A city with no waste figure of its own generates waste at its country's default rate

SWEET_python/city_params.py

@@ -1196,11 +1196,12 @@ def load_andre_params(self, row, backfill=False):
                 self.waste_mass_defaults = True
                 if iso3 in defaults_2019.msw_per_capita_country:
                     waste_per_capita = defaults_2019.msw_per_capita_country[iso3]
-                    year_of_data_msw = 2019
                 else:
                     waste_per_capita = defaults_2019.msw_per_capita_defaults[region]
-                    year_of_data_msw = 2019
                 waste_mass_load = waste_per_capita * population / 1000 * 365
+                # A rate times this population is already the population year's
+                # waste, so the year adjustment below must not move it again.
+                year_of_data_msw = int(year_of_data_pop)
 
             # Subtract mass that is informally collected
             # self.informal_fraction = np.nan_to_num(row['percent_informal_sector_percent_collected_by_informal_sector_percent']) / 100
@@ -3189,11 +3190,12 @@ def import_basics(self, row) -> None:
             waste_mass_defaults = True
             if self.iso3 in defaults_2019.msw_per_capita_country:
                 waste_per_capita = defaults_2019.msw_per_capita_country[self.iso3]
-                year_of_data_msw = 2019
             else:
                 waste_per_capita = defaults_2019.msw_per_capita_defaults[self.region]
-                year_of_data_msw = 2019
             waste_mass_load = waste_per_capita * population / 1000 * 365
+            # A rate times this population is already the population year's
+            # waste, so the year adjustment below must not move it again.
+            year_of_data_msw = int(year_of_data_pop)
 
         # Subtract mass that is informally collected
         # self.informal_fraction = np.nan_to_num(row['percent_informal_sector_percent_collected_by_informal_sector_percent']) / 100

changelog/2026-10.md

@@ -0,0 +1,44 @@
+# SWEET_python Changelog — October 2026
+
+**Highlights:** A city with no waste figure of its own now generates waste at
+its country's per-capita default. `load_andre_params` used to move that default
+from 2019 to the city's population year, although it already described the
+population year, so the 39 cities that take this path came out up to 35% below
+or 9% above the default (model-output change).
+
+## Fixed
+
+- **A city with no waste figure generates waste at the default rate**
+  (model-output change). When a cities-table row has neither
+  `msw_generated_metric_tons_per_year` nor `msw_collected_metric_tons_per_year`,
+  `City.load_andre_params` sizes the city's waste as the IPCC per-capita default
+  (`defaults_2019.msw_per_capita_country`, else the region's
+  `msw_per_capita_defaults`) times the city's population. That population is
+  the one for the row's `population_year`, so the mass already describes that
+  year. The loader labelled it 2019 instead, and the adjustment that moves a
+  surveyed tonnage from its survey year to the population year then moved it a
+  second time. The city got the default × P(population year) / P(2019), or the
+  compounded equivalent for a big city on its own UN rates. The default now
+  takes the population year as its year, so that adjustment leaves it alone. A
+  surveyed tonnage is still moved as before.
+  - **Who moves:** the 39 of 5,962 cities in WasteMAP's June 2026 cities table
+    with "Defaults Used: Waste Mass". Their waste, and so their CH4, changes by
+    −7.9% (Abeokuta, population year 2022) to +53.9% (Kano, 2006): 30 rise and
+    9 fall. Kuwait City goes from 7.92 to 8.36 kg per person per day (+5.5%),
+    Tel Aviv +14.7%, Melbourne +5.4%. Their 2025 total, 395 kt CH4 as
+    published, rises by about 27 kt.
+  - **What else changes for those rows:** "Year of Data Collection (MSW)"
+    becomes the population year (2005–2022) instead of 2019. WasteMAP's map
+    build appends that year to the three "Data Source" columns, and its API
+    returns it as `waste_mass_year`, so those follow.
+  - **When:** nothing moves until WasteMAP's cities table is rebuilt, because
+    the map and the City DST read the published table. Climate TRACE doesn't
+    call `load_andre_params`.
+  - `City.import_basics`, the SINIR loader, had the same rule and gets the same
+    fix. Only `model_city_via_sites` reaches it, and nothing calls that.
+  - The copies in `import_basics_site` are unchanged. They size a site for a
+    placeholder population of 100, so a default there means nothing either
+    way, and Climate TRACE drops a site with no tonnage before SWEET runs.
+
+  Tests: `tests/test_default_waste_per_capita.py`.
+  ([#79](https://github.com/RMI/SWEET_python/pull/79))

changelog/README.md

@@ -15,6 +15,7 @@ The project does not publish semantic version tags, so releases are tracked by
 
