| Literature DB >> 27329411 |
Ting Wei1, Wenjie Dong2,3,4, John Moore5,6,7, Qing Yan8, Yi Song9,10, Zhiyong Yang5, Wenping Yuan3,5, Jieming Chou3, Xuefeng Cui5, Xiaodong Yan5, Zhigang Wei5, Yan Guo5, Shili Yang5, Di Tian5, Pengfei Lin10, Song Yang2,3, Zhiping Wen2,3, Hui Lin11, Min Chen11,12, Guolin Feng13, Yundi Jiang13, Xian Zhu3, Juan Chen3, Xin Wei3, Wen Shi3, Zhiguo Zhang3, Juan Dong14, Yexin Li14, Deliang Chen15.
Abstract
Carbon transfer via international trade affects the spatial pattern of global carbon emissions by redistributing emissions related to production of goods and services. It has potential impacts on attribution of the responsibility of various countries for climate change and formulation of carbon-reduction policies. However, the effect of carbon transfer on climate change has not been quantified. Here, we present a quantitative estimate of climatic impacts of carbon transfer based on a simple CO2 Impulse Response Function and three Earth System Models. The results suggest that carbon transfer leads to a migration of CO2 by 0.1-3.9 ppm or 3-9% of the rise in the global atmospheric concentrations from developed countries to developing countries during 1990-2005 and potentially reduces the effectiveness of the Kyoto Protocol by up to 5.3%. However, the induced atmospheric CO2 concentration and climate changes (e.g., in temperature, ocean heat content, and sea-ice) are very small and lie within observed interannual variability. Given continuous growth of transferred carbon emissions and their proportion in global total carbon emissions, the climatic effect of traded carbon is likely to become more significant in the future, highlighting the need to consider carbon transfer in future climate negotiations.Entities:
Year: 2016 PMID: 27329411 PMCID: PMC4916407 DOI: 10.1038/srep28046
Source DB: PubMed Journal: Sci Rep ISSN: 2045-2322 Impact factor: 4.379
Figure 1Temporal evolution of the simulated atmospheric CO2 concentration changes relative to 1990 using (a) IRF, (b) CESM, (c) BNU-ESM and (d) FGOALS-s2 under the PAX1, PNX1, CAX1 and CNX1 scenarios. (b) Shading shows the range of CO2 changes due to different initial conditions and lines are the ensemble mean.
Contributions of the developed (AX1) and developing (NX1) countries to the rise in atmospheric CO2 concentration and the effectiveness of AKNP and AKNC scenarios (see text for definition).
| IRF | CESM | BNU-ESM | FGOALS | IRF | |
|---|---|---|---|---|---|
| AX1 production-based contribution | 57% | 53% | 50% | 43% | 55% |
| NX1 production-based contribution | 43% | 47% | 50% | 57% | 45% |
| AX1 consumption-based contribution | 60% | 59% | 53% | 52% | 51% |
| NX1 consumption-based contribution | 40% | 41% | 47% | 48% | 49% |
| Transferred contribution | 3% | 6% | 3% | 9% | 4% |
| Effectiveness of AKNP | 1.7% | −0.7% | 1.1% | 8.9% | 2.3% |
| Effectiveness of AKNC | 5.0% | 4.6% | 5.2% | 8.9% | 6.0% |
| Transferred effectiveness | 4.3% | 5.3% | 4.1% | 0% | 3.7% |
aFrom 1990 to 2005.
bFrom 1990 to 2012.
cFrom 1990 to 2008.
Figure 2Temporal evolution of annual mean surface air temperature, upper ocean heat content (0–700 m) and Northern Hemisphere sea ice fraction simulated by CESM (left panel), BNU-ESM (middle panel), and FGOALS-s2 (right panel) under the PAX1, PNX1, CAX1 and CNX1 scenarios.
Left panel: shading shows the range of values due to different initial conditions and lines are the ensemble mean.
Figure 3Same as in Fig. 1 but under the APNP, AKNP, and AKNC scenarios.
Figure 4Same as in Fig. 2 but under the APNP, AKNP, and AKNC scenarios.