Calculate embodied carbon and lifecycle emissions for construction materials and projects. Support sustainable design decisions with carbon data.

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Target platform: OpenClaw
Install method: Manual import
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Prerequisites: OpenClaw
Primary doc: SKILL.md

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Source platform: Tencent SkillHub
What's included: SKILL.md

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Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 1.0.0

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Publisher: datadrivenconstruction
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Documentation

ClawHub primary doc Primary doc: SKILL.md 5 sections Open source page

Overview

Calculate embodied carbon (EC) and lifecycle carbon emissions for construction materials, assemblies, and projects. Support sustainable design decisions and carbon reduction targets.

Business Case

Carbon calculation supports: Regulatory Compliance: Meet carbon reporting requirements Green Certifications: LEED, BREEAM, Living Building Challenge Design Optimization: Choose lower-carbon alternatives Sustainability Goals: Track progress toward net-zero

Technical Implementation

from dataclasses import dataclass, field from typing import List, Dict, Any, Optional from enum import Enum import pandas as pd class LifecycleStage(Enum): A1_A3 = "Product Stage (A1-A3)" # Raw materials, transport, manufacturing A4 = "Transport to Site (A4)" A5 = "Construction (A5)" B1_B7 = "Use Stage (B1-B7)" # Maintenance, repair, replacement C1_C4 = "End of Life (C1-C4)" # Demolition, transport, disposal D = "Beyond Lifecycle (D)" # Reuse, recycling potential @dataclass class MaterialCarbon: material_id: str name: str category: str unit: str carbon_a1_a3: float # kgCO2e per unit carbon_a4: float carbon_a5: float carbon_b: float carbon_c: float carbon_d: float # Usually negative (credit) density: float # kg/m³ if applicable source: str epd_url: str = "" @dataclass class AssemblyCarbon: assembly_id: str name: str materials: List[Dict[str, Any]] total_carbon: float carbon_by_stage: Dict[str, float] @dataclass class ProjectCarbon: project_id: str name: str gross_area: float assemblies: List[AssemblyCarbon] total_embodied_carbon: float carbon_per_area: float carbon_by_stage: Dict[str, float] carbon_by_category: Dict[str, float] benchmark_comparison: Dict[str, Any] class LifecycleCarbonCalculator: """Calculate lifecycle carbon for construction.""" # Sample material carbon data (kgCO2e per unit) DEFAULT_MATERIALS = { 'concrete_30mpa': MaterialCarbon( material_id='C30', name='Concrete 30MPa', category='Concrete', unit='m³', carbon_a1_a3=300, carbon_a4=5, carbon_a5=2, carbon_b=0, carbon_c=10, carbon_d=-20, density=2400, source='EPD Database' ), 'concrete_40mpa': MaterialCarbon( material_id='C40', name='Concrete 40MPa', category='Concrete', unit='m³', carbon_a1_a3=350, carbon_a4=5, carbon_a5=2, carbon_b=0, carbon_c=10, carbon_d=-20, density=2400, source='EPD Database' ), 'steel_rebar': MaterialCarbon( material_id='REBAR', name='Steel Reinforcing Bar', category='Steel', unit='kg', carbon_a1_a3=1.99, carbon_a4=0.05, carbon_a5=0.02, carbon_b=0, carbon_c=0.05, carbon_d=-0.5, density=7850, source='WorldSteel EPD' ), 'steel_structural': MaterialCarbon( material_id='STEEL', name='Structural Steel', category='Steel', unit='kg', carbon_a1_a3=1.55, carbon_a4=0.05, carbon_a5=0.03, carbon_b=0, carbon_c=0.05, carbon_d=-0.8, density=7850, source='AISC EPD' ), 'timber_clt': MaterialCarbon( material_id='CLT', name='Cross-Laminated Timber', category='Timber', unit='m³', carbon_a1_a3=-500, carbon_a4=10, carbon_a5=5, carbon_b=0, carbon_c=50, carbon_d=-100, density=500, source='AWC