 Newest first:
 
+- [2026-10](2026-10.md) — a city with no waste figure of its own generates waste at its country's per-capita default, instead of having that default moved a second time as if it described 2019; the 39 such cities in WasteMAP's cities table change by −8% to +54% (model-output change)
 - [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)

tests/test_default_waste_per_capita.py

@@ -0,0 +1,191 @@
+"""A city with no waste figure of its own generates waste at the per-capita default.
+
+When a cities-table row has neither a generated nor a collected tonnage,
+`City.load_andre_params` sizes the city's waste from the IPCC per-capita default:
+the rate times the city's population, which is the population of the row's
+population year. That mass already describes the population year. The loader
+nonetheless labelled it 2019 and then ran the adjustment that moves a surveyed
+tonnage from its survey year to the population year, so the default was moved a
+second time and the city got default x P(population year) / P(2019).
+
+In the June 2026 cities table, Kuwait City published 7.92 kg/person/day against
+Kuwait's default of 3.05 t/yr (8.36 kg/day), and Eldoret (population year 2005)
+got 57% of Kenya's rate. 39 cities take this path. A default now reaches the model
+unchanged, and is published with the year it describes: the population year.
+"""
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from SWEET_python import defaults_2019
+from SWEET_python.city_params import City
+from SWEET_python.population import country_population_series
+
+NOT_REPORTED = [
+    "msw_generated_metric_tons_per_year",
+    "msw_collected_metric_tons_per_year",
+    "msw_collected_year",
+    "msw_generated_year",
+    "composition_food_organic_waste_percent",
+    "composition_yard_garden_green_waste_percent",
+    "composition_wood_percent",
+    "composition_paper_cardboard_percent",
+    "composition_textiles_percent",
+    "composition_plastic_percent",
+    "composition_metal_percent",
+    "composition_glass_percent",
+    "composition_rubber_leather_percent",
+    "composition_other_percent",
+    "waste_treatment_compost_percent",
+    "waste_treatment_anaerobic_digestion_percent",
+    "waste_treatment_incineration_percent",
+    "waste_treatment_advanced_thermal_treatment_percent",
+    "waste_treatment_recycling_percent",
+    "waste_treatment_sanitary_landfill_landfill_gas_system_percent",
+    "waste_treatment_controlled_landfill_percent",
+    "waste_treatment_landfill_unspecified_percent",
+    "waste_treatment_open_dump_percent",
+]
+
+# (city, country, ISO3, population, population year, the city's own UN rates), as
+# the June 2026 table published them. Kuwait City and Namangan are big enough to
+# keep their own rates; Eldoret and Kadoma City grow with their country's population
+# series. Uzbekistan has no country row, so Namangan takes Central Asia's rate.
+# Kadoma City's population year is after 2019, so the old code moved it up, not down.
+CITIES = [
+    ("Kuwait City", "Kuwait", "KWT", 2_779_000, 2015, 1.0574, 1.0134),
+    ("Eldoret", "Kenya", "KEN", 167_016, 2005, 1.0551, 1.0405),
+    ("Kadoma City", "Zimbabwe", "ZWE", 116_300, 2022, 1.0334, 1.0241),
+    ("Namangan", "Uzbekistan", "UZB", 521_000, 2015, 1.0237, 1.0134),
+]
+IDS = [city[0] for city in CITIES]
+
+
+def _row(country, iso3, population, population_year, historic, future, **reported):
+    """A cities-query row with nothing measured but the population, plus ``reported``."""
+    row = pd.Series(
+        {
+            "population_data_source": "World Bank",
+            "country": country,
+            "iso": iso3,
+            "population_year": float(population_year),
+            "population_count": float(population),
+            "historic_growth_rate": historic,
+            "future_growth_rate": future,
+            "latitude": 0.0,
+            "longitude": 0.0,
+            "mean_yearly_precip_2000_2021": 600.0,
+            "mean_yearly_temp_2000_2021": 20.0,
+            **{column: np.nan for column in NOT_REPORTED},
+            **reported,
+        }
+    )
+    # The cities query selects data_collection_year twice (composition, then
+    # treatment), and the loader falls back to the first when no MSW year is given.
+    return pd.concat([row, pd.Series([2015.0, 2015.0], index=["data_collection_year"] * 2)])
