# ===================================================================
# MODULE 9, LESSON 6: SUSTAINABLE FINANCE AND ESG
# ===================================================================

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime
import warnings
warnings.filterwarnings('ignore')

print("="*70)
print("SUSTAINABLE FINANCE AND ESG – THE FUTURE OF BANKING")
print("="*70)

# ----------------------------------------------------------------
# PART A: ESG PRODUCT PORTFOLIO
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART A: ESG Product Portfolio")
print("-"*60)

esg_products = pd.DataFrame({
    'Product': [
        'ESG ETF',
        'Green Bond Fund',
        'Clean Energy Fund',
        'Sustainable Savings Account',
        'Green Mortgage',
        'ESG Robo-Advisor',
        'Carbon Offset Card'
    ],
    'Category': [
        'Investment', 'Investment', 'Investment', 'Deposit',
        'Lending', 'Investment', 'Payments'
    ],
    'Assets ($M)': [500, 300, 200, 100, 150, 250, 50],
    'Growth (%)': [25, 30, 35, 20, 15, 40, 45],
    'ESG Focus': [
        'Integrated', 'Environmental', 'Environmental', 'Environmental',
        'Environmental', 'Integrated', 'Environmental'
    ]
})

print("ESG Product Portfolio:")
print(esg_products.to_string(index=False))

# Visualise
fig, axes = plt.subplots(1, 2, figsize=(14, 5))

# Assets by Product
ax = axes[0]
esg_sorted = esg_products.sort_values('Assets ($M)', ascending=True)
ax.barh(esg_sorted['Product'], esg_sorted['Assets ($M)'], color='green', alpha=0.7)
ax.set_xlabel('Assets ($M)')
ax.set_title('ESG Assets by Product')
ax.grid(True, alpha=0.3)

# Growth vs Assets
ax = axes[1]
scatter = ax.scatter(esg_products['Assets ($M)'], esg_products['Growth (%)'], 
                     s=esg_products['Assets ($M)'] * 0.5, alpha=0.7)
for i, row in esg_products.iterrows():
    ax.annotate(row['Product'], (row['Assets ($M)'] + 5, row['Growth (%)'] + 0.5))
ax.set_xlabel('Assets ($M)')
ax.set_ylabel('Growth (%)')
ax.set_title('ESG Product Growth vs Assets')
ax.grid(True, alpha=0.3)

plt.tight_layout()
plt.savefig('esg_products.png', dpi=300, bbox_inches='tight')
plt.show()
print("ESG product visualisation saved as 'esg_products.png'")

# ----------------------------------------------------------------
# PART B: ESG SCORING FRAMEWORK
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART B: ESG Scoring Framework")
print("-"*60)

# Define company ESG scores
companies = pd.DataFrame({
    'Company': [
        'Company A', 'Company B', 'Company C', 'Company D', 'Company E'
    ],
    'Sector': [
        'Technology', 'Energy', 'Finance', 'Healthcare', 'Consumer'
    ],
    'E_Score': [85, 45, 65, 70, 55],
    'S_Score': [80, 50, 70, 75, 60],
    'G_Score': [90, 60, 75, 80, 65]
})

# Calculate ESG score (weighted average)
weights = {'E': 0.4, 'S': 0.3, 'G': 0.3}
companies['ESG_Score'] = (
    companies['E_Score'] * weights['E'] +
    companies['S_Score'] * weights['S'] +
    companies['G_Score'] * weights['G']
).round(2)

print("Company ESG Scores:")
print(companies.to_string(index=False))

# Visualise
fig, ax = plt.subplots(figsize=(10, 6))
companies.set_index('Company')[['E_Score', 'S_Score', 'G_Score', 'ESG_Score']].plot(kind='bar', ax=ax)
ax.set_ylabel('Score')
ax.set_title('ESG Scores by Company')
ax.legend(loc='best')
ax.grid(True, alpha=0.3)
plt.tight_layout()
plt.savefig('esg_scores.png', dpi=300, bbox_inches='tight')
plt.show()
print("ESG scores visualisation saved as 'esg_scores.png'")

# ----------------------------------------------------------------
# PART C: CLIMATE RISK ASSESSMENT
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART C: Climate Risk Assessment")
print("-"*60)

climate_risk = pd.DataFrame({
    'Risk Type': ['Physical', 'Transition', 'Liability'],
    'Description': [
        'Risks from climate change impacts',
        'Risks from transitioning to low-carbon economy',
        'Legal and reputational risks'
    ],
    'Examples': [
        'Flooding, extreme weather',
        'Policy changes, technology shifts',
        'Climate litigation'
    ],
    'Banking Impact': [
        'Loan defaults, asset impairment',
        'Stranded assets, policy risk',
        'Reputational damage, fines'
    ],
    'Mitigation': [
        'Physical risk assessment, insurance',
        'Scenario analysis, green lending',
        'Compliance, disclosure'
    ]
})

print("Climate Risk Assessment:")
print(climate_risk.to_string(index=False))

