Lesson 1, Topic 1
In Progress

Monitoring, Data Quality and Management, Impact Quantification for Reporting, Model Evaluation

4.5. Monitoring Plan – Data and Parameters to be Monitored​

List what parameters will be monitored in a table, EXAMPLE Table:

Data / Parameter​Soil organic carbon stocks ​
Unit​ tonnes C / acre at 0 -50 cm depth​
Description​This data is used as an input to Cool Farm Tool Mode​
Source of data​Soil core samples from a site-specific stratified sampling plan​
Value(s) applied​Expected, 75 tonnes C/ acre​
Measurement methods and procedures
including QA/QC​		 See Appendix 4 for proposed soil sampling protocol derived from FAO UN​
Monitoring frequency​Annually​
Roles & responsibilities​Earth Worm Jim: Soil Scientist &  Lead on Soil Sampling and Soil Data Collection ​
Controls​See Appendix 5, Internal data verification procedures​
Data management​See Section 5, Quality Management Procedures and Data Quality​
Data / Parameter​Total goods impacted​
Unit​ tonnes / year​
Description​Total quantity of goods associated with the project activities and project boundaries; used to determine the farmgate emissions factor for supply shed​
Source of data​Producer-reported yields​
Value(s) applied​Expected, 500 tonnes Wheat/ Year​
Measurement methods and procedures
including QA/QC ​		Producer enrollment and annual self-reporting, QA/QC for appropriateness and completeness of parameters including comparison to historical yields​
Monitoring frequency​Annually​
Roles & responsibilities​Enrolled Farmers, self-reporting (See Table of enrolled farms, Section 3.2, Location, Table 4)​Maggie May: Outreach Manager and Lead on Farm Data Collection and QA/ QC​
Controls​See Appendix 5, Internal data verification procedures​
Data management​See Section 5, Quality Management Procedures and Data Quality​

A note on IDD / Program DD section 4.5 – Monitoring

  • ​The monitoring plan is a central piece of the validation​
  • Fill in the template tables in this section with every monitoring parameter ​
    • All monitored parameters are traditionally taken from the selected methodology​
    • This step is needed even when using a modeling tool ​
    • Omitting this information slows down the validation significantly, and as such, auditors may begin checking for completeness at the preliminary review stage.  
Data / Parameter ​<<What data is collected? >>  ​
Unit ​ ​<<Unit used >>  ​
Description ​<<Describe the purpose of this data, how is it used>>  ​
Source of data ​<<Where is the data obtained from (e.g., surveys) >>   ​
Value(s) applied ​<<What is the (expected) monitored value? >>  ​
Measurement methods and procedures, including QA/QC ​<<How is the data collected, how is the quality of data managed? >> ​ ​
Monitoring frequency ​<<How often is the data collected? >>  ​
Roles & responsibilities ​<<Who is responsible for authorizing, approving, documenting changes to recorded data >>  ​
Controls ​<<What mechanism is in place to check data quality, identify issues, and take corrective action where necessary? >>   ​
Data management ​<< Where are stored data located and what are procedures transfers of data between different forms of systems or documentation? >>  ​

4.5.6.1.1. Explain the sampling approach and the rationale behind it.​

4.5.6.1.2. Provide a description of the sampling approach​

  • ​The monitoring and measurement may be carried out at 100% or be based on a sampling plan depending upon the nature of the sources of data. ​
  • In the collection of site-specific data, the IR should ensure that any analyses, sampling, calibrations, and validations for the determination of data for quantification are carried out by applying methods based on recognized International Standards or national standards.​
  • Refer to the selected methodology for sampling guidance and referenced protocols that are relevant to the intervention context​

 

Figure 3. FAO, Sampling designs for national forest assessments ​

KEY QUESTIONS – Monitoring Plan

  • Have you established a Monitoring Plan for both baseline (if dynamic baseline) and project scenarios for the activities and interventions?​
  • Is it clear in the monitoring plan who is doing what, when, and how?

KEY QUESTIONS – Sampling Plan

  • If relevant, have you established a Sampling Plan?​
  • Have you provided a clear description of the sampling approach? ​
  • Is the sampling approach informed by an existing methodology, protocol, or relevant research?​

5.0 – Data Quality and Management

5.1. Quality Management Procedures and Data Quality

Resources:​

  • See GHG-P Corporate Reporting Standard: Chapter 7, Data Collection and Appendix C , Guidance on developing a data management plan​
  • See SC VC Requirements, Section 5.2 Data Quality​

5.1.1 – 5.1.2 Quality Management Procedures and Data Quality​

  • 5.1.1. Describe how quality management procedures to manage data (i.e., data management plan) and information, including the assessment of uncertainty, relevant to the Intervention(s) and baseline(s) scenario(s) have been established and applied.​
  • 5.1.2. Describe how, as far as is practical, uncertainties related to the quantification of GHG emission reductions or removal enhancements have been reduced.​

At a minimum, a data management plan should contain:​

  • Description of the intervention(s) and relevant details. ​
  • Information on the entity(ies) or person(s) responsible for measurement and data collection procedures​
  • Data collection procedures​
  • Data sources, including activity data, emission factors, and other data, and the results of any data quality assessment performed​
  • Calculation methodologies including unit conversions and data aggregation​
  • Length of time the data should be archived​
  • Data transmission, storage, and backup procedures​
  • All QA/QC procedures for data collection, input, and handling activities, data documentation, and emissions calculations.​

5.1. – Quality Management Procedures and Data Quality

  • 5.1.3. Describe the quality of the data used that reduces bias and uncertainty. Data quality shall be characterized by both quantitative and qualitative aspects.

Characterization of data quality should address the following: 

  1. Age of data and the minimum length of time over which data should be collected ​
  2. Geographical area from which data for unit processes should be collected​
  3. Specific technology or technology mix​
  4. Precision: measure of the variability of each data value expressed (e.g., variance)​
  5. Completeness: percentage of total flow that is measured or estimated​
  6. Representativeness: qualitative assessment of the degree to which the data set reflects the true population of interest ​
  7. Consistency: qualitative assessment of whether or not the methodology is applied uniformly​
  8. Reproducibility: qualitative assessment of the extent to which information about the methodology and data values would allow an independent practitioner to reproduce the results reported in the study​
  9. Sources of the data​
  10. Uncertainty of the information
  • 5.1.4. – Describe how site-specific data for unit processes that are most important, and those which together contribute at least 80% to the overall emissions or removals, starting from the largest to the smallest contributions after cut-off (material threshold) were collected.​
Type of Data​Definition ​Example​
Primary Data​Data from specific activities within a company’s (or targeted company’s -i.e., impact unit buyer) value chain​Product-level cradle-to-gate GHG data from suppliers calculated using site-specific data​Site-specific energy use or emissions data from suppliers​
Secondary Data​Data that is not from specific activities within a company’s (or targeted company’s -i.e., impact unit buyer)  value chain​Industry average emission factors per material consumed from life cycle inventory databases​
Level 2 Criteria: Greater reliance on Primary Data

KEY QUESTIONS – Data Quality and Management

  • Have you established a data management plan? ​
  • Are you prepared to describe and justify your data quality? ​
  • Are you relying mainly on specific (primary) data? If not, are you able to justify why primary data collection is not feasible? ​
  • Have you characterized the data quality as follows?
    • Age of data​
    • Geographical area from which data for unit processes should be collected ​
    • Specific technology​
    • Precision ​
    • Completeness​
    • Representativeness ​
    • Consistency ​
    • Reproducibility ​
    • Sources of the data​
    • Uncertainty of information

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