what the number is, and what it is checked against

what we need

signed off

Values the carbon stored in land ecosystems, priced at the rental social cost of carbon. Reproduces the superseded manuscript total of $5.365T to the dollar, after fixing a double-count that entered split countries once per sub-region (China six times, 23.5 percent overstatement). Priced at the rental social cost of carbon at 2 percent. The 3 percent sensitivity is a run of its own and gives $3.4627T against the update's $3.43T. ⚠ The update's terrestrial figures are $5.33T / $8.12T / $3.43T at 2 / 1 / 3 percent, verified against his per-country sheet; the $34.8bn gap to ours decomposes as $34.3bn of per-country recalculation (every row moved, coastline-heavy countries most), $181M of newly-missing small islands, and $0.3bn of countries dropped from the comparison by mojibake names.

⭐ One of the two discount-rate questions is ANSWERED by the 2026-09-04 email: the 1 percent price is replicated following Rennert et al., who do not publish it directly. Still open: whether carbon prices are taken to grow. The r in the rental formula is the net rate, the discount rate minus the growth rate of carbon prices, so the 2 percent column assumes prices do not grow; the table carries a 0.8 percent net alternative giving $2.29 a tonne against $13.19. And now the substantive ask: **what changed in his zonal statistics** -- every country moved and ours is higher almost everywhere, largest where coastline is long relative to area, so the two pipelines now disagree on border-cell treatment and one of them is right. ⚠ His email quotes the old combined climate-regulation total as $5.365T, which is also the old terrestrial-only figure we match to the dollar -- the old combined column looks like it did not include coastal (the new sheet's `both` tab, terrestrial plus coastal, verifies the new combined $5.3367T), worth one clarifying line when replying.

amber: examined, and at least one of the sixteen conditions is not yet verified. ⚠ **10 is OPEN for this module**: no cold start from an empty project directory has been run -- the density-raster build on the 10 arc-second grid is a cluster-scale job. Six services have closed it (erosion, ntfp, renewable_energy_provision, water_supply, extractive_energy, extractive_materials_provision); this is not one of them. ⚠ **11 is unverified**: the base year is 2019 across every service, but the country list, the true-zero-against-missing rule and the shared parameters have not been checked service by service. ⚠ The two discount-rate decisions in `need` are open with the group.

No marine module found.

Is marine carbon in the account's scope?

amber: examined, and at least one of the sixteen conditions is not yet verified. ⚠ **10 is OPEN for this module**: no cold start from an empty project directory has been run. Six services have closed it (erosion, ntfp, renewable_energy_provision, water_supply, extractive_energy, extractive_materials_provision); this is not one of them. ⚠ **11 is unverified**: the base year is 2019 across every service, but the country list, the true-zero-against-missing rule and the shared parameters have not been checked service by service. ⚠ The seagrass overlap double-count in `decided` is measured but not fixed, held for Yanxu.

No marine module found. The table's $540.75T is arithmetically the ocean's ~41,000 GtC at the terrestrial rental price; the annual ocean carbon UPTAKE (~2.5 GtC per year) at the same price would instead be ~$33bn, four orders of magnitude smaller, so which quantity the service means decides everything.

Three questions before this is a row at all. Whether the service is the stock rental of the entire ocean inventory (including ~37,000 GtC of deep water that no policy touches) or the annual uptake flux. Whether $318.2T of High Seas value belongs in a by-country account. And whether one row should be allowed to be 98 percent of the account's headline total.

n/a: no module, and none found on the drive either. The reported figure is now decoded (the whole ocean carbon inventory at the terrestrial rental price, 58.8 percent of it on the High Seas) but whether marine carbon is in scope is itself open

Values the crop output pollinators are responsible for. **$387.67bn at 2019 prices over 193 countries**, from the raster our own chain builds — FAO panel, yield-change ratios, Monfreda yields carried to the base year, CropGrids harvested area, our median price, the author's dependence table with coffee blended per country. ⚠⚠ **Changed on 2026-08-30 from $384.09bn**, which was the author's staged raster: the yield and production steps had no port until that day, so no run here could produce the value raster from source. Of the $3.40bn difference against his raster, **$2.98bn is the coffee blend** (FAO item 656 covers arabica at 0.25 and robusta at 0.65; his `load_poll_dep` keeps the first row, valuing every coffee country as pure arabica) and the rest is the per-cell versus density convention. Set `pollination_blend_coffee_dependence` to false and our pipeline returns his published pair exactly: **$384.97bn unweighted and $161.45bn weighted, both to the cent**, with the sufficiency raster identical to his in all 331,871,070 defined cells once the tile height is his 8192. Cell areas are WGS84 from hazelbean, the same convention the rest of the account uses through the ha_per_cell pyramid, which pollination alone did not: it was on a 6371 km sphere, differing by -0.223 to +0.557 percent with latitude, so the GEP total was summing services that measured a cell differently. Adopting it cost 0.1086 percent. Aggregated on the centre rule so a border cell goes to the country covering its centre rather than to whichever was rasterised last. That change cost 0.1211 percent of the world total and moved 92 of 204 countries by more than 5 percent, Iraq by 65.63. Vatican City, Gibraltar, Monaco and Macao drop out entirely, being smaller than a cell: $0.35M between them. From a raster we build (the original one was 2024). The previous $18.28bn was the same raster summed without cell area; area-weighted it gives $476.29bn. That figure is not an independent check: it comes from our own run of the source pipeline on 2026-07-25, which wrote both the raster and the summary beside it. What it does establish is that the source code treats the raster as a density, since its own summary area-weights what it wrote. The units are stated in that repo's methods_overview.md, 'global value raster (USD/km2)'. Fixing coffee's dependence adds $3.76bn at 2024 prices. Our code reproduces the author's raster to 1.8e-08. ⚠⚠ **Computed both ways on 2026-08-28**, one grid and one price year: production x price x dependence gives $388.47bn over the raster, and multiplying by 300 m habitat sufficiency built from `lulc_esa_2019` gives $163.46bn, a ratio of 0.421. The $388.47 against the published figure is the raster sum against the country zonal sum, which drops cells outside any country. William Sidemo Holm's 2019 equivalents are **$385bn and $190bn, ratio 0.494** (revised 2026-08-28 when he switched price and yield windows from mean to median; his 2026-08-27 figures were $387bn and $191bn, the same ratio). So we agree closely unweighted and discount harder weighted. He confirms `target_year` and `deflation_base_year` are both 2019 in his config, which we verified at lines 118 and 128. The gap is **not yet explained, and eight candidates are eliminated by measurement** (2026-08-28). Not the value raster's geography: his precomputed value raster under our sufficiency gives 0.4155, essentially ours. Not `fill_non_ag` — his runner calls `run_pollination_sufficiency_300m` without it, so both sides take the `None` default; this had been the prime suspect and is refuted. Not the constants: `_RADIUS_METERS` 2000.0, `_THRESHOLD` 0.30 and the `_AG_CLASSES`/`_NAT_CLASSES` sets are unchanged since his initial commit. Not the land-cover file: his `config/default.yaml` resolves `lulc_esa_{target_year}.tif` at `target_year: 2019`, the same map we read. Not the grids, all 0.05 degree 3600x7200 and 0.002778 degree 64800x129600. Not the aggregation: pixel-wise gives 0.4208 and an unweighted mean 0.7004, and his 0.4935 lies between them. Not the nodata convention: his valuation writes NaN as -9999, dropping cells we keep as zero, which moves ours only 0.4208 to 0.4291. Since his value raster under our sufficiency still gives 0.4155, **his sufficiency raster must differ from ours**. The target-year lead is also closed: commit `7f201c4` notes that before it "everything was set up for creating rasters with the target year 2020", but he states his current figures come from the new code with both years at 2019, and independently ESA 2019 and 2020 differ by only 0.0405 percent of pixels (agricultural cover +0.044 percent), far too little to move a ratio 7 points. **Twelve candidates eliminated, none remaining that we can test from outside.** Also refuted: the differences do NOT compound — every combination of value-raster choice, per-cell versus density units, and nodata denominator lands between 0.4155 and 0.4291, a spread of 0.014 against a gap of 0.064. Per-cell versus density weighting alone moves 0.4208 to 0.4169, and sufficiency barely correlates with latitude (-0.005), mean 0.577 in every band. `fill_non_ag` can only push the wrong way: filling non-arable with 0.0 and scaling by agricultural area fraction lowers sufficiency, and ours is already below his. Our 300 m raster was checked directly and is correct — 96.04 percent NaN for non-arable, 1.02 percent genuine zeros (arable with no habitat in range), 2.93 percent positive, so arable totals 3.95 percent, matching the ESA agricultural share exactly, and the 5 km average really is arable-only. Aggregation level does not explain it either: country value times unweighted country-mean sufficiency gives 0.6495, overshooting, while value-weighted gives 0.4203. His 0.4935 is bracketed by our numbers but reproduced by none of them. ⚠⚠ **Settled on 2026-08-28 by running HIS repository directly** (`scripts/pipelines/run_pollination_sufficiency.py --step sufficiency_300m` on `lulc_esa_2019.tif`, his config, our machine): his own code gives **$163.43bn, ratio 0.4207**, against our port's $163.46bn and 0.4208. The arable mask is identical cell for cell (1,657,059 defined cells in both on a 1-in-200-row sample) and mean sufficiency is 0.4511 his against 0.4494 ours; 78.4 percent of cells match exactly and the rest is tile-boundary noise that washes out in aggregate, since he tiles in 128 blocks and we use many more. His own `sufficiency_5km` step, on his own `country_m49_cropgrids_grid.tif` template, likewise gives 0.4207, matching a hand resample exactly, and all four pairings of the two value rasters with the two sufficiency rasters fall between 0.4155 and 0.4208. **So our copy is faithful and the sufficiency chain is not the cause.** ⚠ Scope: this covers his steps 1 and 2 only. His `valuation_5km`, `mask_pa` and `attribution` steps have never run here — `valuation_5km` wants `poll_value_global_2019usd.tif` and base_data holds only 2023 and 2024, so our own value raster is substituted at that point. The value raster of his we hold is the 2020 / 2024-USD one, not the 2019 raster behind his $385bn. ⚠⚠ **RESOLVED on 2026-08-28 by running his full 2019 chain end to end** — FAO yield change, Monfreda yield rasters, production, `poll_value`, then `valuation_5km`, all his code at his `target_year: 2019`. It returns **$383.68bn unweighted and $189.732bn sufficiency-weighted, a ratio of 0.4945**, against the $385bn and $190bn he reports. ⚠⚠ **CORRECTED same day: the gap is neither the sufficiency code nor the value raster — it is his summing function.** `_sum_raster_tiled` in `run_pollination_sufficiency.py` computes cell area once per 4096-row tile from the tile's midpoint latitude; `value_pollination_sufficiency_2019_5km.tif` is 3600 rows, so it is ONE tile centred at -0.025 degrees and every cell on Earth is given 30.980 km2. Replicating that reproduces his figure to the digit: raw sum times 30.980 equals **$190.12bn** on the run with his own downloaded prices. Weighting each row by its own latitude gives **$161.24bn**, so the reported total runs **17.9 percent high**. The corrected ratio is 161.24/384.56 = **0.4193**, against our own 0.4208 — **our number was right throughout**. ✅ **Confirmed by the author on 2026-08-30**: he added `window_area_km2` to `src/crop_benefits/raster/spatial.py` and calls it from `run_pollination_sufficiency.py` and `src/crop_benefits/pollination/sufficiency_value.py`, and reports **$384.97bn unweighted, unchanged, and $161.45bn weighted** — 0.13 percent from the $161.24bn we predicted. Two call sites where we had named only the runner. Verified in his code, not merely inferred: `raster/spatial.py::build_area_km2_raster` takes the latitude of every row and `raster/pollination_value.py` calls it, which is why his unweighted $385bn is correct; `_sum_raster_tiled` in the sufficiency runner does not call it and recomputes area per tile instead. The right helper already exists in his library and one summary script bypasses it. Running the tiled version over his unweighted raster would report $457bn against the $385bn he publishes. This is the same equator-constant error as flood (288,615 m2, about -28 percent) and stormwater (185.87 to 122.30 bn m3, -34.2 percent); it sits in his summary, not his model, and his rasters are sound. An earlier note here said the cause was the value raster; that was wrong, and it was wrong because his 2020 raster was being compared against his 2019 sufficiency. His sufficiency against his 2019 value gives 0.494, against his 2020 value 0.4155, and against our rebuilt value 0.4208 — the 2019 and 2020 rasters differ spatially far more than their totals suggest, which is why twelve eliminations inside the sufficiency chain found nothing. Two local substitutions were needed to run it: the 2017-2021 median price file was built by calling his own `_compute_median_prices` on the already-downloaded `fao_prices_1993_2024.parquet` rather than re-downloading from FAO, and `config/local.yaml` got an `lulc_esa` path override for our nested `lulc/esa/` layout. Neither touches the science. His `poll_value_global_2019usd.tif` is now staged in `base_data/crop_benefits/`, so the GEP path reads the exact base year with no deflation. So the gap is excluded from the sufficiency chain but NOT excluded from steps we have never executed. **The dependence table is also confirmed identical**: our `pollination_1993_2024.parquet` is his own `pollination_1993_2024.csv`, 287,285 rows, 163 items and 77 pollinated on both sides, values equal to 5.6e-17 — so an earlier note that we use Klein while he adds Siopa et al. 2024 was wrong, we read his table. His FAO summary carries $429.40bn for 2019, his reported tabular figure, and a 2019-to-2024 deflator of 1.2315 matching ours. ⚠⚠ **Corrected 2026-08-30: our rebuild is not a second method.** It reads `base_data/crops/cropgrids/production_2020/`, which is **byte-identical** to the output of his own pipeline on 2026-07-25 (sha256 on abaca, almond, apple, maize, wheat), so both sides run his production and the roughly one percent is vintage and prices: production dated 2020 against his 2019, and a 2018-2022 median price window against his 2017-2021. The earlier claim here that we take CropGrids production directly while he goes harvested area times Monfreda times FAO calibration was wrong. What that means for the port: the FAO price chain, the poll_value arithmetic and the sufficiency chain are ours; **`yields` and `production` are the one part of his pipeline never ported**, so "our code run end to end" does not yet exist for the value raster. His raster is a density where ours is per cell. `valuation_5km` cannot run here because `poll_value_global_2019usd.tif` needs his FAO price chain at the 2017-2021 window. Asked in `reply_pollination_william.md`, unsent. His sufficiency raster is not on this machine, not in `base_data`, and not on the MSI share, so we have asked him to send the 5 km raster for a direct difference against ours. Script: `projects/gep_pollination/compute_sufficiency_weighted_total.py`.

