Impacts of nationally determined contributions on 2030 global greenhouse gas emissions: uncertainty analysis and distribution of emissions

Transcription

1 Impacts of nationally determined contributions on 2030 global greenhouse gas emissions: uncertainty analysis and distribution of emissions Céline Guivarch with Hélène Benveniste (IPSL, now Princeton Univ.), Olivier Boucher (LMD, IPSL), Patrick Criqui (GAEL, Univ. Grenoble Alpes), Hervé Le Treut (IPSL) Chaire DDX, Coriolis seminar, 11 december 2017

2 Motivation Nationally Determined Contributions (NDCs) summarize domestic objectives for emissions reductions of greenhouse gases (GHG) at the time horizon. But Ancillary assumptions are needed to interpret some NDCs and project GHG emissions in 2030.

3 Motivation Nationally Determined Contributions (NDCs) summarize domestic objectives for emissions reductions of greenhouse gases (GHG) at the time horizon. But Ancillary assumptions are needed to interpret some NDCs and project GHG emissions in 2030.

4 Motivation Nationally Determined Contributions (NDCs) summarize domestic objectives for emissions reductions of greenhouse gases (GHG) at the time horizon. But Ancillary assumptions are needed to interpret some NDCs and project GHG emissions in 2030.

5 Motivation Nationally Determined Contributions (NDCs) summarize domestic objectives for emissions reductions of greenhouse gases (GHG) at the time horizon. But Ancillary assumptions are needed to interpret some NDCs and project GHG emissions in 2030.

6 Motivation Nationally Determined Contributions (NDCs) summarize domestic objectives for emissions reductions of greenhouse gases (GHG) at the time horizon. But Ancillary assumptions are needed to interpret some NDCs and project GHG emissions in 2030.

7 Motivation Nationally Determined Contributions (NDCs) summarize domestic objectives for emissions reductions of greenhouse gases (GHG) at the time horizon. But Ancillary assumptions are needed to interpret some NDCs and project GHG emissions in Ø Ø Ø Where would current contributions bring us (if exactly implemented)? A systematic analysis of uncertainty sources and their respective impacts on 2030 global GHG emissions, using a high granularity of countries considering the latest available projections of economic growth. Analyze contributions of uncertainty sources, highlight robust results.

8 Methods +100 countries NDC treated individually Direct NDC when no interpretation needed, Assumptions to translate NDC to absolute emissions levels in 2030: Extension when NDC given for 2025, GDP growth when NDC given in intensity, Emissions peak constraint for China, All gases/co2 ratio when target in CO2 only. Others with incomplete NDC (sectoral only, or reduction from a BAU not specified) associated with expert judgement assumptions. 1. iterate a scenario discovery cluster analysis to identify the diversity of combinations of drivers that lead to the selected subset of scenario outcomes.

9 Methods: uncertainty representation 20 different GDP scenarios: 5 Shared Socioeconomic Pathways (SSP) * 4 quantifications (OECD, IIASA, PIK and CEPII).

10 Methods 1. Select a subset of scenario outcomes of interest, and iterate a scenario discovery cluster analysis to identify the diversity of combinations of drivers that lead to the selected subset of scenario outcomes. Evolution of the Chinese Gross Domestic Product (GDP) for a) the 20 original economic scenarios and b) corrected scenarios based on historical data. c) scatter plot of 2030 GDP value versus 2030 GDP growth rate for the corrected scenarios.

11 Methods: uncertainty representation 20 different GDP scenarios: 5 Shared Socioeconomic Pathways (SSP) * 4 quantifications (OECD, IIASA, PIK and CEPII). E (2030) = (1 α) * E (base year) * GDP (2030) / GDP (base year), with α carbon intensity improvement

12 Methods: uncertainty representation 20 different GDP scenarios: 5 Shared Socioeconomic Pathways (SSP) * 4 quantifications (OECD, IIASA, PIK and CEPII). E (2030) = (1 α) * E (base year) * GDP (2030) / GDP (base year), with α carbon intensity improvement Monte Carlo method for each GDP scenarios, varying the following parameters: ambition level of the NDC; our set of assumption for countries with NDCs providing either only sectoral or incomplete information; LULUCF emissions reduction for countries with NDC excluding LULUCF; emissions level for international aviation and shipping.

13 Methods: uncertainty representation 20 different GDP scenarios: 5 Shared Socioeconomic Pathways (SSP) * 4 quantifications (OECD, IIASA, PIK and CEPII). E (2030) = (1 α) * E (base year) * GDP (2030) / GDP (base year), with α carbon intensity improvement Monte Carlo method for each GDP scenarios, varying the following parameters: ambition level of the NDC; our set of assumption for countries with NDCs providing either only sectoral or incomplete information; LULUCF emissions reduction for countries with NDC excluding LULUCF; emissions level for international aviation and shipping. Accounting for the peak before 2030 in China NDC Use a weighted average between an exponential and a linear interpolation of the carbon intensity between 2005 and 2030 (weight taken as uncertain factor in Monte Carlo); Compute the minimal carbon intensity reduction in China allowing the peak to occur before 2030 (peak reached if and only if the carbon intensity reduction rate is larger than the economic growth rate in 2030); If minimal carbon intensity reduction is more stringent than the 65% rate, the peak target is the effective constraint and the carbon intensity reduction target is overachieved.

