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1. Introduction Model Data Results Counterfactuals Conclusions & Extensions Appendix Subsidies, Tariﬀs and Investments in the Solar Power Market Chrystie Burr Department of Economics University of Colorado at Boulder April 26, 2014 1 / 33

2. Introduction Model Data Results Counterfactuals Conclusions & Extensions Appendix “I’d put my money on the sun and solar energy. What a source of power! I hope we don’t have to wait until oil and coal run out before we tackle that.” -Thomas Edison, 1931 ” ”Photovoltaics are threatening to become the costliest mistake in the history of German energy policy.” -Der Spiegel, July 4, 2012” 2 / 33

3. Introduction Model Data Results Counterfactuals Conclusions & Extensions Appendix Overview • Solar power has experienced substantial growth in the U.S. since the early 2000s. Total operating capacity has grown from 4 MW in 2000 to over 4,000 MW in 2011 while the International Energy Agency projects it to grow to 68GW in 2035. • The growth is driven by: • Falling module costs: from costs $110/W in 1975 to $1/W in 2012 • Substantial public policy drivers and financial incentives provided by federal, state, and local governments in the U.S. and abroad: • Capacity-based incentives: upfront buy-down programs • Production-based incentives: Feed-in Tariffs (FITs), net-metering rules • Tax incentives: sales tax exemptions, federal renewable energy tax credit • Financing programs: property assessed clean energy financing (PACE), solar leasing. 3 / 33

9. Introduction Model Data Results Counterfactuals Conclusions & Extensions Appendix The Eﬀect of Incentive Policies Are public policies important? • Germany accounted for 35.6% of worldwide installed capacities in 2011 despite its suboptimal geographical location. Refer to this map • Invested over $11 billion through production subsidies in 2012. • Within the U.S., New Jersey has the second largest solar market (14%) despite its relatively low solar resources. Refer to this map • This suggests that high subsidies lead to adoptions but at what cost? 4 / 33

10. Introduction Model Data Results Counterfactuals Conclusions & Extensions Appendix Research Agenda The questions addressed in this study are: • Evaluation of solar subsidy policies: 1 How to quantify the impact of solar incentive programs on the growth of solar installations? 2 How to assess the relative efficiency of different subsidy programs? What are the welfare costs of displacing GHGs through these programs ? 3 How costly is it to encourage solar adoptions in suboptimal locations? • Impact of counterfactual policies: 1 What is the impact of the subsidy degression design? Is it “better” than the common flat rate subsidies? 2 What would be the effect of lowering the current amount of subsidies to match the $38/ton social cost of carbon suggested by the Interagency Working Group (2013)? How much more does it cost to double the number of current installations? 5 / 33

16. Introduction Model Data Results Counterfactuals Conclusions & Extensions Appendix Methodology • I develop a dynamic structural model to estimate consumer demand for solar panels and apply the model to evaluate the eﬀect of capacity-based subsidies, production revenue, tax credits and upfront system costs. • Why structural? • To conduct counterfactual analysis • To conduct welfare analysis taking into account the change in consumer’s surplus associated with the policy change • Why dynamics? • To capture the consumer’s expectation of falling panel prices and reduction in subsidies. • To provide a realistic model of consumers’ investment decisions in a durable goods market. 6 / 33

17. Introduction Model Data Results Counterfactuals Conclusions & Extensions Appendix Relationship to other studies 1 Theoretical and simulation studies on instrument choice in environmental regulations • Kneese and Bower (1968), Keohane et al. (1998), Goulder and Parry (2008), Fischer and Newell (2008) 2 Empirical studies of the California solar market • Bollinger and Gillingham (2012), Dastrup et al. (2012), Hughes and Podolefsky (2014) 3 Methodology: Single agent optimal stopping model aggregate to the market level • Rust (1987), BLP (1995) 7 / 33

20. Introduction Model Data Results Counterfactuals Conclusions & Extensions Appendix Market and Institution California Solar Initiative Program • Started in January 2007 seeking to install ∼2GW of PV capacity in 10 years with program funding of $2 billion. • It provides ﬁnancial incentives to customers of Investor-Owned Utilities (IOUs): • Residential users (size < 30kW) receive a one-time, up-front payment based on the expected performance of the system. 8 / 33

