diff --git a/SWEET_python/city_params.py b/SWEET_python/city_params.py
index 5c0b7a2..a7b7e8f 100644
--- a/SWEET_python/city_params.py
+++ b/SWEET_python/city_params.py
@@ -23,6 +23,7 @@
 from SWEET_python.singapore_k import compute_singapore_k
 import SWEET_python.defaults_2019 as defaults_2019
 import SWEET_python.mcf as mcf_defaults
+from SWEET_python.population import average_growth_rates, country_population_series
 import psycopg2
 from psycopg2.extras import RealDictCursor
 from sqlalchemy import create_engine, text
@@ -207,6 +208,63 @@ def _resolve_gas_capture(flag, site_type, canonical_row, time_series_rows, years
 # Cities can have multiple sets of CityParameters, one for each scenario.
 # Sets of CityParameters can have one or more landfills, dumpsites, waste to energy, etc.
 # Even for modeling a single landfill, City and CityParameters classes need to be used.
+#: The UN city file behind the cities table's two growth columns (World Urbanization
+#: Prospects 2018, cities over 300,000) lists places of this size and up. Below it, a
+#: city's columns are the nearest listed city's, which can be another city altogether.
+UN_LISTED_CITY_MIN_POPULATION = 300_000
+#: A listed city's yearly growth multiplier outside this band is a data error, not a
+#: city: the city's own population mixed with a larger agglomeration's (Manila City's
+#: 2020-2035 rate came out at +16.9% a year).
+PLAUSIBLE_CITY_GROWTH = (0.98, 1.07)
+
+
+def _city_growth(iso3, anchor_year, population, city_historic, city_future):
+    """How a city's waste grows: ``(population_series, growth_rate_historic, growth_rate_future)``.
+
+    A UN-listed city with plausible rates keeps its own two rates, compounded as before,
+    because a big city outgrows its country. Every other city grows with its country's
+    population year by year (_country_growth).
+    """
+    low, high = PLAUSIBLE_CITY_GROWTH
+    listed = (
+        population is not None
+        and not pd.isna(population)
+        and population >= UN_LISTED_CITY_MIN_POPULATION
+        and all(not pd.isna(r) and low <= r <= high for r in (city_historic, city_future))
+    )
+    if listed:
+        return None, city_historic, city_future
+    return _country_growth(iso3, anchor_year, city_historic, city_future)
+
+
+def _iso3_for(country):
+    """The ISO3 code for a country code or name. Raises LookupError if there is none.
+
+    An exact match first, because the fuzzy search ranks subdivisions: it resolves
+    "MUS" to Turkey, whose province Mus matches, rather than Mauritius.
+    """
+    try:
+        return pycountry.countries.lookup(country).alpha_3
+    except LookupError:
+        return pycountry.countries.search_fuzzy(country)[0].alpha_3
+
+
+def _country_growth(iso3, anchor_year, fallback_historic, fallback_future):
+    """The growth for a place in ``iso3``: its country's population year by year.
+
+    Returns ``(population_series, growth_rate_historic, growth_rate_future)``. The
+    series is what SWEET applies (class_defs.growth_factors_for_years); the two rates
+    are its averages around ``anchor_year`` (population.average_growth_rates), the
+    one-number form a person reads. When SWEET has no series for the country, or no
+    anchor year, the series is None and the fallback rates stand.
+    """
+    population_series = country_population_series(iso3)
+    if population_series is None or anchor_year is None or pd.isna(anchor_year):
+        return None, fallback_historic, fallback_future
+    historic, future = average_growth_rates(population_series, anchor_year)
+    return population_series, historic, future
+
+
 def _population_series_from_pop_data(pop_data, iso3, start_year=MODEL_START_YEAR,
                                      end_year=MODEL_END_YEAR):
     """Extract the WPP2024 per-year population Series for ``iso3`` from ``pop_data``.
@@ -251,6 +309,11 @@ class CityParameters(BaseModel):
     # WPP2024 per-year population series (index = year), used to grow waste and
     # diversion by P(year)/P(pivot). None -> fall back to the growth_rate_* CAGR.
     population_series: Optional[pd.Series] = None
+    # A city's own two growth rates as its loader read them, before _city_growth chose
+    # between them and the country series. Whoever republishes a city's rates should
+    # write these, so a reader re-running that choice reaches the same answer.
+    city_growth_rate_historic: Optional[float] = None
+    city_growth_rate_future: Optional[float] = None
     waste_per_capita: Optional[Union[pd.Series, float]] = None
     precip_zone: Optional[str] = None
     ks: Optional[DecompositionRates] = None
@@ -854,6 +917,17 @@ def load_csv_new(self, db: pd.DataFrame, scenario: int = 0, dst: bool = False) -
         waste_mass = pd.Series(waste_mass, index=years)
 
         year_of_data_pop = city_data["Year of Data Collection (Population)"].values[0]
+        # A big city keeps its own UN city rates; any other grows with its country's
+        # population, year by year (_city_growth).
+        city_growth_historic = city_data["Population Growth Rate: Historic (%)"].values[0] / 100 + 1
+        city_growth_future = city_data["Population Growth Rate: Future (%)"].values[0] / 100 + 1
+        population_series, growth_rate_historic, growth_rate_future = _city_growth(
+            self.country,
+            year_of_data_pop,
+            city_data["Population"].values[0],
+            city_growth_historic,
+            city_growth_future,
+        )
 
