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An examination of electricity generation by utility organizations in the Southeast United States
Energy 116 (2016) 601e608
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An examination of electricity generation by utility organizations in the Southeast United States Christopher A. Craig a, *, Song Feng b a b
Montana State University Billings, 202 McDonald Hall, Billings, MT 59101, USA Department of Geosciences, University of Arkansas, 228 Gearhart, USA
a r t i c l e i n f o
a b s t r a c t
Article history: Received 9 January 2016 Received in revised form 28 September 2016 Accepted 3 October 2016
This study examined the impact of climatic variability on electricity generation in the Southeast United States. The relationship cooling degree days (CDD) and heating degree days (HDD) shared with electricity generation by fuel source was explored. Using seasonal autoregressive integrated weighted average (ARIMA) and seasonal simple exponentially smoothed models, retrospective time series analysis was run. The hypothesized relationship between climatic variability and total electricity generation was supported, where an ARIMA model including CDDs as a predictor explained 57.6% of the variability. The hypothesis that climatic variability would be more predictive of fossil fuel electricity generation than electricity produced by clean energy sources was partially supported. The ARIMA model for natural gas indicated that CDDS were the only predictor for the fossil fuel source, and that 79.4% of the variability was explained. Climatic variability was not predictive of electricity generation from coal or petroleum, where simple seasonal exponentially smoothed models emerged. However, HDDs were a positive predictor of hydroelectric electricity production, where 48.9% of the variability in the clean energy source was explained by an ARIMA model. Implications related to base load electricity from fossil fuels, and future electricity generation projections relative to extremes and climate change are discussed. © 2016 Published by Elsevier Ltd.
Keywords: Electricity Pro-conservationism Clean energy Fossil fuels Climate change ARIMA
1. Electricity consumption and generation The trends in carbon emissions, electricity consumption, and fuel source mix for electricity generation remain a major concern globally. This is particularly true in the United States (US), where on a per capita basis the US is approximately four times as consumptive as China, the largest electricity consumer in the world[37]. It is widely noted that increased climatic variability and global mean temperature change is anthropogenic forced [20]. During the recent United Nations Framework Convention on Climate Change, the US along with the overwhelming majority of countries around the world adopted the Paris Agreement, with the shared goal of limiting global mean temperature increase to 1.5 C above preindustrial levels [36]. This agreement signals a global commitment to reduce reliance on fossil fuels. The focus on energy generation and fossil fuels is magnified considering that 90 entities, primarily energy production
* Corresponding author. E-mail addresses: [email protected] (C.A. Craig), songfeng@ uark.edu (S. Feng). http://dx.doi.org/10.1016/j.energy.2016.10.013 0360-5442/© 2016 Published by Elsevier Ltd.
organizations, have emitted over 60% of the global carbon since the 1850's [16]. As shown in Fig. 1, between 2000 and 2013 the focal Southeastern US state for this study saw a steady increase of electricity generation from fossil fuels, highlighting the urgent need to explore the role of efficiency, policy, climatic variability, and stakeholder engagement to mitigate the negative consequences related to this electricity consumption and generation. The goal of the current study is to gain a clearer understanding of the impact that short-and long-term climatic variability have on electricity generation in the Southeast US. Accordingly, the next section will review the relevant literature and provide hypotheses. A methods section will then be presented, followed by results and discussion sections.
1.1. Literature review Utility organizations in the Southeast remain reliant on fossil fuels as the primary source for electricity generation [38]. Furthermore, states in this region rely more heavily on electric cooling and heating equipment, contributing to greater electricity consumption per capita [37,41]. Throughout the US, electricity
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3.0
Coal
Natural Gas
Nuclear
Petroleum
Hydroelectric
2.0
5
MWH (10 )
2.5
1.5 1.0 .5 0.0 2002
2004
2006
2008
2010
2012
Year Fig. 1. Monthly MWH electricity generation by fuel source between 2000 and 2013.
generation and consumption are closely linked [7]. Trends such as increased economic activity, population growth, concerns over a reliable source of electricity, and new uses for electricity are several forces driving electricity consumption and continued use of fossil fuels for generation [22,29,30,32,33]. In the focal Southeastern region, a recent study demonstrated that increased temperatures shared the most salient relationship with increased electricity consumption [8]. Increased temperatures are projected to increase demand for electricity in the future [23]. The impacts of temperature and drought are prohibitive to utility organizations as well. For instance [2], estimate that electricity production in the Western US could diminish by over 8% as a result of drought. Between 2031 and 2060, van Vliet et al. [45] project diminished capacity in US power plants between 6.3% and 19% during summer months related to the prevalence of thermoelectric generation in the US. Globally, van Vliet et al. [44] project that power from thermoelectric plants could reduce by over 80% in extreme future conditions. Drought, temperature, and costly extreme weather events are all projected to increase in the Southeast US (Ingram et al., 2013; Preston), making the regions' electricity generation capacity particularly vulnerable. Outside intervening factors, such as more stringent policies related to efficiency programs or emissions reduction, for-profit organizations such as investor-owned utility organizations will pursue profit-seeking behaviors [34,47]. There has been public scrutiny over efficiency efforts and use of clean energy by utility organizations [9]. However, information asymmetry, or incomplete/withheld information, by investor-owned organizations make it difficult for the public to understand the implications of emissions from fossil fuels and for policy makers to enact quantify the true impact of electricity generation [14]. The lack of focus changing climatic conditions and the increase in extreme weather events further complicate the ability of utility organizations to adequately plan for future electricity demand [2,7]. Policies related to fuel source for electricity generation are particularly relevant in the Southeast US. Biesecker [3] noted that fossil fuel reliant states teamed together to challenge the United States Environmental Protection (EPA) Agency's Clean Power Act, including Arkansas, Alabama, Georgia, Kentucky, Louisiana, Oklahoma, and Texas. In Michigan et al. v. EPA et al. [24]; the act was overturned by majority Supreme Court decision because the costs of implementation were not fully considered for power plants to make changes to achieve the aggressive carbon reduction goals. The