EPD' ), 'gypsum_board': MaterialCarbon( material_id='GYP', name='Gypsum Board 12.5mm', category='Finishes', unit='m²', carbon_a1_a3=3.2, carbon_a4=0.2, carbon_a5=0.1, carbon_b=0, carbon_c=0.3, carbon_d=-0.1, density=10, source='EUROGYPSUM EPD' ), 'insulation_mineral': MaterialCarbon( material_id='INS_MW', name='Mineral Wool Insulation', category='Insulation', unit='m³', carbon_a1_a3=45, carbon_a4=2, carbon_a5=1, carbon_b=0, carbon_c=5, carbon_d=-2, density=40, source='EURIMA EPD' ), 'glass_double': MaterialCarbon( material_id='GLASS', name='Double Glazed Unit', category='Glazing', unit='m²', carbon_a1_a3=35, carbon_a4=1, carbon_a5=0.5, carbon_b=0, carbon_c=2, carbon_d=-5, density=25, source='Glass for Europe EPD' ), 'aluminum': MaterialCarbon( material_id='ALU', name='Aluminum Profile', category='Metals', unit='kg', carbon_a1_a3=8.0, carbon_a4=0.1, carbon_a5=0.05, carbon_b=0, carbon_c=0.1, carbon_d=-4.0, density=2700, source='EAA EPD' ), } # Building type benchmarks (kgCO2e/m²) BENCHMARKS = { 'Office': {'typical': 500, 'good': 350, 'best': 200}, 'Residential': {'typical': 400, 'good': 280, 'best': 150}, 'Retail': {'typical': 450, 'good': 320, 'best': 180}, 'Industrial': {'typical': 350, 'good': 250, 'best': 150}, 'Healthcare': {'typical': 700, 'good': 500, 'best': 350}, } def __init__(self): self.materials: Dict[str, MaterialCarbon] = dict(self.DEFAULT_MATERIALS) self.assemblies: Dict[str, AssemblyCarbon] = {} def add_material(self, material: MaterialCarbon): """Add or update a material.""" self.materials[material.material_id] = material def calculate_material_carbon(self, material_id: str, quantity: float, stages: List[LifecycleStage] = None) -> Dict: """Calculate carbon for a material quantity.""" if material_id not in self.materials: raise ValueError(f"Unknown material: {material_id}") material = self.materials[material_id] if stages is None: stages = list(LifecycleStage) carbon_by_stage = {} total = 0 for stage in stages: if stage == LifecycleStage.A1_A3: carbon = material.carbon_a1_a3 * quantity elif stage == LifecycleStage.A4: carbon = material.carbon_a4 * quantity elif stage == LifecycleStage.A5: carbon = material.carbon_a5 * quantity elif stage == LifecycleStage.B1_B7: carbon = material.carbon_b * quantity elif stage == LifecycleStage.C1_C4: carbon = material.carbon_c * quantity elif stage == LifecycleStage.D: carbon = material.carbon_d * quantity else: carbon = 0 carbon_by_stage[stage.value] = carbon total += carbon return { 'material_id': material_id, 'material_name': material.name, 'quantity': quantity, 'unit': material.unit, 'total_carbon': total, 'carbon_by_stage': carbon_by_stage } def create_assembly(self, assembly_id: str, name: str, components: List[Dict]) -> AssemblyCarbon: """Create an assembly from multiple materials.""" total_carbon = 0 carbon_by_stage = {stage.value: 0 for stage in LifecycleStage} material_details = [] for comp in components: material_id = comp['material_id'] quantity = comp['quantity'] result = self.calculate_material_carbon(material_id, quantity) total_carbon += result['total_carbon'] for stage, carbon in result['carbon_by_stage'].items(): carbon_by_stage[stage] += carbon material_details.append({ 'material': result['material_name'], 'quantity': quantity, 'unit': result['unit'], 'carbon': result['total_carbon'] }) assembly = AssemblyCarbon( assembly_id=assembly_id, name=name, materials=material_details, total_carbon=total_carbon, carbon_by_stage=carbon_by_stage ) self.assemblies[assembly_id] = assembly return assembly def calculate_project_carbon(self, project_id: str, project_name: str, gross_area: float, building_type: str, quantities: List[Dict]) -> ProjectCarbon: """Calculate total project carbon.""" assemblies = [] total_carbon = 0 carbon_by_stage = {stage.value: 0 for stage in LifecycleStage} carbon_by_category = {} for qty in quantities: if 'assembly_id' in qty: # Use predefined assembly if qty['assembly_id'] in self.assemblies: assembly = self.assemblies[qty['assembly_id']] multiplier = qty.get('multiplier', 1) scaled_carbon = assembly.total_carbon * multiplier assemblies.append(AssemblyCarbon( assembly_id=assembly.assembly_id, name=assembly.name, materials=assembly.materials, total_carbon=scaled_carbon, carbon_by_stage={k: v * multiplier for k, v in assembly.carbon_by_stage.items()} )) total_carbon += scaled_carbon elif 'material_id' in qty: # Direct material result = self.calculate_material_carbon( qty['material_id'], qty['quantity'] ) total_carbon += result['total_carbon'] for stage, carbon in result['carbon_by_stage'].items(): carbon_by_stage[stage] += carbon # Track by category material = self.materials[qty['material_id']] cat = material.category carbon_by_category[cat] = carbon_by_category.get(cat, 0) + result['total_carbon'] # Calculate metrics carbon_per_area = total_carbon / gross_area if gross_area > 0 else 0 # Compare to benchmarks benchmark = self.BENCHMARKS.get(building_type, self.BENCHMARKS['Office']) benchmark_comparison = { 'carbon_per_area': carbon_per_area, 'typical_benchmark': benchmark['typical'], 'good_benchmark': benchmark['good'], 'best_benchmark': benchmark['best'], 'vs_typical': (carbon_per_area / benchmark['typical'] - 1) * 100, 'rating': self._get_rating(carbon_per_area, benchmark) } return ProjectCarbon( project_id=project_id, name=project_name, gross_area=gross_area, assemblies=assemblies, total_embodied_carbon=total_carbon, carbon_per_area=carbon_per_area, carbon_by_stage=carbon_by_stage, carbon_by_category=carbon_by_category, benchmark_comparison=benchmark_comparison ) def _get_rating(self, carbon: float, benchmark: Dict) -> str: """Get rating based on benchmark comparison.""" if carbon <= benchmark['best']: return 'A (Best Practice)' elif carbon <= benchmark['good']: return 'B (Good Practice)' elif carbon <= benchmark['typical']: return 'C (Typical)' else: return 'D (Above Typical)' def compare_alternatives(self, base_project: ProjectCarbon, alternatives: List[Dict]) -> pd.DataFrame: """Compare carbon of design alternatives.""" comparisons = [{ 'Option': 'Base Design', 'Total Carbon (tCO2e)': base_project.total_embodied_carbon / 1000, 'Carbon/m² (kgCO2e)': base_project.carbon_per_area, 'vs Base': '0%', 'Rating': base_project.benchmark_comparison['rating'] }] for alt in alternatives: project = self.calculate_project_carbon( alt['id'], alt['name'], alt['gross_area'], alt.get('building_type', 'Office'), alt['quantities'] ) change = (project.total_embodied_carbon - base_project.total_embodied_carbon) / base_project.total_embodied_carbon * 100 comparisons.append({ 'Option': alt['name'], 'Total Carbon (tCO2e)': project.total_embodied_carbon / 1000, 'Carbon/m² (kgCO2e)': project.carbon_per_area, 'vs Base': f'{change:+.1f}%', 'Rating': project.benchmark_comparison['rating'] }) return pd.DataFrame(comparisons) def suggest_reductions(self, project: ProjectCarbon) -> List[Dict]: """Suggest carbon reduction opportunities.""" suggestions = [] # Analyze by category if 'Concrete' in project.carbon_by_category: concrete_carbon = project.carbon_by_category['Concrete'] if concrete_carbon > project.total_embodied_carbon * 0.3: suggestions.append({ 'category': 'Concrete', 'current_carbon': concrete_carbon, 'suggestion': 'Consider low-carbon concrete (GGBS/PFA replacement)', 'potential_reduction': '20-40%', 'impact': concrete_carbon * 0.3 }) if 'Steel' in project.carbon_by_category: steel_carbon = project.carbon_by_category['Steel'] if steel_carbon > project.total_embodied_carbon * 0.2: suggestions.append({ 'category': 'Steel', 'current_carbon': steel_carbon, 