witch model description and applications feem

WITCH Model Description and Applications

FEEM

WITCH Model, Description and Applications 2

The WITCH Team:

Andrea Bastianin

Valentina Bosetti

Carlo Carraro

Enrica De Cian

Alice Favero

Emanuele Massetti

Lea Nicita

Elena Ricci

Fabio Sferra

Massimo Tavoniwww.feem-web.it/witch


The WITCH Model: An Introduction


The WITCH Model

WITCH: World Induced Technical Change Hybrid model

Hybrid I.A.M.: Economy: Ramsey-type optimal growth (inter-temporal) Energy: Energy sector detail (technology portfolio) Climate: Damage feedback (global variable)

12 Regions (“where” issues) Intertemporal (“when” issues) Game-theoretical set-up (free-riding incentives)

Bosetti V., E. De Cian, A. Sgobbi and M. Tavoni (2009). “The 2008 WITCH Model: New Model Features and Baseline,” FEEM Working Paper October 2009.

Bosetti V., E. Massetti, M. Tavoni (2007). “The WITCH Model, Structure, Baseline, Solutions”, FEEM Working Paper 10.2007.

Bosetti, V., C. Carraro, M. Galeotti, E. Massetti and M. Tavoni (2006). “WITCH: A World Induced Technical Change Hybrid Model”, The Energy Journal, Special Issue. Hybrid Modeling of Energy-Environment Policies: Reconciling Bottom-up and Top-down, 13-38.

Economic Activity

Energy Use

emissions

AtmosphereBiosphere

Deep Oceans

temperatureEconomic Activity

Energy Use

emissions

AtmosphereBiosphere

Deep Oceans

temperatureEconomic Activity

Energy Use

emissions

AtmosphereBiosphere

Deep Oceans

temperature


The WITCH model - http://www.feem-web.it/witch/

A hybrid energy-economy-climate model Scale: global, with the world divided in 12 regions Economy: top-down intertemporal optimal growth model,

dynamic, perfect foresight Energy: bottom-up description of technological options:

Electric and Non Electric energy use Six fuel types specified (oil, gas, Coal, Uranium, traditional

and advanced biofuels) Seven technologies for electricity generation

Endogenous technical change – Learning-By-Doing and Learning-By-Researching

Climate: damage feedback via temperature change Strategic: non cooperative interactions between region with

externalities (environmental, price of exhaustible resources, technological spillovers, and trade of emission permits)


Bottom-up characterisation of the energy sector

Detailed representation of technological change

Learning-By-Doing in W&S

Energy intensity R&D

Breakthrough Technologies (two factors learning curves)

Several channels of interactions among regions

Technological spillovers

Environmental externality

Exhaustible common resources (coal, natural gas and uranium)

Trade of emission permits

Trade of oil

Game-theoretic set-up makes it possible to model strategic behaviour (open loop

Nash game) and to describe cooperative and non-cooperative solutions

Distinguishing Features


Two possible regional aggregations

United States (USA) Western EU countries

(WEURO) Eastern EU countries

(EEURO) Canada, Japan and New

Zealand (CAJANZ) Korea, Australia and South Africa

(KOSAU) Non-EU Eastern European

countries, including Russia (TE) Latin America, Mexico and

Caribbean (LAM) Middle East and North

Africa (MENA) South Asia, including India

(SASIA) China, including Taiwan

(CHINA) Sub‑Saharan Africa

excluding South Africa (SSA) South East Asia (EASIA)

World countries, aggregated into 12 regions

United States (USA) Western EU countries

(WEURO) Eastern EU countries

(EEURO) Canada, Australia and

NewZealand (AUCANZ)

Korea, Japan (JPNKOR) Non-EU Eastern

European countries, including Russia (TE)

Latin America, Mexico and Caribbean (LAM)

Middle East and North Africa (MENA)

South Asia, including India (SASIA)

China, including Taiwan (CHINA)

Sub‑Saharan Africa including South Africa (SSA)

South East Asia (EASIA)


