Security Esg Investing

Provides AI-powered ESG rating comparison, carbon footprint calculation, green bond evaluation, and sustainable portfolio construction for ESG analysts and i...

lingfeng-19@gechengling

Install

openclaw skills install @gechengling/security-esg-investing

ESG Investment Analysis Assistant / ESG投资分析助手

English: AI-powered ESG investing analysis assistant — covers ESG rating comparison, green finance products, carbon accounting, and sustainable portfolio construction. Built for ESG analysts and sustainable investors.

中文: ESG投资分析助手——覆盖ESG评级对比、绿色金融产品筛选、碳核算、可持续组合构建。适用：ESG分析师、可持续基金经理、机构投资者。

证券监管最新动态 [2026-05-25更新]

动态类型	内容摘要	影响范围
证券监管	2026年Q1：ESG信息披露要求扩大至非上市企业	ESG投资分析框架需更新最新信披要求和绿色金融政策
证券监管	绿色金融信贷导向政策升级，ESG投资环境改善	ESG投资分析框架需更新最新信披要求和绿色金融政策
证券监管	证监会加强ESG相关信披监管，ESG评级标准趋严	ESG投资分析框架需更新最新信披要求和绿色金融政策

数据截止: 2026-05-25 | 来源：证监会、NFRA、中证协、安永Q1分析声明: 以上动态供参考，具体以官方最新发布为准

Industry Pain Points / 行业痛点

Pain Point / 痛点	Impact / 影响	Solution / 本Skill解决方案
ESG数据分散	评级机构超过10家，标准不统一	跨评级对比框架+综合评分
"漂绿"风险	虚假绿色宣传导致合规风险	实质性分析+数据核实
碳核算复杂	Scope 1/2/3核算专业门槛高	分级碳核算模板+简化方法
政策变化快	碳市场/ESG披露要求频繁更新	实时政策跟踪+合规提醒

Trigger Keywords / 触发关键词

English Triggers: ESG investing, sustainability, green finance, carbon footprint, ESG rating, responsible investing, ESG analysis, carbon trading, sustainable portfolio, China ESG

中文触发词（优先）： ESG投资 / 可持续发展 / 绿色金融 / 碳足迹 / ESG评级 / 责任投资 / ESG分析 / 碳交易 / 可持续组合 / 碳中和 / 碳达峰 / 绿色债券 / ESG披露 / MSCI ESG / 责任投资 / 影响力投资

Core Capabilities / 核心能力

1. ESG Rating Analysis / ESG评级分析

python

class ESGAnalyzer:
    """ESG评级分析"""
    
    RATING_PROVIDERS = {
        "MSCI": {"scale": "CCC-AAA", "weight": {"E": 0.25, "S": 0.25, "G": 0.50}},
        "Sustainalytics": {"scale": "0-100", "weight": {"E": 0.33, "S": 0.33, "G": 0.33}},
        "商道融绿": {"scale": "D-A+", "weight": {"E": 0.30, "S": 0.30, "G": 0.40}},
        "中证ESG": {"scale": "C-AAA", "weight": {"E": 0.20, "S": 0.20, "G": 0.60}}
    }
    
    def normalize_rating(self, provider: str, raw_score: float) -> float:
        """标准化评分到0-100"""
        if provider == "MSCI":
            scale = {"CCC": 10, "B": 20, "BB": 35, "BBB": 50, "A": 65, "AA": 80, "AAA": 95}
            return scale.get(raw_score, 50)
        elif provider == "Sustainalytics":
            return 100 - raw_score  # 反转，风险分数→ESG分数
        elif provider == "商道融绿":
            scale = {"D": 20, "C": 40, "B": 60, "B+": 70, "A": 85, "A+": 95}
            return scale.get(raw_score, 50)
        return raw_score
    
    def comprehensive_analysis(self, company: str, 
                               ratings: dict) -> dict:
        """综合ESG分析"""
        normalized = {
            provider: self.normalize_rating(provider, score)
            for provider, score in ratings.items()
        }
        
        # 加权综合评分
        weights = [0.30, 0.25, 0.25, 0.20]  # 权重分配
        providers = list(normalized.keys())
        comprehensive = sum(
            normalized[p] * w 
            for p, w in zip(providers, weights)
        )
        
        return {
            "comprehensive_score": round(comprehensive, 1),
            "rating_level": self._score_to_level(comprehensive),
            "individual_ratings": normalized,
            "key_strengths": self._identify_strengths(normalized),
            "key_concerns": self._identify_concerns(normalized),
            "peer_comparison": self._compare_to_peer(company, comprehensive)
        }
    
    def _score_to_level(self, score: float) -> str:
        levels = {
            (90, 100): "AAA - 卓越",
            (80, 90): "AA - 优秀",
            (70, 80): "A - 良好",
            (60, 70): "BBB - 平均偏上",
            (50, 60): "BB - 平均",
            (40, 50): "B - 低于平均",
            (0, 40): "CCC/B - 落后"
        }
        for (low, high), level in levels.items():
            if low <= score <= high:
                return level
        return "未知"

2. Carbon Footprint Analysis / 碳足迹分析