A Decentralized MRV, Tokenization, and Climate Finance Ecosystem for Agroforestry Using Blockchain and AI
DOCTORAL DISSERTATION PROPOSAL
I. TITLE
GreenLedger™: A Decentralized, AI-Enhanced Blockchain Framework for Monitoring, Reporting, Verification (MRV), and Tokenized Carbon Credit Markets in Agroforestry Systems
II. ABSTRACT
This doctoral research proposes the design, development, and validation of GreenLedger™, an integrated digital ecosystem that combines blockchain technology, artificial intelligence (AI), and remote sensing for Monitoring, Reporting, and Verification (MRV) of carbon sequestration in agroforestry systems. The study aims to address systemic inefficiencies in existing carbon markets, including lack of transparency, high verification costs, and exclusion of smallholder farmers.
The proposed framework introduces a decentralized architecture for capturing geospatial and farm-level data, applying AI-driven biomass estimation models, and issuing tokenized carbon credits through smart contracts. The research adopts a multi-phase methodology involving system development, simulation modeling, and empirical validation using pilot agroforestry sites in the Philippines.
Expected contributions include a novel MRV framework, validated carbon estimation algorithms, a blockchain-based tokenization protocol, and policy recommendations for inclusive climate finance systems.
III. INTRODUCTION
3.1 Background of the Study
Global climate mitigation efforts increasingly rely on carbon markets as mechanisms for incentivizing emission reductions and carbon sequestration. Agroforestry systems offer significant carbon storage potential while supporting biodiversity and rural livelihoods. However, current MRV systems are centralized, costly, and inaccessible to smallholder farmers.
Emerging technologies such as blockchain, AI, and remote sensing present opportunities to redesign carbon credit systems into transparent, scalable, and inclusive ecosystems. This study proposes GreenLedger™ as a next-generation platform integrating these technologies.
3.2 Problem Statement
This research addresses the following critical gaps:
- Inefficiency and high cost of traditional MRV systems
- Lack of trust and transparency in carbon credit verification
- Limited inclusion of smallholder agroforestry farmers in carbon markets
- Absence of integrated platforms combining AI, blockchain, and geospatial data
3.3 Research Objectives
General Objective
To develop and validate a decentralized MRV and carbon credit ecosystem using blockchain and AI for agroforestry systems.
Specific Objectives
- Develop AI-based carbon sequestration estimation models
- Design a blockchain-based MRV architecture
- Create a tokenized carbon credit issuance mechanism
- Evaluate system performance, scalability, and usability
- Analyze socio-economic and environmental impacts
3.4 Research Questions
- How can AI improve accuracy of carbon estimation in agroforestry?
- How can blockchain enhance trust and transparency in MRV systems?
- What is the effectiveness of tokenized carbon credits?
- How does the system impact farmer participation and income?
3.5 Hypotheses
H1: AI-based models significantly improve carbon estimation accuracy.
H2: Blockchain-based MRV systems increase trust and data integrity.
H3: Tokenization improves liquidity and accessibility of carbon credits.
3.6 Significance of the Study
- Academic contribution to climate tech and agri-informatics
- Practical solution for carbon markets
- Policy implications for climate finance
IV. REVIEW OF RELATED LITERATURE
4.1 Blockchain for Sustainability
4.2 AI in Environmental Monitoring
4.3 Remote Sensing and GIS in Agroforestry
4.4 Carbon Market Mechanisms
4.5 Tokenization and Digital Assets
4.6 Research Gap
There is no integrated system combining AI, blockchain, and MRV tailored for agroforestry carbon markets.
V. THEORETICAL AND CONCEPTUAL FRAMEWORK
5.1 Theoretical Foundations
- Systems Theory
- Sustainable Development Theory
- Technology Acceptance Model (TAM)
- Diffusion of Innovations Theory
5.2 Conceptual Framework
Input → Process → Output → Outcome
Input: Agroforestry data, satellite data
Process: AI modeling, blockchain validation
Output: Verified carbon credits
Outcome: Increased farmer income and sustainability
VI. METHODOLOGY
6.1 Research Design
Mixed-methods with experimental and developmental components
6.2 System Development Framework
Agile + Design Science Research (DSR)
6.3 System Architecture
- AI Layer (Machine Learning models)
- Blockchain Layer (Smart contracts)
- Data Layer (IoT, GIS, databases)
- Application Layer (Web/mobile interface)
6.4 AI Model Development
- Biomass estimation models
- Regression and machine learning techniques
- Validation using field data
6.5 Blockchain Implementation
- Smart contract development
- Token issuance protocol
6.6 Data Collection
- Field data from agroforestry farms
- Satellite imagery
- Surveys and interviews
6.7 Data Analysis
- Statistical analysis (R/Python)
- Model validation metrics (RMSE, R²)
6.8 Evaluation Metrics
- Accuracy
- Usability (SUS)
- Scalability
- Security
VII. EXPECTED CONTRIBUTIONS
- Novel MRV framework
- AI-based carbon estimation model
- Blockchain tokenization protocol
- Policy and implementation roadmap
VIII. TIMELINE (3–4 YEARS)
| Phase | Duration |
|---|---|
| Literature Review | 6 months |
| Model Development | 12 months |
| System Development | 12 months |
| Validation | 6 months |
| Writing | 6 months |
IX. BUDGET
| Item | Cost |
|---|---|
| Computing Resources | PHP 100,000 |
| Field Work | PHP 150,000 |
| Software & Tools | PHP 80,000 |
| Total | PHP 330,000 |
X. ETHICAL CONSIDERATIONS
- Data privacy
- Informed consent
- Environmental responsibility
XI. REFERENCES
(To be completed with high-impact journal sources)
XII. APPENDICES
- Survey instruments
- System diagrams
- Model equations