SESIT: Industry carbon tax

macro_data/processing/synthetic_country.py

@@ -112,6 +112,7 @@
 from macro_data.readers.emissions.emissions_reader import CH4EmissionsDataCAN, EmissionsData
 from macro_data.readers.exo_prices.exo_prices_reader import SectorExoPrices
 from macro_data.readers.exogenous_data import ExogenousCountryData
+from macro_data.readers.policy_data.obps_can_reader import OBPSCANData
 
 
 @dataclass
@@ -173,6 +174,7 @@ class SyntheticCountry:
     emission_factors: EmissionsData
     emission_fractions: Optional[EmissionFractions] = None
     firm_exo_prices: Optional[SectorExoPrices] = None
+    obps_data: Optional[OBPSCANData] = None
     emission_factors_ch4: Optional[CH4EmissionsDataCAN] = None
     historical_emissions_df: Optional[pd.DataFrame] = None
 
@@ -378,6 +380,7 @@ def eu_synthetic_country(
             firm_exo_prices=(
                 SectorExoPrices.from_reader(readers.exo_prices) if readers.exo_prices is not None else None
             ),
+            obps_data=readers.obps_can.obps_data if readers.obps_can is not None else None,
         )
 
     @classmethod
@@ -594,6 +597,7 @@ def proxied_synthetic_country(
             firm_exo_prices=(
                 SectorExoPrices.from_reader(readers.exo_prices) if readers.exo_prices is not None else None
             ),
+            obps_data=readers.obps_can.obps_data if readers.obps_can is not None else None,
         )
 
     @classmethod

macro_data/readers/default_readers.py

@@ -50,6 +50,7 @@
 )
 from macro_data.readers.io_tables.icio_reader import ICIOReader, split_gfcf_column
 from macro_data.readers.io_tables.industries import AGGREGATED_INDUSTRIES
+from macro_data.readers.policy_data.obps_can_reader import OBPSCANReader
 from macro_data.readers.population_data.compustat_banks_reader import (
     CompustatBanksReader,
 )
@@ -61,6 +62,21 @@
 from macro_data.readers.util.prune_util import DataFilterWarning
 
 
+@dataclass
+class OBPSPaths:
+    """File paths for the Canada Output-Based Pricing System data.
+
+    Attributes:
+        rates_path: CSV of carbon price rates by year and jurisdiction.
+        policy_path: CSV of per-industry reduction factors and tightening rates.
+        policy_elec_path: Optional CSV for electricity-specific tightening rates.
+    """
+
+    rates_path: Path
+    policy_path: Path
+    policy_elec_path: Optional[Path] = None
+
+
 @dataclass
 class DataPaths:
     """Manages file paths for all data sources used in the model.
@@ -113,6 +129,7 @@ class DataPaths:
     emissions_fraction_path: Optional[Path] = None
     firm_prices_path: Optional[Path] = None
     ch4_emissions_path: Optional[Path] = None
+    obps_path: Optional[OBPSPaths] = None
 
     @classmethod
     def default_paths(cls, raw_data_path: Path, icio_years: Iterable[int]):
@@ -150,6 +167,11 @@ def default_paths(cls, raw_data_path: Path, icio_years: Iterable[int]):
             ch4_emissions_path=raw_data_path
             / "emission_factors"
             / "EN-GHG_EconSectByGas-CA_Emissions_2014_2023_v4.csv",
+            obps_path=OBPSPaths(
+                rates_path=raw_data_path / "policy" / "output_based_price_system_rates.csv",
+                policy_path=raw_data_path / "policy" / "output_based_price_system_policy_values_disagg.csv",
+                policy_elec_path=raw_data_path / "policy" / "output_based_price_system_policy_values_elec.csv",
+            ),
         )
 
     # @classmethod
@@ -206,6 +228,7 @@ class DataReaders:
     emission_fractions: Optional[EmissionsFractionReader] = None
     exo_prices: Optional[SectorExoPricesReader] = None
     ch4_emissions: Optional[CH4EmissionsReaderCAN] = None
+    obps_can: Optional[OBPSCANReader] = None
     regions_dict: Optional[dict[Country, list[Region]]] = None
 
     @classmethod
@@ -481,6 +504,14 @@ def get_investment_year(year: int, country_names_: Optional[list[Country | Regio
         if datapaths.ch4_emissions_path is not None and datapaths.ch4_emissions_path.exists():
             ch4_emissions = CH4EmissionsReaderCAN.read_data(datapaths.ch4_emissions_path)
 
