feat(transform): individualise article costs with per-country Eurostat data

Add real per-country cost data to ~30 calculator fields so pSEO articles
show country-specific CAPEX/OPEX instead of hardcoded DE defaults.

Extractor:
- eurostat.py: add 8 new datasets (nrg_pc_205, nrg_pc_203, lc_lci_lev,
  5×prc_ppp_ind variants); add optional `dataset_code` field so multiple
  dict entries can share one Eurostat API endpoint

Staging (4 new models):
- stg_electricity_prices — EUR/kWh by country, semi-annual
- stg_gas_prices         — EUR/GJ by country, semi-annual
- stg_labour_costs       — EUR/hour by country, annual (future staffed scenario)
- stg_price_levels       — PLI indices (EU27=100) for 5 categories, annual

Foundation:
- dim_countries (new) — conformed country dimension; eliminates ~50-line CASE
  blocks duplicated in dim_cities/dim_locations; computes ~29 calculator cost
  override columns from PLI ratios and energy price ratios vs DE baseline;
  NULL for DE so calculator falls through to DEFAULTS unchanged
- dim_cities — replace country_name/slug CASE blocks + country_income CTE
  with JOIN dim_countries
- dim_locations — same refactor as dim_cities

Serving:
- pseo_city_costs_de — JOIN dim_countries; add 29 camelCase override columns
  auto-applied by calculator (electricity, heating, rentSqm, hallCostSqm, …)
- planner_defaults — JOIN dim_countries; same 29 cost columns flow through
  to /api/market-data endpoint

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

extract/padelnomics_extract/src/padelnomics_extract/eurostat.py

@@ -26,6 +26,10 @@ EUROSTAT_BASE_URL = "https://ec.europa.eu/eurostat/api/dissemination/statistics/
 
 # Dataset configs: filters fix dimension values, geo_dim/time_dim are iterated.
 # All other dimensions must either be in filters or have size=1.
+#
+# Optional `dataset_code` field: when present, used for the API URL instead of the dict key.
+# This allows multiple entries to share the same Eurostat dataset with different filters
+# (e.g. five prc_ppp_ind entries with different ppp_cat values).
 DATASETS: dict[str, dict] = {
     "urb_cpop1": {
         "filters": {"indic_ur": "DE1001V"},  # Population on 1 January, total
@@ -51,6 +55,59 @@ DATASETS: dict[str, dict] = {
         "geo_dim": "geo",
         "time_dim": "time",
     },
+    # ── Direct-value datasets (actual EUR figures) ───────────────────────────
+    "nrg_pc_205": {
+        # Electricity prices for non-household consumers, EUR/kWh, excl. taxes
+        "filters": {"freq": "S", "nrg_cons": "MWH500-1999", "currency": "EUR", "tax": "I_TAX"},
+        "geo_dim": "geo",
+        "time_dim": "time",
+    },
+    "nrg_pc_203": {
+        # Gas prices for non-household consumers, EUR/GJ, excl. taxes
+        "filters": {"freq": "S", "nrg_cons": "GJ1000-9999", "currency": "EUR", "tax": "I_TAX"},
+        "geo_dim": "geo",
+        "time_dim": "time",
+    },
+    "lc_lci_lev": {
+        # Labour cost levels EUR/hour — NACE N (administrative/support services)
+        # Stored in dim_countries for future staffed-scenario calculations.
+        "filters": {"lcstruct": "D1_D2_A_HW", "nace_r2": "N", "currency": "EUR"},
+        "geo_dim": "geo",
+        "time_dim": "time",
+    },
+    # ── Price level indices (relative scaling, EU27=100) ─────────────────────
+    # Five entries share the prc_ppp_ind dataset with different ppp_cat filters.
+    # dataset_code points to the real API endpoint; the dict key is the landing filename.
+    "prc_ppp_ind_construction": {
+        "dataset_code": "prc_ppp_ind",
+        "filters": {"ppp_cat": "A050202", "na_item": "PLI_EU27_2020"},
+        "geo_dim": "geo",
+        "time_dim": "time",
+    },
+    "prc_ppp_ind_housing": {
+        "dataset_code": "prc_ppp_ind",
+        "filters": {"ppp_cat": "A0104", "na_item": "PLI_EU27_2020"},
+        "geo_dim": "geo",
+        "time_dim": "time",
+    },
+    "prc_ppp_ind_services": {
+        "dataset_code": "prc_ppp_ind",
+        "filters": {"ppp_cat": "P0201", "na_item": "PLI_EU27_2020"},
+        "geo_dim": "geo",
+        "time_dim": "time",
+    },
+    "prc_ppp_ind_misc": {
+        "dataset_code": "prc_ppp_ind",
+        "filters": {"ppp_cat": "A0112", "na_item": "PLI_EU27_2020"},
+        "geo_dim": "geo",
+        "time_dim": "time",
+    },
+    "prc_ppp_ind_government": {
+        "dataset_code": "prc_ppp_ind",
+        "filters": {"ppp_cat": "P0202", "na_item": "PLI_EU27_2020"},
+        "geo_dim": "geo",
+        "time_dim": "time",
+    },
 }
 
 
@@ -196,22 +253,25 @@ def extract(
     files_skipped = 0
     bytes_written_total = 0
 
-    for dataset_code, config in DATASETS.items():
-        url = f"{EUROSTAT_BASE_URL}/{dataset_code}?format=JSON&lang=EN"
+    for dataset_key, config in DATASETS.items():
+        # Use dataset_code (if set) for the API URL; fall back to the dict key.
+        # This lets multiple entries share one Eurostat dataset with different filters.
+        api_code = config.get("dataset_code", dataset_key)
+        url = f"{EUROSTAT_BASE_URL}/{api_code}?format=JSON&lang=EN"
         for key, val in config.get("filters", {}).items():
             url += f"&{key}={val}"
         dest_dir = landing_path(landing_dir, "eurostat", year, month)
-        dest = dest_dir / f"{dataset_code}.json.gz"
+        dest = dest_dir / f"{dataset_key}.json.gz"
 
-        logger.info("GET %s", dataset_code)
+        logger.info("GET %s", dataset_key)
         bytes_written = _fetch_with_etag(url, dest, session, config)
 
         if bytes_written > 0:
-            logger.info("%s updated — %s bytes compressed", dataset_code, f"{bytes_written:,}")
+            logger.info("%s updated — %s bytes compressed", dataset_key, f"{bytes_written:,}")
             files_written += 1
             bytes_written_total += bytes_written
         else:
-            logger.info("%s not modified (304)", dataset_code)
+            logger.info("%s not modified (304)", dataset_key)
             files_skipped += 1
 
     return {