21. Introduction Model Data Results Counterfactuals Conclusions & Extensions Appendix Preview of Results • Production-based subsidies are more eﬃcient than capacity-based subsidies because they encourage adoptions in sunnier locations. • Total subsidies in CA would be welfare neutral with a $108/ton social cost of carbon (at the end of the sampling period). • If CA has the same solar resource as in Frankfurt, Germany, and to produce the same level of electricity as in the factual world, the welfare neutral CO2 price is at $730/ton. 9 / 33

22. Introduction Model Data Results Counterfactuals Conclusions & Extensions Appendix Household decision model Household’s Decision to Install a Solar Power System • Each month, households (non-installers) make the following choice: • Each household observes 1 price of solar power system (p) 2 capacity-based subsidy (s) 3 proﬁt associated with life-time solar electricity production revenue, O&M cost and inverter replacement cost (r) 4 federal tax credit (τ) • Households choose either to install solar PV (d = 1) or stay with the current utility/electricity setup (d = 0) • After installations, the household leaves the market. 10 / 33

23. Introduction Model Data Results Counterfactuals Conclusions & Extensions Appendix Household decision model • Per period utility is determined by the state variables: X = {p, s, r, τ} and , iid random variables follow a type I extreme value distribution: u(X, d, , θ) = ν(X, d, θ) + (d) where ν(X, 1, θ) =θ0 + θ1p + θ2s + θ3r + θ4τ spec. (I) ν(X, 1, θ) =θ0 + θ1(p − s) + θ2r + θ3τ spec. (I) ν(X, 1, θ) =θ0 + θ1(p + s + r + τ) spec. (II) ν(X, 0, θ) =0 for both specs • Household solves the Bellman equation: Vθ(X, ) = max d={0,1} (0) + β EVθ(X,1) X Vθ(X , )p(X |X)p( |X )dX d , ν(X, 1; θ) + (1) 11 / 33

24. Introduction Model Data Results Counterfactuals Conclusions & Extensions Appendix Household decision model • Per period utility is determined by the state variables: X = {p, s, r, τ} and , iid random variables follow a type I extreme value distribution: u(X, d, , θ) = ν(X, d, θ) + (d) where ν(X, 1, θ) =θ0 + θ1p + θ2s + θ3r + θ4τ spec. (I) ν(X, 1, θ) =θ0 + θ1(p − s) + θ2r + θ3τ spec. (I) ν(X, 1, θ) =θ0 + θ1(p + s + r + τ) spec. (II) ν(X, 0, θ) =0 for both specs • Household solves the Bellman equation: Vθ(X, ) = max d={0,1} (0) + β EVθ(X,1) X Vθ(X , )p(X |X)p( |X )dX d , ν(X, 1; θ) + (1) 11 / 33

25. Introduction Model Data Results Counterfactuals Conclusions & Extensions Appendix Household decision model • The likelihood of observing data {Xz t, di t} for household i in zip code z is i(Xi ; θ) = T t=2 P di t|Xz t ; θ p Xz t |Xz t−1 • where P (d|X; θ) is the conditional choice probability: P(d|X; θ) = exp{ν(X, d, θ) + EVθ(X, d)} d∈{0,1} exp{ν(X, d, θ) + EVθ(X, d)} • The log-likelihood function over the whole dataset is then: Lθ = log θ = z iz t log P(di t|Xz t ; θ) + z iz t log p(Xz t |Xz t−1) =0 perfect foresight assumption 12 / 33

