter is creating a decentralized monitoring, reporting and verification (dMRV) system. We are in the beginning of this dMRV process and countless building blocks are still missing. There will be problems and limitations that we encounter, but as these gradually get addressed it will become possible to use this new set of lego building blocks to build things that we can hardly imagine today.

A. MRV (monitoring, reporting, and verification) overview

MRV consists of three distinct features, all of which are vital to conveying legitimacy of regenerative projects (such as tree planting) to stakeholders. Monitoring is the process of measuring the total ecosystem and social services provided by the project. Measurements may be collected through satellite, IOT, georeferenced imagery from boots on the ground, as well as surveys that empower experts & citizen scientists. Reporting involves describing i) measured data, ii) the methodologies that were used to collect and analyze the data, and iii) potential biases as well as assumptions that went into the data collection/analysis. Verification refers to the review process involving a third party that is utilized to ensure the confidence in the validity of the uploaded data and methodologies.

Decentralized measurement, reporting and verification (dMRV) means that no single entity will own, manage or regulate how MRV is done. Instead a collective of people from many different organizations can build upon and improve this dMRV system. For current accreditation standards a combination of on the ground data collection, satellite sensing and IOT devices are used to back the credits. Utilizing web3 mentality, trust minimized data collection methods can be created. An example of this is to write open codes for actions that used to be performed by humans, like detecting a tree and the species of tree it is. Other data collection technologies can become integrated into this system as they become standard in the future.

Once the data is collected, open source reporting and verification methodologies can be utilized to analyze the data. Entities that collect the data and create the analysis methodologies are paid each time data is used and an analysis done. This inspires entrepreneurs to build specialized dMRV services which can benefit further regenerative projects. The entire dMRV allows for standardization, trustlessness, immutability, transparency, and open accessibility. This paves the way for decentralized data backed credits. These credits are tied to a specific regenerative action and entity who carried out the action continuously updated over time as more data is gathered. This data and the methodologies used to analyze it is stored permanently and immutably. The collectors of this data are the owners of it, allowing for sensitive data to be fully controlled by its owner. The dMRV would assist regenerative projects across blockchains and be interoperable with any new blockchain that came into existence. Every entity that is involved with this dMRV helps to iteratively improve upon it. With this, local communities can become more deeply involved with how the regenerative projects they carry out go through the dMRV process as well as tap into funding that was not previously available to carry out these projects. Along with that the diversity and veracity of Natural Capital assets being valued can exponentially increase.

Monitoring

In this dMRV everything is open to where there is only one final layer of trust- the data itself. Trust minimized data collection can be done through a combination of remote sensing, IoT, and human sensing (such as with smart-phones). The data collected is stored permanently and immutably through current technology such as Filecoin Green. By multiple data sources backing up that a regenerative action has occurred, more accurate calculations can be created. When one of those inputs is inaccurate, the other data sources can openly show a dispute. When a dispute arises, the stakeholders of the dMRV system can confirm or deny the accuracy of the data. Scoring this data for quality, resolution, and temporal frequency can enable any entity utilizing the dMRV to compare the data sources they pay for. Allowing them to quickly choose the best dataset for their needs. Through this, data becomes highly available as many organizations strive to provide the best data for people’s needs. This puts publishers on a level playing field and allows the companies with the best data to naturally succeed. Along with that publishers can not only benefit from the dMRV utilizing the data but other organizations that might want their data (such as parametric insurance). When individuals can decide who gets access to their data, they can ensure it is used ethically. They can also willingly give access to their data to those attempting to create good causes.

Reporting and Verification

Reporting and verification utilize algorithms. Algorithms are a set of instructions followed to solve a problem or accomplish a task (types of algorithms include standard, machine learning and AI). They are used to process data or information. For example georeferenced imagery can be analyzed using an algorithm that detects if a tree is present in that imagery. Another example is an algorithm can choose which data sources are the most appropriate for evaluating a regenerative action based upon how these data sources did previously. This is more efficient than a human could do, reducing the dMRV costs.

