An Empirical Analysis of the Impact of Renewable Portfolio Standards and Feed-in-Tariffs on International Markets. Presentation by Greg Upton Gregory Upton Jr. Louisiana State University Sanya Carley Indiana University Siddhartha Narra Louisiana State University Greg Upton (LSU) FIT and RPS September 24, 2018 1 / 36
Introduction Over the past three decades, countries across the world have implemented policies to promote the growth of renewable energy generation (RE). We focus on two policies: Feed-in-tariffs (FITs) - provides a RE source a long-term guarantee to purchase electricity at a fixed price. (e.g. 30 EURO cents/kwh for 15 years). Renewable portfolio standards (RPSs) - a requirement to produce or procure a percentage of retail sales or generation from RE by a set year (e.g. 20 percent Re by 2020). Greg Upton (LSU) FIT and RPS September 24, 2018 2 / 36
Introduction This paper tests for the impact of FITs and RPSs on four outcomes of interest: Renewable energy generation Emissions Aggregate price levels Electricity demand Greg Upton (LSU) FIT and RPS September 24, 2018 3 / 36
Hypotheses RPS We might expect three potential channels through which a country might comply with an RPS: RPS RE in country (likely most obvious) RPS purchase RECs from other countries no change RE within country RPS fossil fuel generation OR electricity demand Depends on whether the RPS is based on a share of generation or demand. Greg Upton (LSU) FIT and RPS September 24, 2018 4 / 36
Hypotheses RPS RPSs might lead to decreases in emissions through three channels. RPS renewable generation emissions RPS electricity price electricity demand emissions RPS import fossil generation emissions within country Through this channel, the country might decrease emissions within the country, but not necessarily decrease its carbon footprint. Greg Upton (LSU) FIT and RPS September 24, 2018 5 / 36
Hypotheses FIT The impacts of FITs on markets is somewhat more straight forward: FIT RE in country (likely most obvious) substitute away from fossil generation emissions But also potentially offsetting effect! FIT RE in country heat rate loss in fossil generation emissions FIT electricity price electricity demand emissions Greg Upton (LSU) FIT and RPS September 24, 2018 6 / 36
Empirical Specification DD Specification Equation (1) illustrates the commonly used DD estimation strategy that will be used to test for the impact of FIT and RPSs on country electricity markets. Y ct = α + δ(s REP REP c,t ) + γ 1 D c + γ 2 D t + ε ct (1) Where Y ct is the outcome of interest in country c in year t. S REP is an indicator variable corresponding to the countries treated with the respective policy and is zero for the control countries. REP ct is an indicator variable that indicates the time periods after the REP was implemented for a particular country. D c and D t are country and year fixed effects that are included in all regressions. Greg Upton (LSU) FIT and RPS September 24, 2018 7 / 36
Empirical Specification DD Specification But we are concerned about non-random adoption. Country becomes concerned with climate change FIT or RPS But simultaneously, the country invests in EE, allows regulators to approve more expensive RE, etc. Therefore, we will try and mitigate some of this concern through using synthetic control groups. Comparisons of SC results and baseline results might provide insight into the potential importance of selection bias. Greg Upton (LSU) FIT and RPS September 24, 2018 8 / 36
Empirical Specification Synthetic Controls In order to create a synthetic control, choose W that minimizes the following: (X1 X 0 W ) V (X 1 X 0 W ) (2) Where X 1 is a vector of pre-intervention characteristics for the exposed regions (or treatment group). X 0 is a vector of pre-intervention characteristics of the non-exposed regions (or control group). W is a (J 1) vector or positive weights that sum to one. V is some (k k) symmetric and positive semidefinite matrix. We choose V such that the mean squared prediction error of the outcome variable is minimized for the pre intervention periods (see Abadie and Gardeazabel (2003) Appendix for Details). In this context, we create a synthetic country that is most similar along: GDP, population, share urban, ICRG Corruption index and RE potential. Greg Upton (LSU) FIT and RPS September 24, 2018 9 / 36
Renewable Energy Potential Wind - NASA annual average windspeed at 50 m above surface. Solar - NASA annual average global horizontal irradiation (GHI) (kwh/m 2 /day) Population - Global Human Settlement database from European Commission. Exclusive Economic Zones (EEZ) - Flanders Marine Institute. Incompatible Land - includes ecologically protected areas and areas with high terrain slope. All data aggregated into 25 25 km blocks. Greg Upton (LSU) FIT and RPS September 24, 2018 10 / 36
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Renewable Energy Potential Wind Potential Total Wind: W T,i = i,c WindSpeed i (1 %WDPA i ) (3) WindPotential i = i,c Onshore Wind: W T,i SharePop j i, j within 1,000 km (4) d i,j + 1 W On,i = WindSpeed i (1 %WDPA i ) (1 %EEZ i ) (1 %InlandWater i ) i,c (5) Offshore Wind - In contrast to onshore wind, offshore wind only includes wind potential in the exclusive economic zone or over inland water where: W Off,i = i,c WindSpeed i (1 %WDPA i ) (%EEZ i + %InlandWater i ) Greg Upton (LSU) FIT and RPS September 24, 2018 15 / 36 (6)
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Conclusions We find evidence that RE has increased in both RPS and FIT countries relative to SCs. We do not find evidence of emissions reduction associated with either policy. FIT countries have see increases in electricity consumption per capita relative to SCs, while RPS countries see decreases. Neither result is statistically significant. Greg Upton (LSU) FIT and RPS September 24, 2018 34 / 36
The End Thank You! Greg Upton (LSU) FIT and RPS September 24, 2018 35 / 36