French Polynesia vs North Macedonia: Index of carbon stocks in forests, Index (Not Applicable)
French Polynesia
102.61
in 2023
North Macedonia
100.51
in 2023
French Polynesia rank
84th
North Macedonia rank
87th
Index of carbon stocks in forests, Index (Not Applicable) over time
- French Polynesia
- North Macedonia
How they compare
French Polynesia currently reports 102.61 against 100.51 in North Macedonia, a difference of 2.1.
The two have swapped places 1 time across 32 shared years of data; in 1992 it was North Macedonia ahead.
French Polynesia ranks 84th and North Macedonia ranks 87th of 196 countries.
Across the 4 decades both report, French Polynesia averaged higher in 3 and North Macedonia in 1.
Head to head by decade
| Decade | French Polynesia | North Macedonia | Difference | Ahead |
|---|---|---|---|---|
| 1990s | 100.88 | 101.4 | 0.525 | North Macedonia |
| 2000s | 102.28 | 101.78 | 0.5035 | French Polynesia |
| 2010s | 102.61 | 100.34 | 2.27 | French Polynesia |
| 2020s | 102.61 | 100.51 | 2.1 | French Polynesia |
Averages of every year both report within each decade.
Frequently asked questions
- Which has higher index of carbon stocks in forests, index, French Polynesia or North Macedonia?
- French Polynesia, at 102.61 against 100.51 in North Macedonia as of 2023.
- What is the difference in index of carbon stocks in forests, index between French Polynesia and North Macedonia?
- 2.1, with French Polynesia ahead.
- How many years of comparable data are there for French Polynesia and North Macedonia?
- 32 years are reported by both, from 1992 to 2023.
- How do French Polynesia and North Macedonia rank globally for index of carbon stocks in forests, index?
- French Polynesia ranks 84th and North Macedonia ranks 87th of 196 countries.
- Where does this data come from?
- International Monetary Fund, published as Index of carbon stocks in forests, Index (Not Applicable). Statizoid refreshes it automatically from the source and publishes the full history for both places.
Individual pages
About this data
This dataset provides information on global climate and weather patterns, providing insights into temperature variations, land cover accounts, and shifts in mean sea levels.