Sustainable Technologies for Smart Cities: Revolutionary Green Solutions Transforming Urban Living in 2025

import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Any
from dataclasses import dataclass, field
from enum import Enum
import json
import uuid
import time

class SustainabilityMetric(Enum):
    ENERGY_CONSUMPTION = "energy_consumption"
    CARBON_EMISSIONS = "carbon_emissions"
    WASTE_GENERATION = "waste_generation"
    WATER_USAGE = "water_usage"
    AIR_QUALITY = "air_quality"
    RENEWABLE_ENERGY_RATIO = "renewable_energy_ratio"

class CitySystem(Enum):
    ENERGY_GRID = "energy_grid"
    TRANSPORTATION = "transportation"
    WASTE_MANAGEMENT = "waste_management"
    WATER_SYSTEMS = "water_systems"
    BUILDINGS = "buildings"
    GREEN_SPACES = "green_spaces"

@dataclass
class SmartCityZone:
    """Smart city zone with sustainable technologies"""
    zone_id: str
    name: str
    area_km2: float
    population: int
    building_count: int
    renewable_energy_capacity: float  # MW
    green_space_percentage: float
    sustainability_score: float = 0.0
    active_technologies: List[str] = field(default_factory=list)
    
@dataclass
class SustainabilityData:
    """Real-time sustainability measurement"""
    measurement_id: str
    zone_id: str
    timestamp: datetime
    metric_type: SustainabilityMetric
    system_type: CitySystem
    value: float
    unit: str
    target_value: float
    improvement_percentage: float = 0.0
    
@dataclass
class GreenTechnology:
    """Sustainable technology implementation"""
    tech_id: str
    name: str
    category: str
    zones_deployed: List[str] = field(default_factory=list)
    energy_savings_percent: float = 0.0
    emission_reduction_percent: float = 0.0
    cost_savings_annual: float = 0.0
    implementation_status: str = "active"
    
@dataclass
class CircularEconomyProject:
    """Circular economy initiative"""
    project_id: str
    name: str
    zone_id: str
    resource_type: str  # water, waste, energy, materials
    input_materials: Dict[str, float]
    output_products: Dict[str, float]
    waste_reduction_percent: float
    resource_recovery_percent: float
    annual_impact: Dict[str, float] = field(default_factory=dict)
    
class SustainableSmartCity:
    """Comprehensive sustainable smart city management system"""
    
    def __init__(self, city_name: str):
        self.city_name = city_name
        self.zones: Dict[str, SmartCityZone] = {}
        self.sustainability_data: List[SustainabilityData] = []
        self.green_technologies: Dict[str, GreenTechnology] = {}
        self.circular_projects: List[CircularEconomyProject] = []
        
        # Sustainability targets and thresholds
        self.sustainability_targets = {
            SustainabilityMetric.ENERGY_CONSUMPTION: {'reduction_target': 30.0, 'unit': 'kWh/capita'},
            SustainabilityMetric.CARBON_EMISSIONS: {'reduction_target': 50.0, 'unit': 'tons_co2/year'},
            SustainabilityMetric.WASTE_GENERATION: {'reduction_target': 40.0, 'unit': 'kg/capita/day'},
            SustainabilityMetric.WATER_USAGE: {'reduction_target': 25.0, 'unit': 'liters/capita/day'},
            SustainabilityMetric.AIR_QUALITY: {'improvement_target': 45.0, 'unit': 'AQI'},
            SustainabilityMetric.RENEWABLE_ENERGY_RATIO: {'target_percentage': 80.0, 'unit': '%'}
        }
        
