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DRAFT

Requirements of the finance community

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  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 How to minimize costs (e.g. usually > 5 years) and make payments.Verification needs acceptable % of the total project budget spent on MRV, sufficient benefit to farmers) 
  6. Other considerations: Scalability needs, verification needs (e.g. first, second and third-party)How to minimize costs Frequency of estimations  - reporting requirements, timing needed to detect changes (e.g. acceptable % of the total project budget spent on MRV, sufficient benefit to farmers) usually > 5 years) and make payments.

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

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D. Improving accuracy and uncertainty 

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uncertainty 

  1. Move to hybrid approaches: direct measurements with modeling /and remote sensing 
    1. Optimal measurement strategy Measurement, based on project/region characteristics and resources available
      1. activity data collection (e.g. use of smartphone, interviews)
      2. focus focus on few high-quality measurements 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,...)
      Dealing with data gaps
      1. data gaps: filling out gaps (e.g. scientific literature, experts consultation, global databases) 
    2. Choosing Modeling: choosing a model, model calibration, technical requirements and acceptable uncertainties
    3. Remote sensing: application and requirements 
    4. Co-benefits assessment (e.g. generating water/biodiversity indicators from/in tandem? with soil C measurements)
  2. Other MVR aspectsAggregation across larger scales to reduce project-level variation effects 
  3. Setting up baselines (e.g. Baseline v. base year)
  4. Verification and frequency (credibility highest with third-party)
  5. effects (landscapes)

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)

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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 

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.