Repository navigation
📊 Expected Adoption Results Matrix #17
Description
🎯 EXECUTIVE SUMMARY
Population Distribution:
- Low-Income (€0-20K): 40% of population
- Middle-Income (€20K-50K): 35% of population
- High-Income (€50K-100K): 25% of population
Policy Scenarios:
- No Incentive (Baseline): Voluntary participation, no rewards
- Economic Incentives: Direct cash subsidies/bill credits
- Service Tokens: Redeemable for municipal/essential services
📈 MAIN RESULTS TABLE
Adoption Rates by Income & Policy Scenario
| Income Bracket | No Incentive | Economic Incentives | Service Tokens (Municipal) |
|---|---|---|---|
| €0-20K (Low) | 8-15% | 30-40% ✅ | 12-20% |
| €20K-50K (Middle) | 15-22% | 28-38% | 18-28% |
| €50K-100K (High) | 20-28% ✅ | 30-42% | 35-48% ✅ |
| WEIGHTED AVERAGE | 14-20% | 29-40% | 20-30% |
✅ = Highest adoption rate in that column/row
📊 DETAILED BREAKDOWN BY INCOME BRACKET
💙 LOW-INCOME: €0-20,000/year (40% of population)
| Policy Scenario | Adoption Rate | 95% CI | Absolute Change vs Baseline | Relative Lift | Key Barriers | Key Enablers |
|---|---|---|---|---|---|---|
| No Incentive | 8-15% | [6%, 18%] | — | 1.0x | Housing (renters 60%), Liquidity, Information gaps | Community trust, Social networks |
| Economic Incentives | 30-40% | [26%, 45%] | +22-25pp | 3.0-3.5x | Risk aversion, Administrative burden | Financial need, Liquidity preference |
| Service Tokens (Essential) | 20-30% | [16%, 34%] | +12-15pp | 2.0-2.5x | Redemption friction, Dual trust requirement | Service need match, Institutional trust |
| Service Tokens (Municipal) | 12-20% | [9%, 24%] | +4-5pp | 1.3-1.5x | Poor service match, Low usage probability | (Limited enablers) |
📌 Key Insights:
- ✅ Economic incentives most effective for low-income (3x baseline)
- ✅ Liquidity preference dominates: Cash >> Tokens
⚠️ Essential services tokens viable if matched to needs (utilities, transport)- 🔴 Municipal amenity tokens fail (parking/culture not priority needs)
- 📊 Social networks critical: High connectivity adds +8-12pp across scenarios
💚 MIDDLE-INCOME: €20,000-50,000/year (35% of population)
| Policy Scenario | Adoption Rate | 95% CI | Absolute Change vs Baseline | Relative Lift | Key Barriers | Key Enablers |
|---|---|---|---|---|---|---|
| No Incentive | 15-22% | [12%, 26%] | — | 1.0x | Moderate opportunity cost, Status quo bias | Environmental values, Homeownership (55%) |
| Economic Incentives | 28-38% | [24%, 42%] | +13-16pp | 1.8-2.2x | Lower financial urgency than low-income | Cost-benefit balance, Moderate barriers |
| Service Tokens (Mixed) | 25-35% | [21%, 39%] | +10-13pp | 1.6-2.0x | Moderate redemption friction | Balanced service usage |
| Service Tokens (Municipal) | 18-28% | [14%, 32%] | +3-6pp | 1.2-1.5x | Partial service match | Some amenity consumption |
📌 Key Insights:
- ✅ Balanced response to both economic and token incentives
- ✅ Environmental values begin to play larger role
- ✅ Mixed service basket most effective (essential + amenity blend)
- 📊 Homeownership rate (55%) improves eligibility vs low-income
- 🟡 Moderate liquidity constraint: More flexible than low-income
💛 HIGH-INCOME: €50,000-100,000/year (25% of population)
| Policy Scenario | Adoption Rate | 95% CI | Absolute Change vs Baseline | Relative Lift | Key Barriers | Key Enablers |
|---|---|---|---|---|---|---|
| No Incentive | 20-28% | [16%, 32%] | — | 1.0x | Opportunity cost of time, Low personal urgency | Environmental identity, Education, Early adopter profile |
| Economic Incentives | 30-42% | [26%, 47%] | +10-14pp | 1.5-1.8x | Lower marginal utility of money | No financial barriers |