26. Introduction Model Data Results Counterfactuals Conclusions & Extensions Appendix Data Summary Data Summary • Time periods: January 2007 - March 2012 • Data cover 9 counties, 344 zip codes • 2 million owner-occupied households, made 28,103 observed installations during this period. Variable Mean Std. Dev. Min Max Obs. System price 44,702 4,212 34,122 51,318 21,861 Upfront subsidy 8,083 4,516 1,398 13,975 21,861 Revenue (5%) 19,578 1,649 16,729 25,585 21,861 O&M costs (5%) 4,809 113.4 4,533 4,986 21,861 Tax credit 8,071 4,835 2000 14,193 21,861 Irradiation 5.55 0.28 5.08 6.57 21,861 # installations 1.29 2.26 0 42 21,861 13 / 33

27. Introduction Model Data Results Counterfactuals Conclusions & Extensions Appendix Data Summary Consumer forward looking behavior *The red dotted lines indicate one period before a reduction in subsidy amount. 14 / 33

28. Introduction Model Data Results Counterfactuals Conclusions & Extensions Appendix Estimation Results 15 / 33

29. Introduction Model Data Results Counterfactuals Conclusions & Extensions Appendix Welfare analysis The Break-Even Carbon Price • Assume a perfectly elastic supply industry, so total loss in surplus equals program spending adjusted by the change in consumer surplus. • The welfare neutral CO2 price is PCO2 = G − CS γ × Q (1) • G: total government spending • γ: amount of CO2 displaced by the solar power system • Q: number of installations • CS: consumer surplus CS(X) = 1 θ log eβEV (X) + eν(X) × M (2) • M: market size • X: Current state • θ: marginal utility of income 16 / 33

30. Introduction Model Data Results Counterfactuals Conclusions & Extensions Appendix Welfare analysis The Break-Even Carbon Price (subsidies at the last sampling period, 3% discount rate) Without subsidies With subsidies # ﬁrst month adopter 424 753 Change in total adoptions. 38% Subsidy amount 11k - 14k Government Spending $2.80 billion Change in CS $2.28 billion Implied CO2 price SCE $111 PG&E $91 SDG&E $87 Overall $95 *All dollar values are in 2012 dollars 17 / 33

31. Introduction Model Data Results Counterfactuals Conclusions & Extensions Appendix Welfare analysis Loss in Surplus from Suboptimal Siting • If California were as sunny as in Frankfurt, Germany, then how expensive would be the solar subsidy programs? Baseline Frankfurt Irrad. Frankfurt Irrad. Frankfurt Irrad. (California) same # install. same elec. prod. Change in purchase prob. 77-86% 80-87% 96-98% 98-99% Total Installations 777,180 171,508 777,274 1,334,322 Change in total adoptions 62% 65% 81% 90% Total electricity prod. 140 TWh 39 TWh 56 TWh 140 TWh Capacity-based subsidy $0.25/W - $0.65/W $0.25/W - $0.65/W $7094.53+ tax credit $18,153 + tax credit Government spending 0.8 billion 0.4 billion 1.3 billion 5.8 billion Change in CS 0.5 billion 0.2 billion 0.6 billion 2.0 billion CO2 price (per tons) $118.1 $236.6 $418.4 $730.1 18 / 33

32. Introduction Model Data Results Counterfactuals Conclusions & Extensions Appendix Welfare analysis Eﬃciency Comparison • Comparing the welfare costs between a capacity-based subsidy and a production-based subsidy Capacity-based Feed-in Tariﬀ Total Installations 979,244 979,244 Total electricity prod. 163 TWh 189 TWh Per unit Subsidy $1.1/W+tax credits +7.93¢/kWh +tax credits Government spending 1.7 billion 1.7 billion Change in CS 0.9 billion 0.9 billion Implied CO2 price SCE $167 $171 PG&E $173 $170 SDG&E $165 $167 CO2 price (per tons) $169.3 $169.1 19 / 33

33. Introduction Model Data Results Counterfactuals Conclusions & Extensions Appendix Welfare analysis Eﬃciency Comparison • Comparing the welfare costs between a capacity-based subsidy and a production-based subsidy (NJ solar irradiation in N. CA) Capacity-based Feed-in Tariﬀ Total Installations 921,909 921,909 Total electricity prod. 121 TWh 122 TWh Per unit Subsidy $1.1/W+tax credits +8.45¢/kWh +tax credits Government spending 1.5 billion 1.5 billion Change in CS 0.9 billion 0.8 billion Implied CO2 price SCE $167 $176 PG&E $201 $191 SDG&E $165 $171 CO2 price (per tons) $180.6 $179.9 20 / 33