Previous MRV organizations have developed their own algorithms. Some of these algorithms are open source and will become part of the dMRV system if there is no conflict with the creators in doing so. Oftentimes it is only the MRV organization that developed the algorithm that uses it. Allowing more entities to utilize the MRV algorithm, “algorithms-as-a-service” (AaaS) have arisen as a business model. AaaS allows entities of very small to large financial backing to benefit from them. For example, a small business might not have the resources to hire a data scientist to build and train a machine learning model or maintain the infrastructure needed to store the data that the model would be trained on. However, with AaaS, they can simply subscribe to a service and use the algorithms that have been created by someone else. Now through decentralization, algorithm providers can offer their services in a trust minimized and automated way. Along with that they can help financially support the providers of the data that was used to train that algorithm, which in turn gives data providers an incentive to provide high-quality and unique data that otherwise wouldn’t be available for training. Algorithmic transparency allows regenerative actions to demonstrate the ecosystem services they provide in a trustless, decentralized and immutable manner. Which in turn will attract more ReFi investors to support these actions. The more funding that gets channeled towards regenerative actions, the more the dMRV system is used and with each iteration it improves.

Infrastructure

In order to host the data and distribute money to creators of algorithms, among other components of the dMRV system, decentralized infrastructure needs to be created. To permanently store the data collected, decentralized storage systems need to be utilized. For distributing that data to entities that would like to purchase it, data markets need to adequately display it. A new distributed system needs to be created for analyzing that data and paying for each time an analysis methodology is utilized. Some of these systems exist, however they can be improved upon to further support the dMRV system.

Domain Experts

For each regenerative action type there are domain specialists. For example an ocean scientist is unlikely to know how to appropriately value tree planting, vice versa. In order to develop analysis algorithms appropriate to each regenerative action type, this specialized knowledge needs to be brought into the dMRV system.

As these domain experts are the ones likely to start creating tokens from the dMRV system (onboarding non web3 regenerative actions into this space) it is encouraged that a percent of their revenue goes towards supporting the dMRV system and if able provide grants for supporting the initial start of this. These organizations will not have control of the dMRV treasury.

Revenue Sources This dMRV system will be created bottom up from individuals and software, these individuals are who we aim to get rewarded through this software system. The flow of this system is planned to be as follows. An entity pays for collection of data, entities affiliated with this dMRV system collect the data using techniques that are part of this system. The data goes to decentralized storage which is paid for, then the entity that pays for the data is able to access it. The data is interpreted via methodologies either that the entity has proprietary control over or that is open source and part of the dMRV system. If the method is open source, a donation is channeled to the creators of the method for its use. The collected data is also marketed in a data marketplace, when people use the data they pay for the use of it. This payment goes towards both the marketplace and the data creator.

Some of the business cases for dMRV are dMRV setup as a service Restoration project strategy for insurers Ecosystem risk area assessment, regeneration plan, and MRV for insurers, municipalities, and eco-tourism enterprises Environmental Asset Creation- Carbon Credits

Here are some empathy maps showcasing what each business case may be thinking- https://miro.com/app/board/uXjVP9wfNe8=/

Cross Organizational Support Behind all of this are people. These people have formed their own decentralized organizations specializing in different aspects of the dMRV system. There are organizations specializing in data collection, analysis and domain experts. The organizations participating in the dMRV will continue to expand as the system builds continued support.

Here’s a complete map of the organizations that are part of dMeter: https://mm.tt/map/2252789449?t=NhUfVodAtw

B. dMeter organizational structure

The Principles that dMeter Operates By Is:

Legitimacy We build with integrity, with high standards for quality of data and mindfulness with how it’s collected. We build legitimacy with the communities, stakeholders, and internal contributors involved by striving for alignment of our highest intentions with the day-to-day working processes that move our work forward.

Transparency of Process When facilitating data collection, making decisions, or building tools, we provide clear documentation of how it was done, enabling people to understand what went into a specific action, decision, or strategy. In this process we aim to be as holistic as possible.

Collaborative interoperability Data infrastructure needs to be highly interoperable so individuals can take their data with them and use it in different ecosystems, technologies, and use-cases. We create space for collaboration between organizations and focus on the standards that enable interoperability for ecodata.

Distributed & decentralized We distribute and decentralize power. There is no top-down hierarchy in dMeter where you answer to a boss. We self-organize around our strategy and allow leadership to organically arise from the recognition of valuable contributions by the community. We hold ourselves accountable to dMeter as a community and dMRV as a movement, not any individual. Along with that we empower systems that enable anyone anywhere to collect and provide data to the system in a trust minimized way.

