Partech closes its first impact fund at €300M to back Europe’s next generation of industrial and climate tech scaleups

Introduction

In the wake of increasing climate tech investments, understanding how to evaluate and analyze impact-driven startups is becoming crucial for investors and entrepreneurs alike. This tutorial will guide you through creating a structured impact investment analysis framework using Python. We'll build a system that evaluates potential climate tech startups based on key financial metrics, impact indicators, and scalability factors. This approach mirrors the methodology used by venture capital firms like Partech when assessing their impact fund investments.

Prerequisites

• Basic understanding of Python programming
• Python 3.x installed on your system
• Knowledge of financial metrics and impact investing concepts
• Installed libraries: pandas, numpy, matplotlib

Step-by-Step Instructions

Step 1: Setting Up Your Development Environment

Install Required Libraries

First, we need to install the necessary Python libraries for data analysis and visualization. Open your terminal or command prompt and run:

pip install pandas numpy matplotlib

This step ensures we have the tools needed to process financial data and create visual representations of our investment analysis.

Step 2: Create the Data Structure

Define Startup Data Model

We'll create a data structure to represent each startup in our analysis. This model will include both financial metrics and impact indicators:

import pandas as pd
import numpy as np

# Define startup data structure
class Startup:
    def __init__(self, name, revenue, growth_rate, impact_score, scalability_score, funding_stage):
        self.name = name
        self.revenue = revenue  # in millions
        self.growth_rate = growth_rate  # percentage
        self.impact_score = impact_score  # 1-10 scale
        self.scalability_score = scalability_score  # 1-10 scale
        self.funding_stage = funding_stage  # seed, growth, expansion
        
# Sample data for demonstration
startups_data = [
    Startup('GreenTech Solutions', 15, 25, 8, 7, 'growth'),
    Startup('EcoManufacturing Inc', 22, 30, 9, 8, 'growth'),
    Startup('SustainableAgri Systems', 18, 20, 7, 6, 'growth'),
    Startup('CleanBuild Construction', 35, 15, 6, 9, 'expansion'),
    Startup('DigitalHealth Innovations', 12, 40, 8, 7, 'growth')
]

# Convert to DataFrame for easier analysis
df = pd.DataFrame([{
    'name': s.name,
    'revenue': s.revenue,
    'growth_rate': s.growth_rate,
    'impact_score': s.impact_score,
    'scalability_score': s.scalability_score,
    'funding_stage': s.funding_stage
} for s in startups_data])

print(df)

This structure allows us to organize and analyze multiple startups with consistent metrics, making it easier to compare potential investments.

Step 3: Implement Impact Scoring System

Calculate Composite Impact Score

For impact investing, we need to weight different impact factors appropriately. The impact score should reflect both environmental and social impact:

def calculate_impact_score(row):
    # Weighted scoring system
    environmental_impact = row['impact_score'] * 0.6
    social_impact = row['scalability_score'] * 0.4
    return (environmental_impact + social_impact) / 10

# Apply scoring to our dataset
df['composite_impact_score'] = df.apply(calculate_impact_score, axis=1)
print(df[['name', 'composite_impact_score']])

This approach mirrors how Partech might evaluate companies based on their environmental impact and scalability potential, which are crucial factors in climate tech investments.

Step 4: Financial Viability Analysis

Calculate Investment Potential Score

Now we'll create a scoring system that combines financial metrics with impact indicators to determine overall investment potential:

def calculate_investment_potential(row):
    # Revenue factor (higher revenue = lower risk)
    revenue_factor = min(row['revenue'] / 50, 1)  # Normalize to 0-1 scale
    
    # Growth rate factor (higher growth = higher potential)
    growth_factor = min(row['growth_rate'] / 50, 1)  # Normalize to 0-1 scale
    
    # Impact factor (higher impact = better for impact funds)
    impact_factor = row['composite_impact_score']
    
    # Weighted combination (adjust weights based on investment strategy)
    return (revenue_factor * 0.3 + growth_factor * 0.4 + impact_factor * 0.3)

# Apply investment potential calculation
df['investment_potential'] = df.apply(calculate_investment_potential, axis=1)
print(df[['name', 'investment_potential']].sort_values('investment_potential', ascending=False))

This scoring system helps prioritize startups that balance strong financial performance with meaningful environmental impact, aligning with the criteria used by impact-focused VCs.

Step 5: Visualization and Reporting

Create Investment Analysis Dashboard

Visual representation helps quickly identify top investment opportunities:

import matplotlib.pyplot as plt

# Create visualization
def create_investment_dashboard(df):
    fig, axes = plt.subplots(2, 2, figsize=(12, 10))
    
    # Revenue vs Growth Rate
    axes[0,0].scatter(df['revenue'], df['growth_rate'], c=df['investment_potential'], cmap='viridis')
    axes[0,0].set_xlabel('Revenue (Millions)')
    axes[0,0].set_ylabel('Growth Rate (%)')
    axes[0,0].set_title('Revenue vs Growth Rate')
    
    # Impact Score vs Scalability
    axes[0,1].bar(df['name'], df['composite_impact_score'], color='green')
    axes[0,1].set_ylabel('Impact Score')
    axes[0,1].set_title('Impact Score by Company')
    axes[0,1].tick_params(axis='x', rotation=45)
    
    # Investment Potential
    axes[1,0].bar(df['name'], df['investment_potential'], color='blue')
    axes[1,0].set_ylabel('Investment Potential')
    axes[1,0].set_title('Investment Potential Score')
    axes[1,0].tick_params(axis='x', rotation=45)
    
    # Funding Stage Distribution
    stage_counts = df['funding_stage'].value_counts()
    axes[1,1].pie(stage_counts.values, labels=stage_counts.index, autopct='%1.1f%%')
    axes[1,1].set_title('Funding Stage Distribution')
    
    plt.tight_layout()
    plt.show()

# Generate the dashboard
create_investment_dashboard(df)

This dashboard provides a comprehensive view of investment opportunities, helping investors quickly identify which startups meet their criteria for both financial returns and impact.

Step 6: Generate Investment Recommendations

Rank and Recommend Investments

Finally, we'll create a recommendation system that prioritizes startups based on our analysis:

def generate_recommendations(df):
    # Sort by investment potential
    recommendations = df.sort_values('investment_potential', ascending=False)
    
    print("\nTop Investment Recommendations:")
    print("=================================")
    
    for idx, row in recommendations.iterrows():
        print(f"{row['name']}")
        print(f"  Revenue: €{row['revenue']}M")
        print(f"  Growth Rate: {row['growth_rate']}%")
        print(f"  Impact Score: {row['composite_impact_score']:.2f}")
        print(f"  Investment Potential: {row['investment_potential']:.2f}")
        print("  ---")
    
    return recommendations

# Generate final recommendations
top_recommendations = generate_recommendations(df)
print("\nRecommended for Partech-style Impact Fund Investment:")
print(top_recommendations[['name', 'investment_potential']].head(3))

This final step provides actionable insights that mirror the investment decisions made by firms like Partech when evaluating their portfolio companies.

Summary

This tutorial demonstrated how to build a structured analysis framework for impact investing in climate tech startups. By combining financial metrics with impact indicators, we created a scoring system that evaluates potential investments based on revenue, growth, impact score, and scalability. This approach reflects the methodology used by venture capital firms like Partech when allocating their impact funds, focusing on companies that demonstrate both strong business fundamentals and meaningful environmental impact. The framework can be extended with additional metrics such as carbon footprint reduction, job creation, or technological innovation to provide even more comprehensive analysis.