diff --git a/README.md b/README.md
index 34b552f7..95f8715c 100644
--- a/README.md
+++ b/README.md
@@ -20,7 +20,7 @@ These flags are grouped into themes:
 
 | Theme | Example flags |
 |---|---|
-| Schema / taxonomy | `use_cornerstone_2026_model_schema`, `implement_waste_disaggregation`, `implement_electricity_reallocation` |
+| Schema / taxonomy | `implement_waste_disaggregation`, `implement_electricity_reallocation` |
 | Economic IOT (input-output tables) | `apply_io_year_adjustments`, `iot_before_or_after_redefinition` |
 | GHG attribution | `use_cornerstone_ghg_model` |
 | Data vintage | `model_base_year`, `usa_base_io_data_year`, `ipcc_ar_version` |
diff --git a/bedrock/analysis/electricity_disagg_diagnostics/full_trace/decompose_d_n_step.py b/bedrock/analysis/electricity_disagg_diagnostics/full_trace/decompose_d_n_step.py
index 19d12c11..df7e36b7 100644
--- a/bedrock/analysis/electricity_disagg_diagnostics/full_trace/decompose_d_n_step.py
+++ b/bedrock/analysis/electricity_disagg_diagnostics/full_trace/decompose_d_n_step.py
@@ -13,11 +13,11 @@
     _weighted_ef,
 )
 from bedrock.publish.model_objects import get_B, get_D, get_L, get_N, get_q
+from bedrock.transform.allocation.derived import derive_E_usa
 from bedrock.transform.eeio.cornerstone_disagg_pipeline import (
     electricity_conversion_factors,
     electricity_mixed_units_enabled,
 )
-from bedrock.transform.eeio.derived import derive_E_usa
 from bedrock.transform.eeio.derived_cornerstone import (
     derive_cornerstone_Aq_mixed_units,
     derive_cornerstone_Aq_scaled,
diff --git a/bedrock/analysis/electricity_disagg_diagnostics/full_trace/full_trace.py b/bedrock/analysis/electricity_disagg_diagnostics/full_trace/full_trace.py
index 03da88cf..fd4d3804 100644
--- a/bedrock/analysis/electricity_disagg_diagnostics/full_trace/full_trace.py
+++ b/bedrock/analysis/electricity_disagg_diagnostics/full_trace/full_trace.py
@@ -11,12 +11,12 @@
 
 from bedrock.analysis.electricity_disagg_diagnostics.paths import OUT_DIR
 from bedrock.publish.model_objects import get_B, get_D, get_L, get_N, get_q
+from bedrock.transform.allocation.derived import derive_E_usa
 from bedrock.transform.eeio.cornerstone_disagg_pipeline import (
     derive_disagg_Ytot_with_trade,
     electricity_conversion_factors,
     electricity_mixed_units_enabled,
 )
-from bedrock.transform.eeio.derived import derive_E_usa
 from bedrock.transform.eeio.derived_cornerstone import (
     derive_cornerstone_Aq_scaled,
     derive_cornerstone_U_set,
diff --git a/bedrock/extract/allocation/__tests__/test_bea_use_table_schema.py b/bedrock/extract/allocation/__tests__/test_bea_use_table_schema.py
index d7bfe4c2..6d9d911f 100644
--- a/bedrock/extract/allocation/__tests__/test_bea_use_table_schema.py
+++ b/bedrock/extract/allocation/__tests__/test_bea_use_table_schema.py
@@ -1,64 +1,20 @@
-"""Tests for schema-aligned BEA use table (load_bea_use_table)."""
+"""Tests for the Cornerstone-frame BEA use table (load_bea_use_table)."""
 
 from __future__ import annotations
 
-from unittest.mock import patch
-
 import pytest
 
-from bedrock.extract.allocation.bea import (
-    _load_bea_use_table_cached,
-    load_bea_use_table,
-)
-from bedrock.utils.config.usa_config import USAConfig
-from bedrock.utils.taxonomy.bea.ceda_v7 import CEDA_V7_SECTORS
+from bedrock.extract.allocation.bea import load_bea_use_table
 from bedrock.utils.taxonomy.cornerstone.industries import INDUSTRIES
 
 
-def _clear_use_table_cache() -> None:
-    _load_bea_use_table_cached.cache_clear()
-
-
-@pytest.mark.eeio_integration
-def test_load_bea_use_table_ceda_shape() -> None:
-    """With CEDA config, table has CEDA v7 industry rows + PCE row."""
-    _clear_use_table_cache()
-    config = USAConfig(use_cornerstone_2026_model_schema=False)
-    with patch("bedrock.extract.allocation.bea.get_usa_config", return_value=config):
-        table = load_bea_use_table()
-    # Rows = CEDA industries + one PCE row
-    industry_rows = [i for i in table.index if i in CEDA_V7_SECTORS]
-    assert len(industry_rows) == len(CEDA_V7_SECTORS)
-    assert "221200" in table.columns
-    assert table.shape[0] >= len(CEDA_V7_SECTORS)
-    assert table.shape[1] > 0
-
-
 @pytest.mark.eeio_integration
 def test_load_bea_use_table_cornerstone_shape() -> None:
-    """With Cornerstone config, table has Cornerstone industry rows + PCE row."""
-    _clear_use_table_cache()
-    config = USAConfig(use_cornerstone_2026_model_schema=True)
-    with patch("bedrock.extract.allocation.bea.get_usa_config", return_value=config):
-        table = load_bea_use_table()
+    """Table has Cornerstone industry rows + PCE row."""
+    load_bea_use_table.cache_clear()
+    table = load_bea_use_table()
     industry_rows = [i for i in table.index if i in INDUSTRIES]
     assert len(industry_rows) == len(INDUSTRIES)
     assert "221200" in table.columns
     assert table.shape[0] >= len(INDUSTRIES)
     assert table.shape[1] > 0
-
-
-@pytest.mark.eeio_integration
-def test_load_bea_use_table_cache_per_schema() -> None:
-    """CEDA and Cornerstone calls return different shapes (cache keyed by schema)."""
-    _clear_use_table_cache()
-    config_ceda = USAConfig(use_cornerstone_2026_model_schema=False)
-    config_cs = USAConfig(use_cornerstone_2026_model_schema=True)
-    with patch(
-        "bedrock.extract.allocation.bea.get_usa_config", return_value=config_ceda
-    ):
-        table_ceda = load_bea_use_table()
-    with patch("bedrock.extract.allocation.bea.get_usa_config", return_value=config_cs):
-        table_cs = load_bea_use_table()
-    assert table_ceda.shape[0] != table_cs.shape[0]
-    assert set(table_ceda.index) != set(table_cs.index)
diff --git a/bedrock/extract/allocation/bea.py b/bedrock/extract/allocation/bea.py
index 9ac73775..af5e397e 100644
--- a/bedrock/extract/allocation/bea.py
+++ b/bedrock/extract/allocation/bea.py
@@ -6,9 +6,11 @@
 import pandas as pd
 
 from bedrock.transform.eeio.derived_2017 import (
-    derive_2017_U_set_usa,
     derive_2017_V_usa,
-    derive_2017_Y_personal_consumption_expenditure_usa,
+)
+from bedrock.transform.eeio.derived_cornerstone import (
+    derive_cornerstone_U_set,
+    derive_cornerstone_Y_personal_consumption_expenditure,
 )
 from bedrock.utils.config.usa_config import get_usa_config
 from bedrock.utils.io.gcp import load_from_gcs
@@ -45,34 +47,16 @@ def load_bea_make_table() -> pd.DataFrame:
 
 
 @functools.cache
-def _load_bea_use_table_cached(use_cornerstone: bool) -> pd.DataFrame:
-    """Inner loader keyed by schema so both CEDA and Cornerstone can be cached."""
-    if use_cornerstone:
-        from bedrock.transform.eeio import derived_cornerstone  # noqa: PLC0415
-
-        uset = derived_cornerstone.derive_cornerstone_U_set()
-        U_combined = (uset.Udom + uset.Uimp).T
-        Y_cs = (
-            derived_cornerstone.derive_cornerstone_Y_personal_consumption_expenditure()
-            .to_frame()
-            .T
-        )
-        return pd.concat([U_combined, Y_cs])
-    U_set = derive_2017_U_set_usa()
-    Y_usa = derive_2017_Y_personal_consumption_expenditure_usa().to_frame()
-    return pd.concat([(U_set.Udom + U_set.Uimp).T, Y_usa.T])
-
-
 def load_bea_use_table() -> pd.DataFrame:
     """
-    Load BEA Use and Final Demand tables aligned to the model schema.
+    Load BEA Use and Final Demand tables in the Cornerstone frame.
 
-    When use_cornerstone_2026_model_schema is False, returns CEDA v7 industry rows.
-    When True, returns Cornerstone industry rows (from derive_cornerstone_U_set and Y).
-    Rows = industries + one PCE row; columns = commodities. Result is cached per schema.
+    Rows = Cornerstone industries + one PCE row; columns = commodities.
     """
-    use_cornerstone = get_usa_config().use_cornerstone_2026_model_schema
-    return _load_bea_use_table_cached(use_cornerstone)
+    uset = derive_cornerstone_U_set()
+    U_combined = (uset.Udom + uset.Uimp).T
+    Y_cs = derive_cornerstone_Y_personal_consumption_expenditure().to_frame().T
+    return pd.concat([U_combined, Y_cs])
 
 
 def _use_table_value_ceda_sector_cornerstone_aligned(
@@ -81,12 +65,11 @@ def _use_table_value_ceda_sector_cornerstone_aligned(
     ceda_sector: str,
 ) -> float:
     """
-    Value for one CEDA allocator sector from a use table (CEDA or Cornerstone shaped).
+    Value for one CEDA-vocabulary allocator sector from the Cornerstone use table.
 
-    When the table is CEDA-shaped (Cornerstone schema not active), the sector is
-    in the index and we return it directly; alignment rules are skipped. When the
-    table is Cornerstone-shaped, we apply alignment: 562* → 562000, 335220 ↔ 4
-    appliance sectors, 331313 → 331313+33131B.
+    Sectors present in the index are returned directly; otherwise alignment
+    rules apply: 562* → 562000, 335220 ↔ 4 appliance sectors,
+    331313 → 331313+33131B.
     """
     if ceda_sector in table_idx:
         return float(col.loc[ceda_sector])
@@ -120,11 +103,11 @@ def use_table_series_ceda_allocator_to_cornerstone_schema(
     commodity: str,
 ) -> pd.Series:
     """
-    Use-table series for CEDA allocator sectors, aligned to Cornerstone schema.
+    Use-table series for CEDA-vocabulary allocator sectors, aligned to the
+    Cornerstone-shaped use table.
 