+
+
+def _default_rate(country, iso3):
+    """kg/person/day: the country's IPCC row, else its region's."""
+    if iso3 in defaults_2019.msw_per_capita_country:
+        return defaults_2019.msw_per_capita_country[iso3]
+    return defaults_2019.msw_per_capita_defaults[defaults_2019.region_lookup[country]]
+
+
+def _loaded(name, row):
+    city = City(name)
+    city.load_andre_params(row)
+    return city
+
+
+def _emissions(city):
+    """Total CH4 by year, the way the cities pipeline runs a loaded city."""
+    for landfill in city.baseline_parameters.landfills:
+        landfill.estimate_emissions()
+    city.estimate_diversion_emissions(scenario=0)
+    city.sum_landfill_emissions(scenario=0, simple=True)
+    return city.baseline_parameters.total_emissions["total"]
+
+
+@pytest.mark.parametrize("name, country, iso3, population, year, historic, future", CITIES, ids=IDS)
+def test_a_city_with_no_waste_figure_generates_the_default_rate(
+    name, country, iso3, population, year, historic, future
+):
+    city = _loaded(name, _row(country, iso3, population, year, historic, future))
+    params = city.baseline_parameters
+    rate = _default_rate(country, iso3)
+
+    assert city.waste_mass_defaults is True
+    assert params.waste_per_capita == pytest.approx(rate, rel=1e-12)
+    assert params.waste_mass.iloc[0] == pytest.approx(rate * population * 365 / 1000, rel=1e-12)
+
+
+@pytest.mark.parametrize("name, country, iso3, population, year, historic, future", CITIES, ids=IDS)
+def test_a_default_rate_is_published_with_the_population_year(
+    name, country, iso3, population, year, historic, future
+):
+    params = _loaded(name, _row(country, iso3, population, year, historic, future)).baseline_parameters
+
+    assert params.year_of_data_msw == params.year_of_data_pop == year
+
+
+@pytest.mark.parametrize("name, country, iso3, population, year, historic, future", CITIES, ids=IDS)
+def test_a_defaulted_city_is_modelled_as_if_it_reported_the_default_rate(
+    name, country, iso3, population, year, historic, future
+):
+    tonnage = _default_rate(country, iso3) * population * 365 / 1000
+    defaulted = _loaded(name, _row(country, iso3, population, year, historic, future))
+    reported = _loaded(
+        name,
+        _row(
+            country, iso3, population, year, historic, future,
+            msw_generated_metric_tons_per_year=tonnage,
+            msw_generated_year=float(year),
+        ),
+    )
+
+    assert np.allclose(_emissions(defaulted), _emissions(reported), rtol=1e-12, atol=0)
+
+
+def test_a_surveyed_tonnage_still_moves_from_its_survey_year_to_the_population_year():
+    """The adjustment the default skipped is still right for a measured figure."""
+    # A big city with its own rates: compounded from the 2012 survey to 2015.
+    kuwait = _loaded(
+        "Kuwait City",
+        _row(
+            "Kuwait", "KWT", 2_779_000, 2015, 1.0574, 1.0134,
+            msw_generated_metric_tons_per_year=1_500_000.0,
+            msw_generated_year=2012.0,
+        ),
+    ).baseline_parameters
+    assert kuwait.waste_mass.iloc[0] == pytest.approx(1_500_000 * 1.0574**3, rel=1e-12)
+    assert kuwait.year_of_data_msw == 2012
+
+    # A small city: back from the 2010 survey to 2005 along Kenya's population.
+    kenya = country_population_series("KEN")
+    eldoret = _loaded(
+        "Eldoret",
+        _row(
+            "Kenya", "KEN", 167_016, 2005, 1.0551, 1.0405,
+            msw_generated_metric_tons_per_year=30_000.0,
+            msw_generated_year=2010.0,
+        ),
+    ).baseline_parameters
+    assert eldoret.waste_mass.iloc[0] == pytest.approx(
+        30_000 * kenya.loc[2005] / kenya.loc[2010], rel=1e-12
+    )
+    assert eldoret.year_of_data_msw == 2010
+
+
+def test_the_legacy_sinir_loader_generates_the_default_rate_too():
+    """`import_basics` (reached only from `model_city_via_sites`) has the same rule."""
+    basics = City("Kuwait City").import_basics(
+        _row("Kuwait", "KWT", 2_779_000, 2015, 1.0574, 1.0134)
+    )
+    rate = defaults_2019.msw_per_capita_country["KWT"]
+
+    assert basics["waste_mass_defaults"] is True
+    assert basics["waste_per_capita"] == pytest.approx(rate, rel=1e-12)
+    assert basics["waste_mass"] == pytest.approx(rate * 2_779_000 * 365 / 1000, rel=1e-12)
+    assert basics["year_of_data_msw"] == basics["year_of_data_pop"] == 2015