全球航运"碳税"倒计时与合成燃料万吨级产线投产：当远洋巨轮驶入"脱碳深水区"，一场比新能源车企更隐蔽的能源革命正在发生

from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Tuple
import math

class FuelType(Enum):
    """船舶燃料类型"""
    HFO = "重油（HFO，硫含量≤0.5%）"
    VLSFO = "低硫燃油（VLSFO）"
    LNG = "液化天然气（LNG）"
    METHANOL_GREEN = "绿色甲醇"
    METHANOL_GREY = "灰色甲醇（天然气制）"
    AMMONIA_GREEN = "绿色氨"
    AMMONIA_BLUE = "蓝色氨"
    HYDROGEN_LH2 = "液氢"
    BIOFUEL = "生物燃料（HVO/FAE）"

class VesselClass(Enum):
    """船舶级别"""
    ULCS = "超大型集装箱船（>10000TEU）"
    PANAMAX = "巴拿马型集装箱船（4000-10000TEU）"
    FEEDER = "支线集装箱船（<4000TEU）"
    VLCC = "超大型油轮（VLCC）"
    CAPE = "好望角型散货船"
    RO_RO = "滚装船（汽车运输）"
    LNG_CARRIER = "LNG运输船"

class CarbonPricingRegime(Enum):
    """碳定价机制"""
    IMO_GLOBAL_MBM = "IMO全球航运碳定价（2028-）"
    EU_ETS_SHIPPING = "欧盟航运ETS（2024-）"
    UK_ETS = "英国碳市场"
    SINGAPORE_CARBON_TAX = "新加坡碳税"
    NONE = "无碳定价覆盖"

@dataclass
class ShippingRoute:
    """航运航线"""
    route_id: str
    origin_port: str
    destination_port: str
    distance_nm: float                  # 航程距离（海里）
    transit_days: float                 # 航行天数
    cargo_volume_teu: int               # 单次运载量（TEU）
    developing_country_flag: bool       # 是否涉及发展中国家出口

@dataclass
class VesselSpec:
    """船舶规格"""
    vessel_id: str
    vessel_name: str
    vessel_class: VesselClass
    fuel_type: FuelType
    deadweight_ton: float               # 载重吨
    fuel_consumption_ton_per_day: float # 日均油耗（吨/天）
    co2_emission_factor: float          # CO₂排放因子（tCO₂/tFuel）
    engine_efficiency_pct: float        # 主机效率（%）
    scrubber_installed: bool            # 是否安装脱硫塔
    dual_fuel_capable: bool             # 是否双燃料
    build_year: int                     # 建造年份

class ShippingCarbonCostModeler:
    """航运碳成本建模器"""

    # 各碳定价机制参数（2026年估算）
    CARBON_PRICE_USD_PER_TON = {
        CarbonPricingRegime.IMO_GLOBAL_MBM: 100,
        CarbonPricingRegime.EU_ETS_SHIPPING: 115,
        CarbonPricingRegime.UK_ETS: 90,
        CarbonPricingRegime.SINGAPORE_CARBON_TAX: 35,
        CarbonPricingRegime. 31270.t.kuaisou.com
    }

    # 燃料排放因子（tCO₂/tFuel，井到桨Well-to-Wake）
    EMISSION_FACTOR_WTW = {
        FuelType.HFO: 3.85,
        FuelType.VLSFO: 3.65,
        FuelType.LNG: 2.75,
        FuelType.METHANOL_GREEN: 0.15,      # 近零（可再生碳循环）
        FuelType.METHANOL_GREY: 2.10,
        FuelType.AMMONIA_GREEN: 0.0,        # 零碳
        FuelType.AMMONIA_BLUE: 0.50,
        FuelType.HYDROGEN_LH2: 0.0,
        FuelType.BIOFUEL: 0.30,
    }