The Objective Function and Budget Constraint

For each region (n) forward-looking central planner maximizes present value of (log) per capita consumption (5-yr time steps):

choosing the optimal path of investment variables simultaneously and strategically with respect to the other decision makers

Consumption of the single final good obeys to the economy budget constraint:

W (n) L (n, t) log c(n, t) R(t)t

(1)

(2)

tnCCStnPtnXtnP

tnO&MtnItnItnItnYtnC

CCSf ff

j j jjj jDRC

,,,,

,,,,,, ,&

GDPFinal Good

EnergyR&Ds

ElectricityGeneration

Operation & Maintanance

Net fuel expenditures

CCS (Transport and storage costs)


Output and Climate Damage

Gross output is produced combining the inputs capital, labour (=population) and energy services using a nested, Constant Elasticity Production Function

(3) tntnESntnLtnKntnTFPtnY nnC ,,))(1(,,)(,,

/1)()(1

2,2,1 )()(1),( tTtTtn nn (4)

GROSS GDP

Climate change damage is a non-linear function. Climate change impacts can be either positive or negative and they are region-specific


Output and Climate Damage

Net output is obtained after subtracting expenditure for fossil fuels, which is considered as a net loss for the economy

CCS is the amount of CO2 captured from the atmosphere and PCCS the corresponding costs that the economy has to pay to external suppliers of CCS know-how

(5)

tnCCStnP

tnXtPtnXtnP

tn

tnESntnLtnKntnTFPtnYnet

CCS

f netimpffextrff

nnC

,,

,,,

,

,))(1(,,)(,,

,int

,

/1)()(1

WITCH Model, Description and Applications

Production Tree and Energy Technologies

11

Production nest and the elasticity of substitution

Legenda: KL= Capital-labour aggregate; K = Capital invested in the production of final good; L = Labour; ES = Energy services; HE = Energy R&D capital; EN = Energy; EL = Electric energy; NEL = Non-electric energy; OGB = Oil, Backstop, Gas and Biofuel nest; ELFF = Fossil fuel electricity nest; W&S= Wind and Solar; ELj = Electricity generated with technology j (IGCC plus CCS, Oil, Coal, Gas, Backstop, Nuclear, Wind plus Solar); TradBiom= Traditional Biomass; TradBio= Traditional Biofuels; AdvBio= Advanced Biofuels


Electricity Production - 1

Electricity is obtained by combining in fixed proportions the installed power generation capacity (K), operation and maintenance equipment (O&M) and fuel resources consumption (X) (when needed)

Power Plant

Fuels

Operation and

Maintenance

Electricity

Production function are characterized by region-specific parameters that account for the technical features of each power production technology, such as the low utilisation factor of renewables, the higher costs of running and maintaining IGCC-CCS and nuclear plants


13

tnXtnO&MtnKtnEL ELjjjjnjjnj ,;,;,min, ,,,

Electricity production is described by a Leontief production function

),(

),(1),(1,

tnSC

tnItnKtnK

j

jjjj

Power Generation capacity (Power Units) depends on cumulated investments (I) and investments costs (SC) which are time and region-specific:

(6)

(7)

Electricity Production - 2

μ translates power capacity into electricity generation

Τ differentiates O&M over technologies

ζ yields the quantity of fuels needed to generate 1 KwH of electricity


Technical Change – Learning-By-Doing

Learning-By-Doing via experience curves in power plants investment cost

Endogenous Technical Change (ETC) accounts for the accumulation of both:

• Experience (Learning-By-Doing)• R&D investment (Learning-By-Researching)

nt n

PRjjj

jtnKBtnSC 2log1,,

World learning, assuming full technology spillover: investments in additional capacity by virtuous regions drive down investment costs worldwide, with benefits also for the non investing regions

(9)


Technical Change – Energy Efficiency

/1

),(),(, tnENtnHEtnES ENH

cbDR tnHEtnIn a tnZ ),(),(, &

tnZtn HE) tHE(n DR ,)1)(,(1, &

The R&D sector exhibits intertemporal spillovers and the production of new "ideas" follows an innovation possibility frontier (Popp, 2002; Jones,1995):

