法国与巴基斯坦对比
法国与巴基斯坦:热带夜
2014 年热带夜:法国为 3.1 夜,巴基斯坦为 149.9 夜(CMIP6 气候模型中位数模拟)。 2014 年巴基斯坦的数值较高,高出 146.8 夜,是法国的 49 倍。
每年夜数 · CMIP6
数据解读
- 自 1950 年以来,在两国都有数据的每一年,巴基斯坦的数值都更高。
- 根据CMIP6 模型中位数(SSP2-4.5,中间路径),2100 年法国为 19.2 夜,巴基斯坦为 177.6 夜。
逐年数据
每年夜数 · CMIP6
| 年份 | 法国 | 巴基斯坦 |
|---|---|---|
| 2014 | 3.1 | 149.9 |
| 2013 | 3.1 | 149.1 |
| 2012 | 2.9 | 149.9 |
| 2011 | 2.7 | 149 |
| 2010 | 3.9 | 148.4 |
| 2009 | 2.5 | 147.1 |
| 2008 | 2.4 | 147.4 |
| 2007 | 2.1 | 147.3 |
| 2006 | 2.6 | 146.7 |
| 2005 | 2.9 | 146.5 |
| 2004 | 2.7 | 147.5 |
| 2003 | 2.6 | 146 |
| 2002 | 1.8 | 148.3 |
| 2001 | 2.3 | 145.4 |
| 2000 | 1.8 | 144.7 |
| 1999 | 2.1 | 146.9 |
| 1998 | 1.5 | 144.8 |
| 1997 | 1.6 | 143.2 |
| 1996 | 1.5 | 141.2 |
| 1995 | 1.3 | 141.8 |
| 1994 | 1.5 | 142.1 |
| 1993 | 1.1 | 140.4 |
| 1992 | 1 | 139.9 |
| 1991 | 1.8 | 142.5 |
| 1990 | 1.9 | 142.6 |
| 1989 | 1.2 | 142.5 |
| 1988 | 1.2 | 140.3 |
| 1987 | 1.3 | 140 |
| 1986 | 0.9 | 140 |
| 1985 | 0.9 | 140 |
| 1984 | 1.1 | 138.9 |
| 1983 | 1.1 | 136.3 |
| 1982 | 1 | 138.5 |
| 1981 | 1.1 | 141.5 |
| 1980 | 1.2 | 139.5 |
| 1979 | 0.9 | 139.1 |
| 1978 | 1.3 | 139 |
| 1977 | 0.9 | 138.6 |
| 1976 | 0.8 | 139.2 |
| 1975 | 0.9 | 136.5 |
| 1974 | 1.2 | 138.7 |
| 1973 | 0.9 | 137.8 |
| 1972 | 0.9 | 137.4 |
| 1971 | 1 | 136 |
| 1970 | 0.9 | 137.7 |
| 1969 | 1 | 135.9 |
| 1968 | 0.9 | 136.6 |
| 1967 | 0.8 | 135.2 |
| 1966 | 0.8 | 136.9 |
| 1965 | 0.7 | 138.3 |
| 1964 | 1 | 135.1 |
| 1963 | 0.9 | 135.7 |
| 1962 | 1.1 | 137 |
| 1961 | 0.9 | 138.6 |
| 1960 | 1.1 | 139 |
| 1959 | 1.2 | 138.6 |
| 1958 | 1 | 137.7 |
| 1957 | 1 | 140 |
| 1956 | 1.2 | 137.8 |
| 1955 | 1.1 | 137.5 |
| 1954 | 1.3 | 137.8 |
| 1953 | 1 | 136.9 |
| 1952 | 1.3 | 138.1 |
| 1951 | 1.1 | 136.7 |
| 1950 | 1.2 | 139.1 |
CMIP6 模型中位数(SSP2-4.5,中间路径) · 2015–2100
| 年份 | 法国 | 巴基斯坦 |
|---|---|---|
| 2015 | 3.61.5–8.6 | 152.4138.7–163.3 |
| 2016 | 4.11.5–9.9 | 150.6139.4–161.7 |
| 2017 | 3.51.3–10.2 | 151.3138.8–162 |
| 2018 | 3.91.7–10.8 | 152.6139.8–161.8 |
| 2019 | 4.51.6–9 | 151.8138.1–163.3 |
| 2020 | 4.11.6–10.5 | 153.1140.3–164.5 |