    # 发展中国家折扣系数（IMO Global MBM）
    DEVELOPING_COUNTRY_DISCOUNT = {
        "phase1_2028_2030": 0.70,       # 2028-2030：30%折扣
        "phase2_2031_2035": 0.85,       # 2031-2035：15%折扣
        "phase3_2036_onward": 1.00,     # 2036起：无折扣
    }

    def calculate_route_carbon_cost(
        self,
        vessel: VesselSpec,
        route: ShippingRoute,
        regime: CarbonPricingRegime,
        year: int = 2028,
    ) -> Dict[str, any]:
        """计算单航次碳成本"""
        # 航程总油耗
        total_fuel_ton = (
            vessel.fuel_consumption_ton_per_day * route.transit_days
        )

        # CO₂排放量
        emission_factor = self.EMISSION_FACTOR_WTW.get(
            vessel.fuel_type, 3.15
        )
        total_co2_ton = total_fuel_ton * emission_factor

        # 碳价
        base_carbon_price = self.CARBON_PRICE_USD_PER_TON.get(regime, 0)

        # 发展中国家折扣
        discount = 1.0
        if route.developing_country_flag and regime == CarbonPricingRegime.IMO_GLOBAL_MBM:
            if year <= 2030:
                discount = self.DEVELOPING_COUNTRY_DISCOUNT["phase1_2028_2030"]
            elif year <= 2035:
                discount = self.DEVELOPING_COUNTRY_DISCOUNT["phase2_2031_2035"]
            else:
                discount = self.DEVELOPING_COUNTRY_DISCOUNT["phase3_2036_onward"]

        effective_carbon_price = base_carbon_price * discount
        carbon_cost_usd = total_co2_ton * effective_carbon_price

        # 每TEU碳成本分摊
        carbon_cost_per_teu = (
            carbon_cost_usd / route.cargo_volume_teu
            if route.cargo_volume_teu > 0 else 0
        )

        # 对比传统燃油方案
        baseline_co2 = total_fuel_ton * self.EMISSION_FACTOR_WTW[FuelType.HFO]
        baseline_cost = baseline_co2 * effective_carbon_price
        carbon_savings_usd = baseline_cost - carbon_cost_usd
        carbon_savings_pct = (
            carbon_savings_usd / baseline_cost * 100
            if baseline_cost > 0 else 0
        )

        return {
            "route": f"{route.origin_port} → {route.destination_port}",
            "vessel": vessel.vessel_name,
            "fuel": vessel.fuel_type.value,
            "carbon_pricing": regime.value,
            "year": 31270.t.kuaisou.com
            "emission_breakdown": {
                "total_fuel_ton": round(total_fuel_ton, 1),
                "emission_factor_wtw": emission_factor,
                "total_co2_ton": round(total_co2_ton, 1),
                "developing_country_discount": discount,
            },
            "cost_breakdown_usd": {
                "effective_carbon_price_per_ton": round(
                    effective_carbon_price, 1
                ),
                "total_carbon_cost": round(carbon_cost_usd, 0),
                "cost_per_teu": round(carbon_cost_per_teu, 2),
            },
            "vs_baseline_hfo": {
                "carbon_savings_usd": round(carbon_savings_usd, 0),
                "carbon_savings_pct": round(carbon_savings_pct, 1),
                "verdict": (
                    "显著降本" if carbon_savings_pct > 50
                    else "中等降本" if carbon_savings_pct > 20
                    else "有限降本" if carbon_savings_pct > 0
                    else "无优势"
                ),
            },
        }

    def compare_fuel_strategies(
        self,
        vessel_class: VesselClass,
        route: ShippingRoute,
        year: int = 2030,
    ) -> Dict[str, any]:
        """对比不同燃料策略的全生命周期经济性"""
        fuel_options = [
            FuelType.HFO, FuelType.LNG, FuelType.METHANOL_GREEN,
            FuelType.AMMONIA_GREEN, FuelType.BIOFUEL
        ]