         city_instance_attrs = {
             "city_name": self.city_name,
@@ -906,16 +980,11 @@ def load_csv_new(self, db: pd.DataFrame, scenario: int = 0, dst: bool = False) -
                     city_data["Average Annual Precipitation (mm/year)"].values[0]
                 ),
                 temperature=float(city_data["Temperature (C)"].values[0]),
-                growth_rate_historic=city_data[
-                    "Population Growth Rate: Historic (%)"
-                ].values[0]
-                / 100
-                + 1,
-                growth_rate_future=city_data[
-                    "Population Growth Rate: Future (%)"
-                ].values[0]
-                / 100
-                + 1,
+                growth_rate_historic=growth_rate_historic,
+                growth_rate_future=growth_rate_future,
+                population_series=population_series,
+                city_growth_rate_historic=city_growth_historic,
+                city_growth_rate_future=city_growth_future,
                 waste_per_capita=city_data[
                     "Waste Generation Rate per Capita (kg/person/day)"
                 ].values[0],
@@ -1007,13 +1076,27 @@ def load_andre_params(self, row, backfill=False):
             }
             precipitation_zone = defaults_2019.get_precipitation_zone(current_row["Average Annual Precipitation (mm/year)"].iloc[0])
             mef_compost = ((0.0055 * waste_fractions["food"].values[0] / (waste_fractions["food"].values[0] + waste_fractions["green"].values[0])+ 0.0139 * waste_fractions["green"].values[0] / (waste_fractions["food"].values[0] + waste_fractions["green"].values[0]))* 1.1023 * 0.7)
+            # A big city keeps its own UN city rates; any other grows with its
+            # country's population, year by year (_city_growth).
+            city_growth_historic = current_row["Population Growth Rate: Historic (%)"].iloc[0] / 100 + 1
+            city_growth_future = current_row["Population Growth Rate: Future (%)"].iloc[0] / 100 + 1
+            population_series, growth_rate_historic, growth_rate_future = _city_growth(
+                iso3,
+                year_of_data_pop,
+                current_row["Population"].iloc[0],
+                city_growth_historic,
+                city_growth_future,
+            )
             baseline = CityParameters(
                 waste_fractions=waste_fractions,
                 div_fractions=div_fractions,
                 div_component_fractions=div_component_fractions,
                 precip=current_row["Average Annual Precipitation (mm/year)"].iloc[0],
-                growth_rate_historic=current_row["Population Growth Rate: Historic (%)"].iloc[0] / 100 + 1,
-                growth_rate_future=current_row["Population Growth Rate: Future (%)"].iloc[0] / 100 + 1,
+                growth_rate_historic=growth_rate_historic,
+                growth_rate_future=growth_rate_future,
+                population_series=population_series,
+                city_growth_rate_historic=city_growth_historic,
+                city_growth_rate_future=city_growth_future,
                 precip_zone=precipitation_zone,
                 gas_capture_efficiency=None,
                 mef_compost=mef_compost,
@@ -1076,8 +1159,13 @@ def load_andre_params(self, row, backfill=False):
                 population = 200000
                 year_of_data_pop = 2014
 
-            growth_rate_historic = row["historic_growth_rate"]
-            growth_rate_future = row["future_growth_rate"]
+            # A big city keeps its own UN city rates; any other grows with its
+            # country's population, year by year (_city_growth).
+            city_growth_historic = row["historic_growth_rate"]
+            city_growth_future = row["future_growth_rate"]
+            population_series, growth_rate_historic, growth_rate_future = _city_growth(
+                iso3, year_of_data_pop, population, city_growth_historic, city_growth_future
+            )
 
             self.latitude = float(row['latitude'])
             self.longitude = float(row['longitude'])
@@ -1121,7 +1209,14 @@ def load_andre_params(self, row, backfill=False):
 
             # Adjust waste mass to account for difference in reporting years between msw and population
             # if self.data_source == 'World Bank':
-            if year_of_data_msw != year_of_data_pop:
+            if year_of_data_msw != year_of_data_pop and population_series is not None:
+                # From the waste figure's year to the population figure's, by population.
+                waste_mass /= growth_factors_for_years(
+                    np.array([year_of_data_msw]), year_of_data_pop,
+                    growth_rate_historic, growth_rate_future, population_series,
+                )[0]
+                waste_per_capita = waste_mass * 1000 / population / 365
+            elif year_of_data_msw != year_of_data_pop:
                 year_difference = year_of_data_pop - year_of_data_msw
                 if year_of_data_msw < year_of_data_pop:
                     waste_mass *= growth_rate_historic**year_difference
@@ -1625,6 +1720,7 @@ def calculate_component_fractions(
                 year_of_data_pop,
                 growth_rate_historic,
                 growth_rate_future,
+                population_series=population_series,
             )
 
             # Assign to CityParameters
@@ -1636,6 +1732,9 @@ def calculate_component_fractions(
                 precip=precip,
                 growth_rate_historic=growth_rate_historic,
                 growth_rate_future=growth_rate_future,
+                population_series=population_series,
+                city_growth_rate_historic=city_growth_historic,
+                city_growth_rate_future=city_growth_future,
                 waste_per_capita=waste_per_capita,
                 precip_zone=precip_zone,
                 gas_capture_efficiency=gas_capture_efficiency,
@@ -4267,6 +4366,7 @@ def _calculate_divs(self, advanced_baseline=False, advanced_dst=False) -> None:
             year_of_data_pop,
             city_parameters.growth_rate_historic,
             city_parameters.growth_rate_future,
+            population_series=city_parameters.population_series,
         ).df
 
         # if scenario == 0:
@@ -4561,7 +4661,7 @@ def dst_baseline_blank(
 
         # Initialize a new CityParameters instance with all required fields
         try:
-            iso3 = pycountry.countries.search_fuzzy(country)[0].alpha_3
+            iso3 = _iso3_for(country)
         except LookupError:
             raise ValueError(f"Country '{country}' not found.")
 
@@ -4760,6 +4860,11 @@ def calculate_component_fractions(
             **(waste_fractions_normalized * waste_mass).to_dict()
         )
         waste_masses_df = waste_fractions_df * waste_mass
+        # The country's population, year by year. The constants above are one rate for
+        # every country; they stand only for a country SWEET has no series for.
+        population_series, growth_rate_historic, growth_rate_future = _country_growth(
+            iso3, year_of_data_pop, growth_rate_historic, growth_rate_future
+        )
         waste_generated_df = WasteGeneratedDF.create(
             waste_masses_df,
             MODEL_START_YEAR,
@@ -4767,6 +4872,7 @@ def calculate_component_fractions(
             year_of_data_pop,
             growth_rate_historic,
             growth_rate_future,
+            population_series=population_series,
         )
 
