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Requirements of the finance community

A. Identify the goal for climate finance:

1. Green finance: Practices practices aligned with climate change mitigation and co-benefits (e.g. water and biodiversity conservation), where the certainty of directional change is likely, but the impact level is not measured. For example, companies or loans using "green lists" of eligible practices; "good enough" methods (lowest requirements)
2. Results-based payments: payments based on defined climate result, mitigation supported by a MRV that fosters confidence in results delivered, but uncertainties may apply although medium/high quantification uncertainty applies (intermediary requirements)
3. Carbon-credit markets: quantification of climate mitigation results following rules and procedures determined by protocols and standards under third-party verification (e.g. CDM, Verra and Gold Standard standards), which processes is third-party verified (highest requirements)lowers uncertainties and increases credibility of results (highest requirements)

B. MRV design considerations:

1. Scope - other carbon sinks (e.g. trees), GHGs emissions (e.g. emissions from livestock) and opportunities for avoided carbon loss
2. Mitigation co-benefits (e.g. water and biodiversity conservation)
3. Accuracy needs and tolerance for uncertainty
4. Risk of impermanence (e.g. adoption of practices or events that may reduce soil C stocks) and non-performance (e.g. unexpected effect) 
5. Scalability
6. Reporting requirements - given the timing to detect changes (e.g. usually > 5 years) and make payments.
7. Verification needs (e.g. 1^st, 2^nd and 3^rd Party)
8. Costs (e.g. acceptable % of the total project budget)
9. Ensure benefits to farmers 

C. Plan for improving over time in accuracy and uncertainty toward carbon market-grade credits.

(lowest accuracy and highest uncertainty) Practice lists and criteria → Indicators and proxies → Modeling → Measurement (highest accuracy and lowest uncertainty)

D. Improving accuracy and uncertainty 

1. Developing practice-based indicators (e.g. scientific literature review and experts consultation)
2. Using models - chosing a model, technical requirements, caveats, assumptions and uncertainties  
3. Hybrid approach: direct measurements with modeling/remote sensing. 
   1. Optimal measurement strategy based on project/region characteristics and resources available (e.g. how to focus on few high-quality measurements)
      1. prioritization if needed (e.g. sampling design; soil C or bulk density; soil C determination using routine analysis or dry-combustion; use of pedotransfer functions)
   2. Chosing a model, model calibration, technical requirements and acceptable uncertainties
   3. Co-benefits assessment (e.g. generating water/biodiversity indicators from/in conjunction with soil C measurements)
4. Aggregation aspects across larger scales to reduce project-level variation effects 
5. Setting up baselines (e.g. Baseline v. base year)

E. How to deal with risk of impermanence or non-performance: 

1. Discounted carbon credits to account for impermanence and accuracy risks. 
2. Buffers in carbon credits allocated
3. Accounting at the landscape scale to spread risk over large areas. 
4. Verification type and frequency (credibility highest with third-party)

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B. MRV design considerations:

1. Soil carbon sequestration practice MRV goals
2. Scope
   1. Other GHG sources and sinks  
   2. Data needs and dealing with data gaps
3. Identify needs for accuracy and tolerance for uncertainty
4. Address risk of impermanence or non performance
5. Opportunities for avoided carbon loss
6. Identify joint co-benefits 
7. Ensuring benefits to farmers:  credit C/B and MRV as feedback to farmers on immediate benefits
8. Scalability
9. Baseline design and upfront costs
10. Units, stratification and aggregation requirements.
11. Timing of MRV to detect change, make payments and meet reporting requirements.
12. Verification
13. Costs and budget

C. Plan for improving over time in accuracy and uncertainty toward carbon market-grade credits.

1. Practice lists and criteria ; (lowest accuracy and highest uncertainty)
2. Indicators and proxies 
3. Modeling
4. Measurement (highest accuracy and lowest uncertainty)

D. Improving accuracy and uncertainty 

1. Improving accuracy and reducing uncertainty 
   1. Direct measurements and increased sampling frequency (e.g., soil sampling). 
   2. Model improvement based on measurements   
   3. More measurements at deeper depths (> 30 cm). 
   4. Aggregate across larger scales to reduce project-level variation effects 
   5. Improved baselines
      1. Baseline v. base year.  
2. Reducing cost of data collection:
   1. Hybrid approach: complement direct measurements with modeling/remote sensing. Detect the optimal measurement strategy based on region and resources available. 
   2. Focus on few high quality measurements (smaller sample sizes). 
   3. Detect C stock changes by measuring bulk density, in addition to SOC.  
   4. Share costs of carbon accounting with costs for other co-benefits 

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