| 2021 | 4.51.3–13.2 | 154.1140.6–165.7 |
| 2022 | 51.5–12.5 | 153.1140.2–165.7 |
| 2023 | 4.81.6–12.5 | 155.5141.6–165.9 |
| 2024 | 5.51.8–11.2 | 156.8143.3–168.7 |
| 2025 | 5.81.5–12.1 | 155.7141.5–168.6 |
| 2026 | 6.22.1–12.7 | 155.3142.2–167.9 |
| 2027 | 5.41.7–11.2 | 156.8140.9–168.9 |
| 2028 | 5.91.7–13.3 | 156.5143.9–167.6 |
| 2029 | 6.31.9–12.6 | 155.9141.5–167.7 |
| 2030 | 5.82.1–11.9 | 156.3143.3–167.6 |
| 2031 | 5.92–11.8 | 157.3144.4–168.1 |
| 2032 | 5.92.7–12.9 | 157.4142.5–169.6 |
| 2033 | 72.5–13.6 | 157.7143.6–169.4 |
| 2034 | 7.92.4–17.3 | 159.5145.6–173.4 |
| 2035 | 6.92.4–14.4 | 159.3146.7–170.3 |
| 2036 | 7.32.6–14.6 | 159.1147.5–171.4 |
| 2037 | 7.32.6–13.3 | 162.2147.5–175.3 |
| 2038 | 6.53–15.1 | 161.4148.6–171.7 |
| 2039 | 6.92.6–14.3 | 160148.2–170.4 |
| 2040 | 7.72.3–16 | 160.9147.9–171.3 |
| 2041 | 8.12.6–15.2 | 160.8146.7–171.7 |
| 2042 | 6.93–16 | 162.3149.9–175.6 |
| 2043 | 7.92.8–15.6 | 161.1150.3–173.8 |
| 2044 | 7.93.1–16.6 | 163150.9–173.9 |
| 2045 | 9.23.2–19.6 | 163149.5–174.8 |
| 2046 | 8.93.2–17.6 | 163.5149.8–177.2 |
| 2047 | 9.74–19.2 | 165.2151.8–178.5 |
| 2048 | 9.13.4–17 | 166.4154.1–179.4 |
| 2049 | 10.13.7–20.8 | 165150.8–178.2 |
| 2050 | 10.94.8–18.9 | 166.9153.5–179.3 |
| 2051 | 9.23.5–18.4 | 165.6150.8–180 |
| 2052 | 103.4–21.2 | 166.7155.4–179.5 |
| 2053 | 8.73.8–18 | 166.3154.2–178.8 |
| 2054 | 9.74.4–19.2 | 167.5153.9–181 |
| 2055 | 9.93.9–17.2 | 167.8155.8–182.5 |
| 2056 | 114.3–21.8 | 166.3153.6–179.7 |
| 2057 | 10.94.2–19.8 | 168.3153.9–182.3 |
| 2058 | 13.85.1–25.7 | 167.5157–179.9 |
| 2059 | 11.23.3–25 | 170.9155.9–183.8 |
| 2060 | 12.86.1–22 | 170.3156.5–181.8 |
| 2061 | 12.64.7–23.2 | 168.8156.8–179.6 |
| 2062 | 12.36.2–22.4 | 171.6153.5–186.5 |
| 2063 | 125.2–24.9 | 171.3159.5–183.8 |
| 2064 | 125.3–21.6 | 170.2155.3–184.5 |
| 2065 | 12.53.9–30.8 | 169.7156.4–185 |
| 2066 | 11.55.3–22.1 | 172.2157–186.7 |
| 2067 | 18.38.1–33.3 | 173.2160.2–186.3 |
| 2068 | 135.4–23.4 | 172.6157.7–186.3 |
| 2069 | 15.65.7–25.5 | 171.8157.3–185.6 |
| 2070 | 13.55.2–29.1 | 171.7158.9–186.7 |
| 2071 | 154.4–30.2 | 173.4158.7–186.8 |