Clear Responsibility & Accountability We take initiative to define our responsibilities given our role and desires in dMeter, and hold ourselves accountable to these responsibilities on a day-to-day basis. We support each other to take responsibility and accountability by doing it in our own workflow and communications.

Some additional info for a deeper dive into dMeter’s strategy and operations can be found here: https://miro.com/app/board/uXjVPLq3DtI=/?moveToWidget=3458764537112892386&cot=14

Working Groups We’ve adopted working circles. These circles focus on unique aspects of dMeter and work closely together to move forward in sync. Moving forward, we are open to having sub-circles and other circles created as there is a need for more unique work streams that fold into the purpose of dMeter.

Demand Sensing: Understand and develop relationships with users / customers of dMRV

What we do: Analyze the potential markets for holistic ecosystem data collection and engage the relevant customers to identify customer needs and opportunities to add value. Understand unique needs and forge relationships with key users and customers for the holistic data collection and analysis process. Define the use cases that will shape the Engineering circle’s work on designing the data collection and analysis process.

Engineering: Design and implement the ecosystem data collection and analysis process.

What We Do: Co-create the process for setting up and engaging communities in ecosystem data collection with Demand Sensing Define the process to decide on and implement the tech stack that’s needed for each unique data collection scenario Co-create and implement standards for storing ecological data with others working in the MRV space Work on the ground with pilot projects to install the technology and engage —— local stakeholders and independent operators

Governance and Operations: Steward the dMeter System; Develop dMeter’s internal working structures and external appearance.

What We Do: Define the governance structure and roles that are necessary to empower the work of the other working circles. Grow the dMeter community and appeal to potential customers through creating and running external items like the website, social media, and community events. Work with the dMeter community and core group to define high level strategic direction and partnerships. We Will Use the Funding We Raise to Help Complete Our Goals Our 1 Year Goals: By November 2023, we aim to have Have a well functioning governance system that’s aligned with our purpose, enabling greater capacity to execute collectively. Results: We’ve created clear roles, responsibilities, and decision making processes aligned with our purpose that are functioning smoothly Our governance token has been launched, distributed to initial team, and is clearly understood by all holders Design a valuable, holistic, and usable data collection process Results: We’ve deployed full stack of data collection tools on at least 2 pilot sites Texas, Paraguay IOT, Human Sensing, Satellite Data, etc We’ve created a data storage framework and analysis algorithms to cross-reference and make data usable for primary market applications (insurance underwriting, eco or carbon credit creation) We have an operationalized + documented process to design and coordinate flexible data collection setup in different geographical, cultural, and economic contexts Pilot project selection algorithm based on Desci working group work. Prove demand for our process in the market and initial financial sustainability/viability Results: An organization has sold an asset based on dMeter data. dMeter has set up data collection for a project and turned a profit on the infrastructure and labor costs. dMeter has a clear idea of its customers and has at least 5 orgs or individuals that have agreed to paying for data collection setup. Our 3 Month Goals By February 2023, we aim to: Clarify our initial business model and forge relationships with potential customers Results: 10 interviews / conversations with potential customers At least 1 partnership that pays for data collection setup and access to the accompanying data Business model canvases and outline of financial feasibility Research and prioritize business use cases for data and how they might evolve overtime. Create the first concept for full stack data collection design process + conceptualizing the data storage structure Results: Begin receiving input from full stack of data collection tools on a pilot site Research to find patterns in data structures used in established/emerging eco-credit methodologies, insurance underwriting and begin co-creating standards for data collection that fit the majority of current and future requirements V1 of standards Concept flow chart of how data collection is designed in different contexts + test process in a pilot. Build a claims resolution layer for dMRV as outlined in Filecoin Grant: https://www.notion.so/040d8f92b2414720a90d242c1f15efb2 Start with environmental data Map out an aligned governance and tokenomics structure for dMeter DAO Results: Implement an engaging member onboarding and contribution process Define essential roles for governance Understand major decisions that need to be made and conceptualize decision making processes for major decisions Define token value prop and map scenarios of token value flows given the business model Outline technical blueprint or tools for implementing governance Begin to explore legal formats for dMeter

Other Organizational Commitments! Avano, Ogallala Life & unnamed others have committed to donating 20% of what it raises from this Gitcoin Grant Round to dMeter