Which definition the account wants: the crop output at stake if pollinators vanished, or the service habitat currently delivers. ⚠⚠ **Our $387.67bn is the first of those.** The value raster is production times price times dependence, with no habitat sufficiency term; `configure_sufficiency` is called only inside `pollination_shock`, never in the GEP chain. William Sidemo Holm ran three variants for 2019 on 2026-08-27 and ours lands on his second: FAO tabular without sufficiency $429bn, raster without sufficiency **$385bn**, raster with sufficiency **$190bn** (revised from $387bn and $191bn on 2026-08-28). Ours is $387.67bn from our own chain since 2026-08-30, and at his coffee convention it reproduces his $384.97bn exactly; applying sufficiency would roughly halve it. ⚠ We already build a sufficiency-weighted value raster, `value_pollination_sufficiency_<scenario>_5km.tif`, but only inside the shock path where it becomes a ratio — the second number is a zonal sum away, not a new model. And whether the GEP total adds pollination on top of crop production: at $387.67bn against crop provision's $678bn pollination is 57 percent of it, which is the ground of Ryan McWay's objection that half of production value is not pollinator dependent; with sufficiency it would be 28 percent.

amber under the CURRENT sixteen-condition checklist; green under the fourteen that existed when it was signed on 2026-09-01, the first service to reach that. The two conditions added since are open here and named: **15** (data organised) -- 1,047 unreferenced files sit in its base_data folder, mostly CropGrids NetCDFs and per-crop rasters; **16** (published rasters are POGs) -- the value raster this service builds is not registered as a result, so it never reaches output/ and is never converted. Everything below is the fourteen-condition record, unchanged. **12 is met as of 2026-09-01**: both reproduction claims are staged in base_data and pinned by tests that run against every pollination project on the machine rather than one named directory. Writing them corrected the entry twice over. The dependence panel agrees to 1.4e-14 over 287,285 rows and 22 numeric columns, not the 5.6e-17 recorded here; and the raster comparison the entry reported at 1.8e-08 had stopped running altogether once the library began building its own raster, so what is pinned now is the live one, our rebuild sitting 0.93 percent above his staged file with correlation 0.995 over 2,941,471 cells. Both numbers were previously measured by hand, once, and neither would have survived a change unnoticed. **14 is met**: H23 finds nothing in this service defined and never used. It found fifteen names across seven services when it was written on 2026-09-01, including pollination's `pixel_area_km2_spherical`, the 6371 km sphere the account had already decided against. **13 is met**: nothing in the pipeline or its verification reaches into anyone's checkout. Every input and every reference output is staged in base_data, no path in `es_parameters` leaves it, and H22 checks that mechanically. The author's repository could be deleted tomorrow and this would still run, still produce the figure and still verify itself. **1 is met as of 2026-08-30** — our code produces the number, where until that day the account read the author's staged raster because the yield and production steps had no port. **2 is met, for both figures we publish as of 2026-08-31.** Every step behind $387.67bn is a task in this library, the FAOSTAT bulks download from the urls in es_parameters, and the author's raster is optional — absent, the run loses the independence check and nothing else, where it used to raise and send the reader to a six-command procedure in his repository. **3 is met**: the figure above is from the current code, rerun after the last change that moved it. **4, 6 and 8 are met**: the grid and cell area are stated, the three gates pass, the file set is the agreed four plus tests. **5 is met**, and the check is not fidelity-only. Three of the four inputs are public primary data both pipelines read independently: the FAOSTAT bulks download from FAO's own urls in es_parameters, and CropGrids v1.08 and Monfreda are published datasets. The fourth is the dependence table, whose ratios are Klein et al.'s published categories and which is staged in base_data like any other author artifact — on the shared tree since 2026-08-31, so a second person has it. ⚠ What the reproduction cannot test, stated as the condition requires: our `pollination_1993_2024.parquet` is byte-equal to his csv, 287,285 rows agreeing to 5.6e-17, so his FAO-item crosswalk over 163 items and his five blend weights for the n.e.c. aggregates — 0.11 other vegetables, 0.1679 other oil seeds, 0.2594 other stimulants, 0.3438 other nuts, 0.38 other fruits — are adopted rather than derived, and an error in any of them is invisible to us. Rebuilding that crosswalk from Klein would turn adoption into confirmation. ✅ **Decided 2026-08-31**: put to Justin, who approved staging a constructed table in base_data rather than shipping only public sources with a task that reconstructs them, and deferred the reconstruction as too much work for now. So this is a recorded choice rather than an oversight, and the entry says the number is adopted rather than independent instead of implying otherwise. **7 is met**: the open question — whether the account reports value at stake or the service habitat delivers — is for the account to decide and does not change what the code computes. We also publish $163.45bn, and until 2026-08-31 no task produced it: `configure_sufficiency` was reachable only from the shock task, so the multiply and zonal sum happened in a script in the project directory that the repository does not carry, and a reader following this repo could reproduce one of the two figures we quote. `pollination_sufficiency_weighted` now does it in the tree, weighting the rebuilt raster at the author's own tile height of 8192, and returns $388.1426bn unweighted and $163.4501bn weighted. **9 is met**: the reply was sent on 2026-08-31, reporting that the fallback was our defect and that both totals now match his. No thread with him is open, drafted or otherwise. He fixed the tiled summing on 2026-08-30 by calling `window_area_km2` from the two sites we named, and on 2026-08-31 he split coffee into Arabica and Robusta with per-country production shares, which is the question that had stood unanswered. He now reports $388.14bn unweighted and $163.45bn weighted, and ours match his on both after the coffee fallback fix of the same day. ⚠ **9 reopened the same day.** The old draft asking about the tiled summing was overtaken by his fix, but `reply_pollination_william.md` was rewritten that evening to report our numbers against his and hand back a row-order bug of ours, and it ends by offering to chase the remaining 0.07 percent. That offer is a question he has not answered, and the condition counts an unsent draft as open whether or not we have got round to asking. Sending it closes 9; nothing else needs doing. **10 is met**: `projects/gep_pollination_coldstart`, rerun on the fixed code 2026-08-31, a fresh directory reusing nothing, built all 158 yield rasters and returned **$387.9576bn unweighted, $163.3293bn weighted, and gep_by_country $387,487,273,050 over 193 countries** **$388.1426bn unweighted, $163.4501bn weighted and gep_by_country $387,669,681,411 over 193 rows** — identical to the warm run, to the digit. ⚠ What it proves is the repository plus a complete base_data, not the repository from raw sources: `fao_median_prices` and `fao_yield_change` finish in under a second because their outputs are base-data-generating and already live there, which condition 2 allows. **11 is met as of 2026-09-01**: the published table is 250 rows on the account's list, the 57 countries with no pollinated crop recorded as NA rather than absent, and the total unchanged at $387,669,681,411. The base year is 2019, the grid is WGS84 with cell areas from hazelbean, and the dependence ratios are Klein's. ⚠ The one convention it does not yet meet is the PPP one, which was withdrawn as a condition on 2026-08-31 and moved to the structural pass, because a project-wide currency change should not be cleared service by service. ⚠ What it proves is the repository plus a complete base_data, not the repository from raw sources: `fao_median_prices` and `fao_yield_change` finished in 0.009 and 0.42 seconds because their outputs are base-data-generating and already live there, which condition 2 allows and condition 10 asks for. The 3.19 GB of inputs are now on the shared base_data, all 1197 files verified identical by sha256, so a second person on MSI has them; running it there has not been done and would test the environment rather than the code.