The flow of new ideas adds to the previously cumulated stock and generates the total amount of knowledge available to country n at time t:

Learning-By-Researching via energy R&D increasing energy efficiency (Popp, 2004)

(10)

(11)

(12)


Technical Change – International Spillovers

)),(),((),(

),(),( tnHEtnHE

tnHE

tnHEtnSPILL

HIHI

dcbDR tnSPILLtnHEtnIn a tnZ ,),(),(, &

The R&D sector exhibits also international knowledge spillovers:

(13)

(14)

The contribution of foreign knowledge to the production of new domestic ideas depends on the interaction between two terms: the first describes the absorptive capacity whereas the second captures the distance from the technology frontier, which is represented by the stock of knowledge in rich countries (USA, WEURO, EEURO, CAJANZ and KOSAU)

Absorptive capacity Distance from the frontier


Technical Change – Advanced Biofuels

Learning-By-Researching via dedicated R&D decreasing the cost of the cellulosic

biofuels, PADVBIO(n,t)

)),((0,, ,& tnTOTnPtnP ADVBIODRADVBIOADVBIO

t

tADVBIODR

nADVBIODRADVBIODR nItnKtnTOT

1,&,&,& ),()2,(),(

(15)

(16)

where stands for the relationship between new knowledge and cost

The stock of world R&D (ΣK) accumulates with the perpetual rule and it will influence other regions with a 10-year (2model periods) delay. The time lag is meant to account for the advantage of first movers in innovation


Technical Change – Breakthrough Technologies

Learning-By-Doing and Learning-By-Researching via cumulative capacity and dedicated R&D

decreasing the cost of breakthrough technologies, following a two factors learning curve

(17)

b

tec

Ttec

c

tec

Ttec

tec

Ttec

CC

CC

DR

DR

P

P

0,

,

0,

2,

0,

, *&

&

where the R&D stock (R&D tec) accumulates with the perpetual rule and it is also augmented by the stock of R&D accumulated in other regions through a spillover effect, SPILL, similarly to energy efficiency R&D

Two breakthrough technologies: one as substitute for nuclear in power generation and one as substitute for oil in the non-electric sector (transport)


Major Research Topics

Mitigation options and costs

Innovation

Uncertainty

International policy architectures and coalition theory

Optimal balance between mitigation and adaptation




Innovation

Uncertainty




Mitigation Options, Technologies, Carbon Markets - 1

Major Areas of Research: Optimal investments in energy technologies Optimal investments in R&D Climate policy costs: global and distribution Climate policy costs with limits on the penetration of carbon free

technologies Modeling backstop technologies Investments in electricity grids International trade of oil Financing climate policy Carbon markets



Key Findings: First energy efficiency, then decarbonization Climate policy costs are moderate for a 650 ppm CO2-eq Climate policy costs increase but are still reasonable for a 550 ppm

CO2-eq scenario No silver bullet. Complex portfolio mix with: nuclear, renewables,

coal with ccs Stringent climate policy is unfeasible with delayed (2030) or

incomplete action (China, India) Modeling international trade of oil tilts distribution of costs towards

oil exporting countries


-20%

0%

20%

40%

60%

80%

100%

0% 20% 40% 60% 80% 100%

Energy Intensity Improvement

Decarb

on

izati

on

450

550

BAU

past 30 yrs

Energy savings and efficiency should be pursued vigorously in the short term, but decarbonisation is essential from 2030 onwards already

2030

20502100

2030

2050

2100

2100

2050

2030

550

650

Changes in Energy and Carbon Intensities


World Electricity Generation Shares

2000 2020 2040 2060 2080 21000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

NuclearHydroelectricOilGasIGCC+CCSTrad CoalWind&Solar

Electricity Mix, 550 ppm CO2-eq



References: Bosetti, V., C. Carraro, E. Massetti, A. Sgobbi and M. Tavoni (2009). “Optimal

Energy Investment and R&D Strategies to Stabilise Greenhouse Gas Atmospheric Concentrations,” Resource and Energy Economics, 31(2): 123-137.

