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3 fichiers name co2-estimation description Calculate carbon footprint of construction projects. Estimate CO2 emissions from materials, transportation, and construction processes using emission factors databases. homepage https://datadrivenconstruction.io metadata {"openclaw":{"emoji":"🎬","os":["darwin","linux","win32"],"homepage":"https://datadrivenconstruction.io","requires":{"bins":["python3"]}}}
CO2 Estimation for Construction
Overview
Based on DDC methodology (Chapter 3.3), this skill provides comprehensive CO2 and carbon footprint calculations for construction projects. Sustainability is no longer optional - clients and regulations demand accurate environmental impact assessments.
Book Reference: "4D, 6D-8D и расчет CO2" / "4D-8D BIM and CO2 Calculation"
"Расчет углеродного следа становится обязательным требованием для многих проектов. BIM-модель содержит все необходимые данные для автоматизации этого процесса."
— DDC Book, Chapter 3.3
Quick Start
import pandas as pd
df = pd.read_excel("bim_elements.xlsx" )
emission_factors = {
'Concrete' : 0.13 ,
'Steel'
1.85
'Brick'
0.24
'Timber'
1.6
'Glass'
0.85
'Aluminum'
8.14
'CO2_kg'
lambda
'Weight_kg'
'Material'
0
1
'CO2_kg'
sum
print
f"Total Carbon Footprint: {total_co2:,.0 f} kg CO2"
print
f"Equivalent: {total_co2/1000 :,.1 f} tonnes CO2"
Emission Factors Database
Material Emission Factors (Embodied Carbon)
EMISSION_FACTORS_KG = {
'Concrete_C20' : 0.10 ,
'Concrete_C30' : 0.13 ,
'Concrete_C40' : 0.16 ,
'Concrete_C50' : 0.19 ,
'Cement_Portland' : 0.83 ,
'Mortar' : 0.20 ,
'Steel_Reinforcing' : 1.85 ,
'Steel_Structural' : 1.55 ,
'Steel_Recycled' : 0.47 ,
'Aluminum_Primary' : 8.14 ,
'Aluminum_Recycled' : 0.52 ,
'Copper' : 2.71 ,
'Brick_Clay' : 0.24 ,
'Brick_Concrete' : 0.12 ,
'Stone_Natural' : 0.06 ,
'Block_Concrete' : 0.10 ,
'Timber_Softwood' : -1.60 ,
'Timber_Hardwood' : -1.40 ,
'Plywood' : 0.45 ,
'CLT' : -1.20 ,
'Glulam' : -1.10 ,
'Insulation_Mineral' : 1.20 ,
'Insulation_EPS' : 3.29 ,
'Insulation_XPS' : 3.45 ,
'Insulation_Cellulose' : 0.10 ,
'Glass_Float' : 0.85 ,
'Glass_Double' : 1.30 ,
'Glass_Triple' : 1.80 ,
'PVC' : 2.61 ,
'HDPE' : 1.93 ,
'Polycarbonate' : 5.00 ,
'Gypsum_Board' : 0.39 ,
'Ceramic_Tile' : 0.78 ,
'Asphalt' : 0.05
}
EMISSION_FACTORS_M3 = {
'Concrete_C30' : 312 ,
'Steel' : 14430 ,
'Timber' : -800 ,
'Brick' : 432 ,
'Glass' : 2125
}
Carbon Footprint Calculator
Basic Calculator class CarbonCalculator :
"""Calculate carbon footprint for construction projects"""
def __init__ (self, emission_factors=None ):
self .factors = emission_factors or EMISSION_FACTORS_KG
self .results = {}
def calculate_embodied_carbon (self, df, material_col='Material' ,
weight_col='Weight_kg' ):
"""Calculate embodied carbon from materials"""
df = df.copy()
df['Emission_Factor' ] = df[material_col].map (self .factors).fillna(0 )
df['CO2_kg' ] = df[weight_col] * df['Emission_Factor' ]
summary = df.groupby(material_col).agg({
weight_col: 'sum' ,
'CO2_kg' : 'sum'
}).round (2 )
self .results['embodied' ] = {
'total_kg' : df['CO2_kg' ].sum (),
'by_material' : summary,
'details' : df
}
return df
def calculate_transport_carbon (self, df, distance_col='Distance_km' ,