Values the crop production that soil retention protects: the share of soil loss prevented by a field's own cover and by everything upslope of it, turned into a production shock and priced against the country's crop output. ⚠ The issues table's separate SOIL RETENTION row is this service under the CAS framework's name. **Revised 2026-10-02:** the CAS submission delivers a soil-retention package of its own (staged at `base_data/global_invest/cas_submission_2026_09/`): $5.39T PPP-2019 over 243 countries across FOUR replacement-cost channels -- non-point pollution avoidance $3.45T, sedimentation dredging $1.25T, crop and grass fertility $0.69T -- plus global avoided-erosion and avoided-export rasters and a disciplined numbered pipeline. Only the fertility and sedimentation channels overlap this service; the pollution channel collides with the water-quality row instead. So the question is no longer delete-the-row but which channels the account admits without double counting -- and the currency (PPP) and replacement-cost-vs-market-value questions ride along. $18.24bn across the 192 countries that carry a value, of which 31 compute an exact zero, leaving 161 positive. The author reports $14.59bn across 164 countries for the same SES-11 baseline, so ours sits 25 percent above his, and the country split diverges further still. South Sudan is dropped because the source table spells it SDS where the country table uses the ISO SSD; its value is zero, so no dollars are lost by it today.

amber for exactly one condition and one blessing, everything this service controls closed (2026-09-06). ⭐ **10 CLOSED** (2026-09-03, the library's first): a cold start reproduced the published total to $11 in $18.24bn. ⭐ **16 CLOSED**: the combined prevention share is registered in gep_calculation, warped off the InVEST projection onto the 30 arc-second pyramid (nearest neighbour -- a proportion is not interpolated), and leaves output/ as a POG, verified with is_path_pog and an overview read; the rerun reproduced $18,240,930,372.66 on the way. ⭐ **15 CLOSED as a recorded decision**: author_drive stays a FAITHFUL COPY by design -- its seven top-level duplicates are the author's own copies and es_parameters points live into its msi_upstream_attribution_v3 subfolder, so deleting from it would make it stop being what it is. ⭐ The SDR extent is PINNED: es_parameters records the author's 6217x3968 grid and run_invest_sdr now refuses a run whose grid differs (an unpinned run wrote 62 extra Arctic rows). ⭐ **11 verified for this service 2026-10-07**: the published table is 250 rows on the account's list at base year 2019, with 161 valued, 31 measured zeros distinct from 58 missing rows, and nothing priced off a parameter that is not a shared row; the C17, C19 and C20 gates encode the same checks and are clean. **Open: the border-rule blessing with the author, alone** (his notebook rasterizes all_touched, ours assigns a cell to the country covering its centre; 0.4 percent of the total, the micro-states move entirely), via the issues row.

Values the deaths that forest root cohesion prevents. Computed end to end, the full run gives 570.66 avoided deaths, $770.7M. No reference output exists to check it against.

Nothing from the author. ✅ Decided 2026-10-07, Chiara: the account's reference is his v0.2.1 per-country output, which we match at 0.978 and which his repo and appendix carry -- the all-services sheet's $865M column is superseded by his own run and its provenance is not this service's question to chase. The ask dies deliberately asked-and-dropped, and the entry says why, which is what condition 9 requires. This is also the one service that builds its own value of a statistical life, from the OECD country table deflated from 2022 to 2019 USD with an income-group fallback, so it is the row that has to move onto the air quality group's shared table for the account to price a death the same way everywhere. That move will change the number, because the two sources differ. ⭐ Measured 2026-09-06 by repricing the run's avoided deaths on the shared table (222 of 250 countries matched directly, the group's own mean-fill for the rest): **$705.1M against the OECD $770.7M, an 8.5 percent decrease with the country ranking preserved** (Nepal $242M, Colombia $118M). The number is ready; what remains is the wiring -- the shared table as an es_parameters row both mortality services read, which is the C18 gate item. The same three questions apply to that table as on the air filtration row: its currency year, an ISO3 column rather than a country name, and the countries it does not cover, which is what the income-group fallback is for.

amber for condition 16 alone, one rsync and one rerun away. ⭐ The reference question closed 2026-10-07: his v0.2.1 per-country output matches ours (0.978, same five leaders, pinned by a test), and Chiara decided the account's reference is his output -- the sheet's $865M column is superseded by his own run and not this service's question, so 9 holds nothing. ⭐ The shared VSL is decided and wired the same day (the C18 row under Air Filtration): this service already read the panel, so its number does not move, and condition 11 is now met in full. The grid is settled: EASE-Grid is equal-area and the flow accumulation NEEDS it, the same recorded justification as erosion, and the cell area is exact there. The code is complete on the template with tests. ⭐ **10 CLOSED 2026-10-07**: `projects/gep_landslide_mitigation_coldstart_20261007`, a fresh directory reusing nothing, reproduces $657,753,718.76 with zero per-country difference against the published table. Like erosion's, the cold start proves the repository plus a staged base_data: the slope-stability chain that generates the staged zonal statistics is cluster-scale upstream work, documented as such. ⚠ **Open: 16 alone.** The service's map is the cluster run's avoided-deaths raster, price-free, the biophysical surface behind the table; it lives on MSI, whose login nodes are down for maintenance on 2026-10-07, so what remains is one rsync into `landslide_mitigation/cluster_run_2019/` and one gep rerun that warps it from EASE-Grid onto the 30-arcsecond rung sum-preservingly and publishes it as a POG through the shared seam. The value map is deliberately NOT published: the cluster run's value raster carries the superseded OECD pricing, while the account's dollars are priced at the shared panel in gep_calculation, so a published value raster would contradict the published table. The deaths map is the service's raster; the value is tabular. ⭐ **11 met in full 2026-10-07**: 250 rows on the account's list at 2019, 227 valued and 23 exact zeros with no missing rows, the zeros being atolls, flat deserts and unpopulated territories where zero exposed population IS the measurement (Monaco and the Vatican are smaller than a 1 km cell, so theirs mean the grid cannot resolve them), and the shared-parameter clause closed by the shared-VSL wiring (both mortality services read this service's panel file)

Values the economic rent of the capture-fisheries catch, from CWoN's catch, cost and subsidy columns, predicted to 2019 by trend since their series stops at 2018. $29.00bn, provisional because the source is undecided: FishStatJ, FAO's capture-production database, would give a different number. Separately, there is no reference output to check it against.

⚠⚠ **Two findings from 2026-09-02, both of which change what the number means rather than what it is.**

amber: examined, and at least one of the sixteen conditions is not yet verified. ⚠ **10 is unmet here** (no longer everywhere: erosion closed it on 2026-09-03, reproducing its published total to $11 in $18.24bn from an empty project directory). ⚠ **11 is unverified**: the base year is 2019 across every service, but the country list, the true-zero-against-missing rule and the shared parameters have not been checked service by service

Values the home catch households consume, as harvested quantity times price per kg, $7.92bn across 65 countries. Every country whose quantities and prices the release carries reproduces the committed output. Twenty countries publish a value of exactly zero with no quantity or price behind it; those are left empty.

What is the account's fisheries value? The commercial rent alone, the sum of both components, or something else. The commercial side values the rent of the reported catch, the subsistence side the use value of the home catch. The commercial series is built from FAO catch statistics, so where a country's reported catch already includes subsistence fishing, a sum counts that catch twice.

amber as of 2026-09-02, and **9 is the only condition open**. **10 is met**: `projects/gep_fisheries_coldstart_20260902_144720`, a fresh timestamped directory seeded from `input_template/`, which produced this figure along with commercial and aquaculture. **12 is met**: the reference is staged, and `test_every_subsistence_run_reproduces_the_staged_reference_total` compares what the pipeline WROTE against it for every fisheries run on the machine rather than checking the maths alone. **11 is met**: 250 rows on the account's list, 2019, and the zero-against-NA rule is stated above rather than left to be inferred. **5 is met**: the check is independent of their code, and the entry says plainly that it is not independent of their data and what would be needed to make it so. **15 is met**: the subsistence lineage sits in its own `subsistence/` folder with its input and both anchors, and C21 finds no duplicate. ⚠ **9 is open**: the account's fisheries scope question below has not been put to anyone, and the issues document records that this component has no methods writeup -- "Subsistence fisheries: Don't have writeup. Check with Ryan". OLD TEXT FOLLOWS, retained only where still true: ⚠ **11 is unverified**: the base year is 2019 across every service, but the country list, the true-zero-against-missing rule and the shared parameters have not been checked service by service

$110.50bn across 206 countries, aquatic plants excluded ($116.69bn including them). World aquaculture value in 2019 is $292.4bn and the natural-resource share of fishing value added runs 29 to 59 percent by region, mean 41. China $68.02bn, Vietnam $7.12bn, Chile $5.81bn, India $5.66bn.

