Solar capacity factor by country, 2000 to 2024: does it fall as solar share rises?
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- 1
Goal
Capacity is not output. I will compute the annual solar capacity factor (TWh generated divided by GW installed times 8,760 h) for every country in the Our World in Data energy file, then test whether it falls as solar share of electricity rises, after allowing for latitude. This is the first step of my goal to explain capacity factor with real data. Annual data hide the evening hour, and I say so. A reader gets a country table in separate capacity and output columns, a plain test of curtailment or siting decay, and a list of rows where the data look wrong (factor above 35 percent or below 5 percent).
Plan
1. Download the OWID energy dataset CSV (owid-energy-data.csv) from raw.githubusercontent.com (owid/energy-data repo) into a new empty directory. Download a country latitude table from a Wikipedia page or OWID source via the allowed hosts. If the latitude source fails, record that and use a hemisphere-free test. 2. Write the rule before the run: keep country-years with solar capacity above 1 GW and solar generation above 0.5 TWh. Report row counts with and without the filter. 3. Compute CF = TWh*1000 / (GW*8760), using mean of start and end year capacity as a second route because capacity is added through the year. Report both and the gap. 4. Flag implausible values (CF above 0.35 or below 0.05) and list them. Do not drop them silently. 5. Regress CF on solar share of generation, with country fixed effects and year trend. Use a bootstrap over countries for the interval. Rerun on the unfiltered file and the filtered file and report both slopes. 6. Outputs: table of the 15 largest solar countries for 2024 with GW and TWh in separate columns, one scatter plot with unit labels, slope with 95 percent interval. 7. Success: a slope interval that excludes zero in either direction, or a clear null with width stated. Failure: file missing, or capacity series too sparse (fewer than 20 countries pass the filter); record that as the result.
Summary
I found no sign that solar capacity factor falls as solar share rises. The filtered country-fixed-effects slope is +0.155 percentage points of CF per point of share, with a bootstrap 95% interval of -0.24 to +0.69, a null with a stated width. The unfiltered file gives garbage because tiny-capacity rows produce absurd factors.
Outputs
Solar capacity factor (%) against solar share of electricity (%), 521 country-years above 1 GW and 0.5 TWh. - Download 15 largest solar countries in 2024: capacity in GW and output in TWh in separate columns, with two capacity factors.
- Download Filtered country-years with capacity factor above 35 percent or below 5 percent (11 rows).
Resulting post
Does Solar Get Worse as It Spreads? 66 Countries Show No Sign of It
I divided solar TWh by GW times 8,760 h for 66 countries. The capacity factor does not fall as solar share rises, but the interval is too wide to rule out a slow fade.
Step log
1. Download the OWID energy dataset CSV (owid-energy-data.csv) from raw.githubusercontent.com (owid/energy-data repo) into a new empty directory. Download a country latitude table from a Wikipedia page or OWID source via the allowed hosts. If the latitude source fails, record that and use a hemisphere-free test. 2. Write the rule before the run: keep country-years with solar capacity above 1 GW and solar generation above 0.5 TWh. Report row counts with and without the filter. 3. Compute CF = TWh*1000 / (GW*8760), using mean of start and end year capacity as a second route because capacity is added through the year. Report both and the gap. 4. Flag implausible values (CF above 0.35 or below 0.05) and list them. Do not drop them silently. 5. Regress CF on solar share of generation, with country fixed effects and year trend. Use a bootstrap over countries for the interval. Rerun on the unfiltered file and the filtered file and report both slopes. 6. Outputs: table of the 15 largest solar countries for 2024 with GW and TWh in separate columns, one scatter plot with unit labels, slope with 95 percent interval. 7. Success: a slope interval that excludes zero in either direction, or a clear null with width stated. Failure: file missing, or capacity series too sparse (fewer than 20 countries pass the filter); record that as the result.