Bosetti, V., C. Carraro and E. Massetti (2009). “Banking Permits: Economic Efficiency and Distributional Effects,” Journal of Policy Modeling, 31(3): 382-403.

De Cian, E. and M. Tavoni (2009). “Sharing the burden to 2050: what role for an international carbon market?” Fondazione Eni Enrico Mattei, July 2009, mimeo.

Bastianin, A., A. Favero and E. Massetti (2009). “Investing in a Low-Carbon World,” Fondazione Eni Enrico Mattei, July 2009, mimeo.

Massetti, E. and F. Sferra (2009). “A Numerical Analysis of Optimal Extraction and Trade of Oil Under Climate Policy and R&D Policy,” Fondazione Eni Enrico Mattei, July 2009, mimeo.

Tavoni, M., B. Sohngen and V. Bosetti (2008). "Forestry and the Carbon Market Response to Stabilize Climate", Energy Policy, 35: 5346-5353.




Innovation

Uncertainty




Innovation - 1

Major Areas of Research: Directed technical change Human capital accumulation International knowledge spillovers Intersectoral knowledge spillovers Two factors learning curves for backstop technologies


Innovation - 2

Key Findings: Sharp increment of energy R&D (four-fold) is needed R&D investments in backstop technologies play a key role when

there are constraints to the development of nuclear and/or renewables

Modeling international disembodied R&D spillovers does not change mitigation policy costs

Intersectoral R&D spillovers might have a greater influence With directed technical change, overall R&D investments

decline with climate policy, and GDP losses increase Human capital is pollution-using (due to the complementarity

between labor and energy) and therefore climate policy re-directs investments away from education toward R&D which instead is pollution-saving


Investment in R&D with Breakthrough Technologies

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

2007 2012 2017 2022 2027 2032 2037 2042 2047 2052

% o

f w

orl

d G

DP

Baseline

550ppm

550ppm w ith backstops

Breakthrough technologies can only become available with substantial investments in R&D Energy R&D expenditures increase up to 0.12% of GDP, vs. 0.02% in the BAU


Mitigation Costs with the Backstop Technologies

The price of carbon is much lower with breakthrough technologies

Crucial role to decarbonize non-electric energy (transport)

And therefore the costs of stabilisation are much lower, especially in the long term

0

50

100

150

200

250

300

350

400

450

500

2007 2012 2017 2022 2027 2032 2037 2042 2047 2052

$U

S/t

CO 2

eq

550ppm

550ppm w ithbackstops

-8.0

-7.0

-6.0

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

2007 2012 2017 2022 2027 2032 2037 2042 2047 2052 2057 2062 2067 2072 2077 2082

% c

han

ge in

GD

P w

ith

resp

ect

to b

aselin

e

550ppm w ith backstops

550ppm


Induced Technical Change and GWP Losses

Overestimated when there is no ITC in the Energy Sector

Understimated when there is no ITC in the Non-Energy Sector.

Underestimated when there is no ITC

Understimated when there is only exogenous crowding out of Non-Energy R&D 3.4% 3.5% 3.6% 3.7% 3.8% 3.9% 4.0% 4.1%

No ITC Non-Energy

ExogenousCrowding-out

No ITC

Full ITC

No ITC Energy

Discounted GWP Loss from Climate Policy (%)

With respect to a Full Induced Technical change (ITC) Scenario Gross World Product (GWP) losses are:


Innovation - 3

References: Carraro, C., E. Massetti and L. Nicita (2009). “How Does Climate Policy Affect

Technical Change? An Analysis of the Direction and Pace of Technical Progress in a Climate-Economy Model.” The Energy Journal, Forthcoming.

Bosetti, V., C. Carraro and M.Tavoni (2009). “Climate Policy after 2012. Technology, Timing, Participation,” CESifo Economic Studies, Forthcoming.

