Accurate sample size calculation is crucial for reliable emission reduction quantification. This guide implements CDM-compliant sampling methodologies with 95/5 confidence/precision requirements, ensuring the 5% imprecision is factored into emission reduction calculations as per Art. 5 para. 1 let. c No 4 CO2 Ordinance.
Quick Start curl -X POST https://api.emerging.eco/v1/sampling \ -H "Authorization: Bearer YOUR_API_KEY" \ -d '{ "projectId": "did:ixo:project/789", "population": 5000, "confidence": 0.95, "precision": 0.05, "expectedMean": 0.8, "expectedSD": 0.2, "dropoutRate": 0.15, "stratification": { "enabled": true, "strata": [ {"name": "urban", "weight": 0.6}, {"name": "rural", "weight": 0.4} ] } }'
Core Requirements
Confidence/Precision :
95% confidence level mandatory
5% precision requirement
Factor imprecision into ER calculations
Sample Parameters :
Justify expected mean/SD with credible data
Document ER coverage percentage
Account for realistic dropout rates
Adaptation Requirements :
Recalculate after each monitoring period
Use actual variation from monitoring data
Adjust size up/down based on real data
Independent Strata :
Calculate size for each stratum separately
Maintain confidence/precision per stratum
Document stratum-specific parameters
Stratification Criteria :
Based on baseline survey data
Account for socio-economic factors
Consider geographical variations
Initial Phase :
Document selection methodology in MADD
Ensure verifiable randomness
Include non-adopting households
Verification Phase :
Submit project list to FOEN
Include active and planned projects
Allow external random selection
Data Models Sample Design { "designId" : "sample-123" , "projectId" : "did:ixo:project/789" , "parameters" : { "confidence" : 0.95 , "precision" : 0.05 , "population" : 5000 , "calculatedSize" : 357 , "adjustedSize" : 420 , "dropoutRate" : 0.15 , "erCoverage" : 0.85 }, "stratification" : { "method" : "proportional" , "strata" : [{ "name" : "urban" , "population" : 3000 , "sampleSize" : 252 , "mean" : 0.8 , "sd" : 0.2 }, { "name" : "rural" , "population" : 2000 , "sampleSize" : 168 , "mean" : 0.75 , "sd" : 0.25 }] }, "verification" : { "status" : "approved" , "verifier" : "did:ixo:validator/456" , "timestamp" : "2024-02-20T10:00:00Z" } } Sample Size Calculator from emerging import CDMCalculator, StratificationEngine # Initialize CDM calculator calculator = CDMCalculator() # Calculate base sample size base_size = calculator.compute_base_size( mean = 0.8 , sd = 0.2 , confidence = 0.95 , precision = 0.05 ) # Adjust for finite population adjusted_size = calculator.adjust_for_population( base_size = base_size, population = 5000 ) # Account for dropout final_size = calculator.adjust_for_dropout( sample_size = adjusted_size, dropout_rate = 0.15 ) # Stratify if needed stratification = StratificationEngine() strata_sizes = stratification.allocate( total_size = final_size, strata = [ { "name" : "urban" , "weight" : 0.6 }, { "name" : "rural" , "weight" : 0.4 } ] ) Random Selection from emerging import RandomSelector selector = RandomSelector( seed = 20240220 ) # Generate random sample sample = selector.select( population_list = "household_registry.csv" , sample_size = 420 , stratification = { "column" : "region" , "sizes" : strata_sizes } ) # Validate selection validation = selector.validate_randomness( selected_sample = sample, confidence = 0.95 ) Monitoring & Adaptation from emerging import SampleMonitor monitor = SampleMonitor( "did:ixo:project/789" ) # Analyze current data stats = monitor.analyze_current_period( start_date = "2024-01-01" , end_date = "2024-01-31" ) # Recalculate sample size new_size = monitor.recalculate_size( current_mean = stats.mean, current_sd = stats.sd ) # Check if adjustment needed if monitor.requires_adjustment(new_size): adjustment = monitor.generate_adjustment_plan() External Verification from emerging import VerificationSubmission submission = VerificationSubmission( project_id = "did:ixo:project/789" ) # Prepare project list project_list = submission.prepare_project_list( active_projects = True , planned_projects = True ) # Submit for sampling response = submission.submit_to_foen( project_list = project_list, monitoring_period = "2024-Q1" ) Error Handling Invalid sampling parameters
Insufficient population data
Conflicting stratum definitions
Best Practices Sample Design
Document all assumptions
Justify stratification choices
Consider seasonal variations
Plan for dropouts
Maintain stratum independence
Data Quality
Validate input parameters
Monitor dropout rates
Track response rates
Document non-responses
Verify random selection
Adaptation
Regular recalculation
Document changes
Update stratification
Maintain precision levels
Track ER coverage
Verification
Prepare FOEN submissions
Document selection process
Maintain project lists
Track verification status
Archive supporting data
Next Steps Sample Size Adaptation Monitoring Period Updates from emerging import AdaptiveSampling adaptive = AdaptiveSampling( project_id = "did:ixo:project/789" ) # Analyze current monitoring data current_stats = adaptive.analyze_period( start_date = "2024-01-01" , end_date = "2024-01-31" ) # Recalculate using actual data new_sample_size = adaptive.recalculate( actual_mean = current_stats.mean, actual_sd = current_stats.sd, current_dropout_rate = current_stats.dropout_rate ) # Generate adaptation plan if needed if adaptive.requires_adjustment(new_sample_size): plan = adaptive.create_adjustment_plan( current_size = current_stats.sample_size, required_size = new_sample_size, implementation_date = "2024-03-01" ) Dropout Management # Configure with realistic dropout rate calculator = CDMCalculator( base_confidence = 0.95 , base_precision = 0.05 , dropout_buffer = 0.15 # 15% dropout rate ) # Track actual dropouts dropout_analysis = calculator.analyze_dropouts( historical_data = "2023" , current_period = "2024-Q1" ) # Adjust sample size if needed adjusted_size = calculator.compensate_for_dropouts( current_size = 400 , actual_dropout_rate = dropout_analysis.rate ) FOEN Submission Process from emerging import FOENSubmission submission = FOENSubmission( project_id = "did:ixo:project/789" ) # Prepare project list for random selection project_list = submission.prepare_list( active_projects = True , planned_projects = True , monitoring_period = "2024-Q2" ) # Submit for random sampling response = submission.submit_to_foen( project_list = project_list, er_coverage_percentage = 0.85 , # Document ER coverage supporting_data = { "mean_justification" : "historical_data.pdf" , "sd_calculation" : "variance_analysis.pdf" } ) 