        # Green technology catalog
        self._initialize_green_technologies()
        
        # AI optimization parameters
        self.ai_optimization = {
            'energy_prediction_accuracy': 0.95,
            'traffic_optimization_efficiency': 0.30,
            'waste_route_optimization': 0.25,
            'water_leak_detection_rate': 0.85
        }
        
    def _initialize_green_technologies(self):
        """Initialize catalog of sustainable technologies"""
        green_techs = [
            {
                'tech_id': 'SMART_GRID_AI',
                'name': 'AI-Powered Smart Energy Grid',
                'category': 'Energy Management',
                'energy_savings_percent': 28.0,
                'emission_reduction_percent': 35.0,
                'cost_savings_annual': 2500000
            },
            {
                'tech_id': 'ZERO_CARBON_BUILDINGS',
                'name': 'Zero-Carbon Building Systems',
                'category': 'Building Technology',
                'energy_savings_percent': 45.0,
                'emission_reduction_percent': 60.0,
                'cost_savings_annual': 1800000
            },
            {
                'tech_id': 'IOT_WASTE_MGMT',
                'name': 'IoT-Enabled Waste Management',
                'category': 'Waste Systems',
                'energy_savings_percent': 15.0,
                'emission_reduction_percent': 25.0,
                'cost_savings_annual': 800000
            },
            {
                'tech_id': 'SMART_MOBILITY',
                'name': 'Intelligent Transportation Systems',
                'category': 'Transportation',
                'energy_savings_percent': 32.0,
                'emission_reduction_percent': 40.0,
                'cost_savings_annual': 3200000
            },
            {
                'tech_id': 'URBAN_FARMING',
                'name': 'Vertical Urban Agriculture',
                'category': 'Food Systems',
                'energy_savings_percent': 20.0,
                'emission_reduction_percent': 30.0,
                'cost_savings_annual': 1200000
            }
        ]
        
        for tech_data in green_techs:
            tech = GreenTechnology(**tech_data)
            self.green_technologies[tech.tech_id] = tech
            
    def register_city_zone(self, zone: SmartCityZone) -> bool:
        """Register new smart city zone"""
        self.zones[zone.zone_id] = zone
        
        # Calculate initial sustainability score
        zone.sustainability_score = self._calculate_zone_sustainability_score(zone)
        
        print(f"Registered smart city zone: {zone.name}")
        print(f"  Area: {zone.area_km2} km²")
        print(f"  Population: {zone.population:,}")
        print(f"  Renewable Energy Capacity: {zone.renewable_energy_capacity} MW")
        print(f"  Green Space: {zone.green_space_percentage}%")
        print(f"  Initial Sustainability Score: {zone.sustainability_score:.1f}/100")
        
        return True
        
    def deploy_green_technology(self, tech_id: str, zone_id: str) -> bool:
        """Deploy sustainable technology in specific zone"""
        if tech_id not in self.green_technologies:
            return False
        if zone_id not in self.zones:
            return False
            
        technology = self.green_technologies[tech_id]
        zone = self.zones[zone_id]
        
        # Add technology to zone
        if zone_id not in technology.zones_deployed:
            technology.zones_deployed.append(zone_id)
            zone.active_technologies.append(tech_id)
            
            # Update zone sustainability score
            zone.sustainability_score = self._calculate_zone_sustainability_score(zone)
            
            print(f"Deployed {technology.name} in {zone.name}")
            print(f"  Expected Energy Savings: {technology.energy_savings_percent}%")
            print(f"  Expected Emission Reduction: {technology.emission_reduction_percent}%")
            print(f"  Annual Cost Savings: ${technology.cost_savings_annual:,}")
            print(f"  Updated Sustainability Score: {zone.sustainability_score:.1f}/100")
            
        return True
        
    def collect_sustainability_data(self, zone_id: str, metric_type: SustainabilityMetric,
                                  system_type: CitySystem, value: float, unit: str) -> SustainabilityData:
        """Collect real-time sustainability measurements"""
        if zone_id not in self.zones:
            raise ValueError(f"Zone {zone_id} not found")
            
        # Get target value for comparison
        target_info = self.sustainability_targets.get(metric_type, {})
        target_value = target_info.get('reduction_target', 0)
        