        results = []
        for fuel in fuel_options:
            # 模拟不同燃料的日均油耗（简化）
            fuel_density_adjustment = {
                FuelType.HFO: 1.0,
                FuelType.LNG: 0.85,
                FuelType.METHANOL_GREEN: 1.30,     # 能量密度低，耗量更大
                FuelType.AMMONIA_GREEN: 1.50,
                FuelType.BIOFUEL: 1.05,
            }
            base_consumption = {
                VesselClass.ULCS: 180,
                VesselClass.PANAMAX: 120,
                VesselClass.FEEDER: 60,
                VesselClass.VLCC: 200,
                VesselClass.CAPE: 160,
            }
            daily_consumption = base_consumption.get(vessel_class, 120)
            daily_consumption *= fuel_density_adjustment.get(fuel, 1.0)

            v = VesselSpec(
                vessel_id="sim",
                vessel_name=f"模拟{vessel_class.value}",
                vessel_class=vessel_class,
                fuel_type=fuel,
                deadweight_ton=0,
                fuel_consumption_ton_per_day=daily_consumption,
                co2_emission_factor=self.EMISSION_FACTOR_WTW[fuel],
                engine_efficiency_pct=50,
                scrubber_installed= 31271.t.kuaisou.com
                dual_fuel_capable=(
                    fuel in [FuelType.LNG, FuelType.METHANOL_GREEN,
                             FuelType.AMMONIA_GREEN]
                ),
                build_year=2026,
            )

            result = self.calculate_route_carbon_cost(
                v, route, CarbonPricingRegime.IMO_GLOBAL_MBM, year
            )
            results.append({
                "fuel": fuel.value,
                "daily_consumption_ton": round(daily_consumption, 1),
                "total_co2_ton": result["emission_breakdown"]["total_co2_ton"],
                "carbon_cost_usd": result["cost_breakdown_usd"]["total_carbon_cost"],
                "cost_per_teu": result["cost_breakdown_usd"]["cost_per_teu"],
            })

        results.sort(key=lambda x: x["carbon_cost_usd"])

        return {
            "route": f"{route.origin_port} → {route.destination_port}",
            "vessel_class": vessel_class.value,
            "year": year,
            "fuel_ranking": results,
            "winner": results[0]["fuel"],
            "runner_up": results[1]["fuel"] if len(results) > 1 else "N/A",
            "key_insight": (
                "绿色甲醇与绿色氨将在2030年成为碳成本最优解，"
                "但供给端产能瓶颈是最大制约"
            ),
        }

from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Tuple
import math

class TechReadiness(Enum):
    """技术就绪度"""
    COMMERCIAL = "商业化成熟（TRL 9）"
    DEMONSTRATION = "示范验证（TRL 7-8）"
    PILOT = "中试验证（TRL 5-6）"
    LAB = "实验室阶段（TRL 3-4）"

@dataclass
class AlternativeFuel:
    """替代燃料规格"""
    fuel_type: FuelType
    energy_density_mj_kg: float         # 质量能量密度（MJ/kg）
    volumetric_energy_mj_l: float       # 体积能量密度（MJ/L）
    production_cost_usd_per_ton: float  # 生产成本（美元/吨）
    global_annual_capacity_mt: float    # 全球年产能（百万吨）
    shipping_demand_mt: float           # 航运年需求量估算（百万吨）
    supply_gap_ratio: float             # 供给缺口倍数
    storage_temp_c: float               # 储存温度（℃）
    storage_pressure_bar: float         # 储存压力（bar）
    toxicity_level: str                 # "low", "medium", "high"
    bunkering_infrastructure: str       # "mature", "limited", "none"
    engine_trl: TechReadiness           # 发动机技术就绪度
    production_pathway: str             # 生产路径

class ShippingDecarbonizationRouteAnalyzer:
    """航运脱碳路线分析器"""

