SUPPLEMENTARY INFORMATION

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1 SUPPLEMENTARY INFORMATION DOI: 1.138/NCLIMATE1783 The mitigation challenge to stay below two degrees Glen P. Peters 1, Robbie M. Andrew 1, Tom Boden 2, Josep G. Canadell 3, Philippe Ciais 4, Corinne Le Quéré 5, Gregg Marland 6, Michael R. Raupach 3, Charlie Wilson 5 1 Center for International Climate and Environmental Research Oslo (CICERO), Oslo, Norway (glen.peters@cicero.uio.no) 2 Carbon Dioxide Information Analysis Center (CDIAC), Oak Ridge National Laboratory, Tennessee, USA 3 Global Carbon Project, CSIRO Marine and Atmospheric Research, Canberra, Australia 4 Laboratoire des Sciences du Climat et de l'environnement, Gif sur Yvette, France 5 Tyndall Centre for Climate Change Research, University of East Anglia, Norwich, UK 6 Research Institute for Environment, Energy, and Economics, Appalachian State University, Boone, North Carolina, USA Calculation of Growth Rates For comparisons between two data points, growth rates are calculated as (x 1 x )/x and expressed in percent. For comparisons over more than two data points, we calculate the Average Annual Growth Rates as AAGR = d/dt(lnx) = 1/x dx/dt using a linear regression between lnx and t and report the value in %/yr. The use of the logarithm ensures that the growth rates remain additive in the Kaya Identity. Estimates of emissions We follow our earlier work in estimating CO 2 emissions from fossil fuel combustion, cement production, and gas flaring 1 6. With a two year lag, the emissions are estimated by the Carbon Dioxide Information Analysis Center (CDIAC) based on UN energy statistics 7. The most recent two years are estimated by CDIAC based on the growth rate of fossil fuel consumption from the BP energy statistics 8. We forecast the emissions in the current calendar year using estimates of the world Gross Domestic Product (GDP) and estimates of the improvements in the emission intensity 4 6 (CO 2 /GDP). We take the uncertainty in the emissions data as ±5% for one standard deviation 9 ( likely range). A full description of the data and methods for the full carbon budget and the accompanying database are described and available elsewhere 1. Figure SI 1 shows the estimated CO 2 emissions from the last seven carbon budgets showing the different data source for each estimate 1 6. Each year, the UN and BP energy statistics are updated in all years and this leads to revisions in all years. Updates in the UN energy data can lead to changes of up to ±5%, reflecting improvements in data over time. Estimates based on the BP energy statistics for the most recent two years are generally found to be robust compared to the estimates using the UN energy statistics. The forecasts are most uncertain and also depend on the robustness of the previous year s estimated emissions. Except for the forecast in 21 (which was a lower bound) 5, emission estimates lie within the uncertainty bounds of the latest time series (from this study). In general, revisions in the emission estimates confirm a continuing strong growth rate in global emissions. The changes in emission estimates with each new carbon budget reflects different underlying factors, and we now briefly discuss these differences from the year 29 onwards when we have included forecasts for the current calendar year. NATURE CLIMATE CHANGE 1

2 Figure SI 1: The estimated CO 2 emissions from the last seven carbon budgets showing the different data source for each estimate. Each year, the UN and BP energy statistics are updated in all years and thus leads to revisions in all years. The uncertainty bands represent ±5% of the emissions (±1 standard deviation). All estimates, except the forecast for 21, lie within the 5% uncertainty bounds. The 21 forecast (highlighted with an * ) was low since only the lower bound of the growth rate was provided (>3%), and additionally, the 29 value on which the forecast was based was also low. References: Raupach et al 1, Canadell et al 2, Online release 3, Le Quéré et al 4, Friedlingstein et al 5, Peters et al 6. Estimate for 29 Due to the time lag in the availability of the UN energy statistics, this year is the first time for a detailed estimate of the 29 CO 2 emissions (Table SI 1). Le Quéré et al. 4 estimated 29 emissions to be 2.8% below 28 emissions. This estimate was based on an estimated 1.1% decrease in GDP (published by the IMF in October 29), and 1.7% drop in emission intensity based on the long term trend from In a later study, Friedlingstein et al. 5 estimated a drop of only 1.3% based on the BP data, which is markedly different from the Le Quéré et al estimate. This is since the GDP actually decreased by.6% (not 1.1%) and the emission intensity dropped.7% (and not 1.7% as in the long term trend). The large change in the GDP estimate was due to the global financial crisis and the overestimated decrease in the emission intensity is since the emission intensity has decreased at a slower rate in recent years (Figure SI 2 and Figure SI 3) and there may be an asymmetry in the change in emission intensity in times of growth compared to times of declines 11. Peters et al. 6, also using the BP data but based on updated 28 emission estimates using the UN energy statistics, estimated a 1.4% drop owing to minor revisions in the UN and BP datasets. The latest estimated emissions for 29 are based on the UN energy statistics only and give a.4% drop compared with 28 7, markedly different to the estimates using the BP data. The main reason for this change is that the BP energy statistics underestimated growth in Chinese coal consumption and Indian oil 2