+        obps_can = None
+        if datapaths.obps_path is not None:
+            obps_can = OBPSCANReader.read_from_raw_data(
+                rates_path=datapaths.obps_path.rates_path,
+                policy_path=datapaths.obps_path.policy_path,
+                policy_elec_path=datapaths.obps_path.policy_elec_path,
+            )
+
         return cls(
             icio=icio,
             wiod_sea=wiod_sea,
@@ -500,6 +531,7 @@ def get_investment_year(year: int, country_names_: Optional[list[Country | Regio
             emission_fractions=emission_fractions,
             exo_prices=exo_prices,
             ch4_emissions=ch4_emissions,
+            obps_can=obps_can,
             regions_dict=regions_dict,
         )
 

macro_data/readers/policy_data/__init__.py

@@ -0,0 +1,3 @@
+from macro_data.readers.policy_data.obps_can_reader import OBPSCANData, OBPSCANReader
+
+__all__ = ["OBPSCANData", "OBPSCANReader"]

macro_data/readers/policy_data/obps_can_reader.py

@@ -0,0 +1,85 @@
+"""Reader and container for Canada Output-Based Pricing System (OBPS) policy data."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Optional
+
+import pandas as pd
+
+
+@dataclass
+class OBPSCANData:
+    """Container for Canada OBPS policy tables loaded from CSV files.
+
+    Attributes:
+        df_rates: Carbon price trajectory. Rows are years; columns are
+            'Date' plus one column per province/jurisdiction code.
+        df_policy: Per-industry emission standards. Must have columns
+            'Industry', 'reduction_factor', and 'tightening_rate'.
+        df_policy_elec: Optional electricity-specific tightening rates.
+            Same structure as df_policy but for electricity sectors only.
+    """
+
+    df_rates: pd.DataFrame
+    df_policy: pd.DataFrame
+    df_policy_elec: Optional[pd.DataFrame] = None
+
+
+@dataclass
+class OBPSCANReader:
+    """Reader for Canada OBPS CSV data files.
+
+    CSV formats
+    -----------
+    rates_path:
+        Date, CAN, CAN_AB, CAN_BC, ...
+        2014, 15.0, 10.0, 12.0, ...
+        2015, 20.0, 15.0, 15.0, ...
+
+    policy_path:
+        Industry, reduction_factor, tightening_rate
+        B05a, 0.80, 0.02
+        C19,  0.75, 0.02
+
+    policy_elec_path (optional):
+        same structure as policy_path but for electricity sectors.
+    """
+
+    obps_data: Optional[OBPSCANData] = None
+
+    @classmethod
+    def read_from_raw_data(
+        cls,
+        rates_path: Path | str,
+        policy_path: Path | str,
+        policy_elec_path: Optional[Path | str] = None,
+    ) -> OBPSCANReader:
+        """Load OBPS data from CSV files.
+
+        Args:
+            rates_path: Path to carbon price rates CSV.
+            policy_path: Path to per-industry policy values CSV.
+            policy_elec_path: Optional path to electricity-specific CSV.
+
+        Returns:
+            OBPSCANReader with loaded OBPSCANData, or obps_data=None if any
+            required file is absent.
+        """
+        rates_path = Path(rates_path)
+        policy_path = Path(policy_path)
+
+        if not rates_path.exists() or not policy_path.exists():
+            return cls(obps_data=None)
+
+        df_rates = pd.read_csv(rates_path)
+        df_policy = pd.read_csv(policy_path)
+
+        df_policy_elec = None
+        if policy_elec_path is not None:
+            policy_elec_path = Path(policy_elec_path)
+            if policy_elec_path.exists():
+                df_policy_elec = pd.read_csv(policy_elec_path)
+
+        return cls(obps_data=OBPSCANData(df_rates=df_rates, df_policy=df_policy, df_policy_elec=df_policy_elec))

macromodel/agents/firms/firms.py

@@ -1011,6 +1011,7 @@ def compute_price(
         current_estimated_ppi_inflation: np.ndarray,
         previous_average_good_prices: np.ndarray,
         ppi_during: np.ndarray,
+        extra_marginal_taxes: Optional[np.ndarray] = None,
     ) -> np.ndarray:
         """Set prices for each firm's output.
 
@@ -1026,6 +1027,8 @@ def compute_price(
             current_estimated_ppi_inflation (np.ndarray): Expected PPI inflation
             previous_average_good_prices (np.ndarray): Previous period prices
             ppi_during (np.ndarray): Producer price indices
+            extra_marginal_taxes (np.ndarray, optional): Per-sector marginal
+                tax added to sector average prices. Defaults to None.
 
         Returns:
             np.ndarray: New prices for each firm
@@ -1051,6 +1054,7 @@ def compute_price(
             ),
             ppi_during=ppi_during,
             current_time=len(self.ts.historic("price")),
+            extra_marginal_taxes=extra_marginal_taxes,
         )
 
     def compute_unconstrained_demand_for_intermediate_inputs(

macromodel/agents/firms/func/prices.py

@@ -1,4 +1,5 @@
 from abc import ABC, abstractmethod
+from typing import Optional
 
 import numpy as np
 from scipy.interpolate import interp1d
@@ -74,6 +75,7 @@ def compute_price(
         prev_unit_costs: np.ndarray,
         ppi_during: np.ndarray,
         current_time: int,
+        extra_marginal_taxes: Optional[np.ndarray] = None,
     ) -> np.ndarray:
         """Calculate prices for each firm based on market conditions.
 
