Global and regional drivers of accelerating CO2 emissions

Abstract

CO2 emissions from fossil-fuel burning and industrial processes have been accelerating at a global scale, with their growth rate increasing from 1.1% y(-1) for 1990-1999 to >3% y(-1) for 2000-2004. The emissions growth rate since 2000 was greater than for the most fossil-fuel intensive of the Intergovernmental Panel on Climate Change emissions scenarios developed in the late 1990s. Global emissions growth since 2000 was driven by a cessation or reversal of earlier declining trends in the energy intensity of gross domestic product (GDP) (energy/GDP) and the carbon intensity of energy (emissions/energy), coupled with continuing increases in population and per-capita GDP. Nearly constant or slightly increasing trends in the carbon intensity of energy have been recently observed in both developed and developing regions. No region is decarbonizing its energy supply. The growth rate in emissions is strongest in rapidly developing economies, particularly China. Together, the developing and least-developed economies (forming 80% of the world's population) accounted for 73% of global emissions growth in 2004 but only 41% of global emissions and only 23% of global cumulative emissions since the mid-18th century. The results have implications for global equity.

Fig. 1.

Observed global CO₂ emissions including all terms in Eq. 1, from both the EIA (1980–2004) and global CDIAC (1751–2005) data, compared with emissions scenarios (8) and stabilization trajectories (–12). EIA emissions data are normalized to same mean as CDIAC data for 1990–1999, to account for omission of F_Cement in EIA data (see Materials and Methods). The 2004 and 2005 points in the CDIAC data set are provisional. The six IPCC scenarios (8) are spline fits to projections (initialized with observations for 1990) of possible future emissions for four scenario families, A1, A2, B1, and B2, which emphasize globalized vs. regionalized development on the A,B axis and economic growth vs. environmental stewardship on the 1,2 axis. Three variants of the A1 (globalized, economically oriented) scenario lead to different emissions trajectories: A1FI (intensive dependence on fossil fuels), A1T (alternative technologies largely replace fossil fuels), and A1B (balanced energy supply between fossil fuels and alternatives). The stabilization trajectories are spline fits approximating the average from two models (11, 12), which give similar results. They include uncertainty because the emissions pathway to a given stabilization target is not unique.

Fig. 2.

Factors in the Kaya identity, F = Pgef = Pgh, as global averages. All quantities are normalized to 1 at 1990. Intensities are calculated by using G_M (Left) and G_P (Right). In both Left and Right, the black line (F) is the product of the red (P), orange (g), green (e), and light blue (f) lines (Eq. 2) or equivalently of the red (P), orange (g), and dark blue (h) lines (Eq. 3). Because h = ef, the dark blue line is the product of the green and light blue lines. Sources are as in Table 1.

Fig. 3.

Fossil-fuel CO₂ emissions (MtC y⁻¹), for nine regions. Data source is EIA.

Fig. 4.

Factors in the Kaya identity, F = Pgef = Pgh, for nine regions. All quantities are normalized to 1 at 1990. Intensities are calculated with G_Pi (PPP). For FSU, normalizing G_Pi in 1990 was back-extrapolated. Other details are as for Fig. 2.

Fig. 5.

Relative contributions of nine regions to cumulative global emissions (1751–2004), current global emission flux (2004), global emissions growth rate (5 year smoothed for 2000–2004), and global population (2004). Data sources as in Table 1, with pre-1980 cumulative emissions from CDIAC.