Qualitative surveys are conducted at different project stages to validate stove adoption, usage patterns, and SDG impacts. Survey design is project-specific and must be included in the Mitigation Activity Design Document (MADD). Survey effectiveness is continuously assessed during monitoring/verification phases and adapted as needed.

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Survey Types

Baseline Survey

Pre-distribution assessment and eligibility verification

Follow-up Survey

Early adoption and performance evaluation

Monitoring Survey

Ongoing SDG impact and emission reduction validation

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Survey Stages and Objectives

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Baseline Survey (Pre-Project)

  • Timing: Before stove distribution/sales
  • Target Population:
    • Potential adopters
    • Non-adopting households for comparison
  • Objectives:
    • Assess community perception of new stoves
    • Gather socio-economic baseline data
    • Verify household eligibility
    • Validate inclusion criteria
    • Inform sampling design
  • Data Collection:
    • Household demographics
    • Current cooking practices
    • Economic indicators
    • Technology preferences

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Follow-up Survey (Post-Installation)

  • Timing: Shortly after stove deployment
  • Objectives:
    • Identify performance strengths
    • Detect early adoption issues
    • Enable rapid problem resolution
    • Assess user satisfaction
  • Focus Areas:
    • Installation quality
    • Initial user experience
    • Technical challenges
    • Usage patterns

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Monitoring Survey (Ongoing)

  • Purpose:
    • Validate emission reduction calculations
    • Verify SDG impacts
    • Cross-reference with KPT/SUM data
  • Key Parameters:
    • Stove usage patterns
    • Fuel consumption
    • User satisfaction
    • SDG indicators
    • Inclusion criteria validation

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Implementation Requirements

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Quick Start

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Data Models

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Survey Template

{
  "templateId": "baseline-v1",
  "sections": [{
    "id": "eligibility",
    "questions": [{
      "id": "current_cooking",
      "type": "multiple_choice",
      "required": true,
      "options": ["wood", "charcoal", "lpg", "electric"]
    }]
  }, {
    "id": "socio_economic",
    "questions": [{
      "id": "household_size",
      "type": "number",
      "validation": {
        "min": 1,
        "max": 20
      }
    }]
  }],
  "validations": {
    "requiredSections": ["eligibility"],
    "completionThreshold": 0.8
  }
}

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Survey Response

{
  "responseId": "survey-123",
  "surveyId": "baseline-v1",
  "householdId": "did:ixo:household/123",
  "timestamp": "2024-02-20T10:00:00Z",
  "responses": {
    "eligibility": {
      "current_cooking": ["wood", "charcoal"]
    },
    "socio_economic": {
      "household_size": 5
    }
  },
  "verification": {
    "status": "verified",
    "verifier": "did:ixo:validator/456",
    "timestamp": "2024-02-20T10:15:00Z"
  }
}

​
Survey Management

from emerging import SurveyManager, DataValidator, SampleCalculator

# Create survey campaign with CDM-compliant sampling
calculator = SampleCalculator(
    project_id="did:ixo:project/789",
    confidence=0.95,
    precision=0.05
)

sampling = calculator.compute(
    population=1000,
    expected_mean=0.8,
    expected_sd=0.2,
    dropout_rate=0.15
)

# Create and deploy survey
manager = SurveyManager("did:ixo:project/789")
campaign = manager.deploy_survey(
    template_id="baseline-v1",
    sampling=sampling,
    collectors=["did:ixo:agent/123"]
)

For detailed information on sampling methodology and CDM compliance, see the Sample Size Calculator guide.

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Data Handling

from emerging import DataProcessor, SDGValidator

# Process survey responses
processor = DataProcessor()
results = processor.analyze_survey_data(
    survey_id="baseline-v1",
    validation_rules={
        "completeness": 0.8,
        "consistency": True
    }
)

# Validate SDG impacts
sdg_validator = SDGValidator()
sdg_impacts = sdg_validator.assess_impacts(
    survey_data=results,
    sdg_targets=["SDG7", "SDG13"]
)

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Integration with Monitoring Tools

from emerging import MonitoringIntegration

# Create integrated analysis
integration = MonitoringIntegration(
    project_id="did:ixo:project/789"
)

# Cross-validate data sources
validation = integration.cross_validate(
    survey_data="baseline-v1",
    sum_data="sum-456",
    kpt_data="kpt-789"
)

# Generate comprehensive report
report = integration.generate_report(
    period_start="2024-01-01",
    period_end="2024-01-31"
)

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Error Handling

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400
error

Invalid survey configuration

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404
error

Survey or response not found

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409
error

Conflicting response data

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Best Practices

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Survey Design

  • Align with project objectives
  • Include all MADD requirements
  • Enable data cross-validation
  • Support multiple languages
  • Allow for periodic updates

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Data Collection

  • Train survey collectors
  • Implement quality controls
  • Ensure consistent methodology
  • Document collection process
  • Maintain chain of custody

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Data Analysis

  • Cross-reference with SUMs/KPTs
  • Validate response consistency
  • Track temporal changes
  • Generate actionable insights
  • Monitor inclusion criteria

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Documentation

  • Record methodology
  • Track survey updates
  • Document responsibility assignments
  • Maintain data flow records
  • Archive survey versions

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Next Steps

Survey Templates

Standard questionnaire formats

Data Collection

Mobile survey tools

Protocols

Impact assessment methods

Integration Guide

Cross-validation with SUMs

Household Monitoring

Track complete household transitions

Stove Use Monitoring

Validate with sensor data

Kitchen Performance Tests

Measure fuel consumption

Household Reporting

Generate comprehensive reports

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