    # 基准燃料参数
    HFO_ENERGY_DENSITY_MJ_KG = 42.0
    HFO_COST_USD_PER_TON = 580  # 2026年均价

    # 各燃料路线的战略评估维度
    ROUTE_SCORING = {
        FuelType.METHANOL_GREEN: {
            "energy_density_fit": 0.55,
            "engine_maturity": 0.90,
            "bunkering_readiness": 0.60,
            "supply_scalability": 0.30,
            "safety_acceptability": 0.85,
            "cost_competitiveness": 0.40,
        },
        FuelType.AMMONIA_GREEN: {
            "energy_density_fit": 0.65,
            "engine_maturity": 0.40,
            "bunkering_readiness": 0.25,
            "supply_scalability": 0.45,
            "safety_acceptability": 0.35,
            "cost_competitiveness": 0.35,
        },
        FuelType.HYDROGEN_LH2: {
            "energy_density_fit": 0.15,
            "engine_maturity": 0.50,
            "bunkering_readiness": 0.10,
            "supply_scalability": 0.50,
            "safety_acceptability": 0.40,
            "cost_competitiveness": 0.20,
        },
        FuelType.LNG: {
            "energy_density_fit": 0.75,
            "engine_maturity": 0.95,
            "bunkering_readiness": 0.80,
            "supply_scalability": 0.85,
            "safety_acceptability": 0.75,
            "cost_competitiveness": 0.70,
        },
        FuelType.BIOFUEL: {
            "energy_density_fit": 0.90,
            "engine_maturity": 0.95,
            "bunkering_readiness": 0.70,
            "supply_scalability": 0.25,
            "safety_acceptability": 0.90,
            "cost_competitiveness": 0.45,
        },
    }

    def analyze_fuel_route(
        self, fuel: AlternativeFuel
    ) -> Dict[str, any]:
        """分析单条燃料路线的综合可行性"""
        scores = self.ROUTE_SCORING.get(fuel.fuel_type, {})
        if not scores:
            return {"error": f"未定义路线评分: {fuel.fuel_type}"}

        weighted_score = (
            scores["energy_density_fit"] * 0.15
            + scores["engine_maturity"] * 0.20
            + scores["bunkering_readiness"] * 0.15
            + scores["supply_scalability"] * 0.25
            + scores["safety_acceptability"] * 0.15
            + scores["cost_competitiveness"] * 0.10
        )

        # 供给缺口分析
        gap_analysis = {}
        if fuel.shipping_demand_mt > 0:
            coverage = fuel.global_annual_capacity_mt / fuel.shipping_demand_mt
            gap_analysis = {
                "current_capacity_mt": fuel.global_annual_capacity_mt,
                "demand_mt": fuel.shipping_demand_mt,
                "coverage_ratio_pct": round(coverage * 100, 1),
                "gap_multiplier": round(1 / coverage, 0) if coverage > 0 else float('inf'),
                "years_to_close_gap": self._estimate_capacity_scale_years(
                    fuel.global_annual_capacity_mt,
                    fuel.shipping_demand_mt
                ),
            }

        # 成本溢价
        cost_premium_pct = (
            (fuel.production_cost_usd_per_ton - self.HFO_COST_USD_PER_TON)
            / self.HFO_COST_USD_PER_TON * 100
        )

        # 能量密度对比
        energy_ratio = fuel.energy_density_mj_kg / self.HFO_ENERGY_DENSITY_MJ_KG

        # 识别关键瓶颈
        bottlenecks = []
        for dim, score in scores.items():
            if score < 0.4:
                dim_labels = {
                    "energy_density_fit": "能量密度不足",
                    "engine_maturity": "发动机技术不成熟",
                    "bunkering_readiness": "加注基础设施缺失",
                    "supply_scalability": "供给规模化瓶颈",
                    "safety_acceptability": "安全审批阻力大",
                    "cost_competitiveness": "成本竞争力弱",
                }
                bottlenecks.append(dim_labels.get(dim, dim))