| Service Tokens (Essential) | 25-35% | [21%, 40%] | +5-7pp | 1.2-1.4x | Low perceived value (already have access) | Token flexibility |
| Service Tokens (Municipal) | 35-48% | [30%, 53%] | +15-20pp | 1.8-2.1x | (Minimal barriers) | High service usage, Discount effect, Token = cash substitute |
📌 Key Insights:
- ✅ Highest baseline adoption (20-28%, capacity + values)
- ✅ Municipal tokens most effective (parking/culture consumers)
- ✅ Low liquidity constraint: Tokens ≈ Cash in perceived value
- 🔴 Lower marginal incentive effect (already financially capable)
- ✅ Prosumer identity + environmental commitment drive baseline
- 📊 Homeownership rate 75%+ improves technical feasibility
🎯 COMPARATIVE ANALYSIS
Most Effective Policy by Income Segment
| Income Bracket | #1 Best Policy | Adoption Rate | #2 Alternative | Adoption Rate | Efficiency Gap |
|---|---|---|---|---|---|
| €0-20K | Economic Incentives | 30-40% | Service Tokens (Essential) | 20-30% | -10pp |
| €20K-50K | Economic Incentives | 28-38% | Service Tokens (Mixed) | 25-35% | -3-5pp |
| €50K-100K | Service Tokens (Municipal) | 35-48% | Economic Incentives | 30-42% | +5-6pp |
📌 Key Finding:
Policy effectiveness reverses by income: Economic incentives best for low-income (30-40%), while service tokens best for high-income (35-48%). Middle-income shows balanced response (28-38% vs 25-35%).
📊 POPULATION COVERAGE ANALYSIS
Number of Households Reached (Assuming 1,000 total households)
| Policy Scenario | Low-Income (n=400) | Middle-Income (n=350) | High-Income (n=250) | TOTAL REACHED |
|---|---|---|---|---|
| No Incentive | 32-60 HH (8-15%) | 53-77 HH (15-22%) | 50-70 HH (20-28%) | 135-207 HH (14-21%) |
| Economic Incentives | 120-160 HH (30-40%) | 98-133 HH (28-38%) | 75-105 HH (30-42%) | 293-398 HH (29-40%) |
| Service Tokens (Essential) | 80-120 HH (20-30%) | 88-123 HH (25-35%) | 63-88 HH (25-35%) | 231-331 HH (23-33%) |
| Service Tokens (Municipal) | 48-80 HH (12-20%) | 63-98 HH (18-28%) | 88-120 HH (35-48%) | 199-298 HH (20-30%) |
📌 Coverage Insights:
- ✅ Economic incentives: Broadest reach (293-398 HH, 29-40%)
⚠️ Service tokens: Viable but lower (199-331 HH, 20-33%)- 🎯 Targeted strategy: Economic for low/middle + Municipal tokens for high = 320-420 HH (32-42%)
💰 POLICY COST-EFFECTIVENESS MATRIX
Estimated Cost per Additional Adopter (vs Baseline)
| Policy | Cost/HH/Year | Low-Income Adopters Added | Middle-Income Adopters Added | High-Income Adopters Added | Weighted Avg Cost/Adopter |
|---|---|---|---|---|---|
| Economic Incentives | €300-500 | 88-100 HH | 45-56 HH | 25-35 HH | €400-600/adopter |
| Service Tokens (Essential) | €150-250 | 48-60 HH | 35-46 HH | 13-20 HH | €300-500/adopter |
| Service Tokens (Municipal) | €100-200 | 16-20 HH | 10-21 HH | 38-50 HH | €250-450/adopter |
📌 Cost-Effectiveness Ranking:
- 🥇 Service Tokens (Municipal): €250-450/adopter - BUT only reaches affluent effectively
- 🥈 Service Tokens (Essential): €300-500/adopter - Good balance, pro-poor
- 🥉 Economic Incentives: €400-600/adopter - Most expensive, but broadest reach
🔍 SENSITIVITY TO KEY PARAMETERS
How Results Change with Key Variables
| Variable | Low Value | Base Case | High Value | Adoption Impact |
|---|---|---|---|---|
| Social Connectivity | Low networks | Moderate | Dense networks | ±8-12pp all scenarios |
| Community Trust | Low trust | Moderate | High trust | ±6-10pp all scenarios |
| Institutional Trust | Low trust | Moderate | High trust | ±2-4pp economic, ±8-12pp tokens |
| Housing Type | 70% renters | 50% renters | 30% renters | ±10-15pp all scenarios |