         # Assign to CityParameters
@@ -4779,6 +4885,7 @@ def calculate_component_fractions(
             temperature=temperature,
             growth_rate_historic=growth_rate_historic,
             growth_rate_future=growth_rate_future,
+            population_series=population_series,
             waste_per_capita=waste_per_capita,
             precip_zone=precip_zone,
             gas_capture_efficiency=gas_capture_efficiency_series,
@@ -4910,7 +5017,7 @@ def cityparams_obj_for_blank_site(
         temperature: float,
         waste_fractions: float,
         waste_mass_year: dict,
-        growth_rate_override: float,
+        growth_rate_override: Optional[float] = None,
     ) -> None:
         """
         Initializes the baseline scenario with given parameters for a blank/custom city.
@@ -4926,7 +5033,7 @@ def cityparams_obj_for_blank_site(
 
         # Initialize a new CityParameters instance with all required fields
         try:
-            iso3 = pycountry.countries.search_fuzzy(country)[0].alpha_3
+            iso3 = _iso3_for(country)
         except LookupError:
             raise ValueError(f"Country '{country}' not found.")
 
@@ -4944,9 +5051,6 @@ def cityparams_obj_for_blank_site(
         # growth_rate_historic = (population_2020 / population_1950) ** (1 / (2020 - 1950))
         # growth_rate_future = (population_2035 / population_2020) ** (1 / (2035 - 2020))
 
-        growth_rate_historic = 1 + growth_rate_override
-        growth_rate_future = 1 + growth_rate_override
-
         year_of_data_pop = {
             "baseline": waste_mass_year.baseline,
             "scenario": waste_mass_year.scenario,
@@ -4955,6 +5059,23 @@ def cityparams_obj_for_blank_site(
         if year_of_data_pop["scenario"] is None:
             year_of_data_pop["scenario"] = year_of_data_pop["baseline"]
 
+        # A rate the user set is compounded as before. Without one, the waste grows with
+        # the country's population year by year, and the two rates are its averages.
+        if growth_rate_override is None:
+            population_series = country_population_series(iso3)
+            if population_series is None:
+                raise CustomError(
+                    "INVALID_PARAMETERS",
+                    f"There is no UN population series for {country}. Enter a waste growth rate.",
+                )
+            growth_rate_historic, growth_rate_future = average_growth_rates(
+                population_series, year_of_data_pop["baseline"]
+            )
+        else:
+            population_series = None
+            growth_rate_historic = 1 + growth_rate_override
+            growth_rate_future = 1 + growth_rate_override
+
         # Calculate MEF for compost
         try:
             # 0 is food, 1 is green
@@ -4988,6 +5109,7 @@ def cityparams_obj_for_blank_site(
             precip=precipitation,
             growth_rate_historic=growth_rate_historic,
             growth_rate_future=growth_rate_future,
+            population_series=population_series,
             precip_zone=precip_zone,
             mef_compost=mef_compost,
             year_of_data_pop=year_of_data_pop,
@@ -7363,6 +7485,7 @@ def fill_missing_fields(d: dict) -> dict:
             year_of_data_pop=yr_pop,
             growth_rate_historic=scenario_parameters.growth_rate_historic,
             growth_rate_future=scenario_parameters.growth_rate_future,
+            population_series=scenario_parameters.population_series,
         )
 
         pos = self.years_range.index(implement_year)
@@ -7386,6 +7509,7 @@ def fill_missing_fields(d: dict) -> dict:
             scenario_parameters.year_of_data_pop,
             scenario_parameters.growth_rate_historic,
             scenario_parameters.growth_rate_future,
+            population_series=scenario_parameters.population_series,
         )
 
         scenario_parameters.repopulate_attr_dicts()
@@ -7990,7 +8114,7 @@ def sdst_v1_5(
         biocover: Dict = {"baseline": 0.0, "scenario": 0.0},
         oxidation_override: Dict = None,
         baseline_data: pd.DataFrame = None,
-        growth_rate_override: float = None,
+        growth_rate_override: Optional[float] = None,
         country_growth_defaults: List[float] = None,
     ) -> None:
         """
@@ -8084,6 +8208,16 @@ def _align_year_df(df: pd.DataFrame) -> pd.DataFrame:
                 f"Waste mass year must be between {model_year_min} and {model_year_max}.",
             )
 
+        # A rate the user set is compounded as before. Without one, the waste grows as
+        # cityparams_obj_for_blank_site set the site up: by the country's population.
+        if growth_rate_override is None:
+            population_series = self.baseline_parameters.population_series
+            growth_historic = self.baseline_parameters.growth_rate_historic
+            growth_future = self.baseline_parameters.growth_rate_future
+        else:
+            population_series = None
+            growth_historic = growth_future = 1 + growth_rate_override
+
         def _generated_waste_masses() -> tuple[pd.DataFrame, pd.DataFrame]:
             baseline_mass = float(_variant_value(new_waste_mass, "baseline"))
             scenario_mass = _variant_value(new_waste_mass, "scenario")
@@ -8105,9 +8239,10 @@ def _generated_waste_masses() -> tuple[pd.DataFrame, pd.DataFrame]:
                 end_year=model_year_max,
                 year_of_data_pop_baseline=waste_year_baseline,
                 year_of_data_pop_scenario=waste_year_scenario,
-                growth_rate_historic=1 + growth_rate_override,
-                growth_rate_future=1 + growth_rate_override,
+                growth_rate_historic=growth_historic,
+                growth_rate_future=growth_future,
                 implement_year=None,
+                population_series=population_series,
             ).df
             waste_masses_df_scenario = WasteGeneratedDF.create_advanced_2(
                 waste_masses_df=waste_masses_df_scenario_unadjusted,
@@ -8115,9 +8250,10 @@ def _generated_waste_masses() -> tuple[pd.DataFrame, pd.DataFrame]:
                 end_year=model_year_max,
                 year_of_data_pop_baseline=waste_year_baseline,
                 year_of_data_pop_scenario=waste_year_scenario,
-                growth_rate_historic=1 + growth_rate_override,
-                growth_rate_future=1 + growth_rate_override,
+                growth_rate_historic=growth_historic,
+                growth_rate_future=growth_future,
                 implement_year=implement_year,
+                population_series=population_series,
             ).df
             return waste_masses_df_baseline, waste_masses_df_scenario
 
