Post COP21 and Thereafter:
The need for new concepts to monitor progress andcompliance and for assisting policy
arnulf.grubler@yale.edu
ALPS International Symposium
Tokyo, February 10, 2016

Paris INDC by Type of Target

Source: UNFCC, 2015

Paris INDC by Type of Target************ Monitoring and Compliance sensitive to: emissions uncertainty** Sensitive to: emissions AND forecasting uncertainty*** Sensitive to: emissions AND forecasting uncertainty, AND measurement biases

Measurement Issues inTransitions and Decarbonization• Traditional: Input oriented, aggregate (primaryenergy, emissions) - Observed historical rates ofchange are slow: 80-130 yrs• New: Output oriented, sectorial (useful energy,transformation) – Much faster (x2) dynamics anddeeper decarbonization• Reasons for acceleration of transitions anddecarbonization in output measures:
efficiency, granularity, learning, spillovers, and social
network effects – illustrative ALPS modelingInput vs. Output Measures of Growth(example lighting services UK, index 1700=100)
100,000,000

10,000,000

Light service/useful energy
normalized (lumen-hours)

1,000,000

100,000

Light primary energy
normalized

10,000Light (real) expenditures
normalized

1,000100

101
1700

1750

1800

1850

1900

1950

2000

Source: Grubler (in press) based on Fouquet, 2008

New Data• PFUDB: Primary, Final, Useful Energy Balances
countries, regions, world
1900 to 2010
- energy AND exergy- by end-use service- by sector- by fuel• Online: https://tntcat.iiasa.ac.at/PFUDB• Documentation: S. De Stercke, IIASA IR-14-013

World – Useful Energy by Service

Source: EnergyPrimer.org

Global Energy Flows 2005 (EJ)
& Measurement Points for DecarbonizationPETransformation=∆PE-FE

FEUE

Source: EnergyPrimer.org

Decarbonization at 4 Levels: China vs. US
180
30

Transformation
PE-FE

160

Carbon intensity (tC/TJ)

140
120
20

Primary E
FFI
PFPFI

100Final E

PPI

FFI USPFPFI US80

PPI USFUI US

60

1040
200
1900

0
1920

1940

1960

1980

2000

FUI

Useful E

Transitions in Primary Energy Inputs: China vs. USA
trad. Biomass → Coal → modern PE (oil/gas+0-carbon)

Fraction of Primary Energy

100%

China – USA
Lag (of 50% midpoint)
Biomass: 100 yrs
Modern: 130 yrs

75%50%

Transitions speed (dT)
Biomass: 80 vs 120 yrs
Modern: 240 vs 70 yrs
25%0%
1900

1925

1950

1975

2000

Transitions in Energy Outputs (UE) : China vs. USA
Carbon → Hydrogen → Electrons
100%

China – USA
Lag (of 50% midpoint)
C: 70 yrs
e: 0 yrs

Fraction of Useful Energy

75%

50%

Transitions speed (dT)
C: 160 vs 200 yrs
e: 90 vs 120 yrs
25%0%
1900

1925

1950

1975

2000

Transition Dynamics compared(dT useful energy as % of dT primary energy)

percent of dT of Primary Energy

10075

50solids (mean=78%)
grids (mean=64%)

250Why Faster Transitions in Outputs?
• Efficiency gains• Nature of technologies (“granularity”)
leading to faster learning &improvements• Performance, rather than price driven• Social network and peer effects

World Exergy Efficiencies in 2010
(as percent of primary exergy)

World Exergy Efficiencies in 2010
(as percent of primary exergy)

JWLearning Rates of Energy Technologies: Supply vs End-use

Learning rate (% cost change per doubling)

50.0
40.0

A

30.0
20.0
10.0
0.0

M JI F
81295111064 2
3-20.0-30.0
7-40.0
1.E+03

1KG CH EN-10.0-50.0
1.E+00LDB

Mean of “granular”
end use technologies:
LR=20%
CumProd= 10e9

Mean of “lumpy”
supply technologies:
LR=10%
CumProd= 10e4
1.E+06
1.E+091.E+12
1.E+15
Cumulative # of units produced

1.E+18A Transitors
B DRAMs
C Automobiles
D Washing machines
E Refrigerators
F Dishwashers
G Freezers (upright)
H Freezers (chest)
I Dryers
J Calculators
K CF light bulbs
L A/C & heat pumps
M Air furnaces
N Solar hot water heaters
12345678
9101112

PV modulesWind turbines
Heat pumps
Gas turbines
Pulverized coal boilers
Hypropower plants
Nuclear reactors
Ethanol
Coal power plants
Coal power plants
Gas pipelines
Gas combined cycles

Wilson et al. Nature CC S1, 2012

MESSAGE IAM Modeling Implementation• 5 regional, global IAM
MESSAGE+• Demand @ useful energy(output)• 2°C climate constraint (LP)• Endogenous technology
diffusion as a function of:-- unit scaling-- investment risks (granularity)-- learning:- cost reductions (cumUnits)
influencing rel. advantage- spatial knowledge spillovers-- market sizeBase case

Endogenous
scaling & diffusion

ABM - Agent-Based Modeling•••••••

Representation of producers and adoptersof technologies (agents) and policy maker (principal)
micro-level interactions yield aggregate macro-level outcomes
Heterogeneous products(performance, price,…,…, environment)
Heterogeneous agents(producers: technological capability, R&D strategy;
consumers: preferences and preference weights)
Agent interaction 1: producers-consumers
Agent interaction 2: consumers-consumers(“small world network” Watts- Strogatz-1998 model)
depending on:-- nature and size of social network-- peer effectAgent interaction 3:
policy makers – producers – consumers
policy options: education, C-tax, R&D subsidy
Results today: vehicle market sales per product category(Attribute A5: environment <0.5, or >0.5 preference weight)

Results ABM - Network Effects:
Network size (critical threshold level) >> peer effect> # of neighbors and their distanceResults ABM Policy Leverages:∆ consumer preferences >> C-tax > R&D subsidy

Conclusions• INDCs:- Verification, plausibility, “rachetability”
measures needed- Measurement concepts matter- Complement traditional, input measureswith output based measures(less uncertainty in PE accounting,better perspective on “feasibility”, see e.g. SE4All)• Policy implications:- Differentiated, sectorial targets and measures- Renewed emphasis on end-use(efficiency, behavior, organization)- Policies need to consider innovation characteristics