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Requirements of the finance community

A. Identify the goal for climate finance:

  1. Green finance: 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 mitigation result supported by an accounting system that fosters confidence in impacts, 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 lowers uncertainties and increases credibility of results (highest requirements)

B. MRV design considerations:

  1. What needs to be estimated?  Carbon sinks, avoided carbon loss, GHGs emissions, and mitigation co-benefits, e.g. water and biodiversity conservation, to ensure benefits to farmers
  2. How well? Accuracy and uncertainty
  3. How to reduce risks of impermanence or non-performance  
  4. Scalability needs
  5. Frequency of estimations   - reporting requirements, timing needed to detect changes (e.g. usually > 5 years) and make payments.
  6. Verification needs (e.g. first, second and third-party)
  7. How to minimize costs (e.g. acceptable % of the total project budget spent on MRV, sufficient benefit to farmers) 

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

Practice lists and criteria → Indicators and proxies → Modeling → Measurement

from (lowest accuracy and highest uncertainty) to (highest accuracy and lowest uncertainty)

D. Improving accuracy and uncertainty 

  1. Developing "green lists" of eligible practices (e.g. scientific literature review and experts consultation)
  2. Using models/remote sensing for accessing soil C sequestration magnitudes and trends: choosing a model, technical requirements, caveats, assumptions and uncertainties  
  3. Moving to hybrid approaches: direct measurements with modeling/remote sensing 
    1. Optimal measurement strategy based on project/region characteristics and resources available
      1. activity data collection (e.g. use of smartphone, interviews)
      2. focus on few high-quality measurements (e.g. what to measure and how - sampling design; soil C and soil bulk density; frequency)
      3. prioritization (e.g. sampling design; soil C or bulk density; soil C determination method; use of pedo-transfer functions,...)
    2. Dealing with data gaps (e.g. scientific literature, experts consultation, global databases) 
    3. Choosing a model, model calibration, technical requirements and acceptable uncertainties
    4. Remote sensing: application and requirements 
    5. Co-benefits assessment (e.g. generating water/biodiversity indicators from/in tandem? with soil C measurements)
  4. Other MVR aspects
    1. Aggregation across larger scales to reduce project-level variation effects 
    2. Setting up baselines (e.g. Baseline v. base year)
    3. Verification and frequency (credibility highest with third-party)

E. Reducing 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)


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 

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

  1. Managing risk of impermanence or 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: first, second or third-party verification)(credibility highest with third party)
  2. Identify the threshold of C price necessary to make project viable and reduce project risk

Cost/benefit of credits

  • Increase benefits to farmers
  • co-benefits in tandem with soil C increases the value of the C credit;
  • discounts decrease it. 

Key points of convergence amongst Day 1 groups: 

  1. Hybrid approaches (e.g., activity-based and modeling) that are optimally cost-effective and accurate.
    1. Hybrid approaches should also help reduce uncertainty.
    2. Low- and middle-income countries are far behind on data, models, and measurement capacity and present more challenges related to smallholdings, farmer-based monitoring etc.
  2. For the finance community, the level of uncertainty is not always as important as identifying what the uncertainties are.  Methods should be good enough to provide evidence that greenwashing is not occurring. Precision can be improved over time.

  3. Using land use activity data as a proxy for SOC should help reduce cost (the major barrier). Improve how to put together robust data on land use history to define what is eligible for SOC stock measurements. 
  4. Observing co-benefits in tandem with soil C increases the value of the C credit. 
  5. As relationships improve with the farmers in certain projects, the synergy in data collection improves. 
    1. Must engage farmers across scales. 
    2. Companies can help engage farmers by encouraging them to collect data and, in turn, begin to promote behavior changes. However, data collection by farmers can impose an additional cost and be inaccurate. 

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