CO2 Emissions and Drivers (Kaya Decomposition)

This dataset contains the annual historical series of CO2 Emissions and Drivers ( Kaya Decomposition) from 1971-2020Note: Identifying drivers of CO2 emissions trendsThis table presents the decomposition of CO2 emissions into four driving factors following the Kaya identity1, which is generally presented in the form:Kaya identityC = P (G/P) (E/G) (C/E)where:"C = CO2 emissions; P = populationG = GDPE = primary energy consumption""The identity expresses, for a given time, CO2 emissions as the product of population, per capita economic output (G/P), energy intensity of the economy (E/G) and carbon intensity of the energy mix (C/E).Because of possible non-linear interactions between terms, the sum of the percentage changes of the four factors, e.g. (Py-Px)/Px, will not generally add up to the percentage change of CO2 emissions  (Cy-Cx)/Cx. However, relative changes of CO2 emissions in time can be obtained from relative changes of the four factors as follows:"Kaya identity: relative changes in timeCy/Cx = Py/Px (G/P)y/(G/P)x (C/E)y/(C/E)xwhere x and y represent for example two different years.In this table, the Kaya decomposition is presented as:"CO2 emissions and driversCO2 = P (GDP/P) (TES/GDP) (CO2/TES) "where:"C =                       CO2 emissions; P =                       populationGDP/P =               GDP/population *TES/GDP =         Total primary energy consumption per GDP *CO2/TES =          CO2 emissions per unit TES"* GDP in 2015 USD, based on purchasing power parities."The Kaya identity can be used to discuss the primary driving forces of CO2 emissions. For example, it shows that, globally, increases in population and GDP per capita have been driving upwards trends in CO2 emissions, more than offsetting the reduction in energy intensity. In fact, the carbon intensity of the energy mix is almost unchanged, due to the continued dominance of fossil fuels - particularly coal - in the energy mix, and to the slow uptake of low-carbon technologies.However, it should be noted that there are important caveats in the use of the Kaya identity. Most important, the four terms on the right-hand side of equation should be considered neither as fundamental driving forces in themselves, nor as generally independent from each other."

本数据集囊括了1971年至2020年间二氧化碳排放及其驱动因素（凯亚分解）的年度历史序列。备注：识别二氧化碳排放趋势的驱动因素。本表展示了根据凯亚恒等式1分解的二氧化碳排放，分为四个驱动因素，通常以如下形式呈现：

凯亚恒等式：C = P × (G/P) × (E/G) × (C/E)

其中：

C = 二氧化碳排放；
P = 人口；
G = 国内生产总值；
E = 初级能源消费。

该恒等式表达的是，在特定时间点，二氧化碳排放是人口、人均经济产出（G/P）、经济能源强度（E/G）和能源混合碳强度（C/E）的乘积。由于各因素之间可能存在非线性相互作用，四个因素的百分比变化之和，例如( Py-Px )/Px，通常不会等于二氧化碳排放百分比变化(Cy-Cx)/Cx。然而，可以从四个因素的相对变化中获取二氧化碳排放随时间的相对变化，如下所示：

凯亚恒等式：Cy/Cx = Py/Px × (G/P)y/(G/P)x × (C/E)y/(C/E)x

其中，x和y代表例如两个不同的年份。在本表中，凯亚分解以如下形式呈现：

二氧化碳排放及其驱动因素：CO2 = P × (GDP/P) × (TES/GDP) × (CO2/TES)

其中：

C = 二氧化碳排放；
P = 人口；
GDP/P = 国内生产总值/人口；
TES/GDP = 总初级能源消费/国内生产总值；
CO2/TES = 单位初级能源消费的二氧化碳排放。

* 2015年美元，基于购买力平价。

凯亚恒等式可用于讨论二氧化碳排放的主要驱动力量。例如，它表明，在全球范围内，人口和人均国内生产总值的增加推动了二氧化碳排放的上升趋势，这一趋势超过了能源强度的降低。事实上，能源混合的碳强度几乎未变，这归因于化石燃料——尤其是煤炭——在能源混合中的持续主导地位以及低碳技术的缓慢采用。然而，应当注意的是，在使用凯亚恒等式时存在重要的限制。最重要的是，方程右侧的四个项不应被视为根本的驱动力量本身，也不应被视为通常相互独立。