Chapter 6 Sustainabilty and Engineering: Carbon Calculator

Chapter 6 Sustainabilty and Engineering: Carbon Calculator#

Introduction: Carbon Calculator
Simulation: Carbon Calculator
Self-Assessment

1. Introduction#

Descriptive alt text for accessibility — Fig. 38 **Figure 6.3 **: Schematic of Carbon footprint Calculator#

🧮 Carbon Footprint Calculators#

Definition#

A carbon footprint calculator (CFC) estimates the greenhouse gas emissions (GHGs) associated with an individual’s, household’s, organization’s, or product’s activities. Results are typically expressed in CO₂-equivalents (CO₂e) and span domains such as transportation, energy use, diet, and consumption.

⚙️ Methodology#

Carbon calculators use either top-down or bottom-up approaches:

Approach	Description
Top-down	Uses national or sectoral averages (e.g., input–output models)
Bottom-up	Uses user-specific data (e.g., energy bills, travel logs, purchase history)

Key Components:#

Emission factors: kg CO₂e per unit of activity (e.g., per kWh, per mile)
Life cycle analysis (LCA): For products and services
Scope definitions: Often limited to Scope 1 (direct) and Scope 2 (indirect energy)

🧠 Core Mathematical Approaches#

1. Lifecycle Assessment (LCA)#

Scope: Cradle-to-grave emissions of products or services
Equation: $$ \text{Total Emissions} = \sum_{i=1}^{n} A_i \cdot EF_i $$
- $( A_i $): Activity data (e.g., kWh, miles, kg of food)
- $( EF_i $): Emission factor (e.g., kg CO₂e per unit)
Use Case: Product-specific footprints, detailed consumer analysis

2. Input–Output (IO) Model#

Scope: Economy-wide emissions based on sectoral interdependencies
Equation: $$ \mathbf{F} = \mathbf{E} \cdot (\mathbf{I} - \mathbf{A})^{-1} \cdot \mathbf{y} $$
- $( \mathbf{F} $): Final carbon footprint vector
- $( \mathbf{E} $): Emissions per unit output by sector
- $( \mathbf{A} $): Input-output coefficient matrix
- $( \mathbf{y} $): Final demand vector
- $( \mathbf{I} $): Identity matrix
Use Case: National or regional footprint estimation, indirect emissions

🏠 Residential Carbon Footprint Model#

Components:#

Energy Use: $$ \text{Emissions}_{\text{energy}} = \text{kWh} \cdot \text{EF}_{\text{electricity}} + \text{Therms} \cdot \text{EF}_{\text{gas}} $$
Transportation: $$ \text{Emissions}_{\text{transport}} = \text{Miles} \cdot \text{EF}_{\text{vehicle type}} $$
Food Consumption: $$ \text{Emissions}_{\text{food}} = \sum_{j} \text{kg}_{j} \cdot \text{EF}_{j} $$
- $( j $): Food categories (e.g., meat, dairy, vegetables)

📈 Example: T-Shirt Footprint (LCA)#

Cotton production → Water use, pesticides, fuel
Manufacturing → Electricity, labor
Transport → Fuel emissions
Use phase → Washing, drying
Disposal → Waste emissions

Each stage contributes to the total footprint: $$ \text{Total CO₂e} = \text{Raw Material} + \text{Production} + \text{Transport} + \text{Use} + \text{End-of-Life} $$

🔍 Opportunities for Integration#

Combine LCA for product-level detail with IO for systemic analysis
Use quadratic or exponential interpolation for emissions reduction pathways
Enable scenario modeling for policy and behavioral interventions

📚 References#

🚧 Challenges#

Data quality and transparency
Inconsistent emission factors and unclear system boundaries
User engagement
Low retention and limited behavioral change
Overemphasis on individual responsibility
May obscure systemic and policy-level solutions
Cultural and contextual mismatch
Lack of localization for diet, energy mix, or transport norms

🌱 Opportunities#

Behavioral nudges and gamification
Use of challenges, goals, and social comparisons
Integration with financial and lifestyle data
Tools like Svalna use bank transactions for footprint estimation
Policy and education tools
Supports curriculum integration and sustainability campaigns
Standardization and benchmarking
Enables cross-comparison and validation of calculators

📚 References#

🌍 Personal Carbon Footprint Calculator — Description#

This interactive Python tool helps individuals estimate their annual carbon emissions based on detailed lifestyle choices. It enables users to visualize and understand which habits contribute most to their environmental impact.