-    When the use table is CEDA-shaped (Cornerstone schema not active), sectors
-    are looked up directly; alignment is skipped. When Cornerstone-shaped,
-    alignment applies: 562* → 562000, 335220 ↔ 4 appliance sectors, 331313 →
+    Sectors present in the table are looked up directly; otherwise alignment
+    applies: 562* → 562000, 335220 ↔ 4 appliance sectors, 331313 →
     331313+33131B. Missing sectors get 0. Safe to normalize (e.g. pct = s / s.sum()).
     """
     table_idx = use_table.index
diff --git a/bedrock/publish/emission_factors/writer.py b/bedrock/publish/emission_factors/writer.py
index e23baa73..90779b24 100644
--- a/bedrock/publish/emission_factors/writer.py
+++ b/bedrock/publish/emission_factors/writer.py
@@ -10,7 +10,7 @@
     build_purchaser_matrices,
     finalize_cornerstone_ef_table,
 )
-from bedrock.publish.model_objects import apply_loc_suffix, require_cornerstone_config
+from bedrock.publish.model_objects import apply_loc_suffix
 
 logger = logging.getLogger(__name__)
 
@@ -24,7 +24,6 @@ def write_emission_factors(
     write_matrices: bool = False,
 ) -> dict[str, str]:
     """Write CO2e SEF CSV (and optional M/N purchaser matrices) under ``output_dir``."""
-    require_cornerstone_config()
     os.makedirs(output_dir, exist_ok=True)
 
     table = finalize_cornerstone_ef_table(
diff --git a/bedrock/publish/excel/writer.py b/bedrock/publish/excel/writer.py
index b6608359..16b8c62e 100644
--- a/bedrock/publish/excel/writer.py
+++ b/bedrock/publish/excel/writer.py
@@ -90,7 +90,6 @@
     get_Udom,
     get_V,
     get_x,
-    require_cornerstone_config,
 )
 from bedrock.utils.config.settings import GIT_HASH_LONG
 from bedrock.utils.config.usa_config import get_usa_config
@@ -370,11 +369,7 @@ def write_model_to_xlsx(out_path: str, *, config_name: str) -> None:
     Sheets are produced from `_build_matrix_registry(config_name)`. Any
     registry entry whose getter returns `None` is omitted from the
     workbook (useeior-style "skip if NULL").
-
-    Raises `NotImplementedError` on legacy (non-cornerstone) configs --
-    publish is wired only for cornerstone-schema configs today.
     """
-    require_cornerstone_config()
     registry = _build_matrix_registry(config_name)
     materialized = _materialize(registry)
     os.makedirs(os.path.dirname(out_path) or '.', exist_ok=True)
diff --git a/bedrock/publish/model_objects.py b/bedrock/publish/model_objects.py
index ca382272..36922c6d 100644
--- a/bedrock/publish/model_objects.py
+++ b/bedrock/publish/model_objects.py
@@ -9,7 +9,6 @@
 
 import pandas as pd
 
-from bedrock.utils.config.usa_config import get_usa_config
 from bedrock.utils.math.formulas import compute_L_matrix, compute_M_matrix
 
 PUBLISH_LOCATION: str = 'US'
@@ -67,14 +66,6 @@ def assemble_extended_U(
     return out
 
 
-def require_cornerstone_config() -> None:
-    if not get_usa_config().use_cornerstone_2026_model_schema:
-        raise NotImplementedError(
-            'bedrock.publish only supports cornerstone-schema configs '
-            '(use_cornerstone_2026_model_schema=True).'
-        )
-
-
 @functools.cache
 def get_V() -> pd.DataFrame:
     from bedrock.transform.eeio.derived_cornerstone import derive_cornerstone_V
diff --git a/bedrock/transform/allocation/co2/industrial_coal.py b/bedrock/transform/allocation/co2/industrial_coal.py
index 7f34d5f4..499b23cf 100644
--- a/bedrock/transform/allocation/co2/industrial_coal.py
+++ b/bedrock/transform/allocation/co2/industrial_coal.py
@@ -18,12 +18,9 @@
     CORNERSTONE_INDUSTRY_TO_MECS_3_1_NAICS_SUBTRACTION_MAPPING,
 )
 from bedrock.transform.allocation.mappings.v7.ceda_mecs import (
-    CEDA_INDUSTRY_TO_MECS_3_1_NAICS_MAPPING,
-    CEDA_INDUSTRY_TO_MECS_3_1_NAICS_SUBTRACTION_MAPPING,
     NON_MECS_INDUSTRIES,
 )
 from bedrock.transform.allocation.utils import get_allocation_sectors
-from bedrock.utils.config.usa_config import get_usa_config
 from bedrock.utils.economic.units import COAL_MMBTU_PER_SHORT_TONNE, MEGATONNE_TO_KG
 
 load_table_a17_tbtu = functools.cache(_load_table_a17_tbtu)
@@ -38,15 +35,10 @@ def _get_mecs_3_1_naics_mappings() -> tuple[
     dict[tuple[str, ...], tuple[str, ...]],
     dict[tuple[str, ...], tuple[tuple[str, ...], tuple[str, ...]]],
 ]:
-    """Return (mapping, subtraction_mapping) for MECS 3.1 NAICS; use CORNERSTONE when schema flag is on."""
-    if get_usa_config().use_cornerstone_2026_model_schema:
-        return (
-            CORNERSTONE_INDUSTRY_TO_MECS_3_1_NAICS_MAPPING,
-            CORNERSTONE_INDUSTRY_TO_MECS_3_1_NAICS_SUBTRACTION_MAPPING,
-        )
+    """Return the Cornerstone-frame (mapping, subtraction_mapping) for MECS 3.1 NAICS."""
     return (
-        CEDA_INDUSTRY_TO_MECS_3_1_NAICS_MAPPING,
-        CEDA_INDUSTRY_TO_MECS_3_1_NAICS_SUBTRACTION_MAPPING,
+        CORNERSTONE_INDUSTRY_TO_MECS_3_1_NAICS_MAPPING,
+        CORNERSTONE_INDUSTRY_TO_MECS_3_1_NAICS_SUBTRACTION_MAPPING,
     )
 
 
diff --git a/bedrock/transform/allocation/co2/industrial_natural_gas.py b/bedrock/transform/allocation/co2/industrial_natural_gas.py
index 4e8b59ba..ff28f4a5 100644
--- a/bedrock/transform/allocation/co2/industrial_natural_gas.py
+++ b/bedrock/transform/allocation/co2/industrial_natural_gas.py
@@ -17,12 +17,9 @@
     CORNERSTONE_INDUSTRY_TO_MECS_3_1_NAICS_SUBTRACTION_MAPPING,
 )
 from bedrock.transform.allocation.mappings.v7.ceda_mecs import (
-    CEDA_INDUSTRY_TO_MECS_3_1_NAICS_MAPPING,
-    CEDA_INDUSTRY_TO_MECS_3_1_NAICS_SUBTRACTION_MAPPING,
     NON_MECS_INDUSTRIES,
 )
 from bedrock.transform.allocation.utils import get_allocation_sectors
-from bedrock.utils.config.usa_config import get_usa_config
 from bedrock.utils.economic.units import MEGATONNE_TO_KG, NAT_GAS_BCF_TO_TRILLION_BTU
 
 load_table_a17_tbtu = functools.cache(_load_table_a17_tbtu)
@@ -47,15 +44,10 @@ def _get_mecs_3_1_naics_mappings() -> tuple[
     dict[tuple[str, ...], tuple[str, ...]],
     dict[tuple[str, ...], tuple[tuple[str, ...], tuple[str, ...]]],
 ]:
-    """Return (mapping, subtraction_mapping) for MECS 3.1 NAICS; use CORNERSTONE when schema flag is on."""
-    if get_usa_config().use_cornerstone_2026_model_schema:
-        return (
-            CORNERSTONE_INDUSTRY_TO_MECS_3_1_NAICS_MAPPING,
-            CORNERSTONE_INDUSTRY_TO_MECS_3_1_NAICS_SUBTRACTION_MAPPING,
-        )
+    """Return the Cornerstone-frame (mapping, subtraction_mapping) for MECS 3.1 NAICS."""
     return (
-        CEDA_INDUSTRY_TO_MECS_3_1_NAICS_MAPPING,
-        CEDA_INDUSTRY_TO_MECS_3_1_NAICS_SUBTRACTION_MAPPING,
+        CORNERSTONE_INDUSTRY_TO_MECS_3_1_NAICS_MAPPING,
+        CORNERSTONE_INDUSTRY_TO_MECS_3_1_NAICS_SUBTRACTION_MAPPING,
     )
 
 
diff --git a/bedrock/transform/allocation/co2/non_energy_fuels_natural_gas.py b/bedrock/transform/allocation/co2/non_energy_fuels_natural_gas.py
index 4ff8b1d4..df61a42a 100644
--- a/bedrock/transform/allocation/co2/non_energy_fuels_natural_gas.py
+++ b/bedrock/transform/allocation/co2/non_energy_fuels_natural_gas.py
@@ -17,12 +17,7 @@
     CORNERSTONE_INDUSTRY_TO_MECS_2_1_NAICS_MAPPING,
     CORNERSTONE_INDUSTRY_TO_MECS_2_1_NAICS_SUBTRACTION_MAPPING,
 )
-from bedrock.transform.allocation.mappings.v7.ceda_mecs import (
-    CEDA_INDUSTRY_TO_MECS_2_1_NAICS_MAPPING,
-    CEDA_INDUSTRY_TO_MECS_2_1_NAICS_SUBTRACTION_MAPPING,
-)
 from bedrock.transform.allocation.utils import get_allocation_sectors
-from bedrock.utils.config.usa_config import get_usa_config
 from bedrock.utils.economic.units import MEGATONNE_TO_KG
 
 logger = logging.getLogger(__name__)
@@ -32,15 +27,10 @@ def _get_mecs_2_1_naics_mappings() -> tuple[
     dict[tuple[str, ...], tuple[str, ...]],
     dict[tuple[str, ...], tuple[tuple[str, ...], tuple[str, ...]]],
 ]:
-    """Return (mapping, subtraction_mapping) for MECS 2.1 NAICS; use CORNERSTONE when schema flag is on."""
-    if get_usa_config().use_cornerstone_2026_model_schema:
-        return (
-            CORNERSTONE_INDUSTRY_TO_MECS_2_1_NAICS_MAPPING,
-            CORNERSTONE_INDUSTRY_TO_MECS_2_1_NAICS_SUBTRACTION_MAPPING,
-        )
+    """Return the Cornerstone-frame (mapping, subtraction_mapping) for MECS 2.1 NAICS."""
     return (
-        CEDA_INDUSTRY_TO_MECS_2_1_NAICS_MAPPING,
-        CEDA_INDUSTRY_TO_MECS_2_1_NAICS_SUBTRACTION_MAPPING,
+        CORNERSTONE_INDUSTRY_TO_MECS_2_1_NAICS_MAPPING,
+        CORNERSTONE_INDUSTRY_TO_MECS_2_1_NAICS_SUBTRACTION_MAPPING,
     )
 