weight_col='Weight_kg' ,
transport_type='truck' ):
"""Calculate transport emissions"""
transport_factors = {
'truck' : 0.062 ,
'rail' : 0.022 ,
'ship' : 0.016 ,
'air' : 0.602
}
factor = transport_factors.get(transport_type, 0.062 )
df = df.copy()
df['Transport_CO2_kg' ] = (df[weight_col] / 1000 ) * df[distance_col] * factor
self .results['transport' ] = {
'total_kg' : df['Transport_CO2_kg' ].sum (),
'factor_used' : factor,
'transport_type' : transport_type
}
return df
def calculate_construction_carbon (self, df,
equipment_hours=None ,
fuel_consumption=None ):
"""Calculate construction phase emissions"""
equipment_factors = {
'excavator' : 25.0 ,
'crane' : 18.5 ,
'concrete_pump' : 22.0 ,
'loader' : 15.0 ,
'compactor' : 8.0 ,
'generator' : 12.0
}
if equipment_hours:
construction_co2 = sum (
hours * equipment_factors.get(equip, 15.0 )
for equip, hours in equipment_hours.items()
)
elif fuel_consumption:
construction_co2 = fuel_consumption * 2.68
else :
construction_co2 = 0
self .results['construction' ] = {
'total_kg' : construction_co2
}
return construction_co2
def get_total_footprint (self ):
"""Get total carbon footprint"""
total = sum (
r.get('total_kg' , 0 )
for r in self .results.values()
)
return {
'total_kg' : total,
'total_tonnes' : total / 1000 ,
'breakdown' : {k: v.get('total_kg' , 0 ) for k, v in self .results.items()}
}
def generate_report (self ):
"""Generate carbon footprint report"""
footprint = self .get_total_footprint()
report = []
report.append("=" * 50 )
report.append("CARBON FOOTPRINT REPORT" )
report.append("=" * 50 )
report.append("" )
report.append(f"Total Carbon Footprint: {footprint['total_tonnes' ]:,.2 f} tonnes CO2e" )
report.append("" )
report.append("Breakdown:" )
for category, value in footprint['breakdown' ].items():
pct = (value / footprint['total_kg' ] * 100 ) if footprint['total_kg' ] > 0 else 0
report.append(f" {category.capitalize():15s} : {value:>12 ,.0 f} kg ({pct:>5.1 f} %)" )
report.append("" )
report.append("=" * 50 )
return "\n" .join(report)
Usage Example
elements = pd.read_excel("bim_export.xlsx" )
calc = CarbonCalculator()
elements = calc.calculate_embodied_carbon(
elements,
material_col='Material' ,
weight_col='Weight_kg'
)
elements['Distance_km' ] = 50
elements = calc.calculate_transport_carbon(
elements,
distance_col='Distance_km' ,
weight_col='Weight_kg' ,
transport_type='truck'
)
equipment_usage = {
'excavator' : 120 ,
'crane' : 500 ,
'concrete_pump' : 80 ,
'loader' : 200
}
calc.calculate_construction_carbon(equipment_hours=equipment_usage)
print (calc.generate_report())
footprint = calc.get_total_footprint()
Life Cycle Assessment (LCA)
Full LCA Calculation class ConstructionLCA :
"""Life Cycle Assessment for construction projects"""
def __init__ (self, building_lifespan=50 ):
self .lifespan = building_lifespan
self .phases = {}
def calculate_a1_a3 (self, materials_df ):
"""Product stage: Raw material supply, transport, manufacturing"""
materials_df['A1_A3' ] = materials_df.apply(
lambda row: row['Weight_kg' ] * EMISSION_FACTORS_KG.get(row['Material' ], 0 ),
axis=1
)
self .phases['A1-A3' ] = materials_df['A1_A3' ].sum ()
return self .phases['A1-A3' ]