3 consumption compared to the surveyed UN energy statistics 7. In terms of the Kaya Identity, the main reason for the changing estimates for 29 is overestimates of the improvement in the emission intensity (see Table SI 1, Figure SI 2 and Figure SI 3). Growth Rates (%) GDP CO 2 /GDP CO 2 Notes Le Quéré et al GDP estimated, CO 2 /GDP estimated from Friedlingstein et al GDP actual, CO 2 BP, CO 2 /GDP calculated Peters et al GDP actual, CO 2 BP, CO 2 /GDP calculated This study GDP actual, CO 2 UN, CO 2 /GDP calculated Table SI 1: The emission estimates for 29 from the last four carbon budgets, showing the main drivers in the Kaya identify. Estimate for 21 Estimates for 21 have been more robust (Table SI 2), despite one lying outside the uncertainty range (Figure SI 1). Friedlingstein et al 5 estimated the growth rate of CO 2 emissions from 29 to 21 to be at least 3% due to uncertainty in the response after the global financial crisis of The estimated emissions were 5% lower than the estimate of the following year 6. This large variation was due to small improvement in the emission intensity and a large adjustment in the estimated 29 emissions on which the 21 was based. Using the BP energy statistics emissions grew 5.9% from 29 to 21 6 and the estimated emissions are similar in this study but with a revised growth rate from 29 to 21 of 5.1%. Growth Rates (%) GDP CO 2 /GDP CO 2 Notes Friedlingstein et al > 1.7% >3% GDP estimated, CO 2 /GDP greater than long term trend Peters et al GDP actual, CO 2 BP, CO 2 /GDP calculated This study GDP actual, CO 2 BP, CO 2 /GDP calculated Table SI 2: The emission estimates for 21 from the last three carbon budgets, showing the main drivers in the Kaya identify. Estimate for 211 Our estimate of global CO 2 emissions in 211, based on the BP energy statistics is 9.4±.5PgC or 3.% above 21 (Table SI 3). The 211 growth rate is close to our forecast 6 published in 211 of 3.1%, and these are similar to recently published estimates from the IEA 12 of 3.2% and PBL/JRC 13 of 3%. Growth Rates (%) GDP CO 2 /GDP CO 2 Notes Peters et al GDP estimated, CO 2 /GDP estimated from 2 21 This study GDP actual, CO 2 BP, CO 2 /GDP estimated Table SI 3: The emission estimates for 211 from the last two carbon budgets, showing the main drivers in the Kaya identify. 3

4 Estimate for 212 Based on recent growth in economic activity 14, estimated to be 3.3%, and assuming the average decrease in emission intensity over the last ten years of.7%, we estimate 212 emissions to be 9.72PgC (2.6% higher than 211). For the GDP data to calculate the emission intensity we used the IEA 15 Purchasing Power Parity data up to 21 and the IMF 14 growth rate (3.8%) to estimate 211 GDP. We apply a 5% uncertainty range to the 212 estimate, and the earlier discussion on the changes in the 29 estimate suggests this is reasonable and conservative. For the uncertainty in the growth rate of emissions from 211 to 212, we consider two factors. First, the IMF has made four estimates of the GDP from 211 to 212 (January, April, July, and October editions of the World Economic Outlook) and these have been either 3.3% or 3.5%. This indicates that, for 212, the estimates of GDP growth are stable indicating low uncertainty. Second, there is a higher degree of uncertainty in the estimated improvements in the emission intensity. We chose a time period of ten years to estimate the emission intensity, and ten years can be seen as a compromise between short and long term variations. Since the decrease in the emission intensity has declined in recent years (Figure SI 2), the 212 estimate is most sensitive to shortening the time period of the regression (Figure SI 3). Taking the average decrease in emission intensity over the last five years of.1%, we estimate 212 emissions to be 9.78PgC (3.3% higher than 211). Alternatively, taking the average decrease in emission intensity since 199 of 1.2%, we estimate 212 emissions to be 9.67PgC (2.1% higher than 211). Adding the range of the 212 estimates of GDP with the range in estimates of emission intensity, we estimate the 212 emissions to be between 1.9% and 3.5% above 211, with a best estimate of 2.6%. AAGR as a function of start year (%/yr) Figure SI 2: The Average Annual Growth Rate (AAGR) of the emission intensity with various start years and an end year of 211, showing the increase in the emission intensity since 199. The coloured vertical lines represent the different years used in our uncertainty estimate. 4