@@ -8190,9 +8326,10 @@ def _apply_open_close_window(
                             start_year=model_start_year,
                             end_year=model_year_max,
                             year_of_data_pop=waste_year_scenario,
-                            growth_rate_historic=1+growth_rate_override,
-                            growth_rate_future=1+growth_rate_override,
+                            growth_rate_historic=growth_historic,
+                            growth_rate_future=growth_future,
                             implement_year=implement_year,
+                            population_series=population_series,
                         ).df
                         scenario_ratio = 1
                     else:
@@ -8932,7 +9069,7 @@ async def sdst_prepopulate(
             location = geolocator.reverse((latlon[0], latlon[1]), language="en")
             country = location.raw["address"].get("country")
             try:
-                iso3 = pycountry.countries.search_fuzzy(country)[0].alpha_3
+                iso3 = _iso3_for(country)
             except LookupError:
                 raise ValueError(f"Country '{country}' not found.")
             region = defaults_2019.region_lookup_iso3.get(iso3)
@@ -9049,7 +9186,13 @@ async def sdst_prepopulate(
 
         wf_out = waste_fractions_df.iloc[0].to_dict()
 
-        growth_rate = defaults_2019.growth_rate_country[iso3] / 100
+        # The rate a custom site's form starts from: the country's population
+        # projection, which is what the site grows by when no rate is set.
+        population_series = country_population_series(iso3)
+        if population_series is not None:
+            growth_rate = average_growth_rates(population_series, datetime.now().year - 1)[1] - 1
+        else:
+            growth_rate = defaults_2019.growth_rate_country[iso3] / 100
 
         if (not isinstance(latlon, list)) and (not isinstance(latlon, tuple)):
             latlon = latlon.tolist()
diff --git a/SWEET_python/class_defs.py b/SWEET_python/class_defs.py
index 93a59d1..f2f8752 100644
--- a/SWEET_python/class_defs.py
+++ b/SWEET_python/class_defs.py
@@ -226,36 +226,20 @@ def create_advanced(
         growth_rate_historic: float,
         growth_rate_future: float,
         implement_year: Optional[int] = None,
+        population_series: Optional[pd.Series] = None,
     ):
-        years = np.arange(start_year, end_year + 1)
-        t = years - year_of_data_pop
-
-        if (growth_rate_future == growth_rate_future) and (growth_rate_future == 0.0):
-            growth_factors = np.ones(len(years))
-        else:
-            # Create growth rate array, using growth_rate_historic for years before year_of_data_pop and growth_rate_future after
-            growth_rate = np.where(
-                years < year_of_data_pop, growth_rate_historic, growth_rate_future
-            )
-            growth_factors = growth_rate**t
-
-        # Apply growth factors to each row of the DataFrame
-        adjusted_data = waste_masses_df.multiply(growth_factors, axis=0)
-
-        # Repeat with the implement_year if it is provided
-        if implement_year is not None:
-            year_of_data_pop = implement_year
-            t = years - year_of_data_pop
-            growth_rate = np.where(
-                years < year_of_data_pop, growth_rate_historic, growth_rate_future
-            )
-            growth_factors = growth_rate**t
-            adjusted_data2 = waste_masses_df.multiply(growth_factors, axis=0)
-
-            # Update the original DataFrame
-            adjusted_data.loc[implement_year:] = adjusted_data2.loc[implement_year:]
-
-        return cls(df=adjusted_data)
+        # The same as create_advanced_2 with the scenario pivoting at implement_year.
+        return cls.create_advanced_2(
+            waste_masses_df,
+            start_year,
+            end_year,
+            year_of_data_pop,
+            implement_year,
+            growth_rate_historic,
+            growth_rate_future,
+            implement_year=implement_year,
+            population_series=population_series,
+        )
 
     @classmethod
     def create_advanced_2(
@@ -268,32 +252,29 @@ def create_advanced_2(
         growth_rate_historic: float,
         growth_rate_future: float,
         implement_year: Optional[int] = None,
+        population_series: Optional[pd.Series] = None,
     ):
         years = np.arange(start_year, end_year + 1)
-        t = years - year_of_data_pop_baseline
 
-        if (growth_rate_future == growth_rate_future) and (growth_rate_future == 0.0):
+        # By the population series when there is one, else the two rates
+        # (growth_factors_for_years). A future rate of exactly 0.0 with no series
+        # means "no growth model".
+        if population_series is None and growth_rate_future == 0.0:
             growth_factors = np.ones(len(years))
         else:
-            # Create growth rate array, using growth_rate_historic for years before year_of_data_pop and growth_rate_future after
-            growth_rate = np.where(
-                years < year_of_data_pop_baseline, growth_rate_historic, growth_rate_future
+            growth_factors = growth_factors_for_years(
+                years, year_of_data_pop_baseline, growth_rate_historic, growth_rate_future,
+                population_series,
             )
-            growth_factors = growth_rate**t
-
-        # Apply growth factors to each row of the DataFrame
         adjusted_data = waste_masses_df.multiply(growth_factors, axis=0)
 
-        # Repeat with the implement_year if it is provided
+        # From implement_year on, the scenario pivots on its own year.
         if implement_year is not None:
-            t = years - year_of_data_pop_scenario
-            growth_rate = np.where(
-                years < year_of_data_pop_scenario, growth_rate_historic, growth_rate_future
+            growth_factors = growth_factors_for_years(
+                years, year_of_data_pop_scenario, growth_rate_historic, growth_rate_future,
+                population_series,
             )
-            growth_factors = growth_rate**t
             adjusted_data2 = waste_masses_df.multiply(growth_factors, axis=0)
-
-            # Update the original DataFrame
             adjusted_data.loc[implement_year:] = adjusted_data2.loc[implement_year:]
 
         return cls(df=adjusted_data)
@@ -428,8 +409,17 @@ def create_simple(
         year_of_data_pop: int,
         growth_rate_historic: float,
         growth_rate_future: float,
+        population_series: Optional[pd.Series] = None,
     ) -> "DivsDF":
 