@@ -139,11 +141,12 @@ def compute_price(
         current_time: int,
         min_inflation: float = -0.1,
         max_inflation: float = 0.1,
+        extra_marginal_taxes: Optional[np.ndarray] = None,
     ) -> np.ndarray:
         """Calculate prices using the default multi-factor strategy.
 
         The method:
-        1. Maps sector average prices to firms
+        1. Maps sector average prices to firms (plus any OBPS marginal tax)
         2. Calculates demand-pull inflation based on market position
         3. Calculates cost-push inflation from unit costs
         4. Combines all factors with random noise
@@ -158,11 +161,18 @@ def compute_price(
                 Defaults to -0.1 (-10%).
             max_inflation (float, optional): Upper bound on inflation rates.
                 Defaults to 0.1 (10%).
+            extra_marginal_taxes (np.ndarray, optional): Per-sector marginal
+                tax (e.g. OBPS) added to sector average prices seen by firms.
+                Shape (n_industries,). Defaults to None.
 
         Returns:
             np.ndarray: Updated prices by firm, guaranteed to be positive
         """
-        average_price_by_firm = prev_average_good_prices[current_firm_sectors]
+        tax_by_sector = (
+            extra_marginal_taxes if extra_marginal_taxes is not None else np.zeros_like(prev_average_good_prices)
+        )
+        average_price_by_firm = (prev_average_good_prices + tax_by_sector)[current_firm_sectors]
+        tax_by_firm = tax_by_sector[current_firm_sectors]
 
         # Demand-pull inflation
         demand_pull_inflation = np.zeros_like(prev_firm_prices)
@@ -187,12 +197,13 @@ def compute_price(
         )
         demand_pull_inflation = np.maximum(min_inflation, np.minimum(max_inflation, demand_pull_inflation))
 
-        # Cost-push inflation
+        # Cost-push inflation: include the tax in unit costs so positive tax raises prices
+        total_unit_costs = curr_unit_costs + tax_by_firm
         cost_push_inflation = (
             np.divide(
-                curr_unit_costs,
+                total_unit_costs,
                 average_price_by_firm,
-                out=np.ones_like(curr_unit_costs),
+                out=np.ones_like(total_unit_costs),
                 where=average_price_by_firm != 0.0,
             )
             - 1.0
@@ -276,6 +287,7 @@ def compute_price(
         current_time: int,
         min_inflation: float = -0.1,
         max_inflation: float = 0.1,
+        extra_marginal_taxes: Optional[np.ndarray] = None,
     ) -> np.ndarray:
         """Compute prices, overriding listed sectors with exogenous sector paths.
 
@@ -317,6 +329,7 @@ def compute_price(
             current_time=current_time,
             min_inflation=min_inflation,
             max_inflation=max_inflation,
+            extra_marginal_taxes=extra_marginal_taxes,
         )
 
         if self.firm_exo_prices is None or len(self.overriden_industries) == 0:
@@ -328,12 +341,16 @@ def compute_price(
             else prev_average_good_prices
         )
 
+        tax_by_firm = (
+            extra_marginal_taxes[current_firm_sectors] if extra_marginal_taxes is not None else np.zeros_like(price)
+        )
+
         for industry_name in self.firm_exo_prices.prices.columns:
             if industry_name not in self.overriden_industries:
                 continue
             ratio = self._normalised_price(industry_name, current_quarter=current_time)
             for idx in self._indices_for(industry_name):
-                price[idx] = base_prices[idx] * ratio
+                price[idx] = base_prices[idx] * ratio + tax_by_firm[idx]
 
         return price
 
@@ -371,6 +388,7 @@ def compute_price(
         current_time: int,
         min_inflation: float = -0.1,
         max_inflation: float = 0.1,
+        extra_marginal_taxes: Optional[np.ndarray] = None,
     ) -> np.ndarray:
         """Set prices according to exogenous PPI path.
 
@@ -383,6 +401,8 @@ def compute_price(
             current_time (int): Current period index
             min_inflation (float, optional): Unused. Defaults to -0.1.
             max_inflation (float, optional): Unused. Defaults to 0.1.
+            extra_marginal_taxes (np.ndarray, optional): Unused. Accepts for
+                interface consistency. Defaults to None.
 
         Returns:
             np.ndarray: Price level from exogenous PPI path

macromodel/configurations/country_configuration.py

@@ -39,6 +39,7 @@ class CountryConfiguration(BaseModel):
     assume_zero_noise: bool = False
     use_emission_multiplier: bool = False
     CH4_production_emissions_only: bool = False
+    use_obps_reg: bool = False
 
     @classmethod
     def n_industry_default(