The recently published Intergovernmental Panel on Climate Change (IPCC) projections to 2100 give likely ranges of global temperature increase in four scenarios for population, economic growth and carbon use¹. However, these projections are not based on a fully statistical approach. Here we use a country-specific version of Kaya’s identity to develop a statistically based probabilistic forecast of CO₂ emissions and temperature change to 2100. Using data for 1960–2010, including the UN’s probabilistic population projections for all countries^2,3,4, we develop a joint Bayesian hierarchical model for Gross Domestic Product (GDP) per capita and carbon intensity. We find that the 90% interval for cumulative CO₂ emissions includes the IPCC’s two middle scenarios but not the extreme ones. The likely range of global temperature increase is 2.0–4.9 °C, with median 3.2 °C and a 5% (1%) chance that it will be less than 2 °C (1.5 °C). Population growth is not a major contributing factor. Our model is not a ‘business as usual’ scenario, but rather is based on data which already show the effect of emission mitigation policies. Achieving the goal of less than 1.5 °C warming will require carbon intensity to decline much faster than in the recent past.

最近发布的政府间气候变化专门委员会（IPCC）对2100年的预测给出了人口，经济增长和碳使用四种情况下全球温度升高的可能范围¹。但是，这些预测并非基于完全统计的方法。在此，我们使用特定国家/地区的Kaya身份来开发基于统计的CO ₂排放和2100年温度变化的概率预测。使用1960–2010年的数据，包括联合国对所有国家的概率人口预测^2,3,4，我们针对人均国内生产总值（GDP）和碳强度开发了联合贝叶斯分层模型。我们发现90％的累积CO ₂间隔排放包括IPCC的两个中间情景，但不包括极端情景。全球温度升高的可能范围是2.0–4.9°C，中值温度为3.2°C，并且低于2°C（1.5°C）的可能性为5％（1％）。人口增长不是主要因素。我们的模型不是“一切照旧”的情况，而是基于已经显示出减排政策效果的数据。要实现低于1.5°C的升温目标，碳强度的下降速度要比最近的速度快得多。