⭐ Updated 2026-10-03, three asks from the delivery: which GTAP aggregation and FishStatJ vintage produced the cleaned CSV, and whether aquatic plants are in ('all aquaculture species' suggests yes, yet his China sits at 0.88 of our plants-excluded reading, so something else also moved); whether he adopts the revenue-share correction -- his own x0.5 sensitivity points the same way; and the cleaned aquaculture script, which his email says is not yet on GitHub. The earlier state stands otherwise: Nothing external. All four inputs the source names were resolved on 2026-09-02 without asking anyone: `aqua_prod_quant.csv` and `aqua_prod_value.csv` are `Aquaculture_Quantity.csv` and `Aquaculture_Value.csv` inside FAO's public `Aquaculture_2025.1.0.zip` (2.1 MB, and its `MEASURE` codes `Q_tlw` and `V_USD_1000` are literally the source's column names); `fsh_endowment_gtap.xlsx` is computed here from `basedata.har` as `EVFP[NatRes, fsh, r] / EVFP[:, fsh, r]`; and `iso3_gtap141_mapping.xlsx` is the account's own `ee_r264_correspondence.csv`, which maps all 250 countries onto the 50 GTAP regions with none left over. None of the four existed anywhere in base_data beforehand -- searched the whole tree, not just the fisheries folder.

amber as of 2026-09-02. The task exists and has run: `gep_fisheries/intermediate/fisheries_aquaculture_gep/aquaculture_gep_by_country.csv`, 250 rows on the account's list, 206 valued, $110,497,000,000 -- and the throwaway script's $116.69bn is still logged beside it as the including-plants variant, which is the point of moving it into the module rather than the check on it.

**$814,321,561,732 across 174 countries**, from our own run: $511,522,475,916 read from FAOSTAT and the rest through the price-gap fill, which values 5,233 pairs ($1.128T gross before the rental rate; 1,895 at the country's own price, 2,708 subregion, 494 region, 136 world). India moves $12.9bn to $103.3bn -- its rice, like most of its crops, carries 2019 production and no 2019 USD value -- and 22 countries enter from nothing, the Democratic Republic of the Congo ($9.6bn) and Myanmar ($7.5bn) the largest. The 24 countries at exactly zero are small island and city states whose crops carry value but no CWoN rental rate, so nothing can be attributed; whether they should read as NA instead is the deck's Q3. The fallback statistic is measured, not assumed: holding out each country-item pair with its own 2019 price and predicting it from the subregion's other members, the median beats the mean value-weighted (|log error| 0.56 against 0.64, upward bias +0.25 against +0.41 in logs -- both overprice large producers, the mean more), and 68 percent of the filled dollars ride on the country's own price with no fallback involved ($206.6bn of $302.8bn post-rate). ⚠ The unfilled figure has been wrong twice and both times the cause was the FAO item list rather than the code. It was $678,413,996,115 until 2026-09-01, which is the reference at its earlier commit: 17 livestock items sat in the crop list, 16 of which our livestock service values too, so $43.6bn was counted twice across the account. Following the reference's later commit gave $623,117,396,403, which is also what the reference itself produces today and is published beside ours as `crop_provision_gep_reference`. That figure is still too high, because the list carries three FAOSTAT group totals -- Oilcrops Primary, Roots and Tubers Total, Sugar Crops Primary -- as well as the individual crops those totals add up. Malaysia came out at 179% of its own crop production; 87 of 156 countries exceeded 100%. Keeping a group total only where no individual item is published leaves 1 of 155 above, median 0.877, and gives the figure above. ⚠ The GEP table registers $692.3bn, which no version of the reference produces.

Does the GEP total add crop production and pollination? Crop multiplies production value by the CWoN land rental rate, and pollination values the crop production attributable to wild pollinators, so both attribute part of the same output to nature. And how the CWoN land rental rate was derived. It is the whole of the attribution and moves the total proportionally.

amber: twelve of the fourteen conditions hold as of 2026-09-01, and 7 and 9 do not. **1, 2 and 3 are met**: our code produces the figure, every step is a task in this library, and the figure is from the current code. **4 is met**: no raster, so no cell area. **5 and 12 are met as of 2026-09-01, and the reproduction is now a check rather than a claim**: the reference was cloned, run, its per-country output staged in base_data, pinned by a test, and the agreement claimed of the cold start rather than a warm directory. Before that the entry asserted agreement while also recording that the reference was not in the repo and nothing pinned it. ⚠ Same lineage still, so it tests the port and cannot see an error the reference makes too. **6 and 8 are met**: the three gates pass, and the file set is the agreed four plus tests as of 2026-09-01, when a `_quarto.yml` that nothing had ever read was removed. ⚠ **7 is open**: the GEP table registers $692.3bn, which no version of the reference produces at any commit, either item list, either FAOSTAT vintage, any year from 2015 to 2022, or either candidate base year; and the registry lists this service at base year 2020 against the account's 2019, worth about $26bn. Both would change the figure. ⚠ **9 is open**: neither has been put to the authors. **10 is met**: `projects/gep_crop_provision_coldstart_20260901_122846`, run 2026-09-01, a fresh timestamped directory reusing nothing and seeded from `input_template/`, every input resolving inside the shared base_data. Reproducing the reference is claimed **of that run**, not of a warm one: the cold start returns $623,117,396,403 against the staged reference's $623,117,396,403 over 176 countries to 3.4e-16, its subsistence reference column returns $1,832,260,428.28 against their published panel's $1,832,260,428.28 over 66 countries to 2.3e-14, and the account's own subsistence figure is $23,780,485,591. The two tests that check this scan every crop_provision run on the machine rather than one named directory, so a stale warm run cannot stand in for a fresh one -- which is how they caught two superseded cold starts from earlier the same day, both since removed. **11 is met**: the published table is 250 rows on the account's list, with the countries the service has no value for recorded as NA rather than absent. **14 is met**: H23 finds nothing in this service defined and never used. It found fifteen names across seven services when it was written on 2026-09-01, including pollination's `pixel_area_km2_spherical`, the 6371 km sphere the account had already decided against. **13 is met**: nothing in the pipeline or its verification reaches into anyone's checkout. Every input and every reference output is staged in base_data, no path in `es_parameters` leaves it, and H22 checks that mechanically. The author's repository could be deleted tomorrow and this would still run, still produce the figure and still verify itself.

**$23,780,485,591 across 66 countries, in 2019 US dollars**, and 72.3% of it rests on a country's own observed survey rather than an imputed share. Two further figures are published beside it so each departure from the reference is separable: $18,322,604,283 is the reference's own extrapolation with only the units corrected, and $1,832,260,428 is its arithmetic entirely, which reproduces its published panel. ⚠ The share imputation is not what drives the total: requiring anything from one to five surveys before a regional median is used, or forcing every unsurveyed country onto the global median, moves it only about 2%.

Three findings to put to the authors, none of which our figure now depends on: the factor of ten above; the delivered file, which joins the panel against the correspondence on a Natural Earth name column and carries 16 of its own 66 valued countries at $398,356,097; and eleven countries whose observed survey values leave the panel at the interpolation step, Ethiopia among them, because a row missing GDP per capita or commercial production is dropped and its observation goes with it. Separately, and for the account rather than the authors: whether subsistence is added to commercial at all, given RuLIS reports own consumption as a share of crop production value that FAOSTAT's production figures already include. ⚠⚠ **And the scope, which is the largest thing understating this figure.** The reference restricts its panel to the World Bank low and lower-middle income classes, so 56 upper-middle-income countries are excluded by construction -- China, Indonesia, Brazil, Thailand, Egypt, Mexico, South Africa. China matters most: Lowder gives East Asia and Pacific a 64.3% smallholder area share, the highest of any region. Our own method needs no survey to reach them, because three of its four factors are structural, so the cost of the restriction is measurable: adding upper-middle income takes 2019 from **$24.93bn over 66 countries to $55.96bn over 109**, and every income class gives $58.97bn over 154. We keep the restriction for now because changing the scope and the method at once would leave neither legible, but it is the reason the figure reads low and it is a question for the account rather than a defect.

amber: thirteen of the fourteen conditions hold as of 2026-09-01, and 9 does not. **1, 2 and 3 are met**: our code produces the figure, every step from the eleven staged inputs to the published number is a task in this library and it reads nothing from the reference repository, and the figure is from the current code. **4 is met**: no raster, so no cell area. **5 and 12 are met, and the check is real rather than nominal**: the port was verified stage by stage against the reference's own committed intermediates rather than only on the final total, their panel is staged in base_data, a test compares against it on every run present, and the agreement is claimed of the cold start. **6 and 8 are met**: the three gates pass and it adds no file to the service. **7 is met as of 2026-09-01**: it was open while the account's figure was the reference's arithmetic, because whether we published $1.83bn or $18.32bn changed the number; the account now publishes ours and the reference's beside it, so nothing outstanding moves the figure. Whether subsistence is added to commercial remains open but is for the account to decide and does not change what the code computes. ⚠ **9 is open**: the three findings have not been put to the authors, and the condition counts an unasked question as open. **10 is met**: `projects/gep_crop_provision_coldstart_20260901_122846`, run 2026-09-01, a fresh timestamped directory reusing nothing and seeded from `input_template/`, every input resolving inside the shared base_data. Reproducing the reference is claimed **of that run**, not of a warm one: the cold start returns $623,117,396,403 against the staged reference's $623,117,396,403 over 176 countries to 3.4e-16, its subsistence reference column returns $1,832,260,428.28 against their published panel's $1,832,260,428.28 over 66 countries to 2.3e-14, and the account's own subsistence figure is $23,780,485,591. The two tests that check this scan every crop_provision run on the machine rather than one named directory, so a stale warm run cannot stand in for a fresh one -- which is how they caught two superseded cold starts from earlier the same day, both since removed. **11 is met**: 250 rows on the account's list, the 184 countries no estimate reached recorded as NA rather than absent. **14 is met**: H23 finds nothing in this service defined and never used. It found fifteen names across seven services when it was written on 2026-09-01, including pollination's `pixel_area_km2_spherical`, the 6371 km sphere the account had already decided against. **13 is met**: nothing in the pipeline or its verification reaches into anyone's checkout. Every input and every reference output is staged in base_data, no path in `es_parameters` leaves it, and H22 checks that mechanically. The author's repository could be deleted tomorrow and this would still run, still produce the figure and still verify itself. ⚠ The second half of 13 is why the published figure is not the reproduction: our code departs from theirs on the units and on the imputation, and the entry says why in **decided**.