+        years = np.arange(start_year, end_year + 1)
+        # By the population series when there is one, else the two rates, as the waste
+        # it is diverted from (growth_factors_for_years). The same for all four streams.
+        growth_factors = growth_factors_for_years(
+            years, year_of_data_pop, growth_rate_historic, growth_rate_future,
+            population_series,
+        )
+
         def create_div_df(baseline: WasteMasses, scenario: WasteMasses) -> pd.DataFrame:
             # All waste types in order
             waste_types = list(baseline.model_dump().keys())
@@ -438,17 +428,6 @@ def create_div_df(baseline: WasteMasses, scenario: WasteMasses) -> pd.DataFrame:
             baseline_arr = np.array([getattr(baseline, w) for w in waste_types])
             scenario_arr = np.array([getattr(scenario, w) for w in waste_types])
 
-            # Array of years
-            years = np.arange(start_year, end_year + 1)
-            # Compute time offsets
-            t = years - year_of_data_pop
-
-            # Compute growth factors
-            # If year < year_of_data_pop -> use growth_rate_historic, else growth_rate_future
-            growth_factors = np.where(
-                years < year_of_data_pop, growth_rate_historic**t, growth_rate_future**t
-            )
-
             # Mask for baseline vs scenario (before or after implement_year)
             baseline_mask = years < implement_year
 
diff --git a/SWEET_python/constants.py b/SWEET_python/constants.py
index d2f96b0..84c7f77 100644
--- a/SWEET_python/constants.py
+++ b/SWEET_python/constants.py
@@ -26,6 +26,13 @@
 silently drops every country back to the frozen-CAGR growth scalars. Regenerate
 with diagnostic_scripts/generate_pops_yearly.py and upload to blob
 static_data/pops_yearly.csv BEFORE the constant lands in a run.
+
+THERE ARE TWO COPIES OF THAT TABLE, and nothing keeps them in step. Climate TRACE's
+pipeline reads the blob copy; the WasteMAP tools read SWEET's own
+SWEET_python/pops_yearly.csv (SWEET_python.population), which is the same table
+extended back to 1950 because the site tool models from a landfill's real opening
+year. Regenerate both from the same WPP file: the generator starts at
+MODEL_START_YEAR, so SWEET's copy needs its START_YEAR set to 1950.
 """
 
 MODEL_START_YEAR: int = 1970
diff --git a/SWEET_python/pops_yearly.csv b/SWEET_python/pops_yearly.csv
new file mode 100644
index 0000000..0609f25
--- /dev/null
+++ b/SWEET_python/pops_yearly.csv
@@ -0,0 +1,239 @@
+iso3_country,1950,1951,1952,1953,1954,1955,1956,1957,1958,1959,1960,1961,1962,1963,1964,1965,1966,1967,1968,1969,1970,1971,1972,1973,1974,1975,1976,1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021,2022,2023,2024,2025,2026,2027,2028,2029,2030,2031,2032,2033,2034,2035,2036,2037,2038,2039,2040,2041,2042,2043,2044,2045,2046,2047,2048,2049,2050
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diff --git a/SWEET_python/population.py b/SWEET_python/population.py
new file mode 100644
index 0000000..295e8df
--- /dev/null
+++ b/SWEET_python/population.py
@@ -0,0 +1,67 @@
+"""Country population by year, from the UN's World Population Prospects 2024.
+
+SWEET grows a place's waste with its country's population, year by year: the factor
+for a year is P(year) / P(anchor year) (see class_defs.growth_factors_for_years).
+Climate TRACE's pipeline hands SWEET the same numbers through ``pop_data``; the
+WasteMAP tools, which have no ``pop_data``, read them here.
+
+``pops_yearly.csv`` is Climate TRACE's ``static_data/pops_yearly.csv`` (1970-2050)
+unchanged, extended back to 1950 from the same source, because the site tool models
+a landfill from its real opening year. Built with Climate TRACE's
+``diagnostic_scripts/generate_pops_yearly.py`` from
+``WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_COMPACT.xlsx``, "Total Population, as of 1
+January (thousands)": estimates to 2023, medium variant after.
+"""
+
+from __future__ import annotations
+
+from functools import lru_cache
+from pathlib import Path
+from typing import Optional
+
+import pandas as pd
+
+from SWEET_python.constants import MODEL_END_YEAR, MODEL_START_YEAR
+
+_TABLE_PATH = Path(__file__).with_name("pops_yearly.csv")
+
+
+@lru_cache(maxsize=1)
+def _table() -> pd.DataFrame:
+    table = pd.read_csv(_TABLE_PATH, index_col=0)
+    table.columns = [int(c) for c in table.columns]
+    return table
+
+
+def country_population_series(iso3: Optional[str]) -> Optional[pd.Series]:
+    """The country's population for every year in the table, or ``None`` if it has none."""
+    table = _table()
+    if not isinstance(iso3, str) or iso3 not in table.index:
+        return None
+    series = table.loc[iso3].astype(float)
+    if series.isna().any() or not (series > 0).all():
+        return None
+    return series.copy()  # a copy, so no caller can edit the cached table
+
+
+def average_growth_rates(
+    series: pd.Series,
+    anchor_year: int,
+    start_year: int = MODEL_START_YEAR,
+    end_year: int = MODEL_END_YEAR,
+) -> tuple[float, float]:
+    """The two constant yearly multipliers that match ``series`` at the ends of the window.
+
+    Historic: ``(P(anchor) / P(start)) ** (1 / (anchor - start))``. Future:
+    ``(P(end) / P(anchor)) ** (1 / (end - anchor))``. SWEET does not apply these when
+    it has the series; they are the one-number growth rate a person reads, and what
+    the constant-rate fallback compounds. An anchor at either end of the window gives
+    that side the other side's rate.
+    """
+    anchor = min(max(int(anchor_year), start_year), end_year)
+    p = series
+    historic = (p[anchor] / p[start_year]) ** (1 / (anchor - start_year)) if anchor > start_year else None
+    future = (p[end_year] / p[anchor]) ** (1 / (end_year - anchor)) if anchor < end_year else None
+    historic = future if historic is None else historic
+    future = historic if future is None else future
+    return float(historic), float(future)
diff --git a/changelog/2026-09.md b/changelog/2026-09.md
index d171107..74e8b53 100644
--- a/changelog/2026-09.md
+++ b/changelog/2026-09.md
@@ -18,6 +18,45 @@ nowhere near the cause.
 