        # Calculate improvement percentage (simplified)
        baseline_value = value * 1.3  # Assume 30% higher baseline
        improvement_percentage = ((baseline_value - value) / baseline_value) * 100
        
        data = SustainabilityData(
            measurement_id=f"SUSTAIN_{uuid.uuid4()}",
            zone_id=zone_id,
            timestamp=datetime.now(),
            metric_type=metric_type,
            system_type=system_type,
            value=value,
            unit=unit,
            target_value=target_value,
            improvement_percentage=improvement_percentage
        )
        
        self.sustainability_data.append(data)
        
        # Check if target is met
        target_met = improvement_percentage >= target_value
        status_indicator = "✅" if target_met else "⚠️"
        
        print(f"{status_indicator} {metric_type.value}: {value} {unit} ({improvement_percentage:+.1f}% vs baseline)")
        
        return data
        
    def create_circular_economy_project(self, project_name: str, zone_id: str, 
                                      resource_type: str, input_materials: Dict[str, float],
                                      output_products: Dict[str, float]) -> CircularEconomyProject:
        """Create circular economy project"""
        if zone_id not in self.zones:
            raise ValueError(f"Zone {zone_id} not found")
            
        # Calculate waste reduction and resource recovery percentages
        total_input = sum(input_materials.values())
        total_output = sum(output_products.values())
        
        waste_reduction = ((total_input - (total_input - total_output)) / total_input) * 100 if total_input > 0 else 0
        resource_recovery = (total_output / total_input) * 100 if total_input > 0 else 0
        
        project = CircularEconomyProject(
            project_id=f"CIRCULAR_{uuid.uuid4()}",
            name=project_name,
            zone_id=zone_id,
            resource_type=resource_type,
            input_materials=input_materials,
            output_products=output_products,
            waste_reduction_percent=waste_reduction,
            resource_recovery_percent=resource_recovery,
            annual_impact=self._calculate_circular_impact(input_materials, output_products)
        )
        
        self.circular_projects.append(project)
        
        print(f"Created circular economy project: {project_name}")
        print(f"  Resource Type: {resource_type}")
        print(f"  Waste Reduction: {waste_reduction:.1f}%")
        print(f"  Resource Recovery: {resource_recovery:.1f}%")
        print(f"  Annual Environmental Impact: {project.annual_impact}")
        
        return project
        
    def optimize_city_sustainability(self, zone_id: str) -> Dict[str, Any]:
        """AI-powered sustainability optimization for city zone"""
        if zone_id not in self.zones:
            return {'error': 'Zone not found'}
            
        zone = self.zones[zone_id]
        zone_data = [d for d in self.sustainability_data if d.zone_id == zone_id]
        
        # AI-powered optimization recommendations
        optimization_results = {
            'zone_id': zone_id,
            'current_sustainability_score': zone.sustainability_score,
            'optimization_opportunities': [],
            'predicted_improvements': {},
            'recommended_technologies': [],
            'investment_priorities': []
        }
        
        # Analyze energy consumption patterns
        energy_data = [d for d in zone_data if d.metric_type == SustainabilityMetric.ENERGY_CONSUMPTION]
        if energy_data:
            avg_consumption = np.mean([d.value for d in energy_data])
            target_reduction = self.sustainability_targets[SustainabilityMetric.ENERGY_CONSUMPTION]['reduction_target']
            
            if avg_consumption > target_reduction:
                optimization_results['optimization_opportunities'].append({
                    'area': 'Energy Consumption',
                    'current_value': avg_consumption,
                    'target_value': target_reduction,
                    'improvement_potential': f"{((avg_consumption - target_reduction) / avg_consumption) * 100:.1f}%"
                })
                
                # Recommend smart grid if not deployed
                if 'SMART_GRID_AI' not in zone.active_technologies:
                    optimization_results['recommended_technologies'].append({
                        'technology': 'AI-Powered Smart Energy Grid',
                        'expected_savings': '28% energy reduction',
                        'priority': 'High'
                    })
                    