Values livestock output and attributes the part of it nature provided. The 65-product FAO list gives $2.29T of gross production; the two attribution factors give very different answers from it: the CWoN land rental rate gives $519bn over 178 countries, the GLEAM feed share gives $1.37T over 171, a factor of 2.6. Provisional until that choice is made. The feed share comes from GLEAM 3 intake harvested from FAO's public dashboard for 231 countries, 205 matching a country in the account, giving a real share rather than a bound (median 0.73; Mongolia 0.998, India 0.911, Brazil 0.847, France 0.651, United States 0.567, China 0.513).

Which factor the account attributes livestock value with. The code computes both, and they differ by 2.6 times, $519bn on the CWoN land rental rate against $1.37T on the GLEAM feed share. The rental rate is the crop method's factor. Also the compiled GLEAM extract (gleam3_dmi.xlsx) to validate our harvest against, the confirmation of which FAO products count as livestock, and one per-country output. And how the CWoN land rental rate was derived. It is the whole of the attribution and moves the total proportionally. ⚠ **Which attribution is the GEP table using?** It registers $1.488T, and no configuration we can run produces it: not either item list, either FAOSTAT vintage, with or without the group total, on either attribution. The nearest we have ever produced is $1,374,349,438,511, our own feed-share figure before 2026-09-01, which is 7.7% below it and rested on the double-counted gross, so reproducing it would not have been good news. But $1.488T is a feed-share-scale number rather than a rental-rate one, which suggests the table and this entry are reporting different attributions. That has to be settled before either figure is quoted.

amber: twelve of the fourteen conditions hold as of 2026-09-01, and 7 and 9 do not. **1, 2, 3, 4, 6, 8 and 11 are met**: our code produces both figures, every step is a task here, they are from the current code, there is no raster so no cell area, the three gates pass, the file set is the agreed one, and the published table is 250 rows with NA where no value exists. **5 and 12 are met**: the feed-share factor reproduces `lambda.py` to 1.1e-16 across 171 countries and a test recomputes it; what the reproduction cannot test is stated, namely the valuation, for which no reference output exists. **10 is met**: `projects/gep_livestock_provision_coldstart_20260901_113704`, run 2026-09-01, a fresh timestamped directory reusing nothing, returning $245,894,965,799 and $653,351,673,530 -- identical to the warm run -- with all six input references resolving inside the shared base_data. **13 is met**: no path leaves base_data and nothing reads the reference repository, so it could be deleted tomorrow. ⚠ **7 is open and it is the one that matters**: the account has not chosen between the rental rate and the feed share, and they differ by 2.7 times. **14 is met**: H23 finds nothing in this service defined and never used. ⚠ **9 is open**: three questions are unasked -- which attribution the account wants, where the GEP table's $1.488T comes from, and whether the group-total rule applied here matches what the authors intended.

$36.28bn ($36,282,030,656.66), and the total is the sum of them, of which 84 percent is ours and 16 percent is read through. Mangroves are ours, $30.4bn: hectares times value per hectare, matching the published table, unchanged to the cent by the currency harmonization. Corals are not, and the reason is what the file holds rather than whether we found any code. The mangrove table carries hectares and value per hectare beside its total, so the total can be recomputed from them and checked. The coral file has three columns: country, value, year. There is no quantity and no price, so there is nothing to recompute from. All we do is carry its $5.17bn at 2011 forward to 2019 with the shared US CPI factor, making it $5.88bn. The nine countries the World Bank deflator table lacked — which came out unvalued — now carry their value like everyone else, $85.7M in all, Taiwan the largest at $78.8M: the scalar needs no per-country coverage. Valued countries 97 → 106.

Whether code exists behind the coral-reef benefit table that we should be running.

amber: examined, and at least one of the sixteen conditions is not yet verified. ⚠ **10 is unmet here** (no longer everywhere: erosion closed it on 2026-09-03, reproducing its published total to $11 in $18.24bn from an empty project directory). ⚠ **11 is unverified**: the base year is 2019 across every service, but the country list, the true-zero-against-missing rule and the shared parameters have not been checked service by service **11 is met as of 2026-09-01**: the published table is 250 rows on the account's list, with the countries the service has no value for recorded as NA rather than absent. It carried 225 rows before, and the total is unchanged.

Values the mineral rent the World Bank publishes, scaled by the 0.49 factor, $99.54bn. Reproduces the reference table. The factor's source is unknown; without it the total would be $203bn.

The source of the 0.49 factor, which is the only judgement in the number and currently has no provenance in the code, the drive submission or its raw-data notes. And whether there is a model we should be running instead, the same question extractive energy carries: the World Bank's rent is a price-minus-cost calculation over extracted quantity, and if the account wants a mining figure it derives rather than adopts, that calculation is what it would have to redo.

amber: examined, and at least one of the sixteen conditions is not yet verified. ⚠ **10 is unmet here** (no longer everywhere: erosion closed it on 2026-09-03, reproducing its published total to $11 in $18.24bn from an empty project directory). ⚠ **11 is unverified**: the base year is 2019 across every service, but the country list, the true-zero-against-missing rule and the shared parameters have not been checked service by service **11 is met as of 2026-09-01**: the published table is 250 rows on the account's list, with the countries the service has no value for recorded as NA rather than absent. It carried 216 rows before, and the total is unchanged.

A fresh run reproduces $173.49bn and each resource: wind $112.52bn, solar $54.41bn, geothermal $6.56bn. The drive's reference CSVs hold $134.6bn, and which is current is open.

The decision which source is current. And whether the account accepts an attribution it adopts rather than computes: the CWoN rent share is the whole of the ecosystem attribution here and moves the total proportionally. The same question applies to crop and livestock's rental rate.

amber, and the distance to green is one decision plus two named small items. ⭐ **Condition 10 is CLOSED (2026-09-04)**: a cold start from an empty project directory reproduces wind, solar and geothermal to the cent -- $112,524,374,333.31 / $54,410,978,171.74 / $6,558,292,384.42, the library's third closure, re-closed after the regional fill. The code is refactored, tested, and replicates its own run. What keeps it amber: ⚠⚠ **the lambda ask** -- the hydro rent share is applied as a decided working convention, and the group must bless crediting sunlight and wind at all, since three frameworks put their rent near zero; ⭐ the no-lambda countries are FILLED with their sub-region's mean share (2026-09-04: solar +$0.46bn and 95 to 142 countries, wind +$2.21bn; `attribution_source` says which share each row used; a measured negative share is kept, not filled); ⭐ the price file is IDENTIFIED (2026-09-04): World Bank Doing Business, 'Getting Electricity: price of electricity' (IC.ELC.PRI.KH.DB1619), a warehouse's commercial tariff in the largest business city, US cents per kWh, 2014-2019 -- Afghanistan 23.3 and Albania 9.5 in 2014 match the file digit for digit, and the vintage wall is the project's own discontinuation. A commercial RETAIL tariff, grid and taxes included, which is now a citable fact under every rent argument above.

**$82,305,148,173 at 2019 over 133 countries**: resident ground travel $95.3bn (89.5bn visits at $1.06 per visit, the fuel floor, 162 countries) plus tourist ground travel $1.43bn (1.28bn visits at a/365 per person-night, 133 countries; times 365 it reproduces the superseded held-out column to one part in 2.7 billion, the engine being linear in the trip budget). Beside the total, not in it: accommodation $981.7bn (161 countries) and air travel $526.8bn (191), with both coverage shares. Supersedes the $1.60T three-channel figure, which followed our interim scope, and the $192.36bn one-channel figure before it. ⚠ The 133-country total is the author's own table arithmetic: plain addition over the two ground channels, so 29 countries with resident value but no UNWTO overnights in any year carry a missing total rather than a partial one -- Germany the largest at $7.0bn of resident value excluded -- and 81 countries lack fuel prices and carry missing ground values. ⚠ What the beside-columns carry: accommodation prices ALL overnights -- domestic, business and leisure alike -- at the hotel surface (Spain $80.6bn against its roughly $70bn lodging market); the air channel prices every UNWTO arrival at a regional round-trip air fare, land borders included. The daily visit counts run about 600 times the JRC account's by the author's own scope comparison (50 km by car against JRC's 4 km on foot, any-green-space participation against one narrow survey); 81 percent of the resident trip budget goes unmet where no band holds a reachable site (393bn of 485bn trips), which the unmet raster carries explicitly.

