State Policy Impacts on Renewable Energy Development
The State of States 2009 analysis identifies the impacts state policies have on renewable energy development. The five analyses include the:
Impact of Individual Policies
Exploring whether or not the existence of any individual policy is related to higher levels of renewable energy development
Impact of Policy Portfolios
Examining the effectiveness of various policy combinations or policy portfolios
Effectiveness of Individual Policy Best Practice Design Elements
Looking at whether a policy that meets best practice guidelines results in more renewable energy development
Impact of Renewable Portfolio Standard (RPS) Age
Identifying relationships between how long an RPS policy has been in place and the renewable energy development indicators
Removal of Outlying States
Exploring the impact of removing outlying states—states with significantly more or less renewable energy generation than the others.
The table below shows the statistical analysis conducted for each methodology.
|Statistical Analysis||Individual Policies||Policy Portfolios||Policy Best Practice Design Elements||RPS Age Analysis||Removal of Outlying States|
|Time-lag test analysis||✓||✓|
|Graphical validation (box plots)*||✓|
For more information on the methodology, see Chapter 4 of "State of the States 2009: Renewable Energy Development and the Role of Policy".
Because many policies are in early stages of implementation, policy effectiveness is largely evaluated based on design elements seen as successful in previous policy applications. As the market develops, these policies will be evaluated for market impact and other drivers for policy development, such as economic growth.
Statistical analysis shows significant connections between the existence of some state policies and policy portfolios and in-state renewable energy-based electricity generation or capacity, in the case of solar and wind. But, as expected, no causality or direct clear connections can be made between policy and generation increases. As it stands now, the current dataset is too sparse and dependent on too much unrepresented contextual information to allow for a high confidence in the statistical inference. As the dataset grows over the years and incorporates more quantified contextual information, the results will be less provisional.
Important analysis findings include:
The time-lag analysis contributes to the understanding of how long it takes before the effects of a policy can be observed.
The time-lag analysis also reveals that states that had a net-metering policy in effect in 2005 had significantly more renewable energy generation in 2007 than states without the policy. This is in terms of total generation, as a percent of total electricity generation, and per capita.
The portfolio analysis identifies significant relationships between the total number of market transformation policies—including both barrier reduction and technology accessibility policies—and the total renewable energy generation. This is particularly true when considering individual technologies.
Some features of a well-designed RPS policy are found to significantly contribute to renewable energy development when looked at individually. However, none of them can be combined into a model that adequately predicts any of the renewable energy generation indicators.
The age of an RPS policy is found to be correlated with higher levels of installed wind capacity, indicating that wind development may be particularly responsive to RPS policies.
The result of the methodology that omits outlying states emphasizes the role of contextual factors in renewable energy development, whether they're sociological, political, economic, or geographic. This highlights the importance of understanding the contextual factors at play. It also identifies the most effective group of policies for addressing contextual issues and reducing the barriers to renewable energy development. For more information, see Contextual Factors Affecting Renewable Energy Development.
For more information on the results, see Chapter 4 of "State of the States 2009: Renewable Energy Development and the Role of Policy".
Cautions for Interpreting Results
As with all statistical analysis, the analysts encourage careful interpretation of results. Statistically significant t-tests (p<1.0) and correlations (p<0.05) are tools used to understand basic connections between different datasets. In this case, they are used to establish a quantitative connection between policies and renewable energy capacity and generation at the state level.
However, significant relationships do not appoint causality and do not account for other contextual factors. They are used in this analysis to establish connections, and to augment individual state experience in portions of the analysis where the role of policy and other contextual factors in renewable energy development are discussed.