EPA evaluation of health benefits have not been fully realized in the past [13], making it more difficult to effectively enact policy. Graves [14] reiterated the shortcomings with realizing the benefits from environmental policies, noting that oftentimes the “big picture” implications, such as the global implications addressed in the Paris Agreement [36] or anthropogenic forced climate change [20], are overlooked in quantifying benefits resulting in the rejection of policies in the US. Investor-owned utility organizations have turned to energy efficiency programs as a potential market-based solution to reduce consumption and related emissions. In fact, over $7 billion were spent on efficiency programs in 2013, the vast majority of which targeted reduction in electricity consumption [18]. However, a meta-analysis from 1975 until 2012 found that outside rich feedback mechanisms, incentives spent on efficiency improvements resulted in an increase in electricity consumption (Delmas et al., 2013). This has been referred to as the rebound effect, where efficiency improvements are counteracted by outside factors such as adding electrical devices or increased usage behavior [19]. The rebound effect does not always result in counterintuitive results, however, but represents how outside factors prohibit expected efficiencies from being accomplished. The direct rebound effect is often used in electricity studies because the marginal change in demand for usage as operating costs change can be easily quantified (Gillingham et al., 2015). Specific to electricity, a recent study found a residential direct rebound effect between 24% and 37% for households in response to electricity price change [46]. Specific to efficiency upgrades, Davis et al. [10] found that subsidies for efficient air conditioners increased electricity consumption among consumers, while the savings from efficient refrigerators were nominal. Environmental messages have been used to combat the unintended consequences of diminished electricity savings associated with the rebound effect [1,9]. For instance, Asensio and Delmas [1] found that the topic and frequency of which a message is delivered to residential electricity users influenced persistent electricity savings over time in a longitudinal study. Research has shown that levels of awareness about a pro-conservation behavior impact the manner in which gain- and loss-framed messages influence enactment of behaviors [9,27]. Furthermore, Fielder [12] discussed using messages highlighting desirability and/or feasibility to influence future perceptions and/or behaviors. In terms of policy and efficiency spending, the majority of Southeastern states rank in the
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bottom tier compared to the rest of the country on the American Council for an Energy Efficient Economy, and have not enacted comprehensive behavioral programs that incorporate rich feedback mechanisms and effective environmental messages [9,18]. Despite the billions spent on efficiency programs to reduce electricity consumption, in the US and in the focal Southeastern region, electricity consumption among residential and commercial users continues to increase [39], suggesting that additional steps and/or outside intervention are needed to curb consumption and related emissions in response to shifting climatic trends and variability. There have been some efforts to deploy carbon reduction and capture technologies in electricity production plants with various degrees of effectiveness (e.g., [5,15,31]). A recent life-cycle assessment comparing technologies that reduce GHG emissions in existing plants to new clean energy production found that electricity infrastructure that heavily incorporated wind and solar were more effective at reducing negative environmental impacts while meeting demand needs [17]. Across the US the use of carbon-rich coal for electricity has decreased, yet, it should be noted that the majority of new electricity production has shifted to natural gas, also a greenhouse gas emitted fuel source [38]. Despite efforts elsewhere in the US, the focal state in the Southeastern US (see Fig. 1) saw an increase in electricity generation from both coal and natural gas between 2000 and 2013. Complicating deployment of clean energy fuel sources, the lack of knowledge and high-profile failures have made it more difficult to garner support for clean energy in the US [9]. 1.2. Hypotheses In the focal Southeastern US state, the goal of the study is to quantify the relationship between climatic variability in the form of cooling degree days (CDD) and heating degree days (HDD) with electricity generation by fuel source type. CDD and HDD are used here because they are more reliable indicators than temperature alone when considering electricity [25]. To overcome the information asymmetry related to electricity generation, and considering that climatic trends are increasing the demand for electricity (Ingram et al., 2013; [23]), it is vital that a clearer understanding of the relationships between fuel source, long-term climatic trends, and short-term climatic variability are understood. With increased climatic variability (e.g., Ingram et al., 2013), the continued reliance on fossil fuels [37], and the lack of utility-scale clean energy alternatives in the region [40], the following hypotheses are offered: Hypothesis 1. Short- and long-term climatic variability will be predictive of electricity generation for all fuel source types. Hypothesis 2. Short- and long-term climatic variability will be more predictive for fossil fuel sources of electricity generation than clean fuel sources of electricity generation.
2. Materials and methods 2.1. Procedure Monthly electricity generation in megawatt hours (MWH) by fuel source types (i.e., coal, natural gas, nuclear, hydroelectric, and petroleum) was retrieved form the EIA from 2000 to 2013 as well [38]. The daily maximum and minimum temperature on 2.5-min (around 4 km) resolution were both obtained from Di Luzio et al. [11]. The dataset was constructed using the ParametereElevation Regressions on Independent Slopes Model and daily observation from more than 7500 weather stations. The monthly HDD and CDD on individual grid cell during 2000e2013 were calculated using the
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daily maximum and minimum temperature. Degree days are the amount of days above or below a certain threshold temperature [42], 18.33 C or 65 F in the current study. Then the HDD and CDD for the entire state were averaged using areal weight algorithm. 2.2. Statistical analysis IBM SPSS Statistics version 23 was used for statistical analysis. Descriptive analysis was run for all variables (see Table 1). Seasonality is common in climate studies [43], making it necessary to conduct autocorrelation analysis to determine the appropriate model to use. Analysis indicated values for each variable indicated autocorrelation (see Fig. 1), violating the Gauss-Markov assumption for ordinary least square linear regression models. Accordingly, the time series modeler function and expert modeler method were utilized using SPSS to conduct retrospective time series analysis between individual fuel sources and CDD and HDD. The expert modeler method considers both simple exponentially smoothing models and seasonal autoregressive integrated weighted average (ARIMA) models. ARIMA models that leverage the Box-Jenkins approach [4] are appropriate for retrospective studies, and allow the “user to understand not only the relationship between the current state as a function of past states … but also the influence of inputs outside the state of the series” ([21]; p. 2). ARIMA models have been utilized for historical and future models in climate studies [35,43]. ARIMA models use differencing to determine lag periods, autoregressive, and moving average components to achieve stationarity [6]. Goodness-of-fit statistics for each model were run for each fuel source (i.e., coal, hydroelectric, natural gas, nuclear, petroleum) including stationary R2, R2, root mean square error (RMSE), maximum absolute percentage error (MAPE), and normalized Bayesian information criterion (BIC). The default goodness-of-fit used in ARIMA models is the stationary R2 value reported in the results section. Ljung-box statistics were calculated as a diagnostic check to determine the independency of residuals (See Table 2; [6]). Coefficients for each model are provided including constants, seasonality, moving averages, and autoregression (see Table 3). Residual autocorrelation function (ACF) and residual partial autocorrelation function (PACF) were then used to ensure resulting models corrected the autocorrelation of data initially detected (see Fig. 2). 3. Results The Ljung-box statistics for all models except petroleum were greater than the Q value of 18, indicating models had significant levels of autocorrelation. The higher normalized BIC for the simple exponentially smoothing models compared to the ARIMA models indicate a better fit for coal and petroleum (see Table 2). Seasonal ARIMA models emerged as the best predictors for the fuel sources of hydroelectric, natural gas, and nuclear (See Table 3). Time series models addressed the autocorrelation detected in the Ljung-box analysis. Hypothesis 1 posited that climatic variability in terms CDD and HDD would be predictive of total electricity generation. This hypothesis was supported for CDD, as it was the sole predictor in total electricity generation. As shown in Table 3, CDD had a significant impact on total electricity generation, and 57.6% of the variability in total electricity generation was explained in total time series model (Stationary R2 ¼ 0.576). The autoregressive component of the model was to the first order, and the seasonal moving average component of the model was to the first order. Current (p < 0.05), first order (p < 0.001), and second order (p < 0.001) CDD values were significant in the model for total electricity generation.