| 2072 | 15.57.4–26 | 174161.5–186.9 |
| 2073 | 13.96–29.1 | 175.6157.2–187.9 |
| 2074 | 12.84.3–27.3 | 173.5160–186.3 |
| 2075 | 13.96.3–25.6 | 173161.5–187.8 |
| 2076 | 166.1–33.8 | 172.9158.8–186.7 |
| 2077 | 13.25–29.8 | 175161.3–187.8 |
| 2078 | 13.75.1–33.6 | 174.4160.9–189.3 |
| 2079 | 12.95.3–28.4 | 172.5158.3–187.7 |
| 2080 | 14.45.4–27.9 | 175.1160.7–189.2 |
| 2081 | 15.15.8–29.8 | 174.9159.2–190.2 |
| 2082 | 16.46.1–33.9 | 175.4161.8–190.6 |
| 2083 | 15.97.9–32 | 176.5161.8–190.8 |
| 2084 | 14.16.6–27.7 | 176.1160.9–190.5 |
| 2085 | 16.57.3–30.2 | 176.3161.9–193.6 |
| 2086 | 19.55.5–32.4 | 173.9160–189.1 |
| 2087 | 196.9–38 | 176161–193.1 |
| 2088 | 18.47.7–35.2 | 175.4160.5–195.2 |
| 2089 | 1910.6–29.3 | 177.2164.2–191.7 |
| 2090 | 16.36.8–33.5 | 177.7163.9–192.2 |
| 2091 | 19.77.6–32.3 | 177.8162.8–192.2 |
| 2092 | 17.36.1–32.7 | 178.7162.9–192.9 |
| 2093 | 156.5–29.8 | 177164–191 |
| 2094 | 18.68.5–32.8 | 176.8164.1–190.7 |
| 2095 | 19.25.9–38.9 | 178.6163.8–193.1 |
| 2096 | 19.37.6–35.5 | 177.8163.7–192.5 |
| 2097 | 17.36.2–36.1 | 177.3160.4–192.5 |
| 2098 | 18.16.9–36.3 | 178.7162.6–195.7 |
| 2099 | 20.37.4–34.6 | 176159.9–193.2 |
| 2100 | 19.27–36.7 | 177.6162.2–193 |
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须知
- 一年中气温始终高于 20 °C 的夜晚数,取全国平均。
- 来源:World Bank Climate Change Knowledge Portal, CMIP6 multi-model median。
- 虚线为CMIP6 模型中位数(SSP2-4.5,中间路径),是一种情景而非预报;阴影带为其模型第 10–90 百分位。
- 气候模型值为 CMIP6 各模型的中位数:2014 年及以前按历史排放模拟,此后按中间路径 SSP2-4.5 模拟;它们并非实测数据。
各图层如何运作、数据从何而来,以及它们无法告诉你什么: 历年各国数据
法国与巴基斯坦对比
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军用
- 军费开支法国:2025 年为 GDP 的 2.03%巴基斯坦:2025 年为 GDP 的 2.91%
- 军费开支(美元)法国:2025 年为 US$645亿巴基斯坦:2025 年为 US$115亿
- 人均军费开支法国:2025 年为 US$1,021巴基斯坦:2025 年为 US$47
- 军费占政府支出比例法国:2025 年为 3.5%(占政府支出)巴基斯坦:2025 年为 13.8%(占政府支出)
- 武装部队人员法国:2020 年为 304,000 人巴基斯坦:2020 年为 943,000 人
- 国家实力法国:2022 年为 1.09%(占全球实力)巴基斯坦:2022 年为 1.77%(占全球实力)
- 武装部队占劳动力比例法国:2020 年为 1%(占劳动力)巴基斯坦:2020 年为 1.25%(占劳动力)
- 武器进口法国:2025 年为 2.36亿 TIV巴基斯坦:2025 年为 7.06亿 TIV
- 武器出口法国:2025 年为 33.7亿 TIV巴基斯坦:2025 年为 1.27亿 TIV