 
diff --git a/bedrock/transform/allocation/co2/non_energy_fuels_petrol.py b/bedrock/transform/allocation/co2/non_energy_fuels_petrol.py
index 44e8956f..280ac646 100644
--- a/bedrock/transform/allocation/co2/non_energy_fuels_petrol.py
+++ b/bedrock/transform/allocation/co2/non_energy_fuels_petrol.py
@@ -17,12 +17,7 @@
     CORNERSTONE_INDUSTRY_TO_MECS_2_1_NAICS_MAPPING,
     CORNERSTONE_INDUSTRY_TO_MECS_2_1_NAICS_SUBTRACTION_MAPPING,
 )
-from bedrock.transform.allocation.mappings.v7.ceda_mecs import (
-    CEDA_INDUSTRY_TO_MECS_2_1_NAICS_MAPPING,
-    CEDA_INDUSTRY_TO_MECS_2_1_NAICS_SUBTRACTION_MAPPING,
-)
 from bedrock.transform.allocation.utils import get_allocation_sectors
-from bedrock.utils.config.usa_config import get_usa_config
 from bedrock.utils.economic.units import MEGATONNE_TO_KG
 
 logger = logging.getLogger(__name__)
@@ -32,15 +27,10 @@ def _get_mecs_2_1_naics_mappings() -> tuple[
     dict[tuple[str, ...], tuple[str, ...]],
     dict[tuple[str, ...], tuple[tuple[str, ...], tuple[str, ...]]],
 ]:
-    """Return (mapping, subtraction_mapping) for MECS 2.1 NAICS; use CORNERSTONE when schema flag is on."""
-    if get_usa_config().use_cornerstone_2026_model_schema:
-        return (
-            CORNERSTONE_INDUSTRY_TO_MECS_2_1_NAICS_MAPPING,
-            CORNERSTONE_INDUSTRY_TO_MECS_2_1_NAICS_SUBTRACTION_MAPPING,
-        )
+    """Return the Cornerstone-frame (mapping, subtraction_mapping) for MECS 2.1 NAICS."""
     return (
-        CEDA_INDUSTRY_TO_MECS_2_1_NAICS_MAPPING,
-        CEDA_INDUSTRY_TO_MECS_2_1_NAICS_SUBTRACTION_MAPPING,
+        CORNERSTONE_INDUSTRY_TO_MECS_2_1_NAICS_MAPPING,
+        CORNERSTONE_INDUSTRY_TO_MECS_2_1_NAICS_SUBTRACTION_MAPPING,
     )
 
 
@@ -99,7 +89,6 @@ def allocate_non_energy_fuels_petrol() -> pd.Series[float]:
     use = use_table_series_ceda_allocator_to_cornerstone_schema(
         load_bea_use_table(), get_allocation_sectors(), "324110"
     )
-    use_cornerstone = get_usa_config().use_cornerstone_2026_model_schema
     mapping, subtraction_mapping = _get_mecs_2_1_naics_mappings()
     for (
         ceda_industries,
@@ -121,15 +110,6 @@ def allocate_non_energy_fuels_petrol() -> pd.Series[float]:
 
         for ceda_industry in ceda_industries:
             industry_use = float(total_use_ser[ceda_industry])
-            if not use_cornerstone:
-                if len(ceda_industries) == 1:
-                    assert (
-                        industry_use == total_use
-                    ), f"There is only one sector in ceda_industries {ceda_industries}, but use by the industry {industry_use} != total_use ({total_use})"
-                else:
-                    assert (
-                        industry_use <= total_use
-                    ), f"There are more than one sector in ceda_industries {ceda_industries}, but use by a child industry {ceda_industry} ({industry_use}) > total_use ({total_use})"
             if ceda_industry in ("324121", "324122"):
                 # Allocate asphalt and HGL emissions to asphalt industries (324121 and 324122)
                 allocated[ceda_industry] = (
diff --git a/bedrock/transform/allocation/derived.py b/bedrock/transform/allocation/derived.py
index d587552a..797ae32e 100644
--- a/bedrock/transform/allocation/derived.py
+++ b/bedrock/transform/allocation/derived.py
@@ -14,14 +14,10 @@
     get_activitytosector_mapping,
 )
 from bedrock.utils.schemas.cornerstone_schemas import CORNERSTONE_INDUSTRIES_ELEC
-from bedrock.utils.taxonomy.bea.ceda_v7 import CEDA_V7_SECTORS
 from bedrock.utils.taxonomy.cornerstone.industries import (
     INDUSTRIES,
     WASTE_DISAGG_INDUSTRIES,
 )
-from bedrock.utils.taxonomy.mappings.bea_v2017_industry__bea_v2017_commodity import (
-    load_bea_v2017_industry_to_bea_v2017_commodity,
-)
 
 logger = logging.getLogger(__name__)
 
@@ -198,18 +194,12 @@ def map_fbs_sectors_to_model_schema(fbs: pd.DataFrame) -> pd.DataFrame:
         )
         fbs2['NAICS_6'] = fbs2['NAICS_6'].fillna(fbs2['SectorProducedBy'])
 
-        if get_usa_config().use_cornerstone_2026_model_schema:
-            mapping = _build_mapping_with_allocations(
-                get_activitytosector_mapping('Cornerstone_2025'),
-                use_output_weights=False,
-            )
-            if get_usa_config().implement_electricity_disaggregation:
-                mapping = _apply_electricity_disagg_cornerstone_mapping(mapping)
-        else:
-            mapping = _build_mapping_with_allocations(
-                get_activitytosector_mapping('CEDA_2025'),
-                use_output_weights=False,
-            )
+        mapping = _build_mapping_with_allocations(
+            get_activitytosector_mapping('Cornerstone_2025'),
+            use_output_weights=False,
+        )
+        if get_usa_config().implement_electricity_disaggregation:
+            mapping = _apply_electricity_disagg_cornerstone_mapping(mapping)
 
     pre_total = float(fbs2['FlowAmount'].sum())
     fbs2 = (
@@ -433,41 +423,12 @@ def load_E_from_flowsa() -> pd.DataFrame:
     new_index = E_usa.index.map(lambda x: reverse.get(x, x))
     E_usa = E_usa.groupby(new_index).agg('sum')
 
-    # Collapse across sectors (when CEDA: group BEA→CEDA; when Cornerstone: already in schema)
-    if get_usa_config().use_cornerstone_2026_model_schema:
-        if get_usa_config().implement_electricity_disaggregation:
-            target_columns = [str(sector) for sector in CORNERSTONE_INDUSTRIES_ELEC]
-        else:
-            target_columns = [str(sector) for sector in INDUSTRIES]
-        # E_usa already has Cornerstone columns from derive_E_usa_emissions_sources
-        E_usa = E_usa.reindex(columns=target_columns, fill_value=0)
+    # Collapse across sectors (already in Cornerstone schema from
+    # map_fbs_sectors_to_model_schema).
+    if usa.implement_electricity_disaggregation:
+        target_columns = [str(sector) for sector in CORNERSTONE_INDUSTRIES_ELEC]
     else:
-        mapping = load_bea_v2017_industry_to_bea_v2017_commodity()
-        target_columns = [str(sector) for sector in CEDA_V7_SECTORS]
-        col_to_target = {k: v[0] for k, v in mapping.items()}
-        for c in E_usa.columns:
-            if c not in col_to_target and c in target_columns:
-                col_to_target[c] = c  # type: ignore
-        dropped_by_groupby = sorted(set(E_usa.columns) - set(col_to_target.keys()))
-        if dropped_by_groupby:
-            logger.warning(
-                'E_usa columns with no mapping (dropped by groupby): %s',
-                dropped_by_groupby,
-            )
-        E_usa = E_usa.groupby(col_to_target, axis=1).sum()  # type: ignore
-        target_set = set(target_columns)
-        extra = sorted(set(E_usa.columns) - target_set)
-        missing = sorted(target_set - set(E_usa.columns))
-        if extra:
-            logger.warning(
-                'E_usa columns not in target schema (will be dropped by reindex): %s',
-                extra,
-            )
-        if missing:
-            logger.debug(
-                'Target schema columns missing from E_usa (will be filled with 0): %s',
-                missing,
-            )
-        E_usa = E_usa.reindex(columns=target_columns, fill_value=0)
+        target_columns = [str(sector) for sector in INDUSTRIES]
+    E_usa = E_usa.reindex(columns=target_columns, fill_value=0)
 
     return E_usa
diff --git a/bedrock/transform/allocation/utils.py b/bedrock/transform/allocation/utils.py
index 0c06f782..a3a9b953 100644
--- a/bedrock/transform/allocation/utils.py
+++ b/bedrock/transform/allocation/utils.py
@@ -5,21 +5,15 @@
 
 import pandas as pd
 
-from bedrock.utils.config.usa_config import get_usa_config
-from bedrock.utils.taxonomy.bea.ceda_v7 import CEDA_V7_SECTORS
 from bedrock.utils.taxonomy.cornerstone.industries import INDUSTRIES
 
 
 def get_allocation_sectors() -> list[str]:
-    """
-    Return the sector list (taxonomy) for allocation based on model config.
+    """Return the allocation sector list: Cornerstone INDUSTRIES.
 