 ## Changed
 
+- **WasteMAP's tools grow waste with the country's UN population, year by year**
+  (model-output change). Climate TRACE's pipeline already grew each site this way:
+  P(year) / P(anchor year), from the UN's World Population Prospects 2024. The
+  WasteMAP paths compounded two flat rates instead. SWEET now ships the table as
+  `pops_yearly.csv` (Climate TRACE's `static_data/pops_yearly.csv`, 1970–2050,
+  extended back to 1950 from the same source for the site tool), read by the new
+  `SWEET_python.population`.
+  - **Site tool (`cityparams_obj_for_blank_site`, `sdst_v1_5`):**
+    `growth_rate_override=None` now means the country's population. A rate the
+    caller passes is compounded as before.
+  - **City tool and the map's city emissions (`load_csv_new`,
+    `load_andre_params`):** a city under 300,000 people now grows with its
+    country's population. The cities table's two growth columns come from the UN's
+    list of cities over 300,000, and a smaller city had the nearest listed city's
+    rates. Many were wrong: for a small Brazilian town, the town's own 2020
+    population was mixed with a big city's 1950 and 2035 figures, which gave Serra
+    da Saudade (781 people) +83% a year to 2035. A city of 300,000 or more keeps its
+    own UN city rates, unless one is outside −2% to +7% a year, which is the same
+    mix-up (Manila City's +16.9%).
+  - **Custom Location (`dst_baseline_blank`):** grows with its country's population.
+    It used one constant for every country: 2.5% a year to 2020, then 1.4%.
+  - The city scenario (`implement_dst_changes_simple_v1_5`) grows its waste and
+    diversions the same way as its baseline.
+  - `WasteGeneratedDF.create_advanced`, `create_advanced_2` and
+    `DivsDF.create_simple` take `population_series`. Without it they are unchanged,
+    as are Climate TRACE's paths, which pass their own `pop_data`.
+  - A city's own two rates, as its loader read them, are kept on
+    `CityParameters.city_growth_rate_*`. WasteMAP's map build republishes those
+    rather than the rates it applied, so the city tool, re-running the choice on
+    the published table, reaches the same answer for a big city whose own rates
+    were rejected.
+  - Countries are found by exact code or name first, falling back to the fuzzy
+    search. The fuzzy search turned "MUS" into Turkey (its province Muş), which
+    now matters because the code picks the population series; "Niger" also
+    resolved to Nigeria.
+  - The site tool's prefill for a custom site starts the rate from the country's
+    population projection instead of the 2019 default table.
+  ([#71](https://github.com/RMI/SWEET_python/pull/71), [RMI/WasteMAP#839](https://github.com/RMI/WasteMAP/pull/839))
+
 - **`DivsDF.sum` no longer passes `copy=False` to `infer_objects`.** The four
   `reindex(...).infer_objects(...).fillna(0)` chains that combine the compost /
   anaerobic / combustion / recycling streams into the diverted-mass frame passed
diff --git a/changelog/README.md b/changelog/README.md
index 0393293..4bb1323 100644
--- a/changelog/README.md
+++ b/changelog/README.md
@@ -15,7 +15,7 @@ The project does not publish semantic version tags, so releases are tracked by
 
 Newest first:
 