'suggestion': 'Specify high recycled content steel', 'potential_reduction': '10-25%', 'impact': steel_carbon * 0.2 }) # Benchmark-based suggestions if project.benchmark_comparison['vs_typical'] > 0: suggestions.append({ 'category': 'Overall', 'current_carbon': project.total_embodied_carbon, 'suggestion': 'Project exceeds typical benchmark - review high-carbon elements', 'potential_reduction': f"{abs(project.benchmark_comparison['vs_typical']):.0f}%", 'impact': project.total_embodied_carbon * abs(project.benchmark_comparison['vs_typical']) / 100 }) return sorted(suggestions, key=lambda x: -x['impact']) def generate_report(self, project: ProjectCarbon) -> str: """Generate carbon assessment report.""" lines = ["# Embodied Carbon Assessment Report", ""] lines.append(f"**Project:** {project.name}") lines.append(f"**Gross Area:** {project.gross_area:,.0f} m²") lines.append(f"**Assessment Date:** {pd.Timestamp.now().strftime('%Y-%m-%d')}") lines.append("") # Summary lines.append("## Carbon Summary") lines.append(f"- **Total Embodied Carbon:** {project.total_embodied_carbon/1000:,.0f} tCO2e") lines.append(f"- **Carbon Intensity:** {project.carbon_per_area:,.0f} kgCO2e/m²") lines.append(f"- **Rating:** {project.benchmark_comparison['rating']}") lines.append("") # By lifecycle stage lines.append("## Carbon by Lifecycle Stage") for stage, carbon in project.carbon_by_stage.items(): if carbon != 0: pct = carbon / project.total_embodied_carbon * 100 lines.append(f"- {stage}: {carbon/1000:,.1f} tCO2e ({pct:.1f}%)") lines.append("") # By category lines.append("## Carbon by Material Category") for cat, carbon in sorted(project.carbon_by_category.items(), key=lambda x: -x[1]): pct = carbon / project.total_embodied_carbon * 100 lines.append(f"- {cat}: {carbon/1000:,.1f} tCO2e ({pct:.1f}%)") lines.append("") # Benchmark lines.append("## Benchmark Comparison") bc = project.benchmark_comparison lines.append(f"- Project: {bc['carbon_per_area']:.0f} kgCO2e/m²") lines.append(f"- Typical: {bc['typical_benchmark']} kgCO2e/m²") lines.append(f"- Good Practice: {bc['good_benchmark']} kgCO2e/m²") lines.append(f"- Best Practice: {bc['best_benchmark']} kgCO2e/m²") lines.append("") # Reduction opportunities suggestions = self.suggest_reductions(project) if suggestions: lines.append("## Reduction Opportunities") for sug in suggestions[:5]: lines.append(f"\n### {sug['category']}") lines.append(f"- **Suggestion:** {sug['suggestion']}") lines.append(f"- **Potential Reduction:** {sug['potential_reduction']}") lines.append(f"- **Impact:** {sug['impact']/1000:,.1f} tCO2e") return "\n".join(lines)

Quick Start

# Initialize calculator calc = LifecycleCarbonCalculator() # Calculate project carbon project = calc.calculate_project_carbon( project_id="PROJ-001", project_name="Office Building", gross_area=5000, building_type="Office", quantities=[ {'material_id': 'concrete_40mpa', 'quantity': 1500}, # m³ {'material_id': 'steel_rebar', 'quantity': 150000}, # kg {'material_id': 'steel_structural', 'quantity': 200000}, {'material_id': 'gypsum_board', 'quantity': 8000}, # m² {'material_id': 'glass_double', 'quantity': 1200}, # m² ] ) print(f"Total Carbon: {project.total_embodied_carbon/1000:,.0f} tCO2e") print(f"Carbon Intensity: {project.carbon_per_area:,.0f} kgCO2e/m²") print(f"Rating: {project.benchmark_comparison['rating']}") # Get reduction suggestions suggestions = calc.suggest_reductions(project) for sug in suggestions: print(f"- {sug['category']}: {sug['suggestion']}") # Generate full report report = calc.generate_report(project) print(report)

Dependencies

pip install pandas

Category context

Data access, storage, extraction, analysis, reporting, and insight generation.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

1 Docs

SKILL.md Primary doc