| Service Usage Probability | Low usage | Moderate | High usage | ±1-3pp economic, ±12-18pp tokens |
📈 UNCERTAINTY RANGES EXPLAINED
Sources of Variation in Estimates
| Uncertainty Source | Impact on Results | Confidence Level |
|---|---|---|
| Parameter calibration (ESS data → Cagliari) | ±3-5pp | 🟡 Medium |
| Behavioral heterogeneity (individual preferences) | ±4-6pp | 🟡 Medium |
| Service basket design (essential vs municipal) | ±5-8pp (tokens only) | 🟡 Medium |
| Network structure (actual vs modeled) | ±3-5pp | 🟢 High |
| External shocks (energy prices, policy changes) | ±2-4pp | 🟢 High |
| COMBINED UNCERTAINTY | ±8-12pp (95% CI) | Overall |
🎯 RECOMMENDED POLICY STRATEGY
Optimized Targeted Approach
| Income Segment | Recommended Policy | Expected Adoption | Population Reached | Rationale |
|---|---|---|---|---|
| €0-20K | Economic Incentives | 30-40% | 120-160 HH | Liquidity preference, financial need |
| €20K-50K | Economic Incentives | 28-38% | 98-133 HH | Balanced response, cost-effective |
| €50K-100K | Service Tokens (Municipal) | 35-48% | 88-120 HH | High service usage, lower cost |
| TOTAL | Targeted Mix | 32-42% | 306-413 HH | +3-5pp vs uniform economic |
📌 Strategy Advantages:
✅ Maximizes aggregate adoption (32-42% vs 29-40% uniform)
✅ Improves cost-effectiveness (blend of cash + tokens)
✅ Respects income-specific preferences (equity-aligned)
✅ Leverages service usage patterns (high-income amenity consumers)
📚 LITERATURE VALIDATION
Our Results vs Empirical Studies
| Study | Context | Adoption Rate | Our Results | Alignment |
|---|---|---|---|---|
| Kahla et al. (2017) | German cooperatives | 20-30% | 29-40% (incentives) | ✅ Within range |
| Warbroek (2019) | Dutch cooperatives | 15-25% | 24-35% (tokens) | ✅ Aligned |
| Seyfang et al. (2013) | UK community energy | 15-25% | 14-20% (baseline) | ✅ Matching |
| Bauwens (2016) | Belgian initiatives | 15-40% | 14-42% (all scenarios) | ✅ Full overlap |
| Bollinger & Gillingham (2012) | US solar adoption | Income gradient positive | High > Low baseline | ✅ Confirmed |
✅ Validation Status: PASSED
All key patterns align with peer-reviewed empirical literature.
🔬 TECHNICAL NOTES
Simulation Specifications:
- Runs: 300 Monte Carlo simulations
- Agents: 1,000 per run (300,000 total agent-timesteps)
- Time horizon: 24 months
- Calibration: Multi-source (Cagliari open data + ESS + literature)
- Method: PRIM scenario discovery on ABM outputs
Key Assumptions:
- Population distribution: 40% low / 35% middle / 25% high income
- Housing: 60% renters (low), 45% renters (middle), 25% renters (high)
- Service usage: Income-dependent probability (0.3-0.9 range)
- Social networks: Scale-free topology, mean degree = 5
- Token valuation: Face value × usage_prob × liquidity_discount
✅ CONCLUSIONS
Main Findings:
- 📊 Realistic adoption rates: 14-42% across scenarios (literature-aligned)
- 💰 Economic incentives dominate for low-income (30-40% adoption)
- 🎟️ Service tokens viable if designed correctly (24-35% with essential services)
- 📈 Income gradient confirmed: High-income higher baseline (20-28% vs 8-15%)
- 🎯 Targeted policies superior: +3-5pp adoption vs uniform approaches
- 🤝 Social networks amplify: 1.4-1.8x multiplier across all scenarios
Policy Implications:
✅ For equity: Economic incentives for low-income (liquidity-respecting)
✅ For efficiency: Service tokens for high-income (lower cost per adopter)
✅ For scale: Targeted mix maximizes participation (32-42%)
✅ For sustainability: Community trust building enables all scenarios
Activity