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Table 1 Descriptive statistics. Variable
N
Min
Max
Mean
Std.
Coal MWH Hydroelectric MWH Natural Gas MWH Nuclear MWH Petroleum MWH CDD HDD
156 156 156 156 156 156 156
1,062,973.00 47,186.20 1657.00 25,775.00 0 0 0
2,825,843.40 62,292.00 617,253.00 1,395,055.00 268,677.00 344.13 525.81
2,072,916.33 257,991.60 128,776.65 1,217,184.37 15,050.69 87.85 157.25
356,648.79 125,493.39 137,377.98 215,433.60 28,128.80 104.62 166.45
Table 2 Time-series exponentially smoothing and ARIMA models. Variable
Coal (S) Coal Hydro NatGas Nuclear Petro (S) Petro Total
Model fit statistics
Ljung-box Q (18)
Stationary R2
R2
RMSE
MAPE
Normalized BIC
Statistics
DF
Sig.
0.620 0.432 0.489 0.794 0.178 0.792 0.128 0.576
0.583 0.489 0.489 0.802 0.178 0.311 0.128 0.682
231,106.61 256,586.68 90,580.31 63,672.51 205,961.02 23,423.63 26,523.19 302,082.694
9.64 10.66 35.49 135.92 43.069 681.80 545.52 6.32
24.766 24.979 22.926 22.33 24.58 20.19 20.50 25.42
23.944 14.584 18.667 26.87 33.80 16.00 18.69 20.50
16 16 17 14 17 16 14 16
0.091 0.555 0.348 0.020 0.009 0.453 0.177 0.198
*Note. S ¼ simple seasonal exponentially smoothed model; RMSE ¼ root mean square error; MAPE ¼ maximum absolute percentage error; BIC ¼ Bayesian Information Criterion. Where an “S” is not denoted, best-fit models autoregressive integrated weighted average (ARIMA) models.
Table 3 Individual model parameters. Model
Coefficient
Estimates
S.E.
t-value
Sig.
Coal (S)
Alpha (level) Delta (seasonal) Constant AR Lag 1 HDD Delay HDD Numerator Lag 0 Constant MA Lag 1 MA Lag 2 MA Lag 3 AR (S) Lag 1 CDD Delay CDD Numerator Lag 0 Constant MA Lag 1 HDD Delay HDD Numerator Lag 0 Seasonal Difference Alpha (level) Delta (season) AR Lag 1 Seasonal Difference MA Seasonal Lag 1 CDD Delay CDD Numerator Lag 0 CDD Denominator Lag 1 CDD Denominator Lag 2 Seasonal Difference
0.200 4.68E6 228,717.53 0.696 1 179.50 222.06 0.836 0.605 0.305 0.440 12 1.19 1,214,090.24 0.347 13 756.08 1 0.100 5.01E5 0.547 1 0.945 5 1100.33 1.081 0.941 1
0.047 0.057 26,449.91 0.060
4.23 8.26E5 8.65 11.64
0.000 1.00 0.000 0.000
73.51 35.39 0.083 0.096 0.084 0.090
2.44 6.27 10.05 6.29 0.364 4.88
0.016 0.000 0.000 0.000 0.000 0.000
0.164 24,190.26 0.085
7.25 50.19 4.07
0.000 0.000 0.000
331.56
2.28
0.024
0.047 0.034 0.074
4.23 0.001 7.41
0.000 0.999 0.000
0.269
3.52
0.001
424.78 0.031 0.042
2.59 34.90 0.22.14
0.011 0.000 0.000
Hydro
NatGas (Sq. Rt.)
Nuclear
Petro (S) Total
*Note. S ¼ seasonal model. MA ¼ moving average; AR ¼ autoregressive.
Hypothesis 2 posited that increased climatic variability in terms of CDD and HDD and would be more predictive for fossil fuel sources of electricity than clean energy sources of electricity. This hypothesis was partially supported. For CDD, a significant positive relationship emerged with natural gas, but not with coal (Stationary R2 ¼ 0.620) or petroleum (Stationary R2 ¼ 0.790) which were explained by simple seasonal models (see Table 3 for individual model parameters). The natural gas model that included CDD as the
single predictor explained 79.4% of the variability in electricity generation from natural gas (Stationary R2 ¼ 0.794, p < 0.001). The natural gas model was the only model that had a square root transformation of the data. The moving average component was to the third order, and the seasonal autoregressive component was to the first order. The relationship with CDD had no lag. Contrary to Hypothesis 2, a positive significant relationship emerged between HDD and hydroelectric electricity generation
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Fig. 2. Autocorrelation values for time series models.
