Skill: gechengling/security-esg-investing (version 3.0.1)

--- name: ESG Investment Analysis Assistant slug: security-esg-investing description: AI-powered ESG investing analysis assistant — covers ESG rating analysis, green finance screening, carbon footprint assessment, and sustainable investment portfolio construction. Built for ESG analysts, sustainable fund managers, and institutional investors. Keywords: ESG investing, sustainability, green finance, carbon footprint, ESG rating, responsible investing, China ESG, ESG分析, 绿色金融, 可持续发展, 碳足迹, ESG评级, 碳中和, 绿色债券, ESG投资组合, 责任投资, 碳核算, 碳交易. version: "3.0.1" --- # 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 ```python class CarbonAnalyzer: """碳足迹核算""" def calculate_carbon_footprint(self, company_data: dict) -> dict: """ 计算碳足迹（Scope 1, 2, 3） """ # Scope 1: 直接排放 scope1 = ( company_data.get("fuel_combustion", 0) * 2.02 + # CO2系数 company_data.get("vehicle_fleet", 0) * 2.32 + company_data.get("fugitive_emissions", 0) * 25 # CH4当量 ) # Scope 2: 间接排放（电力） scope2 = ( company_data.get("electricity_kwh", 0) * company_data.get("grid_emission_factor", 0.68) # 中国电网系数 ) # Scope 3: 价值链排放（简化版） scope3 = { "上游采购": company_data.get("purchased_goods", 0) * 0.5, "运输配送": company_data.get("transportation", 0) * 0.1, "员工通勤": company_data.get("employee_commute", 0) * 0.02, "产品使用": company_data.get("product_use", 0) * 0.8, "报废处理": company_data.get("end_of_life", 0) * 0.05 } total_scope3 = sum(scope3.values()) return { "scope1_tCO2e": round(scope1, 2), "scope2_tCO2e": round(scope2, 2), "scope3_tCO2e": round(total_scope3, 2), "total_emissions": round(scope1 + scope2 + total_scope3, 2), "intensity_metrics": { "per_revenue": round((scope1+scope2+total_scope3) / max(company_data.get("revenue_yuan", 1), 1) * 1e6, 2), # tCO2e/百万营收 "per_employee": round((scope1+scope2+total_scope3) / max(company_data.get("employees", 1), 1), 2) # tCO2e/人 }, "scope_breakdown": { "Scope 1": round(scope1/(scope1+scope2+total_scope3+0.001)*100, 1), "Scope 2": round(scope2/(scope1+scope2+total_scope3+0.001)*100, 1), "Scope 3": round(total_scope3/(scope1+scope2+total_scope3+0.001)*100, 1) } } ``` ### 3. Green Bond Analysis / 绿色债券分析 ```markdown ## 绿色债券投资分析框架 ### 一、绿色债券认定 | 标准 | 中国绿债标准 | 国际标准（GBP） | |-----|------------|---------------| | 募集资金用途 | ≥80%用于绿色项目 | ≥95%用于绿色项目 | | 项目评估 | 需第三方认证 | 需外部评审 | | 信息披露 | 年度+事件披露 | 发行时+续存期披露 | ### 二、绿色债券筛选矩阵 - [ ] 是否获得绿色债券认证 - [ ] 第三方认证机构资质 - [ ] 募集资金用途透明度 - [ ] 环境效益量化指标 - [ ] 续存期管理机制 ### 三、环境效益量化 ```python def calculate_green_benefits(bond_data: dict) -> dict: """计算绿色债券环境效益""" green_proceeds = bond_data["total_amount"] * bond_data["green_ratio"] benefits = { "annual_co2_reduction": green_proceeds / 10000 * bond_data["carbon_factor"], # 吨CO2/年 "annual_energy_saving": green_proceeds / 10000 * bond_data["energy_factor"], # 吨标煤/年 "equivalent_forests": green_proceeds / 10000 * bond_data["forest_factor"] # 相当于造林面积(公顷) } return benefits ``` ``` --- ## Disclaimer This skill provides ESG analysis tools for educational purposes. ESG ratings and analysis are for reference only and should be combined with other investment research methods.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)