def calculate_a4 (self, materials_df, avg_distance_km=100 ):
"""Transport to site"""
self .phases['A4' ] = (materials_df['Weight_kg' ].sum () / 1000 ) * avg_distance_km * 0.062
return self .phases['A4' ]
def calculate_a5 (self, construction_energy_kwh, waste_factor=0.05 ):
"""Construction/installation process"""
energy_emissions = construction_energy_kwh * 0.4
self .phases['A5' ] = energy_emissions
return self .phases['A5' ]
def calculate_b1_b7 (self, annual_energy_kwh, maintenance_co2_annual=0 ):
"""Use stage: Operation, maintenance, repair, replacement"""
annual_operation = annual_energy_kwh * 0.4
total_operational = (annual_operation + maintenance_co2_annual) * self .lifespan
self .phases['B1-B7' ] = total_operational
return self .phases['B1-B7' ]
def calculate_c1_c4 (self, materials_df, demolition_energy_kwh=0 ):
"""End of life: Deconstruction, transport, processing, disposal"""
demolition = demolition_energy_kwh * 0.4
transport = (materials_df['Weight_kg' ].sum () / 1000 ) * 50 * 0.062
disposal = materials_df['Weight_kg' ].sum () * 0.01
self .phases['C1-C4' ] = demolition + transport + disposal
return self .phases['C1-C4' ]
def calculate_d (self, recycled_materials_df ):
"""Module D: Benefits beyond system boundary (recycling credits)"""
credits = recycled_materials_df.apply(
lambda row: -row['Weight_kg' ] * EMISSION_FACTORS_KG.get(row['Material' ], 0 ) * 0.5 ,
axis=1
).sum () if len (recycled_materials_df) > 0 else 0
self .phases['D' ] = credits
return self .phases['D' ]
def get_total_lca (self ):
"""Calculate total life cycle emissions"""
embodied = self .phases.get('A1-A3' , 0 ) + self .phases.get('A4' , 0 ) + self .phases.get('A5' , 0 )
operational = self .phases.get('B1-B7' , 0 )
end_of_life = self .phases.get('C1-C4' , 0 )
credits = self .phases.get('D' , 0 )
return {
'embodied_carbon' : embodied,
'operational_carbon' : operational,
'end_of_life_carbon' : end_of_life,
'recycling_credits' : credits,
'total_lifecycle' : embodied + operational + end_of_life + credits,
'phases' : self .phases
}
def get_carbon_intensity (self, floor_area_m2 ):
"""Calculate carbon intensity per m²"""
lca = self .get_total_lca()
return {
'embodied_per_m2' : lca['embodied_carbon' ] / floor_area_m2,
'operational_per_m2_year' : lca['operational_carbon' ] / (floor_area_m2 * self .lifespan),
'total_per_m2' : lca['total_lifecycle' ] / floor_area_m2
}
Reporting and Visualization
Carbon Report Generation def generate_carbon_report (df, project_name, floor_area_m2 ):
"""Generate comprehensive carbon footprint report"""
total_co2 = df['CO2_kg' ].sum ()
co2_per_m2 = total_co2 / floor_area_m2
by_category = df.groupby('Category' )['CO2_kg' ].sum ().sort_values(ascending=False )
by_material = df.groupby('Material' )['CO2_kg' ].sum ().sort_values(ascending=False )
report = {
'project' : project_name,
'floor_area_m2' : floor_area_m2,
'total_co2_kg' : total_co2,
'total_co2_tonnes' : total_co2 / 1000 ,
'co2_per_m2' : co2_per_m2,
'by_category' : by_category.to_dict(),
'by_material' : by_material.to_dict(),
'benchmark_comparison' : classify_carbon_intensity(co2_per_m2)
}
return report
def classify_carbon_intensity (co2_per_m2 ):