        return {
            "fuel": fuel.fuel_type.value,
            "overall_score": round(weighted_score, 2),
            "dimension_scores": 31272.t.kuaisou.com
            "route_maturity": fuel.engine_trl.value,
            "energy_density_vs_hfo": round(energy_ratio * 100, 1),
            "cost_premium_vs_hfo_pct": round(cost_premium_pct, 1),
            "supply_gap": gap_analysis,
            "key_bottlenecks": bottlenecks,
            "strategic_verdict": self._generate_fuel_verdict(
                weighted_score, bottlenecks, gap_analysis, fuel
            ),
        }

    def _estimate_capacity_scale_years(
        self, current_mt: float, target_mt: float
    ) -> int:
        """估算产能扩张所需年限（假设年化增长20%）"""
        if current_mt <= 0 or target_mt <= current_mt:
            return 0
        growth_rate = 0.20
        years = math.log(target_mt / current_mt) / math.log(1 + growth_rate)
        return math.ceil(years)

    def _generate_fuel_verdict(
        self,
        score: float,
        bottlenecks: List[str],
        gap: 31273.t.kuaisou.com
        fuel: AlternativeFuel,
    ) -> str:
        """生成燃料路线战略评估"""
        if score >= 0.70:
            return (
                f"{fuel.fuel_type.value}路线综合成熟度较高，"
                f"适合作为2028-2030年主力过渡燃料。"
                f"建议加速加注基础设施建设。"
            )
        elif score >= 0.50:
            gap_note = ""
            if gap and gap.get("gap_multiplier", 0) > 10:
                gap_note = (
                    f"但供给缺口达{gap['gap_multiplier']:.0f}倍，"
                    f"预计需{gap.get('years_to_close_gap', 0)}年才能弥合。"
                )
            return (
                f"{fuel.fuel_type.value}路线具备中期潜力，"
                f"关键瓶颈：{'、'.join(bottlenecks)}。{gap_note}"
                f"建议加大研发投入并布局上游产能。"
            )
        else:
            return (
                f"{fuel.fuel_type.value}路线在远洋航运场景下"
                f"面临系统性障碍：{'、'.join(bottlenecks)}。"
                f"短期内不具备规模化部署条件，建议保持技术跟踪。"
            )

    def generate_fleet_transition_roadmap(
        self,
        fleet_size: int,
        avg_vessel_age_years: float,
        annual_newbuilds: int,
    ) -> Dict[str, any]:
        """生成船队脱碳转型路线图"""
        avg_retirement_age = 25  # 船舶平均退役年限

        # 年度退役量估算
        annual_retirements = math.ceil(
            fleet_size / avg_retirement_age
        )

        # 新造船中双燃料比例（2026年约40%，逐年提升）
        dual_fuel_penetration = {
            2026: 0.40, 2027: 0.55, 2028: 0.70,
            2029: 0.80, 2030: 0.90, 2035: 1.00,
        }

        # 船队燃料结构演变预测
        fleet_composition = {}
        current_fleet = fleet_size
        for year in range(2026, 2036):
            retired = annual_retirements
            new = annual_newbuilds
            df_ratio = dual_fuel_penetration.get(year, 0.95)

            conventional = current_fleet - retired
            new_dual_fuel = int(new * df_ratio)
            new_conventional = new - new_dual_fuel

            current_fleet = conventional + new_dual_fuel + new_conventional
            df_total = new_dual_fuel  # 简化累计
            fleet_composition[year] = {
                "total_fleet": current_fleet,
                "conventional": 31274.t.kuaisou.com
                "dual_fuel_additions": new_dual_fuel,
                "dual_fuel_ratio_pct": round(
                    new_dual_fuel / current_fleet * 100, 1
                ),
            }

        return {
            "initial_fleet_size": fleet_size,
            "avg_vessel_age": avg_vessel_age_years,
            "annual_newbuilds": annual_newbuilds,
            "annual_retirements": annual_retirements,
            "fleet_evolution": fleet_composition,
            "full_transition_year": 2035,
            "strategic_note": (
                "船队脱碳是一个25年周期的渐进过程，"
                "新造船的燃料选择将锁定未来20-25年的碳足迹。"
                "2026-2028年的新造船决策是转型的关键窗口。"
            ),
        }