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explaining 48.9% of the variability (Stationary R2 ¼ 0.489, p < 0.05). The autoregressive component was to the first order, and the relationship with HDD had no lag. However, in support of the hypothesis, a negative significant relationship emerged between HDD and nuclear electricity generation explaining 17.8% of the variability (Stationary R2 ¼ 0.178, p < 0.05). The moving average component of the model was to the first factor, and a seasonal difference emerged, and the relationship with HDD had no lag. 4. Discussion and conclusions The findings of this study demonstrate that seasonality and climatic variability play a salient role specific to the fuel source type used for electricity generation. As increased attention globally and domestically turns toward identifying and mitigating climate change related to emissions [20,36], it is crucial that stakeholders understand the interaction between climate and electricity generation, and hold generation companies accountable. This study is important as it is the first to the authors' knowledge that examine the interaction between monthly electricity generation and climatic variables. With climatic variability projected to increase and demand for electricity to follow (Ingram et al., 2013; [23]), the findings of this study have serious implications for the Southeast US. The findings support previous research that demonstrated a significant relationship between electricity consumption and CDD in the Southeast [8]. The study suggest that as more climate variability and extremes occur in terms of CDD, natural gas in the focal state is the most impacted. Put simply, with increased extremes, the majority of electricity demand is being met by a greenhouse gas emitted fuel source in the focal state. Results from Hypothesis 1 suggest that variability in CDD and seasonality are influencing overall electricity generation. The overall model explains 57.6% of the variability in total electricity generation in the focal state. Subsequent models for individual fuel sources suggest that this relationship is being driven largely by the strong relationship between increasing CDD and natural gas generation. For the overall model, CDD shares a positive relationship with electricity generation in the current month and with a one month lag. However, the second month lag shares a negative relationship with overall electricity generation. This is a signal of the seasonality experienced with total electricity generation in the focal state, which was also demonstrated with a seasonal component in all individual models. These findings support future models that predict an increase in temperatures, extremes, and electricity demand [23]. Hypothesis 2 posited that climatic variability would influence fossil fuel sources more than clean energy sources. This hypothesis was supported for the relationship between natural gas generation and CDDs (Stationary R2 ¼ 0.794), but not for coal or petroleum. Seasonality was related to generation for both coal (Stationary R2 ¼ 0.620) and petroleum (Stationary R2 ¼ 0.792) however. The magnitude of the relationship between natural gas and the climatic factors is a major contribution of the current study. These results also suggest that in the focal state, coal is being used to meet baseload electricity demand conditions. It opens the possibility that non-climatic factors such as population, economy, and/or usage trends may be more influential to electricity generation from the fuel source coal. Regardless, the increase of coal for electricity generation throughout the study period raises serious concerns regarding GHG emissions in the Southeast US. Also supportive of Hypothesis 2 was the finding that there was a negative relationship between HDD and electricity generation from nuclear (Stationary R2 ¼ 0.178). As shown in Fig. 1, electricity production from nuclear decreased throughout the study period. Considering the diminishing production capacity of nuclear plants,
where there was an increase in HDDs, this demand was not be met with nuclear energy. However, a positive relationship did emerge between HDD and the clean energy source of hydroelectric generation. HDD explained almost 50% of the variability in hydroelectric electricity generation during the study period. Alarming, however, is that as CDDs increase throughout the study period, the demand was not associated with electricity generation from hydroelectric. Bartons and Chester [2] noted that climate change is not widely considered by utility organizations who are forecasting future electricity demand. In the focal region, Craig [8] found that there was a significant relationship between CDDs and increased electricity consumption related to cooling equipment. The results of the current study demonstrate that in the Southeast US, utility organizations are largely meeting the growing demand associated with CDDs with GHG emitting fossil fuels. Despite international efforts to reduce GHG emissions, many states continue to fight policies aimed at curbing reliance on GHG fossil fuels. Efficiency programs are used throughout the majority of the US by investor-owned utility organizations [18]. However, Craig & Feng [7] demonstrated that many utility organizations throughout the US are not capturing the true impact of climatic variability on electricity consumption within efficiency programs. The effectiveness of efficiency programs around the US have shown mixed and oftentimes negative results, where more electricity is consumed than expected after efficiency efforts are undertaken ([7]; Delmas et al., 2013; Gillingham et al., 2015). When extreme weather conditions and persistent change are occurring, the potential to further conflate the rebound effect related to electricity consumption increases. Drought is projected in the focal study region in the future (Ingram et al., 2013). While hydroelectric generation was related to increased HDDs in this study, however, the projected increase in temperature and decrease in precipitation in the region make this fuel source questionable to meet growing electricity demand. Furthermore, hydrologic models indicate that drought and adverse future conditions have the potential to greatly diminish the amount of electricity produced from thermoelectric power production as related to generation from coal, natural gas, and nuclear energy [2,44,45]. Policy makers and the public alike should take note that extreme weather trends are increasing, and these trends are significantly related to increases in fossil fuel use to generate electricity and the associated emissions. Clean energy sources including wind and solar have shown great promise at cost effectively producing electricity and reducing environmental impacts over and above technologies aimed at fossil fuel infrastructure [17]. It is not enough for utilities and policy makers to only focus on increased electricity demands, but also the electricity baseload conditions that have traditionally been met with fossil fuel sources. 4.1. Conclusions The results of the current study demonstrate that climatic variability and trends during the study period can play a major impact on electricity generation, particularly in areas that are reliant on electricity to heat and cool homes and are more reliant on fossil fuels to generate electricity [37e41]. While recent global agreements have been made to curb climate [36], increases in the focal region in electricity production and related emissions for carbon rich fuel sources continue [38], and fossil reliant states continue to resist the concept of a cleaner energy supply [3]. The results show that extreme events are positively impacting fossil fuel generation of electricity. With climatic variability and trends projected to increase in the Southeast US (Ingram et al., 2013), the potential for negative impact in terms of socioeconomic factors increases [28]. For policy makers and residents alike, it is important to understand the interactions between the climatic change and
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variability presented here as related to electricity generation to reduce information asymmetry from utility organizations, and to build support for mitigation policies related to existing energy infrastructure. 4.2. Limitations and future research The current study is not without limitation. First, reporting for electricity generation, CDD, and HDD was monthly, and only for a 14 year period. Also, the electricity generation data and climatic data were aggregated to the state-level because of the reporting practices of utility organizations related to electricity generation. There are higher resolution climatic data available, however, without the ability to match this data, state-level aggregation was needed. As demonstrated in this study, climatic data and electricity generation data are often seasonal and auto-correlated. Using exponential smoothing models and seasonal ARIMA models corrected this issue. Future research should expand the current research to the remainder of the US, as states outside the Southeast are not as reliant on fossil fuels and are not as consumptive with regards to electricity [39,41]. Furthermore, states in other regions have more progressive policies regarding utilities and electricity generation which may result in different results [18]. Future research could expand to evaluate other climatic variables for extreme events and the impact on electricity generation, and climatic variables could also be analyzed relative to electricity consumption and energy efficiency savings to get a clearer picture of the causality related to climate. Furthermore, the current study provides a foundation using time series analysis that will make it possible to expand the research to model future electricity demands based on relationships with climatic factors. References [1] Asensio OI, Delmas MA. Nonprice incentives and energy conservation. PNAS 2015;112(6). published online January 12, 2015. [2] Bartons MD, Chester MV. Impacts of climate change on electric power supply in the Western United States. Nat Clim Change 2015;5:748e52. [3] Biesecker M. States and industry groups sue government over new clean air rules. PBS Newshour; 2015. Retrieved 12/15/2015 from: http://www.pbs.org/ newshour/rundown/states-industry-groups-reliant-fossil-fuels-suegovernment-new-clean-air-rules/. [4] Box GEP, Jenkins GM. Time series analysis: forecasting and control. San Francisco, CA: Holden Day Inc.; 1976. [5] Brouwer AS, van den Broek M, Seebregts A, Faaij A. Operational flexibility and economics of power plants in future low-carbon power systems. Appl Energy 2015;156:107e28. [6] Clement EP. Using normalized Bayesian information criterion (Bic) to improve Box- Jenkins model building. Am J Math Stats 2014;4(5):214e21. [7] Craig CA, Feng S. Exploring utility organization electricity generation, residential electricity consumption, and energy efficiency: a climatic approach. Appl Energy 2016 [in review)]. [8] Craig CA. Energy consumption, energy efficiency, and consumer perceptions: a case study for the Southeast United States. Appl Energy 2016;165:660e9. [9] Craig CA, Allen MW. Enhanced understanding of energy ratepayers: factors influencing perceptions of government energy efficiency subsidies and utility alternative energy use. Energy Policy 2014;66:224e33. [10] Davis LW, Fuchs A, Gertler P. Cash for coolers: evaluating a large-scale appliance replacement program in Mexico. Am Econ J Econ Policy 2014;6(4):207e38. [11] Di Luzio M, Johnson GL, Daly C, Eischeid JK, Arnold JG. Constructing retrospective gridded daily precipitation and temperature datasets for the conterminous United States. J Appl Meteorol Climatol 2008;47:475e97. [12] Fielder K. Construal level theory as an integrative framework for behavioral decision-making research and consumer psychology. J Consumer Psychol 2007;17(2):101e6. [13] Fraas AG. The treatment of uncertainty in EPA's analysis of air pollution rules: a status report. J Benefit-Cost Anal 2011;2(2):1e27. [14] Graves PE. Benefit-cost analysis of environmental projects: a plethora of biases understating net benefits. J Benefit-Cost Anal 2012;3(3):1e25. [15] Hanak DP, Biliyok C, Manovic V. Efficiency improvement for the coal-fired power plant retrofit with CO2 capture plant using chilled ammonia process. Appl Energy 2015;151:259e72.