-    When use_cornerstone_2026_model_schema is True, returns Cornerstone INDUSTRIES;
-    otherwise returns CEDA v7 sectors.
+    The Cornerstone frame matches the pinned allocation FBA parquets on GCS.
     """
-    if get_usa_config().use_cornerstone_2026_model_schema:
-        return list(INDUSTRIES)
-    return list(CEDA_V7_SECTORS)
+    return list(INDUSTRIES)
 
 
 def parse_index_with_aggregates(
diff --git a/bedrock/transform/eeio/derived.py b/bedrock/transform/eeio/derived.py
index 4f3d75da..391e22c0 100644
--- a/bedrock/transform/eeio/derived.py
+++ b/bedrock/transform/eeio/derived.py
@@ -13,17 +13,6 @@
     load_2012_URdom_usa,
     load_2012_YR_usa,
 )
-from bedrock.extract.iot.io_2017 import load_summary_Uimp_usa
-from bedrock.transform.allocation.derived import derive_E_usa
-from bedrock.transform.eeio.derived_2017 import (
-    derive_2017_Aq_usa,
-    derive_2017_U_set_usa,
-    derive_2017_Vnorm_scrap_corrected,
-    derive_2017_x_usa,
-    derive_2017_Ytot_usa_matrix_set,
-    derive_summary_Yimp_usa,
-    derive_summary_Ytot_usa_matrix_set,
-)
 from bedrock.transform.eeio.derived_cornerstone import (
     derive_cornerstone_Aq_scaled,
     derive_cornerstone_B_non_finetuned,
@@ -32,36 +21,12 @@
     derive_cornerstone_y_nab,
     derive_cornerstone_ydom_and_yimp,
 )
-from bedrock.transform.eeio.scale_abq_via_summary import (
-    scale_detail_A_based_on_summary_A,
-    scale_detail_B_based_on_summary_q,
-    scale_detail_q_based_on_summary_q,
-)
-from bedrock.utils.config.usa_config import get_usa_config
-from bedrock.utils.economic.inflation_helpers_ceda import (
-    inflate_A_matrix,
-    inflate_B_matrix,
-    inflate_q_or_y,
-)
 from bedrock.utils.emissions.characterization import build_ghg_characterization_matrix
 from bedrock.utils.emissions.ghg import GHG
-from bedrock.utils.math.disaggregation import disaggregate_vector
-from bedrock.utils.math.formulas import (
-    compute_B_ind_matrix,
-    compute_B_matrix,
-    compute_y_for_national_accounting_balance,
-    compute_y_imp,
-)
 from bedrock.utils.math.handle_negatives import handle_negative_vector_values
-from bedrock.utils.math.split_using_aggregated_weights import (
-    split_vector_using_agg_ratio,
-)
 from bedrock.utils.schemas.single_region_schemas import (
-    AMatrix,
-    BMatrix,
     ExportsVectorSchema,
     ImportsVectorSchema,
-    QVectorSchema,
     UMatrix,
     YVectorSchema,
 )
@@ -75,12 +40,6 @@
     USA_2017_FINAL_DEMAND_EXPORT_CODE,
     USA_2017_FINAL_DEMAND_IMPORT_CODE,
 )
-from bedrock.utils.taxonomy.bea.v2017_industry_summary import (
-    USA_2017_SUMMARY_INDUSTRY_CODES,
-)
-from bedrock.utils.taxonomy.bea_v2017_to_ceda_v7_helpers import (
-    get_bea_v2017_summary_to_ceda_corresp_df,
-)
 from bedrock.utils.taxonomy.mappings.ceda_v7__ceda_v5 import CEDA_V5_TO_CEDA_V7_CODES
 
 logger = logging.getLogger(__name__)
@@ -88,254 +47,31 @@
 
 @functools.cache
 def derive_B_usa_non_finetuned() -> pd.DataFrame:
-    if get_usa_config().use_cornerstone_2026_model_schema:
-        return derive_cornerstone_B_non_finetuned()
-    else:
-        E_usa = derive_E_usa()
-        # B_usa_2017 has 2022 emissions but 2017 economic data
-        b_usa_2017 = derive_B_usa_via_vnorm(E_usa=E_usa)
-        # Scale the economic data part of B_usa_2017 to 2022
-        B_usa = inflate_B_matrix(
-            scale_detail_B_based_on_summary_q(
-                B=b_usa_2017,
-                original_year=get_usa_config().usa_detail_original_year,
-                target_year=get_usa_config().usa_io_data_year,
-            ),
-            original_year=get_usa_config().usa_io_data_year,
-            target_year=get_usa_config().model_base_year,
-        )
-
-        return pt.DataFrame[BMatrix](BMatrix.validate(B_usa))
+    return derive_cornerstone_B_non_finetuned()
 
 
 def derive_Y_and_trade_matrix_usa_from_summary_target_year_ytot_and_structural_reflection() -> (
     SingleRegionYtotAndTradeVectorSet
 ):
-    """
-    We get detail Y and Trade Matrix in the following steps:
-    - get `target_year` summary Y and Trade Matrix and 2017 detail Y and Trade Matrix
-    - structurally reflect `target_year` summary Y and Trade Matrix into `target_year` detail Y and Trade Matrix using 2017 detail Y and Trade Matrix
-    - split the reflected `target_year` detail Y and Trade Matrix into `target_year` detail Y and Trade Matrix,
-      using weights derived from `target_year` summary Y and Trade Matrix
-    """
-    if get_usa_config().use_cornerstone_2026_model_schema:
-        return derive_cornerstone_Y_and_trade_scaled()
-    else:
-        detail_2017_YandTradeset = derive_2017_Ytot_usa_matrix_set()
-
-        summary_to_ceda_corresp_df = get_bea_v2017_summary_to_ceda_corresp_df()
-
-        summary_Y_matrix_set = derive_summary_Ytot_usa_matrix_set(
-            get_usa_config().usa_io_data_year
-        )
-
-        ytot = inflate_q_or_y(
-            disaggregate_vector(
-                base_series=summary_Y_matrix_set.ytot,
-                weight_series=detail_2017_YandTradeset.ytot,
-                corresp_df=summary_to_ceda_corresp_df,
-            ),
-            original_year=get_usa_config().usa_io_data_year,
-            target_year=get_usa_config().model_base_year,
-        )
-        exports = inflate_q_or_y(
-            disaggregate_vector(
-                base_series=summary_Y_matrix_set.exports,
-                weight_series=detail_2017_YandTradeset.exports,
-                corresp_df=summary_to_ceda_corresp_df,
-            ),
-            original_year=get_usa_config().usa_io_data_year,
-            target_year=get_usa_config().model_base_year,
-        )
-        imports = inflate_q_or_y(
-            handle_negative_vector_values(
-                disaggregate_vector(
-                    base_series=summary_Y_matrix_set.imports,
-                    weight_series=detail_2017_YandTradeset.imports,
-                    corresp_df=summary_to_ceda_corresp_df,
-                )
-            ),
-            original_year=get_usa_config().usa_io_data_year,
-            target_year=get_usa_config().model_base_year,
-        )
-
-        return SingleRegionYtotAndTradeVectorSet(
-            ytot=YVectorSchema.validate(ytot),
-            exports=ExportsVectorSchema.validate(exports),
-            imports=ImportsVectorSchema.validate(imports),
-        )
+    """Detail Y and trade vectors, structurally reflected to the target year."""
+    return derive_cornerstone_Y_and_trade_scaled()
 
 
 @functools.cache
 def derive_y_for_national_accounting_balance_usa() -> pd.Series[float]:
-    """
-    We get Y for national accounting balance via the following equations:
-
-        y_nab = y_dom + exports
-        y_nab = (y_tot - y_imp) + exports
-        y_nab = (y_tot - (imports - Uimp_row_sum)) + exports
-
-    Because we only have Uimp in 2017 detail, we get 2022 detail y in the
-    following steps:
-    - calculate 2017 detail y_nab
-    - calculate 2022 summary y_nab
-    - scale 2017 detail y_nab to 2022 detail y_nab using 2022 summary y_nab
-    """
-    if get_usa_config().use_cornerstone_2026_model_schema:
-        return derive_cornerstone_y_nab()
-    else:
-        detail_2017_YandTradeset = derive_2017_Ytot_usa_matrix_set()
-
-        y_national_acct_balance_detail_2017 = compute_y_for_national_accounting_balance(
-            y_tot=detail_2017_YandTradeset.ytot,
-            y_imp=compute_y_imp(
-                imports=detail_2017_YandTradeset.imports,
-                Uimp=derive_2017_U_set_usa().Uimp,
-            ),
-            exports=detail_2017_YandTradeset.exports,
-        )
-
-        summary_2022_Y_matrix_set = derive_summary_Ytot_usa_matrix_set(
-            get_usa_config().usa_io_data_year
-        )
-
-        y_national_acct_balance_summary_2022 = (
-            compute_y_for_national_accounting_balance(
-                y_tot=summary_2022_Y_matrix_set.ytot,
-                y_imp=compute_y_imp(
-                    imports=summary_2022_Y_matrix_set.imports,
-                    Uimp=load_summary_Uimp_usa(get_usa_config().usa_io_data_year).loc[
-                        USA_2017_SUMMARY_INDUSTRY_CODES,
-                        USA_2017_SUMMARY_INDUSTRY_CODES,
-                    ],
-                ),
-                exports=summary_2022_Y_matrix_set.exports,
-            )
-        )
-
-        summary_to_ceda_corresp_df = get_bea_v2017_summary_to_ceda_corresp_df()
-        y_national_acct_balance_detail_2022 = inflate_q_or_y(
-            disaggregate_vector(
-                corresp_df=summary_to_ceda_corresp_df,
-                base_series=y_national_acct_balance_summary_2022,
-                weight_series=y_national_acct_balance_detail_2017,
-            ),
-            original_year=get_usa_config().usa_io_data_year,
-            target_year=get_usa_config().model_base_year,
-        )
-
-        # NOTE: original y values have some negative values
-        # that will distort the scaling process that uses the ytot here in non-US countries.
-        # We make a data assumption here to set them to 0 in order to make the scaling process valid.
-        # TODO: this is a temporary solution, we need a y pandera type to enforce this data assumption.
-        return YVectorSchema.validate(
-            handle_negative_vector_values(y_national_acct_balance_detail_2022)
-        )
+    """y for the national accounting balance: y_nab = (y_tot - y_imp) + exports."""
+    return derive_cornerstone_y_nab()
 
 
 def derive_ydom_and_yimp_usa() -> SingleRegionYVectorSet:
-    """
-    This function is only used in derivation of Y_oecd, where we need y_dom and y_imp separately
-    to populate the diagonal and off-diagonal elements of Y_oecd.
-    We get ydom and yimp in the following steps:
-    1. Get 2022 detail ytot
-    2. Get 2022 summary ydom and yimp
-    3. Split 2022 detail ytot to ydom and yimp using 2022 summary ydom and yimp ratios
-    """
-    if get_usa_config().use_cornerstone_2026_model_schema:
-        return derive_cornerstone_ydom_and_yimp()
-    else:
-        # Load summary 2022 ytot and yimp
-        summary_2022_ytot = derive_summary_Ytot_usa_matrix_set(2022).ytot
-        summary_2022_yimp = derive_summary_Yimp_usa(2022).sum(axis=1)
-        # Derive ydom over ytot ratio
-        # NOTE: in case some yimp values are larger than ytot values, causing ydom to be negative,
-        # which could be reasonable but we don't want to take negative ydom values.
-        # We handle this by setting ydom to 0 when yimp is larger than ytot.
-        summary_2022_ydom_over_ytot_ratio = handle_negative_vector_values(
-            1 - (summary_2022_yimp / summary_2022_ytot).fillna(0.0)
-        )
-
-        # Derive 2022 detail ytot
-        detail_2022_ytot = disaggregate_vector(
-            corresp_df=get_bea_v2017_summary_to_ceda_corresp_df(),
-            base_series=summary_2022_ytot,
-            weight_series=derive_2017_Ytot_usa_matrix_set().ytot,
-        )
-        # Split detail 2022 ytot to ydom and yimp
-        ydom, yimp = split_vector_using_agg_ratio(
-            base_series=detail_2022_ytot,
-            agg_ratio_series=summary_2022_ydom_over_ytot_ratio,
-            corresp_df=get_bea_v2017_summary_to_ceda_corresp_df(),
-        )
-        return SingleRegionYVectorSet(
-            ydom=YVectorSchema.validate(ydom), yimp=YVectorSchema.validate(yimp)
-        )
+    """ydom and yimp split, used to populate diagonal/off-diagonal Y_oecd."""
+    return derive_cornerstone_ydom_and_yimp()
 
