Motivation of the analysis

2. 1 Motivation of the analysis Even if the description of technical change in integrated models for climate policy analysis has greatly improved,current approaches rarely include a full description of knowledge externalities Few attempts to incorporate R&D spillovers in integrated models for the study of climate policy have been confined to the inclusion of international spillovers only (e.g. Bosetti et al, 2008) Without spillovers, models unrealistically assume that advance of technological frontiers of different sectors and of different countries are mutually independentandomitto considerthe interactions among different driversof technical change.

3. 2 Spillovers: empirical evidence The majority of empirical works find that R&D spillovers are significantand positive(Coe and Helpman, 1995; Griliches, 1992) Bothdomestic and Internationalknowledgespillovershave asignificant impact on innovationsandproductivity(Bottazzi and Peri, 2007; Keller, 2002; Frantzen, 2002) Spillovers are mainly domestic: more national than international in scope (Weiser, 2007) Inter-sectoral spilloversare extremely significant (Australian Industry Commission, 1995; Weiser, 2007)

4. 3 Aim of the work We introduceinter-sectoral spilloversin the WITCH model with directed technical change (Carraro, Massetti and Nicita, 2009) R&D expenditures arefactor specificand can be directed towards increasingenergy-efficiencyor towards risingproductivity of non-energy inputs The directed technical change approach allows to explicitly modelingspillovers across the two R&D capital stocks We can thus studythe effectthat modelinginter-sectoral knowledge spillovershas on theadvances of the technological frontierand onthe costs of climate policy

5. 4 WITCH: a“TOP DOWN” optimization framework, with a“BOTTOM UP” energy sectordescription as a downward expansion of the energy input. “TOP DOWN “ perspective: strategic optimal investment strategy both on a time dimension (inter-temporality) and a regional dimension (non-cooperative game) “BOTTOM UP” component: enables a strategic assessment of the optimal investment indifferent energy technologies Electric and non electric energy use 6 Fuels types (Oil, Gas, Coal, Uranium, Biofuels, Traditional biomass) 7 Technologies for electricity generation Optimal intertemporal investment strategies are determined as adynamic Nash equilibrium of the gameamong the 12 regions (non-cooperative decision on CO2 emissions, fuel prices, LbD, emission permits market) WITCH model: main features

6. 5 WITCH with Directed Technical Change Output is produced combiningcapital-labour services(KLS) andenergy services(ES) and is reduced byclimate damage: (1) KLS is produced combining thecapital-labour(KL) aggregate and the stock ofcapital-labor knowledge(HKL): (2) (3) ES is produced aggregating theenergy(EN) and the stock ofenergy knowledge(HE): (4)

7. 6 R&D Sectors with inter-sectoral spillovers Knowledge stocks are increased by the flow ofnew ideasand are subject todepreciation (5) (6) Production of new ideas follows an “innovation possibility frontier” specification withintra-sectoral and inter-sectoral spilloversanddiminishing returns: (7) (8)

8. 7 BaU scenario GWP increases over the whole century at a declining rate Population grows at a diminishing rate and start declining at the end of the century Energy Intensity declines Carbon Intensity is constant …..overall carbon emission increases due to economic growth

9. 8 BaU Investments in final good capital, as a share of GWP, decline Capital formation in energy sector also declines R&D expenditures, as share of GWP, slightlyincrease because of increasing path of Non-Energy R&Dinvestments Energy R&Dexpenditures, as share of GWP and as share of total Investments in R&D,decline

10. 9 Stabilization policy Optimal investment and R&D strategies to stabilise GHG concentrations at 550 ppm CO2e Optimal emission time profiles are the solution of the fully cooperative version of the WITCH model It is assumed that regions can trade emissions allowance in an international carbon market Permits are allocated according to the equal per capita scheme, i.e. evenly balanced emissions per person

11. 10 Gross World Product and Consumption both decline wrt Bau Stabilization policy

12. 11 Stabilization: R&D sector R&D investment in energy sector sharply increases R&D investment in Non-energy sector declines

13. 12 Stabilization: R&D sector Overall Total R&D investment declines This result is explained by a contraction of economic activity and by the fact that capital-labor augmenting technical change is energy-biased (Carraro, Massetti and Nicita, 2009)

14. 13 Internalization of R&D externalities and Stabilization policy • R&D in Energy sector increases sharply • Non-Energy R&D increases • Total R&D increases

15. 14 Costs of policy When the stabilization policy and the policy for the internalization of the externalities are jointly implemented GWP increases wrt Bau

16. 15 Policy for the internalization of R&D externalities Policy for theinternalization of externalities in the Energy sectoraffectsdeeplyR&D investments in both sectors, while the effectofinternalizing externalities in the Non-Energy sector is very modest

17. 16 Policythatonly internalizes R&D externalitiesalwaysincreases output and emissions Policy for the internalization of R&D externalities

18. 17 Concluding remarks Even in a model embodying inter-sectoral spillovers, Non-energy R&D investments decrease when a stabilization policy is implemented A joint implementation of R&D policy and stabilization policy reduces the costs of the stabilization policy Implementing only an R&D policy aimed at internalizing externalities increases emissions