        # Analyze waste management efficiency
        waste_data = [d for d in zone_data if d.metric_type == SustainabilityMetric.WASTE_GENERATION]
        if waste_data:
            avg_waste = np.mean([d.value for d in waste_data])
            if avg_waste > 1.2:  # kg per capita per day threshold
                optimization_results['optimization_opportunities'].append({
                    'area': 'Waste Management',
                    'current_value': avg_waste,
                    'improvement_potential': '35% waste reduction possible'
                })
                
                if 'IOT_WASTE_MGMT' not in zone.active_technologies:
                    optimization_results['recommended_technologies'].append({
                        'technology': 'IoT-Enabled Waste Management',
                        'expected_improvement': '35% recycling rate increase',
                        'priority': 'Medium'
                    })
                    
        # Predict overall improvements
        optimization_results['predicted_improvements'] = {
            'sustainability_score_increase': f"{min(15, len(optimization_results['recommended_technologies']) * 5):.1f} points",
            'energy_cost_savings': f"${sum(tech.cost_savings_annual for tech in self.green_technologies.values() if tech.tech_id not in zone.active_technologies) / len(self.green_technologies):,.0f}",
            'carbon_emission_reduction': f"{np.mean([tech.emission_reduction_percent for tech in self.green_technologies.values() if tech.tech_id not in zone.active_technologies]):.1f}%"
        }
        
        # Investment priorities
        optimization_results['investment_priorities'] = [
            'Smart energy grid implementation',
            'Zero-carbon building retrofits', 
            'Intelligent transportation systems',
            'IoT waste management deployment'
        ]
        
        return optimization_results
        
    def generate_sustainability_report(self, time_period_days: int = 30) -> Dict[str, Any]:
        """Generate comprehensive sustainability report"""
        end_date = datetime.now()
        start_date = end_date - timedelta(days=time_period_days)
        
        # Filter data for reporting period
        period_data = [
            d for d in self.sustainability_data 
            if start_date <= d.timestamp <= end_date
        ]
        
        if not period_data:
            return {'error': 'No sustainability data available for reporting period'}
            
        # Calculate city-wide metrics
        city_metrics = {}
        for metric_type in SustainabilityMetric:
            metric_data = [d for d in period_data if d.metric_type == metric_type]
            if metric_data:
                avg_value = np.mean([d.value for d in metric_data])
                avg_improvement = np.mean([d.improvement_percentage for d in metric_data])
                
                city_metrics[metric_type.value] = {
                    'average_value': avg_value,
                    'improvement_percentage': avg_improvement,
                    'target_met': avg_improvement >= self.sustainability_targets[metric_type]['reduction_target'],
                    'measurements_count': len(metric_data)
                }
                
        # Technology deployment analysis
        tech_deployment = {}
        for tech_id, tech in self.green_technologies.items():
            tech_deployment[tech.name] = {
                'zones_deployed': len(tech.zones_deployed),
                'total_zones': len(self.zones),
                'deployment_percentage': (len(tech.zones_deployed) / len(self.zones)) * 100 if self.zones else 0,
                'annual_savings': tech.cost_savings_annual,
                'emission_reduction': tech.emission_reduction_percent
            }
            
        # Circular economy impact
        circular_impact = {
            'active_projects': len(self.circular_projects),
            'total_waste_reduction': np.mean([p.waste_reduction_percent for p in self.circular_projects]) if self.circular_projects else 0,
            'average_resource_recovery': np.mean([p.resource_recovery_percent for p in self.circular_projects]) if self.circular_projects else 0
        }
        