 
 @functools.cache
 def derive_Aq_usa() -> SingleRegionAqMatrixSet:
-    """
-    This function derives Aq_usa in `target_year` USD.
-
-    For Adom and Aimp, we get `target_year` detail matrices by
-    year-scaling 2017 detail Adom and Aimp separately using the
-    `target_year` summary Adom and Aimp, respectively
-
-    For q, we get 2017 detail q from 2017 detail IOTs, then inflate it to `target_year` USD.
-    """
-    if get_usa_config().use_cornerstone_2026_model_schema:
-        return derive_cornerstone_Aq_scaled()
-    else:
-        detail_2017_Aq_set = derive_2017_Aq_usa()
-
-        target_year = get_usa_config().usa_io_data_year
-        original_year = get_usa_config().usa_detail_original_year
-
-        Adom = scale_detail_A_based_on_summary_A(
-            A=detail_2017_Aq_set.Adom,
-            target_year=target_year,
-            original_year=original_year,
-            dom_or_imp_or_total='dom',
-        )
-        Aimp = scale_detail_A_based_on_summary_A(
-            A=detail_2017_Aq_set.Aimp,
-            target_year=target_year,
-            original_year=original_year,
-            dom_or_imp_or_total='imp',
-        )
-
-        q = scale_detail_q_based_on_summary_q(
-            q=detail_2017_Aq_set.scaled_q,
-            target_year=target_year,
-            original_year=original_year,
-        )
-        assert q is not None, 'q in derive_Aq_usa() is None'
-
-        # NOTE: the Adom/Aimp/q being passed in are already in `target_year`.
-        # We just need to inflate them to CEDA base year.
-        # TODO: type inflate_A_matrix as DataFrame[AMatrix]
-        Adom = inflate_A_matrix(  # type: ignore[assignment]
-            Adom,
-            target_year=get_usa_config().model_base_year,
-            original_year=target_year,
-        )
-        Aimp = inflate_A_matrix(  # type: ignore[assignment]
-            Aimp,
-            target_year=get_usa_config().model_base_year,
-            original_year=target_year,
-        )
-        q = inflate_q_or_y(
-            q,
-            target_year=get_usa_config().model_base_year,
-            original_year=target_year,
-        )
-
-        return SingleRegionAqMatrixSet(
-            Adom=pt.DataFrame[AMatrix](Adom),
-            Aimp=pt.DataFrame[AMatrix](Aimp),
-            scaled_q=QVectorSchema.validate(q),
-        )
+    """Year-scaled and inflated A matrices and q (model_base_year USD)."""
+    return derive_cornerstone_Aq_scaled()
 
 
 @functools.cache
@@ -362,18 +98,6 @@ def derive_D_usa() -> pd.DataFrame:
     return D
 
 
-def derive_B_usa_via_vnorm(*, E_usa: pd.DataFrame) -> pd.DataFrame:
-    x = derive_2017_x_usa()
-    Vnorm = derive_2017_Vnorm_scrap_corrected()
-
-    Bi = compute_B_ind_matrix(E=E_usa, x=x)
-    Bc = compute_B_matrix(B_ind=Bi, V_norm=Vnorm)
-
-    Bc.columns.name = 'sector'
-    Bc.index.name = 'ghg'
-    return Bc
-
-
 def derive_v5_U_usa() -> SingleRegionUMatrixSet:
     URtot_usa = load_2012_UR_usa()
     URdom_usa = load_2012_URdom_usa()
@@ -434,40 +158,5 @@ def derive_v5_detail_Ytot_usa_matrix_set() -> SingleRegionYtotAndTradeVectorSet:
 
 @functools.cache
 def derive_v7_detail_Ytot_usa_matrix_set() -> SingleRegionYtotAndTradeVectorSet:
-    """
-    Derive US Ytot and trade vectors using v7 USA IO tables.
-
-    NOTE: Ytot_usa can't be negative, because we need to use it to ABSR Ytot of other countries.
-    """
-    if get_usa_config().use_cornerstone_2026_model_schema:
-        return derive_cornerstone_detail_Ytot_matrix_set()
-    else:
-        detail_2017_YandTradeset = derive_2017_Ytot_usa_matrix_set()
-
-        summary_to_ceda_corresp_df = get_bea_v2017_summary_to_ceda_corresp_df()
-
-        summary_Y_matrix_set = derive_summary_Ytot_usa_matrix_set(year=2022)
-
-        ytot = disaggregate_vector(
-            base_series=summary_Y_matrix_set.ytot,
-            weight_series=detail_2017_YandTradeset.ytot,
-            corresp_df=summary_to_ceda_corresp_df,
-        )
-        exports = disaggregate_vector(
-            base_series=summary_Y_matrix_set.exports,
-            weight_series=detail_2017_YandTradeset.exports,
-            corresp_df=summary_to_ceda_corresp_df,
-        )
-        imports = handle_negative_vector_values(
-            disaggregate_vector(
-                base_series=summary_Y_matrix_set.imports,
-                weight_series=detail_2017_YandTradeset.imports,
-                corresp_df=summary_to_ceda_corresp_df,
-            )
-        )
-
-        return SingleRegionYtotAndTradeVectorSet(
-            ytot=YVectorSchema.validate(ytot),
-            exports=ExportsVectorSchema.validate(exports),
-            imports=ImportsVectorSchema.validate(imports),
-        )
+    """US Ytot and trade vectors (non-negative ytot, used to ABSR other countries)."""
+    return derive_cornerstone_detail_Ytot_matrix_set()
diff --git a/bedrock/transform/eeio/nowcast.py b/bedrock/transform/eeio/nowcast.py
index ab0f4baa..b0a3bb3b 100644
--- a/bedrock/transform/eeio/nowcast.py
+++ b/bedrock/transform/eeio/nowcast.py
@@ -35,14 +35,11 @@
 from __future__ import annotations
 
 import functools
-from collections.abc import Generator
-from contextlib import contextmanager
 
 import pandas as pd
 
 from bedrock.transform.allocation.derived import map_fbs_sectors_to_model_schema
 from bedrock.transform.flowbysector import FlowBySector
-from bedrock.utils.config.usa_config import get_usa_config
 from bedrock.utils.taxonomy.bea.v2017_final_demand import BEA_2017_FINAL_DEMAND_CODES
 
 _SECTOR_SWAP = {
@@ -51,33 +48,13 @@
 }
 
 
-@contextmanager
-def _force_cornerstone_model_schema() -> Generator[None, None, None]:
-    """
-    ``map_fbs_sectors_to_model_schema`` branches on
-    ``get_usa_config().use_cornerstone_2026_model_schema`` (default False) to
-    pick the Cornerstone_2025 vs. CEDA_2025 crosswalk. Temporarily forces it
-    True on the live global config (rather than swapping to a whole different
-    named config via ``temp_usa_config``, which would also reset unrelated
-    fields like ``model_base_year``) and restores the prior value after.
-    """
-    cfg = get_usa_config()
-    previous = cfg.use_cornerstone_2026_model_schema
-    cfg.use_cornerstone_2026_model_schema = True
-    try:
-        yield
-    finally:
-        cfg.use_cornerstone_2026_model_schema = previous
-
-
 def _resolve_both_sector_columns(fbs: pd.DataFrame) -> pd.DataFrame:
     """Apply ``map_fbs_sectors_to_model_schema`` to both SectorProducedBy and
     SectorConsumedBy (via a temporary swap - see module docstring)."""
-    with _force_cornerstone_model_schema():
-        fbs = map_fbs_sectors_to_model_schema(fbs.rename(columns=_SECTOR_SWAP)).rename(
-            columns=_SECTOR_SWAP
-        )
-        return map_fbs_sectors_to_model_schema(fbs)
+    fbs = map_fbs_sectors_to_model_schema(fbs.rename(columns=_SECTOR_SWAP)).rename(
+        columns=_SECTOR_SWAP
+    )
+    return map_fbs_sectors_to_model_schema(fbs)
 