"""Classify building carbon intensity against benchmarks"""
if co2_per_m2 < 300 :
return {'rating' : 'A+' , 'description' : 'Ultra-low carbon' }
elif co2_per_m2 < 500 :
return {'rating' : 'A' , 'description' : 'Low carbon' }
elif co2_per_m2 < 750 :
return {'rating' : 'B' , 'description' : 'Below average' }
elif co2_per_m2 < 1000 :
return {'rating' : 'C' , 'description' : 'Average' }
elif co2_per_m2 < 1250 :
return {'rating' : 'D' , 'description' : 'Above average' }
else :
return {'rating' : 'E' , 'description' : 'High carbon' }
def export_carbon_report (report, filepath ):
"""Export carbon report to Excel"""
with pd.ExcelWriter(filepath, engine='openpyxl' ) as writer:
summary_df = pd.DataFrame({
'Metric' : ['Total CO2 (tonnes)' , 'CO2 per m²' , 'Rating' , 'Floor Area' ],
'Value' : [
f"{report['total_co2_tonnes' ]:,.1 f} " ,
f"{report['co2_per_m2' ]:,.0 f} kg/m²" ,
report['benchmark_comparison' ]['rating' ],
f"{report['floor_area_m2' ]:,.0 f} m²"
]
})
summary_df.to_excel(writer, sheet_name='Summary' , index=False )
cat_df = pd.DataFrame.from_dict(report['by_category' ], orient='index' , columns=['CO2_kg' ])
cat_df.to_excel(writer, sheet_name='By Category' )
mat_df = pd.DataFrame.from_dict(report['by_material' ], orient='index' , columns=['CO2_kg' ])
mat_df.to_excel(writer, sheet_name='By Material' )
Carbon Reduction Strategies
Material Optimization def suggest_carbon_reduction (df, material_col='Material' ):
"""Suggest material substitutions to reduce carbon"""
alternatives = {
'Concrete_C40' : ('Concrete_C30' , 0.19 , 0.13 ),
'Steel_Structural' : ('Steel_Recycled' , 1.55 , 0.47 ),
'Aluminum_Primary' : ('Aluminum_Recycled' , 8.14 , 0.52 ),
'Insulation_EPS' : ('Insulation_Cellulose' , 3.29 , 0.10 ),
'Brick_Clay' : ('Timber_CLT' , 0.24 , -1.20 )
}
suggestions = []
for material, (alt, current, alt_factor) in alternatives.items():
subset = df[df[material_col] == material]
if len (subset) > 0 :
current_co2 = subset['Weight_kg' ].sum () * current
alt_co2 = subset['Weight_kg' ].sum () * alt_factor
saving = current_co2 - alt_co2
suggestions.append({
'current_material' : material,
'alternative' : alt,
'current_co2_kg' : current_co2,
'alternative_co2_kg' : alt_co2,
'potential_saving_kg' : saving,
'saving_percent' : (saving / current_co2 * 100 ) if current_co2 > 0 else 0
})
return pd.DataFrame(suggestions).sort_values('potential_saving_kg' , ascending=False )
Quick Reference Metric Formula Embodied Carbon Weight_kg × Emission_FactorTransport Carbon (Weight_tonnes) × Distance_km × 0.062Carbon Intensity Total_CO2 / Floor_Area_m2LCA Total A1-A3 + A4 + A5 + B1-B7 + C1-C4 + D
Common Emission Factors Material kg CO2e/kg kg CO2e/m³ Concrete C30 0.13 312 Steel (new) 1.85 14,430 Steel (recycled) 0.47 3,666 Timber -1.60 -800 Brick 0.24 432 Aluminum 8.14 21,978
Resources
Book : "Data-Driven Construction" by Artem Boiko, Chapter 3.3Website : https://datadrivenconstruction.io ICE Database : Inventory of Carbon and EnergyEN 15978 : Sustainability of construction works standard
Next Steps
See cost-prediction for cost-carbon optimization
See qto-report for extracting quantities for CO2 calculation
See data-visualization for carbon dashboards
