THE EFFECTIVENESS OF RENEWABLE PORTFOLIO STANDARDS IN REDUCING CARBON EMISSIONS IN THE US ELECTRICITY SECTOR

R. J. Briggs, Dept of Energy and Mineral Engineering, Penn State University

Suman Gautam*, Dept of Energy and Mineral Engineering, Penn State University, 214-542-6697,

Overview

Do renewable portfolio standards (RPS) – a state level policy that requires utility companies to include a minimum percentage of total electricity sales from eligible renewable or “alternative” technologies – lower CO2emissions? A major goal of RPS is to reduce carbon emissions, but to our knowledge no prior study quantifies this impact. The purpose of this paper is to analyze how RPS policy affects carbon emissions and how this impact varies with RPS characteristics.

Methods

We develop a panel dataset integrating state-level annual data on RPS levels, CO2 emissions, electricity generation, electricity market restructuring, electricity price, fuel prices, and demographic characteristics. The final dataset consists of annual state level data for 48 states comprising from 1997 to 2010. Among 48 states considered in the study, 22 states have mandatory RPS yearly goals in effect by 2010.

We use a reduced form econometric analysis with state fixed variables and time fixed effects to study the impacts of RPS yearly targets on carbon emissions. Then, we address the possible selection problem of state’s decision to adopt and design its RPS policy with the help of three-part regression model. The first two stages of the three-part solution form Heckit model. Chandler (2009) uses innovation and diffusion theory to study the impact of neighboring states’ RPS status in adoption of state’s own RPS policy. In this paper, geographical boundaries of neighboring states are defined in four different ways – states that (1) are in same census regions, (2) lie in the same census divisions, (3) share common borders, and (4) fall in the same North American Electric Corporation (NERC) region.

The first stage of the Heckit model is the probit selection equation and the second stage is the linear regression with the RPS yearly targets as the dependent variable. In the first and second stages of the Heckit model, the development of state’s RPS policy is a function of the RPS status of its neighbor states, its own unemployment rate, and exogenous variables of the main structural model. From the second-stage, we calculate the linear prediction of RPS yearly targets by restricting observations to non-zero RPS yearly target variable. In the final stage of the three-part model which is the base model of the study, we use the linear prediction of RPS yearly goals from the second stage instead of actual RPS yearly goals to perform regression analysis.

Results

The ordinary least squares results show that RPS yearly goals are statistically significant in reducing CO2 per MWh – a ten percentage point increase in RPS yearly mandates improves carbon efficiency by at least 10 percent. However, after accounting for the selection issue with the development of the RPS policy, we find no statistical significant relationship between RPS yearly goals and CO2 per MWh.

Conclusion

While the OLS results show that RPS yearly targets are statistically significant in reducing CO2 per MWh, the regression results of the base model after considering selection problem fail to find the significance of RPS yearly targets in affecting carbon efficiency. The closer analysis suggests that the state’s decision to adopt and design its RPS policy is influenced by factors, such as neighboring states’ RPS status, shares of fossil-fired generation, and electricity price. These results suggest that state-level RPS policies do not show any effect in reducing carbon emissions after taking account of the selection problem. This study’s findings do not claim that RPS is not effective in reducing CO2 per MWh; rather we conclude that that the underlying characteristics of states enacting RPS policies have more to do with the apparent success of RPS in reducing CO2 per MWh.

References

Chandler, J., 2009. “Trendy Solutions: Why do states adopt Sustainable Energy Portfolio Standards?” Energy Policy, 37, 3274 – 3281.

Kydes A. S.”Impacts of a Renewable Portfolio Generation Standard on US Energy Markets.” Energy Policy, 35, 809-814.

Yin, H. and N. Powers. 2010. “Do State Renewable Portfolio Standards Promote In-State Renewable Generation?” Energy Policy, 38, 1140-1149.

*corresponding author