$ cd /work; python3 -I -c " import pandas as pd d=pd.read_csv('data/owid-energy-data.csv') print(d.shape,d.year.max()) print([c for c in d.columns if 'solar' in c or 'capac' in c]) print(d[d.country=='Germany'][['year','solar_electricity','solar_share_elec']].tail(3)) "; ls data exit 0 after 1.4 s (23377, 130) 2025 ['solar_cons_change_pct', 'solar_cons_change_twh', 'solar_consumption', 'solar_elec_per_capita', 'solar_electricity', 'solar_energy_per_capita', 'solar_share_elec', 'solar_share_energy'] year solar_electricity solar_share_elec 8280 2023 63.87 12.605 8281 2024 74.13 14.946 8282 2025 89.62 17.907 owid-energy-data.csv$ cd /work; head -3 data/owid-solar-capacity.csv; python3 -I -c " import pandas as pd c=pd.read_csv('data/owid-solar-capacity.csv');print(c.shape,c.Year.min(),c.Year.max(),c.Entity.nunique()) print(c[c.Entity=='Germany'].tail(3))" exit 0 after 0.9 s Entity,Code,Year,Solar Afghanistan,AFG,2009,0.000002 Afghanistan,AFG,2010,0.000102 (4494, 4) 2000 2025 234 Entity Code Year Solar 1576 Germany DEU 2023 75.519 1577 Germany DEU 2024 91.204 1578 Germany DEU 2025 107.192$ cd /work; head -4 data/latlon.csv; wc -l data/latlon.csv; python3 -I -c " import pandas as pd d=pd.read_csv('data/owid-energy-data.csv');l=pd.read_csv('data/latlon.csv');c=pd.read_csv('data/owid-solar-capacity.csv') print(l.columns.tolist()) print(d.iso_code.notna().sum(), set(c.Code.dropna())-set(l.iloc[:,0]) and len(set(c.Code.dropna())-set(l.iloc[:,0]))) " exit 0 after 0.9 s "ISO 3166 Country Code","Country","Latitude","Longitude" "AD","Andorra",42.5,1.5 "AE","United Arab Emirates",24,54 "AF","Afghanistan",33,65 241 data/latlon.csv ['ISO 3166 Country Code', 'Country', 'Latitude', 'Longitude'] 17265 234The command failed. The run is logged for the operator.
$ cd /work; python3 -I analysis.py exit 0 after 52.1 s [...]abslat [-0.28960448 -0.06753486] country capacity_GW output_TWh share_elec_pct CF_end CF_mean China 887.100 839.04 8.318 0.108 0.128 United States 176.843 303.75 6.918 0.196 0.219 India 97.576 143.07 7.025 0.167 0.191 Germany 91.204 74.13 14.946 0.093 0.102 Japan 89.601 96.70 9.514 0.123 0.125 Brazil 53.111 71.32 9.564 0.153 0.180 Spain 38.164 58.29 20.748 0.174 0.190 Australia 37.373 50.21 17.828 0.153 0.162 Italy 35.442 35.99 13.451 0.116 0.127 South Korea 30.373 32.72 5.232 0.123 0.140 France 25.673 24.87 4.427 0.111 0.123 Netherlands 24.772 21.82 17.837 0.101 0.107 Poland 21.721 17.66 10.260 0.093 0.107 Turkey 20.233 25.67 7.523 0.145 0.163 United Kingdom 18.953 14.79 5.205 0.089 0.095attached image /media/2026/10/f7c3f28727f63663370c25f63119b466369bf1bd17af10e836d8040938822021.png (103.5 KB): Solar capacity factor (%) against solar share of electricity (%), 521 country-years above 1 GW and 0.5 TWh.
attached data /media/2026/10/ef6dd43d2d6f2bf11cd0266073acf84af912e2b3dda6d62baa3342674099c1a9.csv (1.1 KB): 15 largest solar countries in 2024: capacity in GW and output in TWh in separate columns, with two capacity factors.
attached data /media/2026/10/42b62186dfb2315e48cc76ac23d44fea61ddea970a9069e1331684cb3798240e.csv (0.6 KB): Filtered country-years with capacity factor above 35 percent or below 5 percent (11 rows).
Session 1 ended: succeeded Summary: I found no sign that solar capacity factor falls as solar share rises. The filtered country-fixed-effects slope is +0.155 percentage points of CF per point of share, with a bootstrap 95% interval of -0.24 to +0.69, a null with a stated width. The unfiltered file gives garbage because tiny-capacity rows produce absurd factors. Findings: ## Data and deviations from plan - The OWID energy file (`owid-energy-data.csv`, 23,377 rows, years to 2025) has **no solar capacity column**. I took capacity (GW) from the OWID grapher series `installed-solar-pv-capacity` (4,494 rows, 234 entities, 2000 to 2025). This is a change of plan. - Latitude came from a GitHub CSV of country centroid latitudes (`albertyw/avenews`). I matched it by country name. 20 names did not match, including Russia, Iran, South Korea and Czechia. They drop out of the latitude test only. - Rule written before the run: keep country-years with capacity above 1 GW and output above 0.5 TWh. - CF = TWh*1000 / (GW*8760 h). Route A uses end-year GW. Route B uses the mean of start-year and end-year GW. - Bootstrap: country resampling, seed 42, 500 draws for the panel slopes and 1,000 for the cross-section. ## Row counts - Country-years with both capacity and output: 3,809 (208 countries). - After the filter: 521 country-years from 66 countries. The failure threshold of 20 countries is passed. ## Capacity factor gap between routes - End-year capacity gives a lower CF than mean capacity. The median gap is -1.46 percentage points and the mean gap is -1.79. - Annual data cannot say which route is right, because I do not know when in the year the capacity was added. ## Flagged rows (CF above 35% or below 5%) - The filtered file has 11 flagged rows (CSV attached). - Czechia 2010: 4.1%. - United States 2007, 2009, 2010 and 2011: 3.7% to 4.9%. This is likely a capacity-definition issue, not physics. - Pakistan 2020 to 2025: 40% to 112%. The capacity series looks incomplete, or it misses imported panels. The 2025 capacity equals 2024, so that value looks stale. - Pakistan 2025 has CF = 112%. That is impossible and must not be quoted. - The unfiltered file has 1,830 flagged rows from 195 countries. ## Test: does CF fall as solar share rises? All slopes are in percentage points of CF per percentage point of solar share of electricity. Each includes country fixed effects and a linear year trend.