5 CO 2 /GDP (gc/usd) AAGR( ) = 1.2% AAGR( ) =.8% AAGR(22 211) =.7% 13 AAGR(27 211) = % Figure SI 3: The emission intensity over time, showing different choices for the start year used to estimate 212 emissions. The analysis in the main paper is based on the 1 year period,

6 The scenario data We obtain the scenario data from the following sources SA9 16 : IS92 17 : SRES 18 : RCP 19 : apps/tnt/rcpdb The scenario data are only provided at intervals of generally ten years or more (see Figure 1, Figure SI 5, and Figure SI 6). When plotting the data we use linear interpolation for transparency as it makes it easier to identify the location of data points and avoids making assumptions on how emissions evolve between the data points. Since Figure 2 has growth rates up to and including 212 and most scenarios have data points at decadal or longer intervals, then we need a method to interpolate between the scenario data points and thereby calculate the growth rates. We used linear interpolation to estimate the 212 scenario values to estimate the growth rates in Figure 2, and Figure SI 4 repeats the figure using a spline interpolation. There was very little change in the AAGR for the different interpolations. Due to the shorter overlap between historical emissions, the RCPs are most sensitive to changes in interpolation. We found the AAGR between 25 and 212 for RCP3 PD was 1.7%/yr for linear and 1.8%/yr for a spline, RCP4.5 was 1.5%/yr for linear and spline, RCP6 was 1.1%/yr for linear and spline, and RCP8.5 was 2.4%/yr for linear and 2.3%/yr for a spline. Figure SI 4: Figure 2 from the main article using a spline interpolation to 212 instead of a linear interpolation. There are minor changes in the growth rates (around.1%) for some scenarios (e.g., RCP3 PD and RCP8.5). 6

7 Additional figures of the scenarios To show alternative perspectives on the historical emissions compared to the scenarios we repeat Figure 1 from the main article for the time period 198 to 21 (Figure SI 5) and for 199 to 23, symmetric about 21 (Figure SI 6). Figure SI 5: Figure 1 from the main article, but over the period to show the full extent of the scenarios. 7

Figure SI 6: Figure 1 from the main article, but over the period 199 23 to

8 Figure SI 6: Figure 1 from the main article, but over the period to show more detail between the scenarios and current emission developments. 8

9 Historical reduction rates Using the country level emission dataset described above, we calculated AAGRs over moving windows with 1 year or greater duration over the historical record (from 19 to 211). We isolated the largest reductions that occurred outside of large and unexpected economic declines (e.g., World War I and II, the Great Depression, the collapse of the former Soviet Union). We cross checked that the emission reductions were due to an energy transition by using the IEA country reports on total primary energy supply ( Figure SI 7 to Figure SI 12 show the CO 2 emission reduction rates discussed in the main text. We have highlighted the Great Depression, World War I and II, and the two oil shocks in the 197s as these events had global ramifications on emissions. 4 BELGIUM 35 3 CO 2 emissions (MtC) %/yr, Figure SI 7: The AAGR for Belgium of 3.7%/yr during the period Grey shaded areas represent major global events that were expected to lead to changes in emission (see text). 9

10 16 FRANCE (INCLUDING MONACO) CO 2 emissions (MtC) %/yr, Figure SI 8: The AAGR for France of 3.7%/yr during the period Grey shaded areas represent major global events that were expected to lead to changes in emission (see text). 3 SWEDEN 25 CO 2 emissions (MtC) %/yr, Figure SI 9: The AAGR for Sweden of 4.5%/yr during the period Grey shaded areas represent major global events that were expected to lead to changes in emission (see text). 1