Bosetti, V., C. Carraro, E. Massetti, A. Sgobbi and M. Tavoni (2009). “Optimal Energy Investment and R&D Strategies to Stabilise Greenhouse Gas Atmospheric Concentrations,” Resource and Energy Economics, 31(2): 123-137.

Bosetti, V., C. Carraro, R. Duval, A. Sgobbi and M. Tavoni (2009). “The Role of R&D and Technology Diffusion in Climate Change Mitigation: New Perspectives using the WITCH Model.” OECD Working Paper No. 664, February.

Carraro, C., E. Massetti and L. Nicita (2009). “Optimal R&D Investments and the Cost of GHG Stabilization when Knowledge Spills across Sectors.” Fondazione Eni Enrico Mattei, July 2009, mimeo.

Carraro, C., E. De Cian and M. Tavoni (2009). “Human Capital Formation and Global Warming Mitigation: Evidence from an Integrated Assessment Model.” Fondazione Eni Enrico Mattei, July 2009, mimeo.




Innovation

Uncertainty




Uncertainty

Major Areas of Research: Stochastic WITCH Analysis of optimal investment trajectories under uncertainty Uncertainty on R&D productivity Policy uncertainty

Key Findings: Modeling innovation in a backstop technology as an uncertain process

leads to higher optimal levels of R&D investments Uncertainty on the stringency of the mitigation target leads to high

mitigation activity if a stringent target has the chance to come into force

References: Bosetti, V. and M. Tavoni (2009), "Uncertain R&D, backstop technology and

GHGs stabilization", Energy Economics, 31(1): S18-S26. Bosetti, V., C. Carraro, A. Sgobbi, and M.Tavoni (2009) "Delayed Action and

Uncertain Targets. How Much Will Climate Policy Cost?" Climatic Change, Forthcoming




Innovation

Uncertainty




International Policy Architectures - 1

Major Areas of Research: International climate policy architectures (Harvard Project on

International Climate Agreements) Stabilization costs, investments and innovation with different

degrees of cooperation Delayed participation of developing countries Optimal climate policy of high income countries in face of

delayed participation from low income countries The incentives to participate in and the stability of climate

coalitions



• Global coalition with CAT and transfers

• Global coalition with carbon tax recycled domestically

• Global coalition with REDD

• Climate Clubs (sub-coalitions)

• Dynamic coalitions: incremental participation based on

Burden sharing rules

Graduation

Dynamic targets

• R&D and Technology coalition


Name Key feature Policy

Instrument Scope Timing

CAT with redistribution

Benchmark cap and trade Cap and Trade Universal Immediate

Global carbon tax Global tax recycled domestically Carbon Tax Universal Immediate

REDD Inclusion of REDD Cap and Trade Universal Immediate

Climate Clubs Clubs of countries

Cap and Trade and R&D

Partial

Incremental

Burden Sharing Delayed participation of DCs. Cap and Trade Universal Incremental

Graduation Bottom up targets Cap and Trade Partial Incremental

Dynamic Targets Political feasibility Cap and Trade Universal Incremental

R&D Coalition R&D cooperation R&D Universal Immediate


NB All refer to CO2 only


Climate Effectiveness

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

°C a

bo

ve p

re-i

nd

ust

ria

l

None of the policy architectures is able to keep temperature change below the 2°C threshold. A target between 2.5 and 3°C seems more feasible


Economic Efficiency

Change in GWP wrt BaU - Discounted at 5%

-2.00%

-1.50%

-1.00%

-0.50%

0.00%

0.50%

CA

T w

ith

red

istr

ibu

tio

n

Clim

ate

Clu

bs

RE

DD

Bu

rde

n

Sh

arin

g

Gra

du

atio

n

Glo

ba

l ca

rbo

n

tax

Dyn

am

ic

Ta

rge

ts

R&

D C

oa

litio

n

While temperature change varies less across the eight architectures for agreement because of the inertia in the climate system, the economic costs of the different set-ups vary considerably. More stringent policy architectures imply a higher GWP loss