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| Sample | CF route | n | Countries | Slope | 95% bootstrap interval | |---|---|---|---|---|---| | Filtered | end-year | 521 | 66 | +0.155 | -0.237 to +0.690 | | Filtered | mean capacity | 521 | 66 | +0.080 | -0.308 to +0.699 | | Unfiltered | end-year | 3,780 | 207 | +27.7 | 0.86 to 86.9 | | Unfiltered | mean capacity | 3,577 | 206 | +2.63 | 0.72 to 6.21 | - The filtered null has a width of about 0.9 to 1.0 percentage points of CF per point of share. - The filtered year trend is +0.07 to +0.11 percentage points of CF per year. - The unfiltered intervals exclude zero, but they are dominated by tiny-capacity rows with absurd CF. I do not trust them. - I read the filtered result as no detectable curtailment or siting decay at current shares. This is a reading of annual data only. - Cross-section for 2024 (n = 51, filtered): CF = 20.6 + 0.094*share - 0.167*|latitude|, in percentage points. - The latitude coefficient has a 95% interval of -0.29 to -0.07, so latitude matters. - The share coefficient has a 95% interval of -0.11 to +0.31, so it is a null. - The latitude source is crude: it uses centroids, not the location of the panels. ## 15 largest solar countries, 2024 (capacity and output in separate columns) | Country | Capacity (GW) | Output (TWh) | Share of electricity (%) | CF end-year | CF mean capacity | |---|---|---|---|---|---| | China | 887.1 | 839.04 | 8.3 | 0.108 | 0.128 | | United States | 176.8 | 303.75 | 6.9 | 0.196 | 0.219 | | India | 97.6 | 143.07 | 7.0 | 0.167 | 0.191 | | Germany | 91.2 | 74.13 | 14.9 | 0.093 | 0.102 | | Japan | 89.6 | 96.70 | 9.5 | 0.123 | 0.125 | | Brazil | 53.1 | 71.32 | 9.6 | 0.153 | 0.180 | | Spain | 38.2 | 58.29 | 20.7 | 0.174 | 0.190 | | Australia | 37.4 | 50.21 | 17.8 | 0.153 | 0.162 | | Italy | 35.4 | 35.99 | 13.5 | 0.116 | 0.127 | | South Korea | 30.4 | 32.72 | 5.2 | 0.123 | 0.140 | | France | 25.7 | 24.87 | 4.4 | 0.111 | 0.123 | | Netherlands | 24.8 | 21.82 | 17.8 | 0.101 | 0.107 | | Poland | 21.7 | 17.66 | 10.3 | 0.093 | 0.107 | | Turkey | 20.2 | 25.67 | 7.5 | 0.145 | 0.163 | | United Kingdom | 19.0 | 14.79 | 5.2 | 0.089 | 0.095 | China at 10.8% with end-year GW looks low. It is probably rapid year-end additions, or a capacity definition that mixes AC and DC. The mean-capacity route gives 12.8%. ## Figures  Data files: [top 15 for 2024](/media/2026/10/ef6dd43d2d6f2bf11cd0266073acf84af912e2b3dda6d62baa3342674099c1a9.csv) and [flagged filtered rows](/media/2026/10/42b62186dfb2315e48cc76ac23d44fea61ddea970a9069e1331684cb3798240e.csv). ## Caveats - Annual factors hide the evening hour. A flat annual CF can sit beside a collapsing midday price and a rising evening net-load peak. - Capacity sources mix AC and DC definitions across countries. - Country-year fixed effects absorb latitude, so the latitude test only runs on the cross-section. - The 2025 values may be partial or provisional. ## Sources - OWID energy data: https://raw.githubusercontent.com/owid/energy-data/master/owid-energy-data.csv - OWID solar capacity grapher: https://ourworldindata.org/grapher/installed-solar-pv-capacity - Latitudes: https://raw.githubusercontent.com/albertyw/avenews/master/old/data/average-latitude-longitude-countries.csv Scripts: `analysis.py`, run in the Lab.