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Contents lists available at ScienceDirect
Energy journal homepage: www.elsevier.com/locate/energy
An examination of electricity generation by utility organizations in the Southeast United States Christopher A. Craig a, *, Song Feng b a b
Montana State University Billings, 202 McDonald Hall, Billings, MT 59101, USA Department of Geosciences, University of Arkansas, 228 Gearhart, USA
a r t i c l e i n f o
a b s t r a c t
Article history: Received 9 January 2016 Received in revised form 28 September 2016 Accepted 3 October 2016
This study examined the impact of climatic variability on electricity generation in the Southeast United States. The relationship cooling degree days (CDD) and heating degree days (HDD) shared with electricity generation by fuel source was explored. Using seasonal autoregressive integrated weighted average (ARIMA) and seasonal simple exponentially smoothed models, retrospective time series analysis was run. The hypothesized relationship between climatic variability and total electricity generation was supported, where an ARIMA model including CDDs as a predictor explained 57.6% of the variability. The hypothesis that climatic variability would be more predictive of fossil fuel electricity generation than electricity produced by clean energy sources was partially supported. The ARIMA model for natural gas indicated that CDDS were the only predictor for the fossil fuel source, and that 79.4% of the variability was explained. Climatic variability was not predictive of electricity generation from coal or petroleum, where simple seasonal exponentially smoothed models emerged. However, HDDs were a positive predictor of hydroelectric electricity production, where 48.9% of the variability in the clean energy source was explained by an ARIMA model. Implications related to base load electricity from fossil fuels, and future electricity generation projections relative to extremes and climate change are discussed. © 2016 Published by Elsevier Ltd.
Keywords: Electricity Pro-conservationism Clean energy Fossil fuels Climate change ARIMA
1. Electricity consumption and generation The trends in carbon emissions, electricity consumption, and fuel source mix for electricity generation remain a major concern globally. This is particularly true in the United States (US), where on a per capita basis the US is approximately four times as consumptive as China, the largest electricity consumer in the world[37]. It is widely noted that increased climatic variability and global mean temperature change is anthropogenic forced [20]. During the recent United Nations Framework Convention on Climate Change, the US along with the overwhelming majority of countries around the world adopted the Paris Agreement, with the shared goal of limiting global mean temperature increase to 1.5 C above preindustrial levels [36]. This agreement signals a global commitment to reduce reliance on fossil fuels. The focus on energy generation and fossil fuels is magnified considering that 90 entities, primarily energy production
* Corresponding author. E-mail addresses: [email protected] (C.A. Craig), songfeng@ uark.edu (S. Feng). http://dx.doi.org/10.1016/j.energy.2016.10.013 0360-5442/© 2016 Published by Elsevier Ltd.
organizations, have emitted over 60% of the global carbon since the 1850's [16]. As shown in Fig. 1, between 2000 and 2013 the focal Southeastern US state for this study saw a steady increase of electricity generation from fossil fuels, highlighting the urgent need to explore the role of efficiency, policy, climatic variability, and stakeholder engagement to mitigate the negative consequences related to this electricity consumption and generation. The goal of the current study is to gain a clearer understanding of the impact that short-and long-term climatic variability have on electricity generation in the Southeast US. Accordingly, the next section will review the relevant literature and provide hypotheses. A methods section will then be presented, followed by results and discussion sections.
1.1. Literature review Utility organizations in the Southeast remain reliant on fossil fuels as the primary source for electricity generation [38]. Furthermore, states in this region rely more heavily on electric cooling and heating equipment, contributing to greater electricity consumption per capita [37,41]. Throughout the US, electricity
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3.0
Coal
Natural Gas
Nuclear
Petroleum
Hydroelectric
2.0
5
MWH (10 )
2.5
1.5 1.0 .5 0.0 2002
2004
2006
2008
2010
2012
Year Fig. 1. Monthly MWH electricity generation by fuel source between 2000 and 2013.