34. Introduction Model Data Results Counterfactuals Conclusions & Extensions Appendix Welfare analysis Eﬃciency Comparison • Comparing the welfare costs between a capacity-based subsidy and a production-based subsidy (AK solar irr. in N. CA, AZ solar irr. in S. CA ) Capacity-based Feed-in Tariﬀ Total Installations 859,358 859,358 Total electricity prod. 109 TWh 114 TWh Per unit Subsidy $1.1/W+tax credits +8.94¢/kWh +tax credits Government spending 1.4 billion 1.5 billion Change in CS 0.7 billion 0.8 billion Implied CO2 price SCE $159 $176 PG&E $297 $249 SDG&E $150 $167 CO2 price (per tons) $184.5 $182.2 21 / 33

35. Introduction Model Data Results Counterfactuals Conclusions & Extensions Appendix Welfare analysis Capacity vs. Production Subsidy (Flat-rate Capacity-Based Subsidy) 22 / 33

36. Introduction Model Data Results Counterfactuals Conclusions & Extensions Appendix Welfare analysis Capacity vs. Production Subsidy (FIT with Greater Solar Disparity Between North and South) 23 / 33

37. Introduction Model Data Results Counterfactuals Conclusions & Extensions Appendix Welfare analysis Comparison of the Subsidy Degression and the Equivalent Flat-rate Subsidy Taking system prices as exogenous, overall installations would be 15% higher under the ﬂat-rate design and the welfare cost would be lower. 24 / 33

38. Introduction Model Data Results Counterfactuals Conclusions & Extensions Appendix Impact from changes in Policy Impacts from Varying Incentive Levels • Match the $38/ton ($42.08 in 2012 $) social cost of carbon (Interagency Working Group, 2013) • Doubling the current installations Current Lowering sub. Doubling install. Total Installations 722,829 583,685 1,445,464 % change in adoptions 38% 23% 69% Total electricity prod. 140 TWh 26.5 TWh 193 TWh Upfront subsidy $0.25-$0.65/W + tax credits $1.114/W 22k Government spending 2.8 billion 1.2 billion 20.7 billion Change in CS 2.3 billion 1.1 billion 12.2 billioin Equivalent CO2 price $96 $42.08 $342.8 25 / 33

39. Introduction Model Data Results Counterfactuals Conclusions & Extensions Appendix Conclusions & Extensions • The dynamic consumer PV demand model is versatile and can provide quantitative evaluations of potential policy changes. • Encouraging solar adoptions in sunny locations can reduce the welfare costs of the incentive programs. • Policy makers may prefer to provide production-based subsidies instead of capacity-based subsidies to encourage the adoption of solar power systems for the gains in eﬃciency. • The declining schedule of the subsidy design does encourage more adoption earlier on, however the equivalent ﬂat-rate design would have encouraged more adoptions at a lower welfare cost. 26 / 33

40. Introduction Model Data Results Counterfactuals Conclusions & Extensions Appendix Map of Solar Resource in US, Germany and Spain Back 27 / 33

41. Introduction Model Data Results Counterfactuals Conclusions & Extensions Appendix Cumulative Installed Capacity in the US States Back 28 / 33

42. Introduction Model Data Results Counterfactuals Conclusions & Extensions Appendix Eﬀect of Lifting the $2000 Tax Credit Cap Back 29 / 33

43. Introduction Model Data Results Counterfactuals Conclusions & Extensions Appendix Geographical distribution of installations in California 30 / 33

44. Introduction Model Data Results Counterfactuals Conclusions & Extensions Appendix System size distribution over the years Back 31 / 33

45. Introduction Model Data Results Counterfactuals Conclusions & Extensions Appendix PV module price trend Back 32 / 33

46. Introduction Model Data Results Counterfactuals Conclusions & Extensions Appendix Data Correlations 33 / 33

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