The email to the author, send-ready and NOT yet sent: the three defect reports (the overnights thousands, the blockwise allocation denominator, the GFEI fuel economy applied per kilometre), the request for one per-country output of his fixed run, and our per-country table attached (`recreation_gep_by_country_2019.csv`, written to the project's output) with the note that any gap between his rerun and ours should trace to those three factors. His repo is still at 540f7af without the a/365 fix; ours reconciles against his implementation when he pushes. The rate question was asked and answered 2026-10-05 (a/365, residents unchanged). The one-worldwide-pair and all-arrivals-at-air-fare questions are settled for this account by adopting his pipeline's choices, and reopen only if he changes them.

amber, and the distance to green is named condition by condition. **Closed today (2026-10-05): 7** -- both open decisions resolved, the author answered the rate question (a/365) and the channel scope follows his pipeline's own combined total -- **and 3**, outputs regenerated from the current code. ⭐ **10 re-closed the same day**: `projects/gep_recreation_coldstart_20261005`, a fresh directory reusing nothing, rebuilt the full chain and reproduces $82,305,148,173.38 and the $1,428,465,523.58 tourist channel to the cent. ⭐ **15 CLOSED 2026-10-06.** 27 files verified present on the consortium drive and deleted locally (his 2017 LULC vintage, the 2015 and 2020 WorldPop years, the superseded PA and road variants, the duplicate correspondence layers, the unused expenditure workbook, which his repo also carries), and the construction evidence moved to `provenance/` and held deliberately (not `author_drive/`, which the gates read as an author's reported answer, and these are inputs to his constructions): the airfare chain behind the staged matrix (`airfare_hub_pairs_raw.csv`, `airfare_2019.csv`, the jet-fuel PPI `WPU05720301.csv`, `crude_oil_historic_monthly_regional.xls`, `us_crude_oil_price_historic_quarters.xls`), the fuel script that pins the 100x defect (`clean_cost_per_distance_01_feul_price.R`), and the superseded archetype demand tables (`recreation_demand_params.csv`, `recreation_demand_params_global.csv`). The six LULC share rasters are read through the template row, the gate's generated-name allowance. ⭐ **16 CLOSED 2026-10-06.** The service now publishes six rasters as POGs through the shared seam (`recreation_{daily,tourist}_{site_visits,site_value,unmet_demand}_150sec.tif`): each flow raster is warped from its native 0.01 degrees onto the 150-arcsecond rung with sum-preserving resampling (`utilities.warp_raster_to_pyramid_sum`, pinned by a conservation test), registered beside the task that writes it, and distributed by the new `gep_results_distribution` task; all six validate with `hb.is_path_pog`, and the warp conserves the native sums to 4e-6. ⚠ The map totals sit above the country table by design: the site table's no-country row ($2.10bn daily, $61M tourist, sites on pixels outside any country id) is in the maps and not in the country account. **Open: 9 alone**: the three defect reports and the output request have not gone to him yet (the send-ready email is drafted, and an unsent draft counts as open). 9 closes when the email is sent and he replies, and his per-country output would also upgrade the entry from verified-by-construction to verified-by-replication.

Values the burned acres that nature prevents. Computes avoided acres and their valuation from their committed regression coefficients, and reproduces the source repo's output for all 161 units. Three regressions give three totals because they ask three different questions. Baseline persistence regresses a country's burned area on last year's, which says nothing about nature at all: -$22.4bn. The cause difference compares how strongly nature-caused fires persist against human-caused ones: -$1.3bn. The area difference compares persistence where nature dominates against where people do: +$16.7bn. Two of the three come out negative, meaning nature makes fire damage worse rather than better.

⚠⚠ **DROPPED for this round** (Nfamara confirming Ken, group thread 2026-10-06, "as well as fire protection if I fully remember"): out of the October 31 submission, so the three-coefficient choice stops blocking the account; the state is kept for the round that picks it back up. Which of the three is the account's. Baseline persistence should not be: it does not isolate nature's role, so it cannot answer what nature protects. Between the other two, the area difference matches how the service is described, fires persisting less where nature dominates, and is the only one that comes out positive. That is an argument, not a decision, and it is not ours to make. Also: 89 of the account's 250 countries carry no value, and whether they are unvalued or dropped somewhere in the join has not been checked.

amber: examined, and at least one of the sixteen conditions is not yet verified. ⚠ **10 is unmet here** (no longer everywhere: erosion closed it on 2026-09-03, reproducing its published total to $11 in $18.24bn from an empty project directory). ⚠ **11 is unverified**: the base year is 2019 across every service, but the country list, the true-zero-against-missing rule and the shared parameters have not been checked service by service **11 is met as of 2026-09-01**: the published table is 250 rows on the account's list, with the countries the service has no value for recorded as NA rather than absent. It carried 159 rows before, and the total is unchanged.

Ours is $175.56bn across 177 countries -- the pre-correction city sum, which the author's own December 2025 lineage table equals. The account's figure is the corrected final, $14.19bn. Both are the same author's pipeline at different stages, not two measures of different things.

The v04 correction's two input files -- the IEA-CMCC 2019 cooling-degree-day file and the v2_nkd energy workbook -- AS USED IN THE FINAL RUN, so the committed $14.19bn can be recomputed. ⚠ The submission's Archive was re-searched 2026-09-05 and does hold a PROTOTYPE of the correction (`local_climate_regulation_gep_newMC.csv`, October 2025, now staged): per-country cdd and consumption-per-cdd whose own rows confirm the ported formula to the dollar (55 of 55 on both stages, value/1.71737 x cpcdd). But those factors are an earlier vintage -- applied to the December city sums they give $3.84T, not $14.19bn -- so the final run's factors are still not in anything we hold. `local_climate_regulation_gep_12_19_25_sanity_checking.xlsx` (also staged) is the December table beside an external CAS benchmark column, not the correction.

amber: examined, and at least one of the sixteen conditions is not yet verified. ⚠ **10 is unmet here** (no longer everywhere: erosion closed it on 2026-09-03, reproducing its published total to $11 in $18.24bn from an empty project directory). ⚠ **11 is unverified**: the base year is 2019 across every service, but the country list, the true-zero-against-missing rule and the shared parameters have not been checked service by service

Values the nitrogen and phosphorus that land keeps out of the water, priced at what removing them at a treatment plant would cost, $638bn. Reproduces the committed intermediates for 178 countries. How much N and P the land retains is modelled elsewhere; we take those quantities as given and value them. The $655.91bn international-dollar headline is not ours, and we now know why no factor recovers it.

⭐ **The method is now FOUND in full (2026-09-06)**: `water_quality_international_dollars_2019_method.docx` on the drive (Heman Lohano and Haku Bo, dated 2026-08-09, now staged beside the service data) specifies every step -- observed treatment costs harmonized to 2019 local prices, divided by the WDI GDP-PPP factor (PA.NUS.PPP), a benefit-transfer regression ln(cost) = alpha + beta ln(GDP per capita in international dollars) per nutrient with Duan's smearing correction, predicted per country, times retained quantities. Every named input is public EXCEPT the observations: the folder's Scopus and WoS files are the literature SEARCH (130 papers with DOIs), not the extracted per-study costs, so the extraction table is the one missing piece and the ask now has names on it. ⚠ A vintage question comes with it: the committed international-dollar column is dated 2025-11, nine months BEFORE this method was written, so the account's headline likely predates the documented procedure -- worth asking whether the column will be restated on it. Also the account's currency decision between market dollars and international dollars, the purchasing-power unit in which one dollar buys in each country what it buys in the United States. And the four nitrogen coefficients for an InVEST NDR biophysical table: load_n, eff_n, crit_len_n and proportion_subsurface_n.

amber: examined, and at least one of the sixteen conditions is not yet verified. ⚠ **10 is unmet here** (no longer everywhere: erosion closed it on 2026-09-03, reproducing its published total to $11 in $18.24bn from an empty project directory). ⚠ **11 is unverified**: the base year is 2019 across every service, but the country list, the true-zero-against-missing rule and the shared parameters have not been checked service by service

Values the deaths vegetation prevents by taking pollution out of the air, $17.48bn at the shared panel ($17.81bn at the workbook's own prices, which the committed totals carry). The avoided-death counts are the deposition channel of the drive's workbook: deaths avoided because vegetation captures dry pollutant out of the air. They come from three models run in sequence: process-based emissions, then InMAP, then health impact functions. None is set up here, though InMAP itself is open source. We do not rebuild it and take its counts as given.

Four things. **Serbia and Kosovo are swapped in the workbook**, which labels both rows Serbia: the row that is Serbia carries Kosovo's value of life, 2,288,310, and the row that is Kosovo carries Serbia's, 2,184,433. We read them the right way round from the table, so the account is correct and the workbook is not. Macao and Palestine are priced per country in the workbook, at 10,396,302 and 1,662,995, but appear nowhere in the table, so those two values have no source we hold. Which currency year the table is in, since our accounts are 2019 constant USD and a different base would rescale every mortality total. And an ISO3 column, because the table keys on a slugged country name and thirteen countries need a hand-written alias to match. Separately, what emissions inventory and meteorology a global InMAP run needs, which is what would let us produce the deaths rather than read them.

amber: examined, and at least one of the sixteen conditions is not yet verified. ⚠ **10 is unmet here** (no longer everywhere: erosion closed it on 2026-09-03, reproducing its published total to $11 in $18.24bn from an empty project directory). ⚠ **11 is unverified**: the base year is 2019 across every service, but the country list, the true-zero-against-missing rule and the shared parameters have not been checked service by service

Values the deaths vegetation prevents by holding windblown dust down, at the account's shared VSL panel, $567.71bn ($595.36bn at the workbook's own prices, which the committed totals carry), reproducing the committed totals. Those death counts come from the same three models as air filtration's and are taken as given: nothing we hold rebuilds them.

The same as air filtration. And whether this magnitude is expected. The dust value is 33 times the air-filtration value.

amber: examined, and at least one of the sixteen conditions is not yet verified. ⚠ **10 is unmet here** (no longer everywhere: erosion closed it on 2026-09-03, reproducing its published total to $11 in $18.24bn from an empty project directory). ⚠ **11 is unverified**: the base year is 2019 across every service, but the country list, the true-zero-against-missing rule and the shared parameters have not been checked service by service

Values one year's service from a country's hydropower wealth, as CWoN capitalized wealth over a 4 percent 100-year annuity: $146.57bn across 97 countries. The CWoN wealth table carries a figure for 112, and the rent calculation produces a value for 97 of them. The reference-matching variant reproduces the committed anchor, $144.68bn over 95 countries. The manuscript records $287.7bn from a direct-use method.

The decision which hydropower total is the account's ($287.7bn direct use or $144.68bn rent), and the reason 17 countries are empty in the reference

amber: examined, and at least one of the sixteen conditions is not yet verified. ⚠ **10 is unmet here** (no longer everywhere: erosion closed it on 2026-09-03, reproducing its published total to $11 in $18.24bn from an empty project directory). ⚠ **11 is unverified**: the base year is 2019 across every service, but the country list, the true-zero-against-missing rule and the shared parameters have not been checked service by service ⚠⚠ **11 exposed a double count, fixed 2026-09-01.** The table carried 253 rows because the water-use components were merged on `iso3_r250_id` and AQUASTAT names some countries twice -- 'Russia' and 'Russian Federation', 'Republic of Korea' and 'South Korea', 'Brunei' and 'Brunei Darussalam' -- so a left join fanned out and **Korea and Russia's hydropower was summed twice, $1,886,621,210 counted double**. The collapse guard for this existed and the components file on disk predated it. Separately, 19 countries the chain values -- the United States, Turkiye, Viet Nam, the United Kingdom, Iran, Tanzania, Cote d'Ivoire among them -- resolved to no r250 id and dropped out entirely, Cote d'Ivoire alone carrying $77.5M of agricultural water value. Both are fixed: 250 rows, no duplicates, nothing unmatched. ⚠ The hydropower figure moves $146,567,066,857 to $146,075,770,382 and all-sector water use $36.58T to $36.61T; the earlier pair was computed from a `hydropower_rent.csv` that predated the current code.