generation and consumption are closely linked [7]. Trends such as increased economic activity, population growth, concerns over a reliable source of electricity, and new uses for electricity are several forces driving electricity consumption and continued use of fossil fuels for generation [22,29,30,32,33]. In the focal Southeastern region, a recent study demonstrated that increased temperatures shared the most salient relationship with increased electricity consumption [8]. Increased temperatures are projected to increase demand for electricity in the future [23]. The impacts of temperature and drought are prohibitive to utility organizations as well. For instance [2], estimate that electricity production in the Western US could diminish by over 8% as a result of drought. Between 2031 and 2060, van Vliet et al. [45] project diminished capacity in US power plants between 6.3% and 19% during summer months related to the prevalence of thermoelectric generation in the US. Globally, van Vliet et al. [44] project that power from thermoelectric plants could reduce by over 80% in extreme future conditions. Drought, temperature, and costly extreme weather events are all projected to increase in the Southeast US (Ingram et al., 2013; Preston), making the regions' electricity generation capacity particularly vulnerable. Outside intervening factors, such as more stringent policies related to efficiency programs or emissions reduction, for-profit organizations such as investor-owned utility organizations will pursue profit-seeking behaviors [34,47]. There has been public scrutiny over efficiency efforts and use of clean energy by utility organizations [9]. However, information asymmetry, or incomplete/withheld information, by investor-owned organizations make it difficult for the public to understand the implications of emissions from fossil fuels and for policy makers to enact quantify the true impact of electricity generation [14]. The lack of focus changing climatic conditions and the increase in extreme weather events further complicate the ability of utility organizations to adequately plan for future electricity demand [2,7]. Policies related to fuel source for electricity generation are particularly relevant in the Southeast US. Biesecker [3] noted that fossil fuel reliant states teamed together to challenge the United States Environmental Protection (EPA) Agency's Clean Power Act, including Arkansas, Alabama, Georgia, Kentucky, Louisiana, Oklahoma, and Texas. In Michigan et al. v. EPA et al. [24]; the act was overturned by majority Supreme Court decision because the costs of implementation were not fully considered for power plants to make changes to achieve the aggressive carbon reduction goals. The
EPA evaluation of health benefits have not been fully realized in the past [13], making it more difficult to effectively enact policy. Graves [14] reiterated the shortcomings with realizing the benefits from environmental policies, noting that oftentimes the “big picture” implications, such as the global implications addressed in the Paris Agreement [36] or anthropogenic forced climate change [20], are overlooked in quantifying benefits resulting in the rejection of policies in the US. Investor-owned utility organizations have turned to energy efficiency programs as a potential market-based solution to reduce consumption and related emissions. In fact, over $7 billion were spent on efficiency programs in 2013, the vast majority of which targeted reduction in electricity consumption [18]. However, a meta-analysis from 1975 until 2012 found that outside rich feedback mechanisms, incentives spent on efficiency improvements resulted in an increase in electricity consumption (Delmas et al., 2013). This has been referred to as the rebound effect, where efficiency improvements are counteracted by outside factors such as adding electrical devices or increased usage behavior [19]. The rebound effect does not always result in counterintuitive results, however, but represents how outside factors prohibit expected efficiencies from being accomplished. The direct rebound effect is often used in electricity studies because the marginal change in demand for usage as operating costs change can be easily quantified (Gillingham et al., 2015). Specific to electricity, a recent study found a residential direct rebound effect between 24% and 37% for households in response to electricity price change [46]. Specific to efficiency upgrades, Davis et al. [10] found that subsidies for efficient air conditioners increased electricity consumption among consumers, while the savings from efficient refrigerators were nominal. Environmental messages have been used to combat the unintended consequences of diminished electricity savings associated with the rebound effect [1,9]. For instance, Asensio and Delmas [1] found that the topic and frequency of which a message is delivered to residential electricity users influenced persistent electricity savings over time in a longitudinal study. Research has shown that levels of awareness about a pro-conservation behavior impact the manner in which gain- and loss-framed messages influence enactment of behaviors [9,27]. Furthermore, Fielder [12] discussed using messages highlighting desirability and/or feasibility to influence future perceptions and/or behaviors. In terms of policy and efficiency spending, the majority of Southeastern states rank in the
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bottom tier compared to the rest of the country on the American Council for an Energy Efficient Economy, and have not enacted comprehensive behavioral programs that incorporate rich feedback mechanisms and effective environmental messages [9,18]. Despite the billions spent on efficiency programs to reduce electricity consumption, in the US and in the focal Southeastern region, electricity consumption among residential and commercial users continues to increase [39], suggesting that additional steps and/or outside intervention are needed to curb consumption and related emissions in response to shifting climatic trends and variability. There have been some efforts to deploy carbon reduction and capture technologies in electricity production plants with various degrees of effectiveness (e.g., [5,15,31]). A recent life-cycle assessment comparing technologies that reduce GHG emissions in existing plants to new clean energy production found that electricity infrastructure that heavily incorporated wind and solar were more effective at reducing negative environmental impacts while meeting demand needs [17]. Across the US the use of carbon-rich coal for electricity has decreased, yet, it should be noted that the majority of new electricity production has shifted to natural gas, also a greenhouse gas emitted fuel source [38]. Despite efforts elsewhere in the US, the focal state in the Southeastern US (see Fig. 1) saw an increase in electricity generation from both coal and natural gas between 2000 and 2013. Complicating deployment of clean energy fuel sources, the lack of knowledge and high-profile failures have made it more difficult to garner support for clean energy in the US [9]. 1.2. Hypotheses In the focal Southeastern US state, the goal of the study is to quantify the relationship between climatic variability in the form of cooling degree days (CDD) and heating degree days (HDD) with electricity generation by fuel source type. CDD and HDD are used here because they are more reliable indicators than temperature alone when considering electricity [25]. To overcome the information asymmetry related to electricity generation, and considering that climatic trends are increasing the demand for electricity (Ingram et al., 2013; [23]), it is vital that a clearer understanding of the relationships between fuel source, long-term climatic trends, and short-term climatic variability are understood. With increased climatic variability (e.g., Ingram et al., 2013), the continued reliance on fossil fuels [37], and the lack of utility-scale clean energy alternatives in the region [40], the following hypotheses are offered: Hypothesis 1. Short- and long-term climatic variability will be predictive of electricity generation for all fuel source types. Hypothesis 2. Short- and long-term climatic variability will be more predictive for fossil fuel sources of electricity generation than clean fuel sources of electricity generation.