Purpose#

Quantify carbon emissions from daily routines
Compare lifestyle choices (diet, transportation, consumption)
Identify high-impact areas to target for sustainability improvements
Empower users to make informed decisions toward a lower footprint

What It Calculates#

The tool includes these components:

🚗 Transportation#

Km driven per week — with emission factors based on fuel type
Flights per year — considers average hours per flight and per-hour emissions

⚡ Home Energy Use#

Electricity consumption (kWh/year) — based on national energy mix
Gas use (m³/year) — natural gas combustion for heating/cooking

🍽️ Diet Choice#

Select between Meat, Vegetarian, or Vegan
Assesses daily food-related emissions using published averages

👕 Consumption#

Clothes purchased per month
Electronics purchased per year
Estimates manufacturing-related emissions

Visualization#

Generates a pie chart showing the relative share of each category
Displays total annual carbon footprint in kg CO₂
Text output summarizes your personal impact

Emission Factors Used#

Category	Emission Factor	Unit
Car travel	0.21 kg CO₂/km	Distance
Flights	90 kg CO₂/hour	Flight time
Electricity	0.45 kg CO₂/kWh	Based on regional mix
Natural Gas	2.05 kg CO₂/m³	Fossil fuel combustion
Meat Diet	7.2 kg CO₂/day	High animal consumption
Vegetarian	3.8 kg CO₂/day	Reduced animal products
Vegan	2.5 kg CO₂/day	Plant-based only
Clothing	25 kg CO₂/item	Manufacturing impact
Electronics	100 kg CO₂/item	Production + logistics

Use Cases#

Evaluate your current lifestyle’s carbon impact
Compare emission sources to find top contributors
Test improvement scenarios (e.g. switching to public transport or vegan diet)
Support sustainability goals, climate pledges, or carbon offset planning

Reference#

[Berners-Lee, 2022]- A comprehensive, item-by-item breakdown of carbon footprints—from bananas to bitcoin - Based on rigorous life cycle analysis and updated with post-pandemic data. [United Nations Climate Change Learning Partnership, 2009] Provides standardized methodologies for calculating carbon footprints across UN agencies, suitable for adaptation in academic and operational contexts.

2. Simulation#

import numpy as np
import matplotlib.pyplot as plt
from ipywidgets import FloatSlider, IntSlider, interact

# 📊 Emission factors (in kg CO₂ per unit)
factors = {
    'car': 0.21,         # kg CO₂/km (average car)
    'flight': 90,        # kg CO₂ per hour of flying
    'electricity': 0.45, # kg CO₂/kWh (varies by country)
    'gas': 2.05,         # kg CO₂/m³
    'meat': 7.2,         # kg CO₂/day (high meat diet)
    'vegetarian': 3.8,   # kg CO₂/day (vegetarian diet)
    'vegan': 2.5,        # kg CO₂/day (vegan diet)
    'clothing': 25,      # kg CO₂ per item
    'electronics': 100   # kg CO₂ per item
}

# 🧮 Carbon calculation function
def carbon_footprint(km_per_week, flights_per_year, flight_hours,
                     electricity_kwh, gas_m3, diet_choice,
                     clothes_per_month, electronics_per_year):

    annual_car = km_per_week * 52 * factors['car']
    annual_flight = flights_per_year * flight_hours * factors['flight']
    annual_electric = electricity_kwh * factors['electricity']
    annual_gas = gas_m3 * factors['gas']

    if diet_choice == 'Meat':
        annual_diet = 365 * factors['meat']
    elif diet_choice == 'Vegetarian':
        annual_diet = 365 * factors['vegetarian']
    else:  # Vegan
        annual_diet = 365 * factors['vegan']

    annual_clothes = clothes_per_month * 12 * factors['clothing']
    annual_electronics = electronics_per_year * factors['electronics']

    total = (annual_car + annual_flight + annual_electric +
             annual_gas + annual_diet + annual_clothes + annual_electronics)

    categories = ['Car', 'Flight', 'Electricity', 'Gas', 'Diet', 'Clothing', 'Electronics']
    values = [annual_car, annual_flight, annual_electric, annual_gas,
              annual_diet, annual_clothes, annual_electronics]