14 Results 57.8 to 65.6 Gt CO2eq (10%-90% range) UNFCCC: Gt CO2eq UNEP Gap: Gt CO2eq Rogelj et al.: 52-58Gt CO2eq Vandyck et al.: 56 Gt CO2eq den Elzen et al.: Gt CO2eq Probability distribution function of global greenhouse gas emissions in 2030 for 20 growth scenarios, 5 Shared Socioeconomic Pathways (SSP) * 4 data sources (CEPII, OECD, IIASA, PIK)

15 Results Fraction (%) of the total variance in 2030 global greenhouse gas emissions explained by the identified set of drivers.

16 Results LEA: Large Emitters with Absolute target LENA: Large Emitters with Non Absolute target Relative contributions of countries to 2010 and 2030 global emissions, for the 5 Shared Socioeconomic Pathways (SSP) growth scenarios * 4 data sources (from left to right: CEPII, OECD, IIASA, PIK).

17 Results [-5.1%/yr +5.1%/yr] at countries level Global emissions and uncertainty ranges in 2010, 2030 and The 2030 range is determined considering NDCs, while the 2050 range corresponds to the Intergovernmental Panel on Climate Change (IPCC) milestone if global warming is to be limited to 2 C since preindustrial times (i.e., 40 to 70% emissions reductions in 2050 compared to 2010).

18 Discussions/limitations A substantial source of uncertainty not accounted for here will come from whether countries miss, meet or surpass the announced NDC target. Accounting for targets in terms of share of non-fossil fuels, e.g. of the total primary energy supply for China (20% by 2030) and of the power generation capacity for India (40% by 2030). For China, no consensus in recent literature regarding which of the three elements of the Chinese NDC (carbon intensity reduction, CO2 emission peak before 2030, share of non-fossil fuels) is the most constraining. For India non-fossil target, the few elements published suggest that the intensity target could be overachieved if the non-fossil target is reached.

19 Conclusions There are large uncertainties surrounding the 2030 global GHG emissions level, to be reduced in upcoming negotiation cycles? Despite these uncertainties, NDCs provide robust evidence of a shift in GHG emissions towards emerging and developing countries and a noticeable reduction in international inequalities in per capita GHG emissions. The growing share of emerging and developing countries emissions in global emissions illustrates the importance of a universal agreement including all countries such as the Paris Agreement. The post-2030 emissions reduction challenge is emphasized. Thankyou for your attention! Céline Guivarch (guivarch@centre-cired.fr) with Hélène Benveniste, Olivier Boucher, Patrick Criqui, Hervé Le Treut

20 Methods 1. Select a subset of scenario outcomes of Large Emitters with Absolute target (LEA) interest, and iterate a scenario discovery cluster analysis to identify the diversity of combinations of drivers that lead to the Large Emitters with Not Absolute target (LENA) selected subset of scenario outcomes.

21 Methods 1. Select a subset of scenario outcomes of interest, and iterate a scenario discovery cluster analysis to identify the diversity of combinations of drivers that lead to the selected subset of scenario outcomes.

22 Methods 1. Select a subset of scenario outcomes of interest, and iterate a scenario discovery cluster analysis to identify the diversity of combinations of drivers that lead to the selected subset of scenario outcomes.

23 Supplementary Material Probability distribution function of global greenhouse gas emissions distributions in 2030 for 20 growth scenarios grouped by: a. SSP narrative, and b. GDP data source.

24 Supplementary Material Fraction (%) of the total variance in 2030 global greenhouse gas emissions explained by the identified set of drivers.

25 Supplementary Material Emissions uncertainty ranges per country or group of countries.

26 Supplementary Material

27 Supplementary Material LEA: Large Emitters with Absolute target LENA: Large Emitters with Non Absolute target Evolution of greenhouse gas emissions per capita (in ton CO2eq per capita) between 2010 (in red) and 2030 (in blue) for various countries and groups of countries.

28 Supplementary Material Gini coefficients for emissions and GDP.

29 Supplementary Material Whisker plots of Chinese CO 2 emissions in 2030 with (in light grey) and without (in dark grey) the peak constraint (left scale), and corresponding 2030 GDP growth rate (right scale) under the 20 GDP scenarios considered. The box plots show the median, 25 and 75 th percentiles and the lines show minimum and maximum values.

30 Supplementary Material Probabilistic projections of Chinese CO 2 emissions over and comparison with the latest historical emission data.

31 Supplementary Material

32 Supplementary Material

33 Supplementary Material Probability distribution function of global greenhouse gas emissions distributions in 2030 for 20 growth scenarios without corrections on historical Chinese emissions. with corrections on historical Chinese emissions.

34 Supplementary Material