2. Materials and methods 2.1. Procedure Monthly electricity generation in megawatt hours (MWH) by fuel source types (i.e., coal, natural gas, nuclear, hydroelectric, and petroleum) was retrieved form the EIA from 2000 to 2013 as well [38]. The daily maximum and minimum temperature on 2.5-min (around 4 km) resolution were both obtained from Di Luzio et al. [11]. The dataset was constructed using the ParametereElevation Regressions on Independent Slopes Model and daily observation from more than 7500 weather stations. The monthly HDD and CDD on individual grid cell during 2000e2013 were calculated using the
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daily maximum and minimum temperature. Degree days are the amount of days above or below a certain threshold temperature [42], 18.33 C or 65 F in the current study. Then the HDD and CDD for the entire state were averaged using areal weight algorithm. 2.2. Statistical analysis IBM SPSS Statistics version 23 was used for statistical analysis. Descriptive analysis was run for all variables (see Table 1). Seasonality is common in climate studies [43], making it necessary to conduct autocorrelation analysis to determine the appropriate model to use. Analysis indicated values for each variable indicated autocorrelation (see Fig. 1), violating the Gauss-Markov assumption for ordinary least square linear regression models. Accordingly, the time series modeler function and expert modeler method were utilized using SPSS to conduct retrospective time series analysis between individual fuel sources and CDD and HDD. The expert modeler method considers both simple exponentially smoothing models and seasonal autoregressive integrated weighted average (ARIMA) models. ARIMA models that leverage the Box-Jenkins approach [4] are appropriate for retrospective studies, and allow the “user to understand not only the relationship between the current state as a function of past states … but also the influence of inputs outside the state of the series” ([21]; p. 2). ARIMA models have been utilized for historical and future models in climate studies [35,43]. ARIMA models use differencing to determine lag periods, autoregressive, and moving average components to achieve stationarity [6]. Goodness-of-fit statistics for each model were run for each fuel source (i.e., coal, hydroelectric, natural gas, nuclear, petroleum) including stationary R2, R2, root mean square error (RMSE), maximum absolute percentage error (MAPE), and normalized Bayesian information criterion (BIC). The default goodness-of-fit used in ARIMA models is the stationary R2 value reported in the results section. Ljung-box statistics were calculated as a diagnostic check to determine the independency of residuals (See Table 2; [6]). Coefficients for each model are provided including constants, seasonality, moving averages, and autoregression (see Table 3). Residual autocorrelation function (ACF) and residual partial autocorrelation function (PACF) were then used to ensure resulting models corrected the autocorrelation of data initially detected (see Fig. 2). 3. Results The Ljung-box statistics for all models except petroleum were greater than the Q value of 18, indicating models had significant levels of autocorrelation. The higher normalized BIC for the simple exponentially smoothing models compared to the ARIMA models indicate a better fit for coal and petroleum (see Table 2). Seasonal ARIMA models emerged as the best predictors for the fuel sources of hydroelectric, natural gas, and nuclear (See Table 3). Time series models addressed the autocorrelation detected in the Ljung-box analysis. Hypothesis 1 posited that climatic variability in terms CDD and HDD would be predictive of total electricity generation. This hypothesis was supported for CDD, as it was the sole predictor in total electricity generation. As shown in Table 3, CDD had a significant impact on total electricity generation, and 57.6% of the variability in total electricity generation was explained in total time series model (Stationary R2 ¼ 0.576). The autoregressive component of the model was to the first order, and the seasonal moving average component of the model was to the first order. Current (p < 0.05), first order (p < 0.001), and second order (p < 0.001) CDD values were significant in the model for total electricity generation.
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Table 1 Descriptive statistics. Variable
N
Min
Max
Mean
Std.
Coal MWH Hydroelectric MWH Natural Gas MWH Nuclear MWH Petroleum MWH CDD HDD
156 156 156 156 156 156 156
1,062,973.00 47,186.20 1657.00 25,775.00 0 0 0
2,825,843.40 62,292.00 617,253.00 1,395,055.00 268,677.00 344.13 525.81
2,072,916.33 257,991.60 128,776.65 1,217,184.37 15,050.69 87.85 157.25
356,648.79 125,493.39 137,377.98 215,433.60 28,128.80 104.62 166.45
Table 2 Time-series exponentially smoothing and ARIMA models. Variable
Coal (S) Coal Hydro NatGas Nuclear Petro (S) Petro Total
Model fit statistics
Ljung-box Q (18)
Stationary R2
R2
RMSE
MAPE
Normalized BIC
Statistics
DF
Sig.
0.620 0.432 0.489 0.794 0.178 0.792 0.128 0.576
0.583 0.489 0.489 0.802 0.178 0.311 0.128 0.682
231,106.61 256,586.68 90,580.31 63,672.51 205,961.02 23,423.63 26,523.19 302,082.694
9.64 10.66 35.49 135.92 43.069 681.80 545.52 6.32
24.766 24.979 22.926 22.33 24.58 20.19 20.50 25.42
23.944 14.584 18.667 26.87 33.80 16.00 18.69 20.50
16 16 17 14 17 16 14 16
0.091 0.555 0.348 0.020 0.009 0.453 0.177 0.198
*Note. S ¼ simple seasonal exponentially smoothed model; RMSE ¼ root mean square error; MAPE ¼ maximum absolute percentage error; BIC ¼ Bayesian Information Criterion. Where an “S” is not denoted, best-fit models autoregressive integrated weighted average (ARIMA) models.
Table 3 Individual model parameters. Model
Coefficient
Estimates
S.E.
t-value
Sig.
Coal (S)
Alpha (level) Delta (seasonal) Constant AR Lag 1 HDD Delay HDD Numerator Lag 0 Constant MA Lag 1 MA Lag 2 MA Lag 3 AR (S) Lag 1 CDD Delay CDD Numerator Lag 0 Constant MA Lag 1 HDD Delay HDD Numerator Lag 0 Seasonal Difference Alpha (level) Delta (season) AR Lag 1 Seasonal Difference MA Seasonal Lag 1 CDD Delay CDD Numerator Lag 0 CDD Denominator Lag 1 CDD Denominator Lag 2 Seasonal Difference
0.200 4.68E6 228,717.53 0.696 1 179.50 222.06 0.836 0.605 0.305 0.440 12 1.19 1,214,090.24 0.347 13 756.08 1 0.100 5.01E5 0.547 1 0.945 5 1100.33 1.081 0.941 1
0.047 0.057 26,449.91 0.060
4.23 8.26E5 8.65 11.64
0.000 1.00 0.000 0.000
73.51 35.39 0.083 0.096 0.084 0.090
2.44 6.27 10.05 6.29 0.364 4.88
0.016 0.000 0.000 0.000 0.000 0.000
0.164 24,190.26 0.085
7.25 50.19 4.07
0.000 0.000 0.000
331.56
2.28
0.024
0.047 0.034 0.074
4.23 0.001 7.41
0.000 0.999 0.000
0.269
3.52
0.001
424.78 0.031 0.042
2.59 34.90 0.22.14
0.011 0.000 0.000
Hydro
NatGas (Sq. Rt.)
Nuclear
Petro (S) Total
*Note. S ¼ seasonal model. MA ¼ moving average; AR ¼ autoregressive.
Hypothesis 2 posited that increased climatic variability in terms of CDD and HDD and would be more predictive for fossil fuel sources of electricity than clean energy sources of electricity. This hypothesis was partially supported. For CDD, a significant positive relationship emerged with natural gas, but not with coal (Stationary R2 ¼ 0.620) or petroleum (Stationary R2 ¼ 0.790) which were explained by simple seasonal models (see Table 3 for individual model parameters). The natural gas model that included CDD as the
single predictor explained 79.4% of the variability in electricity generation from natural gas (Stationary R2 ¼ 0.794, p < 0.001). The natural gas model was the only model that had a square root transformation of the data. The moving average component was to the third order, and the seasonal autoregressive component was to the first order. The relationship with CDD had no lag. Contrary to Hypothesis 2, a positive significant relationship emerged between HDD and hydroelectric electricity generation
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Fig. 2. Autocorrelation values for time series models.