11 2 UNITED KINGDOM 18 CO 2 emissions (MtC) %/yr, %/yr, Figure SI 1: The AAGR for the United Kingdom of 1.6%/yr during the period and Grey shaded areas represent major global events that were expected to lead to changes in emission (see text). 2 DENMARK 18 CO 2 emissions (MtC) %/yr, Figure SI 11: The AAGR for Denmark of 2.2%/yr during the period Grey shaded areas represent major global events that were expected to lead to changes in emission (see text). 11

12 Carbon Emissions (GtC) Solid Liquid Gas Cement Flaring Total UNITED STATES OF AMERICA 1.4%/yr, Figure SI 12: The AAGR for the United States of America of 1.4%/yr during the period The emissions by fuel type identify the decrease in coal and oil consumption and substitution with gas during this period. Grey shaded areas represent major global events that were expected to lead to changes in emission (see text). 12

13 References 1 Raupach, M. R. et al. Global and regional drivers of accelerating CO 2 emissions. Proc. Natl. Acad. Sci. 14, , doi:1.173/pnas (27). 2 Canadell, J. G. et al. Contributions to accelerating atmospheric CO 2 growth from economic activity, carbon intensity, and efficiency of natural sinks. Proc. Natl. Acad. Sci. 14, , doi:1.173/pnas (27). 3 Global Carbon Project. Carbon budget and trends 28. ( 29). 4 Le Quéré, C. et al. Trends in the sources and sinks of carbon dioxide. Nature Geosci. 2, , doi:1.138/ngeo689 (29). 5 Friedlingstein, P. et al. Update on CO 2 emissions. Nature Geosci. 3, , doi:1.138/ngeo122 (21). 6 Peters, G. P. et al. Rapid growth in CO 2 emissions after the global financial crisis. Nature Clim. Change 2, 2 4, doi:1.138/nclimate1332 (212). 7 Boden, T. A., Marland, G. & Andres, R. J. Global, Regional, and National Fossil Fuel CO 2 Emissions in Trends (Carbon Dioxide Information Analysis Center, doi:1.3334/cdiac/gcp_v212, 212). 8 BP. BP Statistical Review of World Energy June 212, (212). 9 Andres, R. J. et al. A synthesis of carbon dioxide emissions from fossil fuel combustion. Biogeosciences 9, , doi:1.5194/bg (212). 1 Le Quéré, C. et al. The Global Carbon Budget Earth Syst. Sci. Data Discuss. 5, , doi:1.5194/essdd (212). 11 York, R. Asymmetric effects of economic growth and decline on CO 2 emissions. Nature Clim. Change 2, , doi:1.138/nclimate1699 (212). 12 International Energy Agency, Global carbon dioxide emissions increase by 1. Gt in 211 to record high. (212). 13 Olivier, J. G. J., Janssens Maenhout, G. & Peters, J. A. H. W. Trends in global CO emissions; 212 Report (PBL Netherlands Environmental Assessment Agency, 212). 14 International Monetary Fund. World Economic Outlook October 212: Coping with High Debt and Sluggish Growth (IMF, 212). 15 International Energy Agency. CO 2 emissions from fuel combustion highlights 212 edition (International Energy Agency, 212). 16 Tirpak, D. & Vellinga, P. in Climate Change: The IPCC Response Strategies (eds F. Bernthal et al.) 9 42 (Intergovernmental Panel on Climate Change, 199). 17 Leggett, J. et al. in Climate Change 1992: The Supplementary Report to The IPCC Scientific Assessment (eds J.T. Houghton, B.A.Callander, & S.K. Varney) (Intergovernmental Panel on Climate Change, 1992). 18 Nakicenovic, N. & Swart, R. Special Report on Emissions Scenarios (Cambridge Univ. Press, 2). 19 van Vuuren, D. P. et al. The representative concentration pathways: an overview. Climatic Change 19, 5 31, doi:1.17/s z (211). 13

Budget10 released on 5 December 2011 ppt version 8 December Carbon Budget

Budget10 released on 5 December 2011 ppt version 8 December 2011 Carbon Budget 2010 GCP - Carbon Budget 2010 Contributors Thomas A. Boden Carbon Dioxide Information Analysis Center, Oak Ridge National