-- [2026-09](2026-09.md) — Model output frames get one fixed waste-type column order instead of a per-process one (a `set` fed the ordering, and Python randomizes set iteration per process), moving values in the last bit only, where a sum runs across the reordered columns; City DST's diversion sum stops passing pandas a keyword pandas is removing, which would have broken the model path on pandas 4 behind a misleading `AttributeError` (no output change); annual model no longer charges a flare's inefficiency twice (a hardcoded 2% slip stacked on the `flaring` destruction efficiency), so it agrees with the monthly model again and a site with gas capture emits ~3.5% less (model-output change); `gas_capture_efficiency` bounded to [0, 1], where an out-of-range value used to produce negative emissions silently; one methane density constant instead of two 9.1% apart
+- [2026-09](2026-09.md) — WasteMAP's site tool, city tool and Custom Location grow waste with the country's UN population year by year, as Climate TRACE does; big cities keep their own UN city rates (model-output change); model output frames get one fixed waste-type column order instead of a per-process one (a `set` fed the ordering, and Python randomizes set iteration per process), moving values in the last bit only, where a sum runs across the reordered columns; City DST's diversion sum stops passing pandas a keyword pandas is removing, which would have broken the model path on pandas 4 behind a misleading `AttributeError` (no output change); annual model no longer charges a flare's inefficiency twice (a hardcoded 2% slip stacked on the `flaring` destruction efficiency), so it agrees with the monthly model again and a site with gas capture emits ~3.5% less (model-output change); `gas_capture_efficiency` bounded to [0, 1], where an out-of-range value used to produce negative emissions silently; one methane density constant instead of two 9.1% apart
 - [2026-08](2026-08.md) — Single-site adst gains optional `depth` (deep-dump MCF bump) and `k_override` (caller-supplied decomposition rate) inputs, restoring the last two site-DST levers; `/sdst` flaring efficiency reaches the model again after a variable-name bug silently forced flare destruction to 0.98; annual model applies cover oxidation by emission year not deposit year, fixing biocover having no effect on closed landfills (WasteMAP #719); `City.sdst_v1_5` custom-site path holds the scenario equal to the baseline before the implementation year even when composition changes (was back-dating the new composition onto pre-implementation deposits) (model-output change); MCF consolidated into a new `SWEET_python.mcf` module and both dump types moved to the IPCC uncategorised-SWDS 0.6 (open dumps up from 0.4, controlled dumps down from 0.7), with a supplied depth now selecting the deep/shallow category (model-output change)
 - [2026-07](2026-07.md) — All ten waste types eligible for combustion (metal/glass/other added); methane-only model treats combustion as landfill diversion (model-output change)
 - [2026-06](2026-06.md) — New single-site and city-level ADST modeling modules, min-cost max-flow rewrite of the city DST diversion allocator, physical-k fix for cold/dry sites, no more spurious negative food-waste mass
diff --git a/tests/test_population_growth.py b/tests/test_population_growth.py
new file mode 100644
index 0000000..b37c36d
--- /dev/null
+++ b/tests/test_population_growth.py
@@ -0,0 +1,240 @@
+"""WasteMAP's tools grow waste with the country's UN population, year by year.
+
+Climate TRACE's pipeline hands SWEET the UN WPP2024 population for every year and
+SWEET grows a site's waste by P(year) / P(anchor year). The WasteMAP tools compounded
+two flat rates instead: the site tool's form value, the city tool's cities-table
+columns, and for Custom Location one constant for every country. These tests hold the
+WasteMAP paths to the population series, and hold the paths that still take a rate
+(a typed site-tool rate, a big city's own UN city rates) to what they did before.
+"""
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from SWEET_python import city_params
+from SWEET_python.city_params import City
+from SWEET_python.class_defs import DivMasses, DivsDF, DiversionFractions, Variant, WasteGeneratedDF, WasteMasses
+from SWEET_python.population import average_growth_rates, country_population_series
+from test_sdst_v1_5_preimplement_composition import (
+    BASELINE_FRACTIONS,
+    CLOSE_YEAR,
+    COMPONENT_ORDER,
+    MODEL_YEAR_MAX,
+    OPEN_YEAR,
+)
+
+BRA = country_population_series("BRA")
+USA = country_population_series("USA")
+WINDOW = range(1970, 2051)
+
+
+def _shape(frame_or_series, anchor):
+    total = frame_or_series.sum(axis=1) if isinstance(frame_or_series, pd.DataFrame) else frame_or_series
+    return (total / total.loc[anchor]).loc[list(WINDOW)].to_numpy()
+
+
+def _population_shape(series, anchor):
+    return (series / series.loc[anchor]).loc[list(WINDOW)].to_numpy()
+
+
+# --- the table ---------------------------------------------------------------
+
+
+def test_a_country_has_a_population_for_every_year_the_site_tool_models():
+    assert list(USA.index) == list(range(1950, 2051))
+    assert (USA > 0).all()
+    assert USA.loc[2023] == pytest.approx(342475.098)  # WPP2024, thousands
+
+
+def test_an_unknown_country_has_no_series():
+    assert country_population_series("XXX") is None
+    assert country_population_series(None) is None
+
+
+def test_a_caller_cannot_edit_the_table():
+    mine = country_population_series("USA")
+    mine.loc[2023] = 0.0
+    assert country_population_series("USA").loc[2023] == pytest.approx(342475.098)
+
+
+def test_the_average_rates_reach_the_series_at_both_ends():
+    historic, future = average_growth_rates(USA, 2022)
+    assert USA.loc[2022] * historic ** (1970 - 2022) == pytest.approx(USA.loc[1970])
+    assert USA.loc[2022] * future ** (2050 - 2022) == pytest.approx(USA.loc[2050])
+
+
+# --- the growth constructors ---------------------------------------------------
+
+
+def _masses(years):
+    return pd.DataFrame({"food": 10.0, "paper_cardboard": 5.0}, index=years)
+
+
+@pytest.mark.parametrize("builder", ["create_advanced", "create_advanced_2"])
+def test_the_site_tool_constructors_follow_the_series_when_given_one(builder):
+    years = np.arange(1970, 2051)
+    args = (_masses(years), 1970, 2050, 2025) + (() if builder == "create_advanced" else (2025,))
+    grown = getattr(WasteGeneratedDF, builder)(*args, 1.5, 1.5, population_series=USA).df
+    assert np.allclose(_shape(grown, 2025), _population_shape(USA, 2025))
+
+
+@pytest.mark.parametrize("builder", ["create_advanced", "create_advanced_2"])
+def test_the_site_tool_constructors_compound_a_rate_as_before(builder):
+    years = np.arange(1970, 2051)
+    args = (_masses(years), 1970, 2050, 2025) + (() if builder == "create_advanced" else (2025,))
+    grown = getattr(WasteGeneratedDF, builder)(*args, 1.02, 1.03).df
+    expected = np.where(years < 2025, 1.02, 1.03) ** (years - 2025)