        # Zone performance comparison
        zone_performance = {}
        for zone_id, zone in self.zones.items():
            zone_performance[zone.name] = {
                'sustainability_score': zone.sustainability_score,
                'active_technologies': len(zone.active_technologies),
                'green_space_percentage': zone.green_space_percentage,
                'renewable_capacity_mw': zone.renewable_energy_capacity
            }
            
        report = {
            'city_name': self.city_name,
            'report_period': {
                'start_date': start_date.isoformat(),
                'end_date': end_date.isoformat(),
                'days': time_period_days
            },
            'city_wide_metrics': city_metrics,
            'technology_deployment': tech_deployment,
            'circular_economy_impact': circular_impact,
            'zone_performance': zone_performance,
            'overall_assessment': self._generate_overall_assessment(city_metrics, tech_deployment),
            'improvement_recommendations': self._generate_improvement_recommendations(city_metrics)
        }
        
        return report
        
    # Helper methods for calculations and analysis
    def _calculate_zone_sustainability_score(self, zone: SmartCityZone) -> float:
        """Calculate sustainability score for zone"""
        base_score = 50.0  # Base score
        
        # Green space bonus
        green_bonus = min(zone.green_space_percentage * 0.5, 20.0)
        
        # Renewable energy bonus
        renewable_bonus = min(zone.renewable_energy_capacity / 100.0 * 10, 15.0)
        
        # Technology deployment bonus
        tech_bonus = len(zone.active_technologies) * 3.0
        
        total_score = min(base_score + green_bonus + renewable_bonus + tech_bonus, 100.0)
        return total_score
        
    def _calculate_circular_impact(self, inputs: Dict[str, float], outputs: Dict[str, float]) -> Dict[str, float]:
        """Calculate annual impact of circular economy project"""
        total_input = sum(inputs.values())
        total_output = sum(outputs.values())
        
        return {
            'waste_diverted_tons': (total_input - total_output) * 365 / 1000,
            'resources_recovered_tons': total_output * 365 / 1000,
            'carbon_savings_tons_co2': (total_input - total_output) * 0.5 * 365 / 1000  # Simplified calculation
        }
        
    def _generate_overall_assessment(self, metrics: Dict, tech_deployment: Dict) -> Dict[str, Any]:
        """Generate overall city sustainability assessment"""
        # Calculate average improvement across all metrics
        improvements = [data['improvement_percentage'] for data in metrics.values() if 'improvement_percentage' in data]
        avg_improvement = np.mean(improvements) if improvements else 0
        
        # Calculate technology adoption rate
        adoption_rates = [data['deployment_percentage'] for data in tech_deployment.values()]
        avg_adoption = np.mean(adoption_rates) if adoption_rates else 0
        
        assessment_level = "Excellent" if avg_improvement > 25 else "Good" if avg_improvement > 15 else "Developing"
        
        return {
            'overall_improvement': avg_improvement,
            'technology_adoption_rate': avg_adoption,
            'assessment_level': assessment_level,
            'strengths': self._identify_strengths(metrics),
            'areas_for_improvement': self._identify_improvement_areas(metrics)
        }
        
    def _generate_improvement_recommendations(self, metrics: Dict) -> List[str]:
        """Generate specific improvement recommendations"""
        recommendations = []
        
        for metric_name, data in metrics.items():
            if not data.get('target_met', True):
                if metric_name == 'energy_consumption':
                    recommendations.append("Accelerate smart grid deployment and building energy efficiency programs")
                elif metric_name == 'waste_generation':
                    recommendations.append("Implement comprehensive IoT waste management and circular economy initiatives")
                elif metric_name == 'carbon_emissions':
                    recommendations.append("Increase renewable energy capacity and electric transportation adoption")
                    
        return recommendations[:5]  # Top 5 recommendations
        
    def _identify_strengths(self, metrics: Dict) -> List[str]:
        """Identify sustainability strengths"""
        strengths = []
        for metric_name, data in metrics.items():
            if data.get('target_met', False) and data.get('improvement_percentage', 0) > 20:
                strengths.append(f"Strong performance in {metric_name.replace('_', ' ')}")
        return strengths
        
    def _identify_improvement_areas(self, metrics: Dict) -> List[str]:
        """Identify areas needing improvement"""
        areas = []
        for metric_name, data in metrics.items():
            if not data.get('target_met', True) or data.get('improvement_percentage', 0) < 10:
                areas.append(f"Focus needed on {metric_name.replace('_', ' ')}")
        return areas