 
 @functools.cache
diff --git a/bedrock/transform/eeio/scale_abq_via_summary.py b/bedrock/transform/eeio/scale_abq_via_summary.py
deleted file mode 100644
index b0f8bd62..00000000
--- a/bedrock/transform/eeio/scale_abq_via_summary.py
+++ /dev/null
@@ -1,141 +0,0 @@
-from __future__ import annotations
-
-import typing as ta
-
-import numpy as np
-import pandas as pd
-import pandera.pandas as pa
-import pandera.typing as pt
-
-from bedrock.transform.eeio.derived_2017 import (
-    derive_summary_Adom_usa,
-    derive_summary_Aimp_usa,
-    derive_summary_q_usa,
-)
-from bedrock.utils.math.formulas import compute_total_industry_inputs
-from bedrock.utils.schemas.single_region_schemas import AMatrix
-from bedrock.utils.taxonomy.bea.ceda_v7 import CEDA_V7_SECTORS
-from bedrock.utils.taxonomy.bea.matrix_mappings import USA_SUMMARY_MUT_YEARS
-from bedrock.utils.taxonomy.bea_v2017_to_ceda_v7_helpers import (
-    load_bea_v2017_summary_to_ceda_v7,
-)
-
-
-@pa.check_output(AMatrix.to_schema())
-def scale_detail_A_based_on_summary_A(
-    A: pd.DataFrame,
-    target_year: USA_SUMMARY_MUT_YEARS,
-    original_year: USA_SUMMARY_MUT_YEARS,
-    dom_or_imp_or_total: ta.Literal["dom", "imp", "total"],
-) -> pt.DataFrame[AMatrix]:
-    """
-    Derive A_ratio from the summary tables and scale A element-wise based on A_ratio.
-    """
-
-    match dom_or_imp_or_total:
-        case "dom":
-            A_summary_base = derive_summary_Adom_usa(original_year)
-            A_summary_target = derive_summary_Adom_usa(target_year)
-        case "imp":
-            A_summary_base = derive_summary_Aimp_usa(original_year)
-            A_summary_target = derive_summary_Aimp_usa(target_year)
-        case "total":
-            A_summary_base = derive_summary_Adom_usa(
-                original_year
-            ) + derive_summary_Aimp_usa(original_year)
-            A_summary_target = derive_summary_Adom_usa(
-                target_year
-            ) + derive_summary_Aimp_usa(target_year)
-
-    summary_to_ceda_v7 = load_bea_v2017_summary_to_ceda_v7()
-    summary_ratios = (A_summary_target / A_summary_base).fillna(1.0)
-    summary_ratios[np.isinf(summary_ratios)] = 1.0
-
-    A_scaled = A.copy()
-
-    block_rows = []
-    for i, row in summary_ratios.iterrows():
-        if i not in summary_to_ceda_v7:
-            continue
-        block_mat = pd.DataFrame(
-            index=summary_to_ceda_v7[i],  # type: ignore
-            columns=CEDA_V7_SECTORS,
-            data=0,
-            dtype=float,
-        )
-        for col_summary_sector, val in row.items():
-            if val == 0:
-                continue
-            if col_summary_sector in ["Used", "Other"]:
-                continue
-            col_ceda_sectors = summary_to_ceda_v7[col_summary_sector]  # type: ignore
-            block_mat.loc[:, col_ceda_sectors] = val
-        block_rows.append(block_mat)
-    ratio_multiplier = pd.concat(block_rows, axis=0).loc[
-        A_scaled.index, A_scaled.columns
-    ]
-    A_scaled_by_A_summary = A_scaled * ratio_multiplier
-
-    # Adjust A if column sum exceeds 1, force the column sum to be 0.98
-    A_scaled_by_A_summary = _rescale_A_matrix_by_capping_total_industry_inputs(
-        A=A_scaled_by_A_summary, max_total_industry_inputs=0.98
-    )
-
-    assert (
-        compute_total_industry_inputs(A=A_scaled_by_A_summary) <= 1
-    ).all(), "A column sums exceed 1 after scaled via A_ratio."
-
-    return pt.DataFrame[AMatrix](A_scaled_by_A_summary)
-
-
-def scale_detail_q_based_on_summary_q(
-    q: pd.Series[float],
-    target_year: USA_SUMMARY_MUT_YEARS,
-    original_year: USA_SUMMARY_MUT_YEARS,
-) -> pd.Series[float]:
-    q_scaled = q.copy()
-    q_summary_base = derive_summary_q_usa(original_year)
-    q_summary_target = derive_summary_q_usa(target_year)
-    q_ratio = (q_summary_target / q_summary_base).fillna(1.0)
-
-    summary_to_ceda_v7 = load_bea_v2017_summary_to_ceda_v7()
-    for i, val in q_ratio.items():
-        if i not in summary_to_ceda_v7:
-            continue
-        ceda_sectors = summary_to_ceda_v7[i]  # type: ignore
-        q_scaled.loc[ceda_sectors] *= val
-
-    return q_scaled
-
-
-def scale_detail_B_based_on_summary_q(
-    B: pd.DataFrame,
-    target_year: USA_SUMMARY_MUT_YEARS,
-    original_year: USA_SUMMARY_MUT_YEARS,
-) -> pd.DataFrame:
-    B_scaled = B.copy()
-    q_summary_base = derive_summary_q_usa(original_year)
-    q_summary_target = derive_summary_q_usa(target_year)
-    # b_ratio is the opposite of q_ratio because
-    # the price value for B is in the denominator
-    b_ratio = (q_summary_base / q_summary_target).fillna(1.0)
-
-    summary_to_ceda_v7 = load_bea_v2017_summary_to_ceda_v7()
-    for i, val in b_ratio.items():
-        if i not in summary_to_ceda_v7:
-            continue
-        ceda_sectors = summary_to_ceda_v7[i]  # type: ignore
-        B_scaled.loc[:, ceda_sectors] *= val
-
-    return B_scaled
-
-
-def _rescale_A_matrix_by_capping_total_industry_inputs(
-    *, A: pd.DataFrame, max_total_industry_inputs: float  # noqa: ARG001
-) -> pd.DataFrame:
-    A_rescaled = A.copy()
-    total_industry_inputs = compute_total_industry_inputs(A=A)
-    oob_idx = total_industry_inputs[total_industry_inputs > 1].index
-    for col in oob_idx:
-        A_rescaled[col] *= 0.98 / total_industry_inputs[col]
-    return A_rescaled
diff --git a/bedrock/utils/config/configs/2025_usa_cornerstone_full_model_electricity_disaggregation.yaml b/bedrock/utils/config/configs/2025_usa_cornerstone_full_model_electricity_disaggregation.yaml
index a80a07a3..76ca203b 100644
--- a/bedrock/utils/config/configs/2025_usa_cornerstone_full_model_electricity_disaggregation.yaml
+++ b/bedrock/utils/config/configs/2025_usa_cornerstone_full_model_electricity_disaggregation.yaml
@@ -1,5 +1,4 @@
 # Fully implemented Cornerstone 2025 model + waste + electricity reallocation + disaggregation.
-use_cornerstone_2026_model_schema: True
 use_cornerstone_ghg_model: true
 use_E_data_year_for_x_in_B: True
 implement_waste_disaggregation: True
diff --git a/bedrock/utils/config/configs/2025_usa_cornerstone_taxonomy_and_B_transformation.yaml b/bedrock/utils/config/configs/2025_usa_cornerstone_taxonomy_and_B_transformation.yaml
index 3dab08f2..054d461c 100644
--- a/bedrock/utils/config/configs/2025_usa_cornerstone_taxonomy_and_B_transformation.yaml
+++ b/bedrock/utils/config/configs/2025_usa_cornerstone_taxonomy_and_B_transformation.yaml
@@ -11,7 +11,6 @@
 #####
 # Methodology selection
 #####
-use_cornerstone_2026_model_schema: True
 use_E_data_year_for_x_in_B: True
 
 
diff --git a/bedrock/utils/config/configs/2025_usa_cornerstone_v0_2_electricity_disaggregation.yaml b/bedrock/utils/config/configs/2025_usa_cornerstone_v0_2_electricity_disaggregation.yaml
index 283b53dc..75cf4395 100644
--- a/bedrock/utils/config/configs/2025_usa_cornerstone_v0_2_electricity_disaggregation.yaml
+++ b/bedrock/utils/config/configs/2025_usa_cornerstone_v0_2_electricity_disaggregation.yaml
@@ -1,5 +1,4 @@
 # v0.2 + waste + electricity reallocation + 3-way split (no mixed units, no margins).
-use_cornerstone_2026_model_schema: True
 use_cornerstone_ghg_model: true
 use_E_data_year_for_x_in_B: True
 implement_waste_disaggregation: True
diff --git a/bedrock/utils/config/configs/2025_usa_cornerstone_v0_2_electricity_mixed_units.yaml b/bedrock/utils/config/configs/2025_usa_cornerstone_v0_2_electricity_mixed_units.yaml
index 840b6eab..2ca190f8 100644
--- a/bedrock/utils/config/configs/2025_usa_cornerstone_v0_2_electricity_mixed_units.yaml
+++ b/bedrock/utils/config/configs/2025_usa_cornerstone_v0_2_electricity_mixed_units.yaml
@@ -1,5 +1,4 @@
 # v0.2 electricity-disagg config + mixed units (221110 in MWh).
-use_cornerstone_2026_model_schema: True
 use_cornerstone_ghg_model: true
 use_E_data_year_for_x_in_B: True
 implement_waste_disaggregation: True
diff --git a/bedrock/utils/config/configs/2025_usa_cornerstone_v0_2_electricity_reallocation.yaml b/bedrock/utils/config/configs/2025_usa_cornerstone_v0_2_electricity_reallocation.yaml
index 0abaab18..87c76dc3 100644
--- a/bedrock/utils/config/configs/2025_usa_cornerstone_v0_2_electricity_reallocation.yaml
+++ b/bedrock/utils/config/configs/2025_usa_cornerstone_v0_2_electricity_reallocation.yaml
@@ -1,5 +1,4 @@
 # Fully implemented Cornerstone 2025 model + waste disagg + electricity co-production reallocation.
-use_cornerstone_2026_model_schema: True
 use_cornerstone_ghg_model: true
 use_E_data_year_for_x_in_B: True
 implement_waste_disaggregation: True
diff --git a/bedrock/utils/config/configs/2025_usa_cornerstone_v0_3.yaml b/bedrock/utils/config/configs/2025_usa_cornerstone_v0_3.yaml
index 2d44ac50..2775a23e 100644
--- a/bedrock/utils/config/configs/2025_usa_cornerstone_v0_3.yaml
+++ b/bedrock/utils/config/configs/2025_usa_cornerstone_v0_3.yaml
@@ -24,7 +24,6 @@ usa_ghg_data_year: 2024
 #####
 # Methodology selection
 #####
-use_cornerstone_2026_model_schema: True
 