+    assert np.array_equal(grown["food"].to_numpy(), 10.0 * expected)
+
+
+def test_scenario_diversions_follow_the_series_when_given_one():
+    zero = WasteMasses(**{w: 0.0 for w in COMPONENT_ORDER})
+    compost = WasteMasses(**{**{w: 0.0 for w in COMPONENT_ORDER}, "food": 100.0})
+    divs = DivMasses(compost=compost, anaerobic=zero, combustion=zero, recycling=zero)
+    grown = DivsDF.create_simple(divs, divs, 1970, 2050, 2030, 2025, 1.5, 1.5, population_series=BRA)
+    assert np.allclose(_shape(grown.compost["food"], 2025), _population_shape(BRA, 2025))
+
+
+# --- the city tool -------------------------------------------------------------
+
+
+def test_a_big_city_with_plausible_rates_keeps_its_own():
+    series, historic, future = city_params._city_growth("NGA", 2020, 15_000_000, 1.0556, 1.036)
+    assert series is None and (historic, future) == (1.0556, 1.036)
+
+
+@pytest.mark.parametrize(
+    "population, historic, future, why",
+    [
+        (781, 0.9144, 1.8303, "a town under 300k borrows the nearest big city's rate"),
+        (1_800_000, 1.0022, 1.1687, "Manila City: its own population mixed with the agglomeration's"),
+        (np.nan, 1.02, 1.01, "no population to tell"),
+        (2_000_000, np.nan, 1.01, "no rate"),
+    ],
+)
+def test_any_other_city_grows_with_its_country(population, historic, future, why):
+    series, got_historic, got_future = city_params._city_growth("BRA", 2022, population, historic, future)
+    assert series is not None, why
+    assert (got_historic, got_future) == pytest.approx(average_growth_rates(BRA, 2022))
+
+
+def test_custom_location_grows_with_its_country_not_one_constant():
+    city = City("custom")
+    city.dst_baseline_blank("United States", 100_000, 800.0, 15.0)
+    baseline = city.baseline_parameters
+
+    assert np.allclose(_shape(baseline.waste_generated_df, 2022), _population_shape(USA, 2022))
+    # Was (4.3e9 / 751e6) ** (1/70), 2.5% a year, for every country.
+    assert baseline.growth_rate_historic == pytest.approx(average_growth_rates(USA, 2022)[0])
+
+
+def test_the_city_scenario_grows_as_its_baseline_does():
+    city = City("custom")
+    city.dst_baseline_blank("United States", 100_000, 800.0, 15.0)
+    for landfill in city.baseline_parameters.landfills:
+        landfill.estimate_emissions()
+
+    divs = DiversionFractions(compost=0.1, anaerobic=0.0, combustion=0.0, recycling=0.1)
+    city.implement_dst_changes_simple_v1_5(divs, False, False, 0.0, 0.0, 2030, 1, 0.0)
+    scenario = city.scenario_parameters[0]
+
+    after = list(range(2030, 2051))
+    waste = scenario.waste_generated_df.df.sum(axis=1)
+    compost = scenario.divs_df.compost.sum(axis=1)
+    usa = (USA / USA.loc[2035]).loc[after].to_numpy()
+    assert np.allclose((waste / waste.loc[2035]).loc[after].to_numpy(), usa)
+    assert np.allclose((compost / compost.loc[2035]).loc[after].to_numpy(), usa)
+
+
+# --- the site tool -------------------------------------------------------------
+
+
+def _custom_site(growth_rate_override):
+    years = pd.Index(range(OPEN_YEAR, MODEL_YEAR_MAX + 1))
+    fractions = pd.DataFrame([BASELINE_FRACTIONS] * len(years), index=years, columns=COMPONENT_ORDER, dtype=float)
+    year = Variant[int](baseline=2025, scenario=2025)
+    city = City("custom site")
+    city.cityparams_obj_for_blank_site(
+        country="BRA",
+        population=None,
+        precipitation=500.0,
+        temperature=10.0,
+        waste_fractions=Variant(baseline=list(BASELINE_FRACTIONS), scenario=list(BASELINE_FRACTIONS)),
+        waste_mass_year=year,
+        growth_rate_override=growth_rate_override,
+    )
+    city.sdst_v1_5(
+        precipitation=500.0,
+        new_waste_fractions={"baseline": fractions, "scenario": fractions},
+        new_landfill_types=Variant(baseline=[2], scenario=[2]),
+        new_gas_efficiency=Variant(baseline=[0.0], scenario=[0.0]),
+        new_landfill_open_close_dates=Variant(baseline=[(OPEN_YEAR, CLOSE_YEAR)], scenario=[(OPEN_YEAR, CLOSE_YEAR)]),
+        scenario=1,
+        landfill_split_timeline=Variant(baseline={y: [1.0] for y in years}, scenario={y: [1.0] for y in years}),
+        new_landfill_latlons=None,
+        new_landfill_areas=None,
+        new_covertypes=None,
+        new_coverthicknesses=None,
+        waste_burning=Variant(baseline=0.0, scenario=0.0),
+        new_landfill_flaring=Variant(baseline=[0.98], scenario=[0.98]),
+        fancy_ox=None,
+        new_waste_mass=Variant(baseline=10000.0, scenario=10000.0),
+        waste_mass_year=year,
+        depths=Variant(baseline=[3.0], scenario=[3.0]),
+        ks_overrides=Variant(baseline=0.2, scenario=0.2),
+        biocover={"baseline": 0.0, "scenario": 0.0},
+        oxidation_override=None,
+        baseline_data=None,
+        implement_year=2030,
+        growth_rate_override=growth_rate_override,
+        country_growth_defaults=[1.0, 1.0],
+    )
+    deposits = city.baseline_parameters.landfills[0].waste_mass_df
+    return deposits.sum(axis=1)
+
+
+def test_a_custom_site_with_no_rate_grows_with_its_country():
+    deposits = _custom_site(None)
+    open_years = list(range(OPEN_YEAR, CLOSE_YEAR))
+    assert np.allclose(
+        (deposits / deposits.loc[2025]).loc[open_years].to_numpy(),
+        (BRA / BRA.loc[2025]).loc[open_years].to_numpy(),
+    )
+
+
+def test_a_typed_rate_is_compounded_as_before():
+    deposits = _custom_site(0.02)
+    assert deposits.loc[2035] == pytest.approx(10000.0 * 1.02**10)
+
+
+# --- review fixes --------------------------------------------------------------
+
+
+@pytest.mark.parametrize(
+    "given, iso3",
+    [("MUS", "MUS"), ("Mauritius", "MUS"), ("Niger", "NER"), ("Nigeria", "NGA"), ("United States", "USA")],
+)
+def test_a_country_is_found_by_its_code_before_the_fuzzy_search(given, iso3):
+    """search_fuzzy('MUS') is Turkey (province Mus), and 'Niger' was Nigeria."""
+    assert city_params._iso3_for(given) == iso3
+
+
+def test_mauritius_grows_with_mauritius():
+    city = City("custom")
+    city.dst_baseline_blank("MUS", 100_000, 1500.0, 24.0)
+    assert city.baseline_parameters.population_series.equals(country_population_series("MUS"))
+
+
+def test_republishing_a_citys_own_rates_keeps_its_growth_the_same():
+    """The map build writes a city's rates back into the cities table and the city tool
+    re-runs _city_growth on them. Republishing the rates the loader READ keeps the
+    choice; republishing the averages it applied flipped Manila City to compounding."""
+    own = (1.0022, 1.1687)  # Manila City: its own population mixed with the agglomeration's
+    series, historic, future = city_params._city_growth("PHL", 2020, 1_800_000, *own)
+    assert series is not None
+
+    reread, *_ = city_params._city_growth("PHL", 2020, 1_800_000, *own)
+    assert reread is not None
+    flipped, *_ = city_params._city_growth("PHL", 2020, 1_800_000, historic, future)
+    assert flipped is None  # why the applied averages must not be republished