# Example usage and demonstration
def run_sustainable_smart_city_demo():
    print("=== Sustainable Smart City Management Demo ===")
    
    # Initialize smart city system
    smart_city = SustainableSmartCity("EcoMetropolis 2025")
    
    # Register city zones
    zones = [
        SmartCityZone(
            zone_id="DOWNTOWN_01",
            name="Downtown Innovation District",
            area_km2=15.2,
            population=125000,
            building_count=2500,
            renewable_energy_capacity=45.0,
            green_space_percentage=25.0
        ),
        SmartCityZone(
            zone_id="RESIDENTIAL_01",
            name="Green Residential Quarter",
            area_km2=22.8,
            population=180000,
            building_count=4500,
            renewable_energy_capacity=32.0,
            green_space_percentage=40.0
        ),
        SmartCityZone(
            zone_id="INDUSTRIAL_01",
            name="Sustainable Industrial Park",
            area_km2=8.5,
            population=15000,
            building_count=150,
            renewable_energy_capacity=85.0,
            green_space_percentage=15.0
        )
    ]
    
    for zone in zones:
        smart_city.register_city_zone(zone)
        
    print(f"\nRegistered {len(zones)} smart city zones")
    
    # Deploy green technologies
    print("\n=== Green Technology Deployment ===")
    
    technology_deployments = [
        ("SMART_GRID_AI", "DOWNTOWN_01"),
        ("ZERO_CARBON_BUILDINGS", "DOWNTOWN_01"),
        ("SMART_MOBILITY", "DOWNTOWN_01"),
        ("IOT_WASTE_MGMT", "RESIDENTIAL_01"),
        ("URBAN_FARMING", "RESIDENTIAL_01"),
        ("SMART_GRID_AI", "INDUSTRIAL_01"),
        ("IOT_WASTE_MGMT", "INDUSTRIAL_01")
    ]
    
    for tech_id, zone_id in technology_deployments:
        smart_city.deploy_green_technology(tech_id, zone_id)
        print()  # Add spacing
        
    # Collect sustainability data
    print("\n=== Sustainability Data Collection ===")
    
    sustainability_measurements = [
        ("DOWNTOWN_01", SustainabilityMetric.ENERGY_CONSUMPTION, CitySystem.ENERGY_GRID, 145.2, "kWh/capita/month"),
        ("DOWNTOWN_01", SustainabilityMetric.CARBON_EMISSIONS, CitySystem.ENERGY_GRID, 8.5, "tons_co2/capita/year"),
        ("RESIDENTIAL_01", SustainabilityMetric.WASTE_GENERATION, CitySystem.WASTE_MANAGEMENT, 0.85, "kg/capita/day"),
        ("RESIDENTIAL_01", SustainabilityMetric.WATER_USAGE, CitySystem.WATER_SYSTEMS, 180, "liters/capita/day"),
        ("INDUSTRIAL_01", SustainabilityMetric.RENEWABLE_ENERGY_RATIO, CitySystem.ENERGY_GRID, 72.0, "%"),
        ("INDUSTRIAL_01", SustainabilityMetric.AIR_QUALITY, CitySystem.GREEN_SPACES, 35, "AQI")
    ]
    
    for zone_id, metric, system, value, unit in sustainability_measurements:
        smart_city.collect_sustainability_data(zone_id, metric, system, value, unit)
        