    # 📈 Pie chart
    plt.figure(figsize=(8, 6))
    plt.pie(values, labels=categories, autopct='%1.1f%%', startangle=140)
    plt.title(f"Estimated Annual Carbon Footprint: {total:.0f} kg CO₂")
    plt.tight_layout()
    plt.show()

    print(f"🌍 Your Estimated Annual Carbon Footprint: {total:.0f} kg CO₂")

# 🎛️ Interactive sliders & dropdown
interact(carbon_footprint,
         km_per_week=IntSlider(value=100, min=0, max=500, step=10, description='Km Driven/Week'),
         flights_per_year=IntSlider(value=2, min=0, max=20, step=1, description='Flights/Year'),
         flight_hours=IntSlider(value=2, min=1, max=12, step=1, description='Flight Hours Avg'),
         electricity_kwh=IntSlider(value=4000, min=1000, max=10000, step=500, description='kWh/Year'),
         gas_m3=IntSlider(value=800, min=0, max=3000, step=50, description='Gas (m³/Year)'),
         diet_choice=['Meat', 'Vegetarian', 'Vegan'],
         clothes_per_month=IntSlider(value=2, min=0, max=10, step=1, description='Clothes Bought/Month'),
         electronics_per_year=IntSlider(value=1, min=0, max=10, step=1, description='Electronics/Year'));

3. Self-Assessment#

Emission Factors#

What does an emission factor represent, and how is it used to estimate carbon output?
Why are different units used for different activities (e.g., kg CO₂/km for cars, kg CO₂/day for diet)?
How do emission factors vary by region, technology, or behavior?

Activity-Based Modeling#

Why is car travel modeled on a weekly basis while electricity and gas are annualized?
What assumptions are made about the consistency of user behavior over time?
How does the model aggregate emissions from diverse sources into a single footprint?

Visualization & Interpretation#

What does the pie chart reveal about the relative contribution of each category?
Why might diet or flights dominate the footprint for some users?
How could the model be adapted to show monthly or lifetime emissions?

Reflective Questions#

These questions encourage users to think critically about their lifestyle and sustainability choices.

Which category in your footprint surprised you the most, and why?
What changes could you make to reduce your footprint without compromising quality of life?
How would your footprint change if you switched to renewable electricity or public transport?
What are the limitations of using static emission factors for dynamic behaviors?
How could this model be used to support policy decisions or personal goal-setting?

Multiple Choice#

Which activity has the highest emission factor per unit in this model?
A. Car travel
B. Electricity use
C. Flight hours
D. Clothing purchases
Answer: C
If you fly 5 times a year for 3 hours each, what is your flight-related footprint?
A. 270 kg CO₂
B. 450 kg CO₂
C. 1,350 kg CO₂
D. 90 kg CO₂
Answer: C
Which diet has the lowest carbon footprint per day?
A. Meat
B. Vegetarian
C. Vegan
D. Flexitarian
Answer: C

True/False#

Electricity and gas emissions are calculated using annual consumption.
Answer: True
The model assumes that all flights have the same duration.
Answer: False
Buying fewer electronics has no impact on your carbon footprint.
Answer: False

Short Answer#

Explain how the model calculates total emissions from car travel.
Answer: It multiplies kilometers driven per week by 52 weeks and then by the car emission factor (kg CO₂/km).
Why might the diet category have a large impact on total emissions?
Answer: Because diet is a daily activity, even small differences in emission factors accumulate significantly over a year.

Chapter 6 Sustainabilty and Engineering: Carbon Calculator

Contents

Chapter 6 Sustainabilty and Engineering: Carbon Calculator#

1. Introduction#

🧮 Carbon Footprint Calculators#

Definition#

⚙️ Methodology#

Key Components:#

🧠 Core Mathematical Approaches#

1. Lifecycle Assessment (LCA)#

2. Input–Output (IO) Model#

🏠 Residential Carbon Footprint Model#

Components:#

📈 Example: T-Shirt Footprint (LCA)#

🔍 Opportunities for Integration#

📚 References#

🚧 Challenges#

🌱 Opportunities#

📚 References#

🌍 Personal Carbon Footprint Calculator — Description#

Purpose#

What It Calculates#

🚗 Transportation#

⚡ Home Energy Use#

🍽️ Diet Choice#

👕 Consumption#

Visualization#

Emission Factors Used#

Use Cases#

Reference#

2. Simulation#

3. Self-Assessment#

Emission Factors#

Activity-Based Modeling#

Visualization & Interpretation#

Reflective Questions#

Multiple Choice#

True/False#

Short Answer#