Factors Affecting the Rise of Renewable Energy in the U.S.: Concern over Environmental Quality or Rising Unemployment?

• Author: Ohler, Adrienne M.

1. INTRODUCTION

This paper analyzes the transition between renewable and nonrenewable energy sources by empirically examining the relationship between per capita income and the relative use of RE sources. Schmalensee, Stoker, and Judson (1998) stress that examining this relationship is important to understanding whether energy transitions are due to fundamental economic trends or environmental policy. Using 1990-2008 state level panel data from the U.S. electricity market, I examine two measures of relative RE use: the percent of capacity that utilizes RE sources and the development of RE capacity, defined as the change in the percent of RE capacity. The basic regression results report a U-shaped relationship between income and RE capacity.

Literature on the empirical relationship between renewable energy (RE) and income typically finds a positive relationship. Research on an individual's willingness-to-pay (WTP) for RE suggests that demand for RE increases with income. Bollino (2009) shows that high income individuals are willing to pay more for electricity from RE, and Long (1993) presents results that suggest high-income individuals spend more on RE investments. Oliver, Volschenk, and Smit (2011) study the developing country of South Africa and also find a positive link between household income and WTP for green electricity. On a more aggregate level, Carley (2009) finds evidence that the percentage of RE generation increases with a state's Gross State Product, and Burke (2010) finds that the share of electricity generation from wind, and biomass electricity increases with per capita GDP. Finally, several papers concerning the causality between RE consumption and GDP growth find a positive bidirectional causality relationship (Apergis and Payne, 2010, 2012, 2011; Sadorsky, 2009). These results suggest that income is an important factor impacting RE development, but fail to consider a possible quadratic relationship.

To understand the U-shaped empirical findings, I consider and test several theories presented in the literature. First, environmental quality (EQ) is often stressed in the promotion of RE (Ellis, 1996; Fischer and Preonas, 2010), and several theories have been presented to explain the U-shaped relationship between income and environmental quality (Y-EQ). (1) Explanations include: a) economies and diseconomies of scale of production for pollution, b) a changing economic composition from agrarian to manufacturing to service oriented industries, c) a change in demand for EQ at higher income levels, and d) an evolving property rights structure such that a common resource evolves into a well-defined private property structure (Andreoni and Levinson, 2001; Bhattacharya and Lueck, 2009; Cropper and Griffiths, 1994; Dinda, 2004; Stern, 2004; Panayotou, 1999). This paper contributes to the environmental literature by classify RE generating capacity as a new metric for EQ. Increases in relative RE use imply increases in EQ in terms of improved C[O.sub.2], methane, nitrous oxide, and sulfur dioxide emissions. Much of the environmental literature measures EQ using C[O.sub.2] emissions, water pollutants, deforestation, risk to hazardous waste exposure, or environmental actions. To date, the Y-EQ literature has not explicitly measured EQ through the relative use of RE.

Applying the Y-EQ theories to the U.S. electricity market from 1990-2008, I note several changes that potentially impact the use and development of RE. The four applications of the theories for the electricity markets include a) electricity market deregulation, which fundamentally changed the market and property rights structure, b) the implementation of the Clean Air Act Amendments of 1990, which developed air pollution markets for S[O.sub.2] and changed the property rights for pollution c) the transition of the U.S. economy from a manufacturing based economy toward a service oriented economy, and d) the increase in renewable portfolio standards (RPS), which increase the demand for RE electricity generation. The empirical results for these control variables fail to provide support to the Y-EQ theories. Additionally, states with a large manufacturing share of GDP typically have more RE capacity than states with a small share.

Alternatively, I consider possible economic factors that impact RE capacity rather than focusing on EQ. For example, Tahvonen and Salo (2001) propose a model where economies transition from renewables, to non-renewables, and back to renewables as the economy develops, fossil fuels become scarce, and consumption and production costs change. Because advocates for RE typically promote the development of wind and solar projects as a means of job creation, I consider the hypothesis that unemployment rates impact RE capacity (Wei, Patadia, and Kammen, 2010). Sari, Ewing, and Soytas (2008) support this hypothesis, finding a long run relationship between energy measures, industrial output and employment. I extend the model to examine the impact of lagged unemployment on RE. The results support Sari et al. (2008) and provide evidence that a high unemployment rate promotes RE development, but the impact decreases as a state's per capita income increases, explaining the U-shaped relationship. These results suggest that economic factors, such as unemployment and manufacturing GDP, are better predictors of RE development than environmental policies, supporting the existence of an electricity ladder (Burke, 2010; Tahvonen and Salo, 2001). These results suggest that improvements in EQ can occur without an increase in income, when EQ projects are presented as a means of job creation.

