Decomposing the drivers of projected long-term CO 2 emissions across socio-economic pathways

Transcription

1 Decomposing the drivers of projected long-term CO 2 emissions across socio-economic pathways Giacomo Marangoni 1, Massimo Tavoni 1, Valentina Bosetti 1,2, Emanuele Borgonovo 2, Eoin O Broin 3, Pantelis Capros 4, Oliver Fricko 5, David Gernaat 6, Celine Guivarch 3, Petr Havlik 5, Daniel Huppmann 5, Nils Johnson 5, Panagiotis Karkatsoulis 4, Ilkka Keppo 7, Volker Krey 5, Leonidas Paroussos 4, James Price 7, Detlef P. van Vuuren 6,8. 1 FEEM, CMCC, Polytechnic of Milan (Italy); 2 Bocconi University (Italy); 3 CIRED (France); 4 National Technical University of Athens (Greece); 5 IIASA (Austria); 6 PBL (The Netherlands); 7 University College London (UK); 8 Utrecht University (The Netherlands). IEW UCC, Cork, Ireland June 1-3, 2016

2 Motivation Wide uncertainty space spanned by future socio-economic-technologic realizations leads to a wide range of potential future mitigation/adaptation challanges.

3 Motivation

4 Research Questions What factor drives this uncertainty the most? Is each factor influencing the output individually or via interactions? How do results change across models? over time? under a climate mitigation scenario? across regions?

5 Methodology Focus on cumulative CO2 FFI emissions (e), being an important metric in climate policy assessment. e can be expressed as a function of a set of input parameters, which have been grouped for convenience into 5 factors: e = f (POP, GDPPC, END, FF, LC) (1) where: f POP GDPPC END FF LC IAM black box Population GDP per capita Energy intensity Fossil fuels availability Low-carbon technology development

6 Methodology This factors have been quantified to represent the expectations of 3 possible futures: a green world (SSP1), a challenging world (SSP3), an inbetween scenario (SSP2). Factor SSP1 SSP2 SSP3 POP Low Med High GDPPC High Med Low END Low Med High FF Low Med High LC High Med Low

7 Methodology Going from SSP2 assumptions to SSP1 we obtain lower emissions: e 2 = f (POP 2, GDPPC 2, END 2, FF 2, LC 2 ) (2) e 1 = f (POP 1, GDPPC 1, END 1, FF 1, LC 1 ) (3) e = e 1 e 2 < 0 (4) On the contrary, going from SSP2 to SSP3 we obtain a e > 0.

8 Scenario decomposition We tackle the sensitivity problem with the scenario decomposition approach of Borgonovo (2010). 1 Φ 3 = individual effect of factor 3 in moving from point 0 to point 1 3 Φ 1 Φ T 3 = individual effect of factor 3 in the reverse case = total effect of factor 3 (individual + all interaction effects)

9 Scenario protocol

10 Implementation 6 IAMs GEM-E3 National Technical University of Athens, Greece IMACLIM CIRED, France IMAGE PBL, Netherlands TIAM-UCL University College London, England WITCH FEEM, Italy MESSAGE IIASA, Austria 2 climate policies BAU no specific climate policy; CTAX30 carbon tax, starting in 2020, increasing at 5%/yr, and equal to 30 US$2005/tCO2eq in 2040;

11 Implementation - END/FF/LC Model Energy Demand Fossil Fuels Low-carbon Techs GEM-E3 Regional energy intensity improvements from WITCH IMACLIM IMAGE TIAM-UCL Motorisation rate / residential space / industrial goods consumption increase w/ wealth. Preference factor for low carbon fuels, energy intensity for services, reliance on trad. fuels Growth in the service / industrial sectors via demand curves exponents. WITCH Factor productivity of energy in the production function tree. Transport fuel eff. and travel intensity. Reserves from ROSE project. Prices follow. Extraction costs and availability of unconventional oil/gas. Learning factors, trade barriers. Extraction costs and availability of oil/gas. Reserves and extraction costs from ROSE project. LbD rates of Non-BIO REN. LbD rates and max potential of REN (opposite for NUC). Learning factors of REN and H2 (opposite preference for NUC), support for REN capacity. Inv./O&M costs of REN, max potential of BIO, support for NUC capacity. LbD rates of REN and battery. Inv./O&M costs of NUC.

12 Proxy variables for factors harmonization

13 CO2 FFI Emissions

14 CO2 FFI Emissions - Delta w/ SSP2

15 Sensitivity indicators - CO2 FFI

16 Sensitivity indicators - Over time

17 Sensitivity indicators - Across policies

18 Sensitivity indicators - By region

19 Conclusions In the SSPs context, taking cumulative CO2FFI as the output of interest: END and GDPPC occur to be the most influential factors; FF and LC play an intermediate role, while POP scored last. Results are conditional to the width of uncertainty span by SSPs storylines; the specific modelling choices in implementing the storylines; the different modelling responses. Ranking is also affected by the fact that individual impacts of factors can be dampened or reinforced when these are varied together (e.g. GDPPC, getting behind END in SSP1 and above END in SSP3 in the ranking). Results exhibit similarities over time and with the addition of a mild carbon tax, even though they generally blur the rankings.

20 Thanks