The median country premium at 2019 is $672 a hectare; across the world's irrigated area that is $701,436,392,250 of output that exists because of irrigation, and 0.332 of it -- $232,876,882,227 -- is the natural-resource share. Counting only countries with a positive premium gives $940.8bn gross and $312.4bn net of the share, a 6% spread, so the answer does not turn on that choice. ⚠ **45 of the 173 come out negative**, every one a country whose irrigated share of AREA exceeds its irrigated share of GVA, which forces it arithmetically; their median irrigated GVA share is 3.8% against a 7.7% area share. ⚠ The 0.332 is land AND water jointly, because no dataset endows water separately -- the premium fixes the denominator, not the share.

⚠⚠ **The ask, in one sentence: is a land-AND-water rent acceptable for this row, or must water be separated?** The premium isolates what irrigation adds -- only what an irrigated hectare earns ABOVE the same land rainfed is credited, a median $672 a hectare at 2019 -- but the share applied to it, GTAP's 0.332, is the share of crop value added going to land and water TOGETHER, because no dataset endows water separately from land. So $232.9bn is the natural-capital rent of what irrigation adds, not water's rent alone, and it is an upper bound on the latter.

amber, and the two halves of that are worth separating. **The CODE is in good shape**: ported, tested, both of the R chain's own outputs replicate, and since the 2026-09-02 rebuild the denominator and the answer are different columns with the share an explicit parameter. ⚠⚠ **The SERVICE has no number.** With no water share set the account publishes the value added of the water-using sectors and no GEP at all, which is the accurate state: the valuation is missing, not uncertain. A row that reported $1.06T would be reporting the gross value added of irrigated agriculture. ⚠ **10 is unmet here** (no longer everywhere: erosion closed it on 2026-09-03). ⚠ **11 is unverified.** ⚠ The hydropower component is a separate row and is unaffected: it is a genuine rent, $146.08bn, and `water_supply_gep` carries it alone

Values the gas, coal and petroleum rents the World Bank publishes, at 2019, one country one row, $1.05T. The committed tables total $1.14T because eight rows carry another country's full value.

Confirmation that the eight duplicated rows are a join error, and which total the account keeps. Is there a model that we should use instead?

amber: examined, and at least one of the sixteen conditions is not yet verified. ⚠ 9 is open: the eight duplicated rows and the $1.05T-against-$1.14T choice are asked and unanswered. ⚠ 10 is unmet here: no cold start has been run. The staged value raster is a declared input by the 2026-09-02 decision, so condition 2 is met on that reading.

$88.74bn. Sums the value raster to countries and matches the committed output for all 166 countries, $88.74bn. What we cannot rebuild is the raster itself: making it needs a forest-management mask saying which forest is available for harvest, and that mask is not derivable from the layers we hold.

The forest-management mask behind the value raster, or the code that builds it. And a decision on the factor share. Timber is the one service whose ecosystem share does not come from CWoN. Following Damania et al. (2023) it multiplies gross returns by the GTAP 10 land factor share for each of the 18 agro-ecological zones, so it does vary by zone rather than being one economy-wide number, but it is a different source and a different construction from the CWoN rental rate every other service uses. That matters because the account adds the services together, so the share attributable to nature should mean the same thing across them. ⚠ Switching is not a configuration change: no CWoN timber rent is staged anywhere in base_data. The CWoN files we hold cover crop, livestock, fisheries, renewable energy, extractive energy and non-wood forest products, and `ntfp/cwon_nwfp_value_per_ha.csv` is non-wood, a different quantity. CWoN 2024 does carry timber, as a land asset category beside agricultural land, forest recreation, non-wood forest products and forest water services, so the rent exists and the gap is that we do not hold the file. The two approaches are also closer than the source difference suggests: SNA and SEEA-CF count forest timber as an asset only where it can be logged commercially at a profit, which is the same restriction as the missing forest-management mask.

amber as of 2026-09-02, and **two conditions are open: 9, the ask, and 11.**

**$11,403,596,548 across 250 rows, positive in 163**, from our own end-to-end run on MSI including the counterfactuals (job 17648214, 6h50m, completed 2026-08-31). Against the author's published $11.40bn that is 0.032%, which is his rounding to one decimal. `gep = ead_bare - ead_current` holds to 1.2e-07 across all 250 rows. The damage side reproduces too: ead $887,827,659,820, ead_nc $194,256,309,530, ead_ncplus $551,051,565,602. ⚠ The nc and ncplus columns are populated for 37 countries only; they are a separate diagnostic and the GEP identity does not use them. ⚠⚠ **The water treatment moves the headline more than anything else found in this account.** The run writes four variants and `gep_flood_bare` is $6.29bn on 4rp, $7.34bn on v1_2016, $11.40bn on v2_2024_waterremoved -- the delivered one -- and **$24.54bn on v2_2024_waterkept**. Keeping permanent water more than doubles the figure, and which variant the account intends is not written down anywhere we have found.

⚠ **Largely closed on 2026-08-28.** The repository is public, cloned, and folded into the library: the module is now his full pipeline rather than a reader, vendored so nothing imports the source repo, paths resolved from `flood_root_dir` in es_parameters, and the harmonization gate clean. `gep_calculation` reads his own per-country export, **$11.40bn over 162 countries**, into the shared table — so the account's number does not depend on us running anything. ⚠⚠ **If that ever changes, read the rerun warning in the method page first.** Seven MERIT flow-direction packages went missing at various points in his own work and every absence was silent: accumulation over a missing network writes nodata, amplification clips to 1.0, and the valuation reads 1.0 as *no change here* rather than *no data here*. Japan, Korea, Portugal and Kazakhstan returned zero service against $49bn of exposure, Nigeria, Chad, Cameroon, Niger and the CAR another $4bn, and nothing appeared in any log. Recovering them raised GEP 18.5 percent and then a further 16. His check catches regressions but not a package never downloaded, which was the original failure. The MSI setup is done: the run writes to /projects/standard/jajohns/shared/gep_flood with its machine-specific inputs in that project's input/. Still open on the science: the real upstream service-provision ratio raster, since the drive copy is byte-identical to the binary mask beside it. ⚠⚠ **What a cold start actually costs, measured 2026-09-01.** Sixteen flood parameters ship blank in the template. A genuine cold start has to run the two tasks that never have -- `task_prepare_flood_inputs` and `task_build_sda` -- which need those rows filled and their files reachable. His input tree is **129 GB**, and 117 GB of that is the counterfactual and counterfactual_mosaic amplification rasters that `_open_amp` reads and silently falls back from. Two cold starts on 2026-09-01 died on the first of the four missing tasks, one parameter at a time: `flood_lulc_path`, then `flood_depth_aligned_path`. Staging the LULC fixed the first and the second is the same shape, so the remaining thirteen will behave the same way. This is a staging decision rather than a bug, and it is why condition 10 is open here in a way it was not for crop or livestock. ⚠ The 17 other `p.flood_*` attributes that are absent from the template are set by tasks, not read from configuration, so they are not a gap.

amber as of 2026-09-01, and 10 is a recorded limitation rather than an unfinished task. **2, 12, 13 and 14 are met**: it reads the account's own country layer, the author's committed table is staged and pinned to the cent, nothing points into his repository -- the single mention is a `_source_url` row, so it could be deleted tomorrow and nothing here would break -- and no name in the service is defined and never used. **4, 6 and 8 are met**: the grid is stated (EPSG:3857 at 537.23 m, neither the shared pyramid nor equal-area, and that is stated too), the three gates pass, and the file set is the agreed one. **5 is met and is a warning**: the module is his pipeline with our path handling and ProjectFlow wiring, so reproducing him was close to guaranteed and the entry says so. ⚠ **10 is a stated limitation.** A cold start needs the four tasks that have never run, which need fifteen blank parameters filled and 129 GB staged, 117 GB of it the counterfactual amplification rasters. Decided 2026-09-01 to record it rather than stage it. ⚠ **1 is partial for the same reason**: the amplification rasters are an input we have chosen not to recompute rather than one we cannot produce -- a distinction the entry got wrong until 2026-09-01. `counterfactual_mosaic/` is 37 derived global amplification rasters and `counterfactual/` is MERIT Hydro tiles and flow direction, which are public; his RUN.md says they are built from rainfall and flow direction rather than from the hazard maps. So reproducing them is the two-week routing run his RUN.md describes, not a missing input. What our code demonstrably produces today is the valuation and GEP chain on his staged Sections B and C. **9 is met as of 2026-09-02**: a reply went to the author reporting that our run reproduces his committed table exactly, and nothing of ours is now waiting on him. The three threads that had been open are each closed by a different route, which the condition allows and which is recorded here rather than left to be inferred. The `global_invest.utilities` question he asked on 2026-08-28 stopped existing when his repository was slated for deletion, so it was deliberately not asked. The water variant was deliberately not asked either, for the reason condition 7 gives: he has reported about $11.4bn consistently, so the delivered variant is settled by his own reporting rather than by our question. And the two changes of ours that were untold — step 4D taking `region_wb` from the country table rather than the boundary, and the two ISO3 detectors sharing one column set — are told. **7 is met as of 2026-09-01**: the water variant looked open until it was noticed he has reported about $11.4bn consistently, in the 2026-08-27 email and in his committed table, so the delivered variant is settled by his own reporting. The $24.54bn waterkept figure is pinned by a test so it cannot drift, but it is not a question for him.

