AgentSkillsCN

Analyze Esg Score

分析 ESG 评分

SKILL.md

Skill: Analyze ESG Score

Domain

private_equity

Description

Analyzes Environmental, Social, and Governance (ESG) factors to calculate composite ESG scores for investment screening and reporting.

Tags

ESG, sustainability, investing, governance, social, environmental

Use Cases

  • •ESG screening for investments
  • •Portfolio ESG analysis
  • •Regulatory ESG reporting
  • •Stakeholder disclosure

Proprietary Business Rules

Rule 1: Environmental Metrics

Carbon footprint, resource usage, and environmental impact scoring.

Rule 2: Social Assessment

Labor practices, community impact, and diversity metrics.

Rule 3: Governance Evaluation

Board composition, ethics policies, and transparency.

Rule 4: Materiality Weighting

Industry-specific factor materiality application.

Input Parameters

  • •entity_id (string): Entity identifier
  • •environmental_data (dict): Environmental metrics
  • •social_data (dict): Social metrics
  • •governance_data (dict): Governance metrics
  • •industry (string): Industry classification
  • •peer_data (list): Peer comparison data

Output

  • •esg_score (float): Composite ESG score
  • •pillar_scores (dict): E, S, G individual scores
  • •materiality_assessment (dict): Material factor analysis
  • •peer_comparison (dict): Relative peer ranking
  • •improvement_areas (list): Areas for ESG improvement

Implementation

The analysis logic is implemented in esg_analyzer.py and references data from CSV files:

  • •pillar_weights.csv - Reference data
  • •environmental_benchmarks.csv - Reference data
  • •social_benchmarks.csv - Reference data
  • •governance_requirements.csv - Reference data
  • •scoring_thresholds.csv - Reference data
  • •parameters.csv - Reference data.

Usage Example

python
from esg_analyzer import analyze_esg

result = analyze_esg(
    entity_id="COMP-001",
    environmental_data={"carbon_intensity": 150, "renewable_pct": 0.35, "waste_recycled": 0.6},
    social_data={"diversity_pct": 0.40, "safety_incidents": 2, "turnover_rate": 0.12},
    governance_data={"board_independence": 0.7, "ethics_policy": True, "audit_committee": True},
    industry="manufacturing",
    peer_data=[{"id": "PEER-001", "esg_score": 65}]
)

print(f"ESG Score: {result['esg_score']}")

Test Execution

python
from esg_analyzer import analyze_esg

result = analyze_esg(
    entity_id=input_data.get('entity_id'),
    environmental_data=input_data.get('environmental_data', {}),
    social_data=input_data.get('social_data', {}),
    governance_data=input_data.get('governance_data', {}),
    industry=input_data.get('industry'),
    peer_data=input_data.get('peer_data', [])
)