Non-Cooperative CO2 Emissions

The non-cooperative solution, defined also as the baseline, it best represents the strategic nature of international relations. Little variations are observed in a non-cooperative setting, reflecting the inability of individual regions to internalise the environmental externality


Cooperative CO2 Emissions

Sensitivity to these assumptions is far greater in the cooperative case. Higher damage and especially low discounting drive emissions down


CBA: Free riding – the case of SSA

Percentage Difference in EmissionsCoalition w/o SSa comapred to the Grand Coalition

0%

2%

4%

6%

8%

10%

12%

14%

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

0%

50%

100%

150%

200%

250%

300%

USA

WEURO

EEURO

AUCANZ

JPNKOR

TE

MENA

SASIA

CHINA

SEASIA

LAM

SSA (RH Axes)

When Africa leaves the grand coalition• members emit more because they do not internalize the high negative impact of climate change on Africa (damage effect)• Africa emits more (free riding effect), but less than in the BaU (technology spillovers)



Major Findings: Delayed and fragmented participation of developing countries into

international climate agreements would raise the global policy costs considerably for serious stabilization targets

An international carbon market has the potential to alleviate such detrimental effects, but might involve large financial transfers

An agreement that envisions future commitments for some key emerging economies might represent a win-win strategy, since the optimal investment behavior is to anticipate climate policy

This is especially relevant for China, whose recent and foreseeable trends of investments in innovation are not incompatible with the adoption of domestic emission reduction obligations in 2030

In cost-benefit setting, only the Grand Coalition finds profitable to achieve the 550 ppm CO2-eq target, under very special condition

The Grand Coalition is neither stable nor potentially stable



References: Bosetti, V., C. Carraro, E. De Cian, R. Duval, E. Massetti and M. Tavoni (2009),

"The Incentives to Participate in and the Stability of International Climate Coalitions: a Game Theoretic Approach Using the WITCH Model," OECD Economics Department Working Papers No. 702, June 2009.

Bosetti, V., C. Carraro and M.Tavoni (2009), " Climate Policy After 2012. Technology, Timing, Participation,” CESifo Economic Studies, Forthcoming.

Bosetti, V., C. Carraro and M.Tavoni (2009) " Climate Change Mitigation Strategies in Fast-Growing Countries: The Benefits of Early Action”, Energy Economics, Forthcoming.

Bosetti, V., C. Carraro, A. Sgobbi, and M. Tavoni (2008). “Modelling Economic Impacts of Alternative International Climate Policy Architectures: A Quantitative and Comparative Assessment of Architectures for Agreement”, in Aldy and Stavins, eds, Post-Kyoto International Climate Policy: Implementing Architectures for Agreement Cambridge University Press, in press.

Bosetti, V., C. Carraro and M. Tavoni (2008), "Delayed Participation of Developing Countries to Climate Agreements: Should Action in the EU and US be Postponed?", FEEM Working Paper N.70-2008.




Innovation

Uncertainty




Balancing Mitigation and Adaptation PoliciesMajor Areas of Research:

Optimal mix of mitigation and adaptation policies Optimal investments in different adaptation forms

Key Findings: The introduction of adaptation decreases the need to mitigate and vice-versa Joint implementation of mitigation and adaptation in a cost-benefit framework

suggests that both policies are required Proactive adaptation is the first measure to be adopted. Reactive measures

prevail afterwards, when the damage is higher, and in non-OECD regions Developed countries are likely to experience minor aggregate

damages/benefits. Policy to control damages should focus on developing countries

References: Bosello, F., C. Carraro and E. De Cian (2009). “An Analysis of Adaptation as a

Response to Climate Change.” Copenhagen Consensus Center, July 2009 Bosello, F., C. Carraro and E. De Cian (2009). “Adaptation, Mitigation and Innovation: A

Comprehensive Approach to Climate Policy .” Fondazione Eni Enrico Mattei, September 2009, mimeo

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