    # Create circular economy projects
    print("\n=== Circular Economy Projects ===")
    
    circular_projects = [
        {
            'name': 'Urban Waste-to-Energy Plant',
            'zone_id': 'INDUSTRIAL_01',
            'resource_type': 'waste',
            'input_materials': {'organic_waste': 100, 'recyclables': 50, 'residual_waste': 30},
            'output_products': {'biogas': 40, 'compost': 35, 'recycled_materials': 45}
        },
        {
            'name': 'Rainwater Harvesting System',
            'zone_id': 'RESIDENTIAL_01', 
            'resource_type': 'water',
            'input_materials': {'rainwater': 1000},
            'output_products': {'clean_water': 850, 'irrigation_water': 100}
        }
    ]
    
    for project_data in circular_projects:
        smart_city.create_circular_economy_project(
            project_data['name'],
            project_data['zone_id'],
            project_data['resource_type'],
            project_data['input_materials'],
            project_data['output_products']
        )
        print()  # Add spacing
        
    # Optimize city sustainability
    print("\n=== AI-Powered Sustainability Optimization ===")
    
    for zone_id, zone in smart_city.zones.items():
        optimization = smart_city.optimize_city_sustainability(zone_id)
        
        print(f"\n{zone.name} Optimization Analysis:")
        print(f"Current Sustainability Score: {optimization['current_sustainability_score']:.1f}/100")
        
        if optimization['optimization_opportunities']:
            print(f"Optimization Opportunities ({len(optimization['optimization_opportunities'])}):") 
            for opp in optimization['optimization_opportunities']:
                print(f"  • {opp['area']}: {opp['improvement_potential']}")
                
        if optimization['recommended_technologies']:
            print(f"Recommended Technologies:")
            for tech in optimization['recommended_technologies']:
                print(f"  • {tech['technology']} - {tech['expected_savings']} (Priority: {tech['priority']})")
                
        predicted = optimization['predicted_improvements']
        print(f"Predicted Improvements:")
        print(f"  • Sustainability Score: +{predicted['sustainability_score_increase']}")
        print(f"  • Annual Cost Savings: {predicted['energy_cost_savings']}")
        print(f"  • Emission Reduction: {predicted['carbon_emission_reduction']}")
        
    # Generate comprehensive sustainability report
    print("\n=== Comprehensive Sustainability Report ===")
    report = smart_city.generate_sustainability_report(30)
    
    print(f"Sustainability Report for {report['city_name']}")
    print(f"Report Period: {report['report_period']['days']} days\n")
    
    print("City-Wide Performance:")
    for metric_name, data in report['city_wide_metrics'].items():
        status = "✅ TARGET MET" if data['target_met'] else "⚠️  NEEDS IMPROVEMENT"
        print(f"  {metric_name.replace('_', ' ').title()}: {data['improvement_percentage']:+.1f}% improvement [{status}]")
        
    print("\nTechnology Deployment Status:")
    for tech_name, deployment in report['technology_deployment'].items():
        print(f"  {tech_name}: {deployment['deployment_percentage']:.0f}% coverage ({deployment['zones_deployed']}/{deployment['total_zones']} zones)")
        
    circular = report['circular_economy_impact']
    print(f"\nCircular Economy Impact:")
    print(f"  Active Projects: {circular['active_projects']}")
    print(f"  Average Waste Reduction: {circular['total_waste_reduction']:.1f}%")
    print(f"  Average Resource Recovery: {circular['average_resource_recovery']:.1f}%")
    
    assessment = report['overall_assessment']
    print(f"\nOverall Assessment: {assessment['assessment_level']}")
    print(f"  Average Improvement: {assessment['overall_improvement']:.1f}%")
    print(f"  Technology Adoption: {assessment['technology_adoption_rate']:.1f}%")
    
    if assessment['strengths']:
        print(f"\nKey Strengths:")
        for strength in assessment['strengths']:
            print(f"  • {strength}")
            
    print(f"\nImprovement Recommendations:")
    for i, rec in enumerate(report['improvement_recommendations'], 1):
        print(f"  {i}. {rec}")
        
    return smart_city

# Run demonstration
if __name__ == "__main__":
    demo_city = run_sustainable_smart_city_demo()