 # GHG model: Cornerstone GHG FBS (2024 = UMD GHGIA inventory + 2022 MECS attribution).
 use_cornerstone_ghg_model: True
diff --git a/bedrock/utils/config/configs/test_usa_config_waste_disagg.yaml b/bedrock/utils/config/configs/test_usa_config_waste_disagg.yaml
index 7ced4da3..de38b204 100644
--- a/bedrock/utils/config/configs/test_usa_config_waste_disagg.yaml
+++ b/bedrock/utils/config/configs/test_usa_config_waste_disagg.yaml
@@ -1,4 +1,3 @@
 model_base_year: 2023
 usa_ghg_data_year: 2023
-use_cornerstone_2026_model_schema: True
 implement_waste_disaggregation: True
diff --git a/bedrock/utils/config/configs/test_usa_config_waste_disagg_electricity.yaml b/bedrock/utils/config/configs/test_usa_config_waste_disagg_electricity.yaml
index 61bf5589..ccbc84eb 100644
--- a/bedrock/utils/config/configs/test_usa_config_waste_disagg_electricity.yaml
+++ b/bedrock/utils/config/configs/test_usa_config_waste_disagg_electricity.yaml
@@ -1,5 +1,4 @@
 model_base_year: 2023
 usa_ghg_data_year: 2023
-use_cornerstone_2026_model_schema: True
 implement_waste_disaggregation: True
 implement_electricity_reallocation: True
diff --git a/bedrock/utils/config/configs/test_usa_config_waste_disagg_electricity_disaggregation.yaml b/bedrock/utils/config/configs/test_usa_config_waste_disagg_electricity_disaggregation.yaml
index 2dfbfb3f..18281052 100644
--- a/bedrock/utils/config/configs/test_usa_config_waste_disagg_electricity_disaggregation.yaml
+++ b/bedrock/utils/config/configs/test_usa_config_waste_disagg_electricity_disaggregation.yaml
@@ -1,6 +1,5 @@
 model_base_year: 2023
 usa_ghg_data_year: 2023
-use_cornerstone_2026_model_schema: True
 implement_waste_disaggregation: True
 implement_electricity_reallocation: True
 implement_electricity_disaggregation: True
diff --git a/bedrock/utils/config/configs/test_usa_config_waste_disagg_electricity_mixed_units.yaml b/bedrock/utils/config/configs/test_usa_config_waste_disagg_electricity_mixed_units.yaml
index dc01945b..13a21496 100644
--- a/bedrock/utils/config/configs/test_usa_config_waste_disagg_electricity_mixed_units.yaml
+++ b/bedrock/utils/config/configs/test_usa_config_waste_disagg_electricity_mixed_units.yaml
@@ -1,6 +1,5 @@
 model_base_year: 2023
 usa_ghg_data_year: 2023
-use_cornerstone_2026_model_schema: True
 implement_waste_disaggregation: True
 implement_electricity_reallocation: True
 implement_electricity_disaggregation: True
diff --git a/bedrock/utils/config/configs/useeio_phoebe_23.yaml b/bedrock/utils/config/configs/useeio_phoebe_23.yaml
index d2ae2a7e..aa1cc93f 100644
--- a/bedrock/utils/config/configs/useeio_phoebe_23.yaml
+++ b/bedrock/utils/config/configs/useeio_phoebe_23.yaml
@@ -16,7 +16,6 @@ usa_ghg_data_year: 2023
 #####
 # Methodology selection
 #####
-use_cornerstone_2026_model_schema: true
 use_E_data_year_for_x_in_B: true
 implement_waste_disaggregation: true
 
diff --git a/bedrock/utils/config/configs/v03_waterfall_ceda_g1a_schema_ghg.yaml b/bedrock/utils/config/configs/v03_waterfall_ceda_g1a_schema_ghg.yaml
index 5be21c1b..82415403 100644
--- a/bedrock/utils/config/configs/v03_waterfall_ceda_g1a_schema_ghg.yaml
+++ b/bedrock/utils/config/configs/v03_waterfall_ceda_g1a_schema_ghg.yaml
@@ -7,7 +7,6 @@ price_type: producer
 
 usa_ghg_data_year: 2023
 
-use_cornerstone_2026_model_schema: true
 use_cornerstone_ghg_model: true
 apply_io_year_adjustments: false
 implement_waste_disaggregation: false
diff --git a/bedrock/utils/config/configs/v03_waterfall_ceda_g1b_waste_disagg.yaml b/bedrock/utils/config/configs/v03_waterfall_ceda_g1b_waste_disagg.yaml
index 67a4d90c..5af3937d 100644
--- a/bedrock/utils/config/configs/v03_waterfall_ceda_g1b_waste_disagg.yaml
+++ b/bedrock/utils/config/configs/v03_waterfall_ceda_g1b_waste_disagg.yaml
@@ -6,7 +6,6 @@ price_type: producer
 
 usa_ghg_data_year: 2023
 
-use_cornerstone_2026_model_schema: true
 use_cornerstone_ghg_model: true
 apply_io_year_adjustments: false
 implement_waste_disaggregation: true
diff --git a/bedrock/utils/config/configs/v03_waterfall_final.yaml b/bedrock/utils/config/configs/v03_waterfall_final.yaml
index a476fec5..2c9bbf24 100644
--- a/bedrock/utils/config/configs/v03_waterfall_final.yaml
+++ b/bedrock/utils/config/configs/v03_waterfall_final.yaml
@@ -6,7 +6,6 @@ price_type: producer
 
 usa_ghg_data_year: 2024
 
-use_cornerstone_2026_model_schema: true
 
 use_cornerstone_ghg_model: true
 
diff --git a/bedrock/utils/config/configs/v03_waterfall_g2_methods.yaml b/bedrock/utils/config/configs/v03_waterfall_g2_methods.yaml
index c72272fb..bae5e1d7 100644
--- a/bedrock/utils/config/configs/v03_waterfall_g2_methods.yaml
+++ b/bedrock/utils/config/configs/v03_waterfall_g2_methods.yaml
@@ -6,7 +6,6 @@ price_type: producer
 
 usa_ghg_data_year: 2023
 
-use_cornerstone_2026_model_schema: true
 use_cornerstone_ghg_model: true
 apply_io_year_adjustments: true
 implement_waste_disaggregation: true
diff --git a/bedrock/utils/config/configs/v03_waterfall_g3_data.yaml b/bedrock/utils/config/configs/v03_waterfall_g3_data.yaml
index 5a5ae0d5..e7b0f706 100644
--- a/bedrock/utils/config/configs/v03_waterfall_g3_data.yaml
+++ b/bedrock/utils/config/configs/v03_waterfall_g3_data.yaml
@@ -6,7 +6,6 @@ price_type: producer
 
 usa_ghg_data_year: 2024
 
-use_cornerstone_2026_model_schema: true
 use_cornerstone_ghg_model: true
 apply_io_year_adjustments: true
 implement_waste_disaggregation: true
diff --git a/bedrock/utils/config/configs/v03_waterfall_useeio_g1_schema_ghg.yaml b/bedrock/utils/config/configs/v03_waterfall_useeio_g1_schema_ghg.yaml
index 5314e2b9..b5cfee48 100644
--- a/bedrock/utils/config/configs/v03_waterfall_useeio_g1_schema_ghg.yaml
+++ b/bedrock/utils/config/configs/v03_waterfall_useeio_g1_schema_ghg.yaml
@@ -7,7 +7,6 @@ iot_before_or_after_redefinition: before
 
 usa_ghg_data_year: 2023
 
-use_cornerstone_2026_model_schema: true
 use_E_data_year_for_x_in_B: true
 implement_waste_disaggregation: true
 
diff --git a/bedrock/utils/config/usa_config.py b/bedrock/utils/config/usa_config.py
index a788d2b2..2c9ed350 100644
--- a/bedrock/utils/config/usa_config.py
+++ b/bedrock/utils/config/usa_config.py
@@ -61,7 +61,6 @@ class USAConfig(BaseModel):
     # Methodology selection
     #####
     ### Schema/Taxonomy selection
-    use_cornerstone_2026_model_schema: bool = False  # DRI: mo.li
     use_useeio_schema: bool = False
     ### IO Methodology selection
     # "IO year adjustments" bucket: CEDA A/q scaling to usa_io_data_year with
diff --git a/bedrock/utils/economic/inflation_helpers_ceda.py b/bedrock/utils/economic/inflation_helpers_ceda.py
index bf04923d..7d17f734 100644
--- a/bedrock/utils/economic/inflation_helpers_ceda.py
+++ b/bedrock/utils/economic/inflation_helpers_ceda.py
@@ -3,7 +3,6 @@
 import functools
 import os
 
-import numpy as np
 import pandas as pd
 
 from bedrock.utils.io.gcp import download_gcs_file_if_not_exists
@@ -52,34 +51,3 @@ def obtain_inflation_factors_from_reference_data() -> pd.DataFrame:
 get_price_index = functools.cache(
     lambda: obtain_inflation_factors_from_reference_data()
 )
-
-
-def inflate_A_matrix(
-    A: pd.DataFrame, original_year: int, target_year: int
-) -> pd.DataFrame:
-    price_index = get_price_index()
-
-    price_ratio = price_index[target_year] / price_index[original_year]
-    return pd.DataFrame(
-        (np.diag(price_ratio) @ A @ np.diag(1 / price_ratio)).values,
-        index=A.index,
-        columns=A.columns,
-    )
-
-
-def inflate_B_matrix(
-    B: pd.DataFrame, original_year: int, target_year: int
-) -> pd.DataFrame:
-    price_index = get_price_index()
-
-    price_ratio = price_index[original_year] / price_index[target_year]
-    return B * price_ratio.loc[B.columns].values
-
-
-def inflate_q_or_y(
-    q_or_y: pd.Series[float], original_year: int, target_year: int
-) -> pd.Series[float]:
-    price_index = get_price_index()
-
-    price_ratio = price_index[target_year] / price_index[original_year]
-    return q_or_y * price_ratio.loc[q_or_y.index]
diff --git a/bedrock/utils/validation/__tests__/test_calculate_ef_diagnostics.py b/bedrock/utils/validation/__tests__/test_calculate_ef_diagnostics.py
index 41d8141a..df90f9f7 100644
--- a/bedrock/utils/validation/__tests__/test_calculate_ef_diagnostics.py
+++ b/bedrock/utils/validation/__tests__/test_calculate_ef_diagnostics.py
@@ -69,7 +69,6 @@ def mock_load(name: str) -> pd.DataFrame:
         ]
 
         mock_config = MagicMock()
-        mock_config.use_cornerstone_2026_model_schema = False
         mock_config.use_ghg_year_x_in_B = False
         mock_config.diagnostics_baseline_source = "gcs_snapshot"
 
@@ -98,6 +97,14 @@ def mock_load(name: str) -> pd.DataFrame:
                 "bedrock.utils.validation.calculate_ef_diagnostics.SIGNIFICANT_SECTORS",
                 mock_significant_sectors,
             ),
+            patch(
+                "bedrock.utils.validation.calculate_ef_diagnostics.align_efs_across_schemas",
+                return_value=(efs, {}),
+            ),
+            patch(
+                "bedrock.utils.validation.calculate_ef_diagnostics.get_aligned_sector_desc",
+                return_value={s: s for s in SECTORS},
+            ),
         ):
             calculate_ef_diagnostics(sheet_id="test_sheet")
 
@@ -114,6 +121,7 @@ def test_tab_names_are_correct(self) -> None:
             "D_and_diffs",
             "D_and_N_significant_sectors",
             "N_and_D_summary_stats",
+            "sector_mapping_notes",
             "output_contrib_new_vs_old",
         ]
 