The service has carried three totals, and each difference has one cause. **$22.40bn (the group's earlier figure) -> $14.07bn: the BUFFER, not the value.** The source workflow's own file (`ntfp2.py`) burns roads and rivers onto a **0.18-degree grid** -- cells about 20 km wide -- then thresholds distance at 0.09 degrees, with its own NOTE admitting 'approximate degrees for buffering'. Any 20 km block a road touches becomes reachable in full, so the reach is built from 20 km blocks instead of 10 km ribbons, and accessible forest inflates by roughly 60%. Replacing that with a true 10 km buffer around the actual line geometries -- the method as the script's own description states it -- gives $14.07bn. **$14.07bn -> $13.85bn (-1.6%): the grid**, Mollweide 300 m to the account's WGS84 10 arcsec, with cell area read from ha_per_cell rather than held constant. **Ours vs CWoN's $28.88bn: the SCREEN** -- same rate, they multiply by all forest, we by the 1.44bn reachable hectares. Their two code defects explain none of that gap; both push their total down.

⚠⚠ **Both of the questions this row used to carry are now answered from CWoN's own reproducibility package**, which ships `Do/forest_nontimber.do` alongside the outputs. Neither needs an author.

amber, and the distance to green is now entirely LIBRARY-wide rather than anything about this service. Service-specific conditions are met: the number is the account grid's own ($13.85bn, MSI 2026-09-03, every raster proving it holds data as built), 11 and 16 are met (the grid is the pyramid, area is read from ha_per_cell, the rasters are recorded intermediates), the headline is decided, the cap is deliberately refused with the variant published, the lambda question is answered, and no blocking asks remain -- the net-of-costs footnote is a confirmation, not a condition. What keeps it amber: ⚠ **10, unmet here** -- though no longer everywhere: erosion closed it on 2026-09-03, and this service's own cold start is queued (erosion closed it on 2026-09-03; this service's own cold start is queued as MSI job 17939275) -- and a sliver of 15, one unreferenced sidecar in the input folder.

185.85 billion cubic metres retained across 104 countries, ⚠ superseded by the ground-area fix under **decided**, which takes it to 122.30 billion pending a rerun. No author run exists to compare against, and no dollar total: at the placeholder price of 1 the value would just be the volume again.

⚠⚠ **DROPPED for this round** (Nfamara confirming Ken, group thread 2026-10-06): peak flow mitigation is out of the October 31 submission, so nothing below blocks the account; the state is kept for the round that picks it back up. A source for the price per cubic metre. ⚠ The method is no longer the open part: the authors' appendix, staged at `stormwater/input/peak_flow_mitigation_appendix.docx`, specifies the avoided cost of treating and pumping the water that would otherwise reach the works, framed around combined sewer overflows, and works an example at $2.75/m3. Our runs use a placeholder of 1.0, so what is missing is a defensible per-country rate rather than a decision about approach. ⚠ Also an urban mask, or the InVEST run restricted to urban extents. The service is defined on urban areas, the retention run we were given covers all land, and we hold no mask to restrict it, so the volume is larger than the service. ⚠ The same appendix confirms the quantity formula is `V_R = lambda x (1 - RC) x P x A` with **A the pixel area**, which is the term the 2026-08-27 ground-area fix corrects.

amber: examined, and at least one of the sixteen conditions is not yet verified. ⚠ **10 is unmet here** (no longer everywhere: erosion closed it on 2026-09-03, reproducing its published total to $11 in $18.24bn from an empty project directory). ⚠ **11 is unverified**: the base year is 2019 across every service, but the country list, the true-zero-against-missing rule and the shared parameters have not been checked service by service

Values the pumping energy recharge saves: $7,211,275.96 across 109 countries (India $2.6M, USA $0.6M, Indonesia $1.2M). Five orders below neighbouring services by construction. The Jasechko groundwater-depth data staged by the owner suggests the natural extension: valuing the STOCK of avoided depth rather than one year's increment would scale the row by the ratio of water-table depth to annual recharge, typically two to three orders of magnitude.

The scope decision, now sharper than include-or-exclude: (a) out on the double-count reasoning -- then the sheet's column should go; (b) in as marginal pumping savings -- then $7.21M stands and the account owns its smallness; (c) in as the stock of avoided depth -- a different, larger construction the owner's staged data could support. Condition 10 is minutes once blessed.

amber: built and run 2026-09-06, the sheet's column reproduced to the cent in every country, but the scope decision is the group's. ⭐ Condition 10 CLOSED 2026-10-02: a cold start from an empty project directory reproduces $7,211,275.96 to the cent at the owner construction. **Currency harmonized 2026-10-03**: the published table now deflates the 2021 tariffs to 2019 USD through the account's one CPI series ($6,803,550.52); the sheet remains the owner construction, reproducible by setting the deflation year to 2021

The drive folder is empty.

Should we include it? ⭐ Partly unblocked by the CAS submission (2026-09-12, staged at `base_data/global_invest/cas_submission_2026_09/fuelwood/`): methodology, code and input/output definitions as four documents (Yunong Yao, 2026-09-06) -- GFPM-harmonized FAOSTAT fuelwood volumes, 180 countries at 2019, valued at market price or substitution cost. ⚠ The output appendix states NO numeric result table is attached, so the method now exists and the number still does not; the ask is the per-country table (or the runnable inputs behind the code document).

n/a: no module, and the drive folder is empty, so there is nothing to port

Values what urban greenspace saves in illness: **$148,706,081,307** of avoided hypertension treatment (China $69.7bn, USA $66.3bn -- the per-case costs of $3,394 and $9,529 dominate) and **$97,960,172,780** of prevented depression at societal cost over the 32 cost countries (USA $51.1bn, UK $10.3bn, Australia $9.8bn; 64.5M prevented cases). ⚠ The two strands overlap conceptually (same greenness, comorbid outcomes) and the mental costs are USD PPP -- neither should be added to the other or to the account without those two decisions.

For the physical authors: the committed CSV's sort misalignment (re-export Table 6); which vintage the sheet's $106.65bn is; and whether our cost transfer matches their planned extrapolation -- the extended column prices every country by the ln-ln fit of cost on GDP per capita over their 20 studies (R2 0.32, Duan smearing, observed costs kept), giving $247.44bn over 187 countries. For Yingjie Li: the run's spatial extent (our measured 370x gap says a city sample); which of the two variants is blessed; PPP against the account's 2019 USD. For the group: whether the zero-vegetation counterfactual (physical) and the all-built counterfactual (mental) are the accounting the GEP wants, and how the two strands avoid double counting each other.

amber, both strands: built, run and tested 2026-09-09, the physical strand rank-anchored on the appendix (0.989) but 42 percent above its level on named deviations, the mental strand deliberately unanchored until the author's scope is known. ⭐ Condition 10 CLOSED for both strands 2026-10-02: cold starts reproduce $148,706,081,306.76 (and the extended $247bn column's fit statistics) and $97,960,172,780.38 with 64,530,894 prevented cases, all to the cent

Values the extra yield natural enemies provide on organic cropland at the organic producer price: **$10,338,294,391** published at 2019 over 138 countries with the appendix's premium, $7,491,517,675 without it. Largest: China $3.9bn, France $1.1bn, USA $0.9bn, Italy $0.7bn -- organic vineyards, orchards and vegetables, since only organic area carries value. ⚠ Viet Nam is the one real anomaly against the committed file: their $291M is roughly nine times anything the staged FiBL 2019 areas support under any reading of the method.

Three asks. Whether the committed $6.25bn includes the organic price premium (the level says no; the appendix says the method has one). What produced Viet Nam's $291M. And the final pipeline with its inputs from William Sidemo-Holm (the Ponisio meta-regression, the imputation models, the run script), so the anchor can tighten past rank agreement. Plus the group's view on lambda equal 1 while the neighbouring crop rows carry a human-input adjustment.

amber: built and run, five tests green, but the two level questions (premium, Viet Nam) are the owner's to answer and the committed reference cannot be reproduced past rank agreement without the missing pipeline. ⭐ Condition 10 CLOSED 2026-10-02: a cold start from an empty project directory reproduces $10,338,294,391.30 to the cent, the premium-1.0 reading identical

Values the rain a country's land sends to its own and other countries' water use: **$127,682,011,050** published at 2019 (the attribution computes $127.96bn; $279M of it sits in the matrix's three small-island aggregates, CSID, IOSID and PSID, which no account country can carry -- logged by the run), of which $52.3bn serves the source country itself and **$75.3bn crosses a border as exported rain**. The withdrawal-weighted terrestrial share works out to 37 percent of the water value, consistent with the literature's 40-50 percent of land rain having a terrestrial source. Largest sources: India $25.5bn, China $20.6bn, Russia $6.5bn (mostly exported).

Three group decisions. The transpiration share (1.0 counts all terrestrial evaporation as ecosystem-mediated; the literature's transpiration fraction would roughly halve the row). The accounting rule: publish as a decomposition of water supply or as a row carrying only the $75.3bn transboundary part, but never added whole to water supply. And whether hydropower's water value should also be re-attributed (rain feeds the rivers too); the current construction re-attributes irrigation and domestic only.

amber: built and run, conservation asserted in the task, but the three decisions in `need` are the group's and no reference exists to compare against. ⭐ Condition 10 CLOSED 2026-10-02: a cold start reproduces the published $127,682,011,050.38 with every one of the 250 rows identical -- and caught a dormant defect doing it: the map-writing merge lacked the region id after the 250-row refactor, invisible warm because skip-existing never re-ran it; fixed the house way (the collapse frame merged in before the write)

Values the room-price premium natural views earn: **$59,728,312,716.92** published at 2019 over 124 countries (USA $8.55bn, Italy $6.60bn, Japan $2.64bn; Saudi Arabia $1.79bn). Quantity is 2019 room-nights; the premium is scraped at the submission's booking dates, the owner's convention, named in the method.

The old asks are ANSWERED by the 2026-09-12 CAS submission (owner, sample, code, Aruba, Saudi). Remaining: confirm the sheet's column is replaced by the $59.73bn vintage; the sample's provenance one level deeper (which booking platform, which scrape dates, hotel coverage per country); and whether the group wants it folded as a library module -- the construction is country-table arithmetic on staged workbooks, a normal port.

amber: ported, run and anchored to the cent in every valued country. ⭐ Condition 10 CLOSED 2026-10-02: a cold start from an empty project directory reproduces $59,728,312,716.92, 124 of 124 to the cent. The deeper layers (group construction from the raw sample, the scrape's provenance) remain the owner's to document