The paper proceeds by examining the basic empirical relationship between income and RE in section 2. Section 3 examines three possible Y-EQ theories that explain the relationship between income and environmental quality. I apply the theories to the electricity industry to include controls in the empirical model. Results from the fixed-effects model are presented. Section 4 examines the alternative hypothesis that the unemployment rate impacts the use of RE. Section 5 concludes.

2. RENEWABLE ENERGY AND INCOME

Energy Information Administration (EIA) provides information for the 50 states on the capacity of electricity generation by source. (2) To measure RE relative to non-RE use, I calculate the percent of RE capacity excluding hydroelectric power, and the development of RE capacity, defined as the change in the percent of RE capacity. Table 1 provides summary statistics for both measures. The metric for relative RE capacity captures the level of investment in environmentally friendly, cleaner technologies, whereas the change in relative RE capacity captures the growth in investment towards environmentally friendly capital. Note that investment in more wind capacity does not necessarily imply more electricity generation from wind; I highlight the notion that relative RE capacity is a measure for the appearance of environmental-friendliness, and not necessarily the production of cleaner goods. (3)

For comparison, Figure 1 plots per capita income with the percent of RE capacity. Six states with high renewable potential and/or development are represented: Arizona, California, Iowa, Illinois, New York, and Texas. The figures illustrate that between 1990-2008, these six states experienced a growth in capacity dedicated to RE. The rise in real income for these same six states exhibits an overall upward trend in income with dips around 2000 and 2007. These illustrations suggest that higher income levels are correlated with a higher percent of RE capacity; however, the correlation is not perfect, as building large-scale capacity may result in a step-change increase in the percent of renewable capacity. Most notably in California around 2000 the Calpine Corporation acquired The Geysers for geothermal electricity production, increasing renewable capacity from under 2.1% to over 2.6%. Thus, the variation in relative RE capacity may be due to other factors such as the increased demand for environmental quality, or changes in economic composition. I consider these factors in section 3.

First, to examine the relationship between income and RE, consider the following fixed effect model common in the Y-EQ literature

R[E.sub.i,t] = [[beta].sub.0,i] + [[beta].sub.1]Log([Income.sub.i,t]) + [[beta].sub.2]Log([Income.sub.i,t]).sup.2] + [[beta].sub.3]t + [[epsilon].sub.i,t] (1)

where i represents a state in time period t. I examine a fixed effects model with a time trend to account for technological change, following Aslanidis and Iranzo (2009); Cole, Rayner, and Bates (1997); Stern and Common (2001). (4) R[E.sub.i,t] is measured using relative RE capacity or RE development.

Table 2 summarizes the basic fixed-effects regression results with standard errors that control for heteroskedasticity and autocorrelation and a time trend. Columns 1 and 2 provide results for the percent of RE capacity, and columns 3 and 4 provide results for RE development. The estimated coefficients provide evidence of a quadratic relationship between income and relative RE capacity. LogIncome and Log[Income.sup.2] are jointly statistically significant for both RE capacity and RE capacity development. The estimated income turning point (ITP) for models 2 and 4 are $19,205 and $17,510. States that continually fall below $19,205 include Alabama, Arkansas, Idaho, Ken tucky, Louisiana, Mississippi, Montana, New Mexico, Oklahoma, South Carolina, Utah, and West Virginia. Most of these states are not known for renewable energy production. Idaho utilizes mostly hydropower which I exclude. Montana and Oklahoma have only recently begun developing wind when their per capita income levels were above $19,205. States above this estimated ITP averaged 2.65% RE capacity and a 0.185% increase. States below the ITP averaged 1.74% RE capacity and a 0.048% increase.

Thus, the results suggest a U-shaped...