@@ -172,7 +180,6 @@ def mock_load(name: str) -> pd.DataFrame:
 
         mock_significant_sectors = [{"sector": "1111A0"}, {"sector": "221110"}]
         mock_config = MagicMock()
-        mock_config.use_cornerstone_2026_model_schema = True
         mock_config.use_E_data_year_for_x_in_B = False
         mock_config.diagnostics_baseline_source = "gcs_snapshot"
         mock_config.usa_ghg_data_year = 2023
@@ -254,7 +261,7 @@ def mock_load(name: str) -> pd.DataFrame:
 
     def test_output_contribution_has_expected_columns(self) -> None:
         tabs = self._run_ef_diagnostics()
-        oc = tabs[4][2]
+        oc = tabs[5][2]
 
         expected_cols = {
             "EF_sector",
diff --git a/bedrock/utils/validation/__tests__/test_diagnostics_helpers.py b/bedrock/utils/validation/__tests__/test_diagnostics_helpers.py
index b9f582b8..570a7a89 100644
--- a/bedrock/utils/validation/__tests__/test_diagnostics_helpers.py
+++ b/bedrock/utils/validation/__tests__/test_diagnostics_helpers.py
@@ -21,7 +21,6 @@
 class TestDNNewInflatedEligibility:
     def test_emit_when_deflate_path(self) -> None:
         cfg = USAConfig(
-            use_cornerstone_2026_model_schema=True,
             use_E_data_year_for_x_in_B=True,
             deflate_x_to_detail_io_year_for_B=True,
         )
@@ -31,7 +30,6 @@ def test_emit_when_deflate_path(self) -> None:
 
     def test_skip_when_model_base_year_equals_detail_original_year(self) -> None:
         cfg = USAConfig(
-            use_cornerstone_2026_model_schema=True,
             use_E_data_year_for_x_in_B=True,
             deflate_x_to_detail_io_year_for_B=True,
             model_base_year=2017,
@@ -42,7 +40,6 @@ def test_skip_when_model_base_year_equals_detail_original_year(self) -> None:
 
     def test_skip_use_e_without_deflate(self) -> None:
         cfg = USAConfig(
-            use_cornerstone_2026_model_schema=True,
             use_E_data_year_for_x_in_B=True,
             deflate_x_to_detail_io_year_for_B=False,
         )
@@ -52,7 +49,6 @@ def test_skip_use_e_without_deflate(self) -> None:
 
     def test_skip_default_b_inflation_path(self) -> None:
         cfg = USAConfig(
-            use_cornerstone_2026_model_schema=True,
             deflate_x_to_detail_io_year_for_B=False,
             use_E_data_year_for_x_in_B=False,
         )
@@ -60,16 +56,6 @@ def test_skip_default_b_inflation_path(self) -> None:
         assert ok is False
         assert 'double-apply' in reason
 
-    def test_skip_legacy_non_cornerstone(self) -> None:
-        cfg = USAConfig(
-            use_cornerstone_2026_model_schema=False,
-            use_E_data_year_for_x_in_B=True,
-            deflate_x_to_detail_io_year_for_B=True,
-        )
-        ok, reason = d_n_new_inflated_eligibility(cfg)
-        assert ok is False
-        assert 'legacy' in reason.lower()
-
 
 SECTORS = ["1111A0", "1111B0", "221100"]
 
diff --git a/bedrock/utils/validation/calculate_ef_diagnostics.py b/bedrock/utils/validation/calculate_ef_diagnostics.py
index 0a7c0fa8..67c7ee0b 100644
--- a/bedrock/utils/validation/calculate_ef_diagnostics.py
+++ b/bedrock/utils/validation/calculate_ef_diagnostics.py
@@ -152,13 +152,12 @@ def calculate_ef_diagnostics(sheet_id: str) -> None:
     - N_and_D_summary_stats: Summary statistics of percent diffs.
     - output_contrib_new_vs_old: Top N contributing sectors to each EF's change,
       derived from the output contribution matrix.
-    - sector_mapping_notes (cornerstone only): Documents mapped/excluded sectors.
+    - sector_mapping_notes: Documents mapped/excluded sectors.
 
     Old EFs are inflation-adjusted to the current base year before comparison.
 
-    When ``use_cornerstone_2026_model_schema`` is active, old (CEDA v7) and new
-    (cornerstone) EF vectors are aligned before comparison so that sectors with
-    different granularity are still comparable.
+    Old (CEDA v7) and new (Cornerstone) EF vectors are aligned before comparison
+    so that sectors with different granularity are still comparable.
 
     Args:
         sheet_id: Google Sheets spreadsheet ID to write results to.
@@ -175,7 +174,6 @@ def calculate_ef_diagnostics(sheet_id: str) -> None:
     from bedrock.utils.validation.diagnostics_helpers import pull_efs_for_diagnostics
 
     config = get_usa_config()
-    use_cornerstone = config.use_cornerstone_2026_model_schema
 
     t0 = time.time()
     efs_raw = pull_efs_for_diagnostics()
@@ -183,15 +181,10 @@ def calculate_ef_diagnostics(sheet_id: str) -> None:
         f'[TIMING] pull_efs_for_diagnostics completed in {time.time() - t0:.1f}s'
     )
 
-    # When the cornerstone schema is active, align old/new sector indices
-    active_mappings: ta.Dict[str, str] = {}
-    if use_cornerstone:
-        logger.info('Aligning EF vectors across CEDA v7 / cornerstone schemas')
-        efs, active_mappings = align_efs_across_schemas(efs_raw)
-        sector_desc: ta.Optional[ta.Dict[str, str]] = get_aligned_sector_desc()
-    else:
-        efs = efs_raw
-        sector_desc = None  # use default CEDA_V7_SECTOR_DESC
+    # Align old (CEDA v7) / new (Cornerstone) sector indices before comparison.
+    logger.info('Aligning EF vectors across CEDA v7 / cornerstone schemas')
+    efs, active_mappings = align_efs_across_schemas(efs_raw)
+    sector_desc: ta.Optional[ta.Dict[str, str]] = get_aligned_sector_desc()
 
     logger.info('------ Calculating EF Diagnostics ------')
 
@@ -211,9 +204,8 @@ def calculate_ef_diagnostics(sheet_id: str) -> None:
         sector_desc=sector_desc,
     )
 
-    if use_cornerstone:
-        _add_comparison_type_column(N_comparison, active_mappings)
-        _add_comparison_type_column(D_comparison, active_mappings)
+    _add_comparison_type_column(N_comparison, active_mappings)
+    _add_comparison_type_column(D_comparison, active_mappings)
 
     if efs.D_new_inflated is not None:
         assert efs.N_new_inflated is not None
@@ -352,9 +344,7 @@ def calculate_ef_diagnostics(sheet_id: str) -> None:
     # When aligned, some significant sectors may not be in the index (e.g. if
     # they were removed).  Filter to those present.
     available_significant = [s for s in significant_sectors if s in D_comparison.index]
-    drop_cols = ['sector_name']
-    if use_cornerstone:
-        drop_cols.append('comparison_type')
+    drop_cols = ['sector_name', 'comparison_type']
     d_sig = D_comparison.loc[available_significant]
     n_sig = N_comparison.loc[available_significant].drop(
         columns=drop_cols, errors='ignore'
@@ -408,17 +398,14 @@ def calculate_ef_diagnostics(sheet_id: str) -> None:
         f'[TIMING] Write N_and_D_summary_stats to Google Sheets in {time.time() - t0:.1f}s'
     )
 
-    # Sector mapping notes (cornerstone only)
-    if use_cornerstone:
-        mapping_notes = _build_sector_mapping_notes(
-            active_mappings,
-            old_ef=efs_raw.D_old.raw,
-            new_ef=efs_raw.D_new,
-        )
-        update_sheet_tab(
-            sheet_id, 'sector_mapping_notes', mapping_notes, clean_nans=True
-        )
-        logger.info('Wrote sector_mapping_notes tab')
+    # Sector mapping notes
+    mapping_notes = _build_sector_mapping_notes(
+        active_mappings,
+        old_ef=efs_raw.D_old.raw,
+        new_ef=efs_raw.D_new,
+    )
+    update_sheet_tab(sheet_id, 'sector_mapping_notes', mapping_notes, clean_nans=True)
+    logger.info('Wrote sector_mapping_notes tab')
 
     # Compare output contribution (parquet baseline only; omitted for gcs_useeio_xlsx)
     if config.diagnostics_baseline_source != 'gcs_useeio_xlsx':
@@ -449,11 +436,10 @@ def calculate_ef_diagnostics(sheet_id: str) -> None:
             L=L_old, D=ta.cast('pd.Series[float]', efs_raw.D_old.inflated.squeeze())
         )
 
-        if use_cornerstone:
-            full_idx = OC_new.index.union(OC_old.index).sort_values()
-            full_cols = OC_new.columns.union(OC_old.columns).sort_values()
-            OC_new = OC_new.reindex(index=full_idx, columns=full_cols, fill_value=0.0)
-            OC_old = OC_old.reindex(index=full_idx, columns=full_cols, fill_value=0.0)
+        full_idx = OC_new.index.union(OC_old.index).sort_values()
+        full_cols = OC_new.columns.union(OC_old.columns).sort_values()
+        OC_new = OC_new.reindex(index=full_idx, columns=full_cols, fill_value=0.0)
+        OC_old = OC_old.reindex(index=full_idx, columns=full_cols, fill_value=0.0)
 
         OC_comparison = diff_and_perc_diff_two_output_contribution_matrices(
             OC_old,
diff --git a/bedrock/utils/validation/diagnostics_helpers.py b/bedrock/utils/validation/diagnostics_helpers.py
index 2fd92edf..e03cb1e9 100644
--- a/bedrock/utils/validation/diagnostics_helpers.py
+++ b/bedrock/utils/validation/diagnostics_helpers.py
@@ -129,12 +129,6 @@ def d_n_new_inflated_eligibility(cfg: USAConfig) -> tuple[bool, str]:
         ``derive_cornerstone_B_non_finetuned`` already inflates ``B`` to
         ``model_base_year``.
     """
-    if not cfg.use_cornerstone_2026_model_schema:
-        return (
-            False,
-            'use_cornerstone_2026_model_schema is false (legacy B path; no '
-            'cornerstone D_new_inflated hook)',
-        )
     if cfg.deflate_x_to_detail_io_year_for_B:
         if cfg.model_base_year == cfg.usa_detail_original_year:
             return (