# ----------------------------------------------------------------
# PART D: ESG REGULATORY COMPLIANCE
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART D: ESG Regulatory Compliance")
print("-"*60)

esg_regulations = pd.DataFrame({
    'Regulation': ['SFDR', 'EU Taxonomy', 'CSRD', 'TCFD', 'ISSB'],
    'Region': ['EU', 'EU', 'EU', 'Global', 'Global'],
    'Status': ['Active', 'Active', 'Active', 'Active', 'Active'],
    'Compliance Status': ['✅', '🟡', '🟡', '🟡', '🟡']
})

print("ESG Regulatory Compliance:")
print(esg_regulations.to_string(index=False))

# ----------------------------------------------------------------
# PART E: ESG METRICS DASHBOARD
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART E: ESG Metrics Dashboard")
print("-"*60)

esg_metrics = pd.DataFrame({
    'Metric': [
        'ESG Assets Under Management',
        'Green Loan Portfolio',
        'ESG Fund Performance',
        'Carbon Reduction Impact',
        'ESG Client Adoption',
        'SFDR Article 8/9 Funds',
        'TCFD Compliance Score',
        'ESG NPS'
    ],
    'Current Value': [
        '$1.2B',
        '$250M',
        '8.2%',
        '15,000 tonnes',
        '35%',
        '12',
        '65%',
        '58'
    ],
    'Target Value': [
        '$5.0B',
        '$1.0B',
        '> 10%',
        '50,000 tonnes',
        '> 60%',
        '25+',
        '> 90%',
        '> 70'
    ],
    'Status': ['🟡', '🟡', '🟡', '🟡', '🔴', '🟡', '🟡', '🟡']
})

print("ESG Metrics Dashboard:")
print(esg_metrics.to_string(index=False))

# ----------------------------------------------------------------
# PART F: SUSTAINABLE FINANCE ROADMAP
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART F: Sustainable Finance Roadmap")
print("-"*60)

roadmap = {
    "Phase 1 (0-12 months) – Foundation": {
        "Focus": "Build sustainable finance foundation.",
        "Activities": [
            "Launch ESG ETF and green bond funds.",
            "Develop ESG scoring framework.",
            "Implement green loan product.",
            "Comply with SFDR Article 8 requirements."
        ],
        "Success Metrics": ["ESG AUM > $500M", "Green loan portfolio > $100M"]
    },
    "Phase 2 (12-24 months) – Scale": {
        "Focus": "Scale ESG products.",
        "Activities": [
            "Launch ESG robo-advisory.",
            "Add sustainable savings account.",
            "Implement green mortgage product.",
            "Achieve SFDR Article 9 for select funds."
        ],
        "Success Metrics": ["ESG AUM > $1.5B", "ESG client adoption > 40%"]
    },
    "Phase 3 (24-36 months) – Advanced": {
        "Focus": "Advanced ESG capabilities.",
        "Activities": [
            "Launch impact investing products.",
            "Implement carbon offset products.",
            "Build ESG analytics platform.",
            "Achieve industry-leading ESG credentials."
        ],
        "Success Metrics": ["ESG AUM > $3B", "Carbon reduction > 30,000 tonnes"]
    },
    "Phase 4 (36+ months) – Leadership": {
        "Focus": "Industry-leading ESG.",
        "Activities": [
            "Launch full ESG banking suite.",
            "Build global ESG capabilities.",
            "Achieve sustainability leadership.",
            "Continuous innovation."
        ],
        "Success Metrics": ["Industry-leading ESG products", "Continuous improvement"]
    }
}

for phase, details in roadmap.items():
    print(f"\n{phase}:")
    print(f"  Focus: {details['Focus']}")
    print("  Activities:")
    for activity in details['Activities']:
        print(f"    • {activity}")
    print("  Success Metrics:")
    for metric in details['Success Metrics']:
        print(f"    • {metric}")

# ----------------------------------------------------------------
# PART G: SUMMARY AND RECOMMENDATIONS
# ----------------------------------------------------------------

print("\n" + "="*70)
print("PART G: Summary and Recommendations")
print("="*70)

print("""
Sustainable Finance and ESG – Key Takeaways:

1. Sustainable finance integrates ESG criteria into financial services.
2. ESG pillars: Environmental, Social, Governance.
3. Key frameworks: GRI, SASB, TCFD, ISSB, EU Taxonomy, SFDR.
4. ESG products: green loans, ESG funds, green bonds, impact investing.
5. Climate risk: physical, transition, liability.
6. Regulatory landscape: SFDR, EU Taxonomy, CSRD, TCFD, ISSB.
7. Key metrics: ESG AUM, green loan portfolio, ESG performance, carbon reduction.

Recommendations:
  - Launch ESG ETF and green bond funds.
  - Implement green loan products.
  - Develop ESG scoring and analytics.
  - Comply with SFDR and EU Taxonomy.
  - Build ESG robo-advisory capabilities.
  - Lead in sustainable finance and ESG.
""")

print("="*70)
print("END OF LESSON 6 – MODULE 9")
print("="*70)