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explaining 48.9% of the variability (Stationary R2 ¼ 0.489, p < 0.05). The autoregressive component was to the first order, and the relationship with HDD had no lag. However, in support of the hypothesis, a negative significant relationship emerged between HDD and nuclear electricity generation explaining 17.8% of the variability (Stationary R2 ¼ 0.178, p < 0.05). The moving average component of the model was to the first factor, and a seasonal difference emerged, and the relationship with HDD had no lag. 4. Discussion and conclusions The findings of this study demonstrate that seasonality and climatic variability play a salient role specific to the fuel source type used for electricity generation. As increased attention globally and domestically turns toward identifying and mitigating climate change related to emissions [20,36], it is crucial that stakeholders understand the interaction between climate and electricity generation, and hold generation companies accountable. This study is important as it is the first to the authors' knowledge that examine the interaction between monthly electricity generation and climatic variables. With climatic variability projected to increase and demand for electricity to follow (Ingram et al., 2013; [23]), the findings of this study have serious implications for the Southeast US. The findings support previous research that demonstrated a significant relationship between electricity consumption and CDD in the Southeast [8]. The study suggest that as more climate variability and extremes occur in terms of CDD, natural gas in the focal state is the most impacted. Put simply, with increased extremes, the majority of electricity demand is being met by a greenhouse gas emitted fuel source in the focal state. Results from Hypothesis 1 suggest that variability in CDD and seasonality are influencing overall electricity generation. The overall model explains 57.6% of the variability in total electricity generation in the focal state. Subsequent models for individual fuel sources suggest that this relationship is being driven largely by the strong relationship between increasing CDD and natural gas generation. For the overall model, CDD shares a positive relationship with electricity generation in the current month and with a one month lag. However, the second month lag shares a negative relationship with overall electricity generation. This is a signal of the seasonality experienced with total electricity generation in the focal state, which was also demonstrated with a seasonal component in all individual models. These findings support future models that predict an increase in temperatures, extremes, and electricity demand [23]. Hypothesis 2 posited that climatic variability would influence fossil fuel sources more than clean energy sources. This hypothesis was supported for the relationship between natural gas generation and CDDs (Stationary R2 ¼ 0.794), but not for coal or petroleum. Seasonality was related to generation for both coal (Stationary R2 ¼ 0.620) and petroleum (Stationary R2 ¼ 0.792) however. The magnitude of the relationship between natural gas and the climatic factors is a major contribution of the current study. These results also suggest that in the focal state, coal is being used to meet baseload electricity demand conditions. It opens the possibility that non-climatic factors such as population, economy, and/or usage trends may be more influential to electricity generation from the fuel source coal. Regardless, the increase of coal for electricity generation throughout the study period raises serious concerns regarding GHG emissions in the Southeast US. Also supportive of Hypothesis 2 was the finding that there was a negative relationship between HDD and electricity generation from nuclear (Stationary R2 ¼ 0.178). As shown in Fig. 1, electricity production from nuclear decreased throughout the study period. Considering the diminishing production capacity of nuclear plants,
where there was an increase in HDDs, this demand was not be met with nuclear energy. However, a positive relationship did emerge between HDD and the clean energy source of hydroelectric generation. HDD explained almost 50% of the variability in hydroelectric electricity generation during the study period. Alarming, however, is that as CDDs increase throughout the study period, the demand was not associated with electricity generation from hydroelectric. Bartons and Chester [2] noted that climate change is not widely considered by utility organizations who are forecasting future electricity demand. In the focal region, Craig [8] found that there was a significant relationship between CDDs and increased electricity consumption related to cooling equipment. The results of the current study demonstrate that in the Southeast US, utility organizations are largely meeting the growing demand associated with CDDs with GHG emitting fossil fuels. Despite international efforts to reduce GHG emissions, many states continue to fight policies aimed at curbing reliance on GHG fossil fuels. Efficiency programs are used throughout the majority of the US by investor-owned utility organizations [18]. However, Craig & Feng [7] demonstrated that many utility organizations throughout the US are not capturing the true impact of climatic variability on electricity consumption within efficiency programs. The effectiveness of efficiency programs around the US have shown mixed and oftentimes negative results, where more electricity is consumed than expected after efficiency efforts are undertaken ([7]; Delmas et al., 2013; Gillingham et al., 2015). When extreme weather conditions and persistent change are occurring, the potential to further conflate the rebound effect related to electricity consumption increases. Drought is projected in the focal study region in the future (Ingram et al., 2013). While hydroelectric generation was related to increased HDDs in this study, however, the projected increase in temperature and decrease in precipitation in the region make this fuel source questionable to meet growing electricity demand. Furthermore, hydrologic models indicate that drought and adverse future conditions have the potential to greatly diminish the amount of electricity produced from thermoelectric power production as related to generation from coal, natural gas, and nuclear energy [2,44,45]. Policy makers and the public alike should take note that extreme weather trends are increasing, and these trends are significantly related to increases in fossil fuel use to generate electricity and the associated emissions. Clean energy sources including wind and solar have shown great promise at cost effectively producing electricity and reducing environmental impacts over and above technologies aimed at fossil fuel infrastructure [17]. It is not enough for utilities and policy makers to only focus on increased electricity demands, but also the electricity baseload conditions that have traditionally been met with fossil fuel sources. 4.1. Conclusions The results of the current study demonstrate that climatic variability and trends during the study period can play a major impact on electricity generation, particularly in areas that are reliant on electricity to heat and cool homes and are more reliant on fossil fuels to generate electricity [37e41]. While recent global agreements have been made to curb climate [36], increases in the focal region in electricity production and related emissions for carbon rich fuel sources continue [38], and fossil reliant states continue to resist the concept of a cleaner energy supply [3]. The results show that extreme events are positively impacting fossil fuel generation of electricity. With climatic variability and trends projected to increase in the Southeast US (Ingram et al., 2013), the potential for negative impact in terms of socioeconomic factors increases [28]. For policy makers and residents alike, it is important to understand the interactions between the climatic change and
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variability presented here as related to electricity generation to reduce information asymmetry from utility organizations, and to build support for mitigation policies related to existing energy infrastructure. 4.2. Limitations and future research The current study is not without limitation. First, reporting for electricity generation, CDD, and HDD was monthly, and only for a 14 year period. Also, the electricity generation data and climatic data were aggregated to the state-level because of the reporting practices of utility organizations related to electricity generation. There are higher resolution climatic data available, however, without the ability to match this data, state-level aggregation was needed. As demonstrated in this study, climatic data and electricity generation data are often seasonal and auto-correlated. Using exponential smoothing models and seasonal ARIMA models corrected this issue. Future research should expand the current research to the remainder of the US, as states outside the Southeast are not as reliant on fossil fuels and are not as consumptive with regards to electricity [39,41]. Furthermore, states in other regions have more progressive policies regarding utilities and electricity generation which may result in different results [18]. Future research could expand to evaluate other climatic variables for extreme events and the impact on electricity generation, and climatic variables could also be analyzed relative to electricity consumption and energy efficiency savings to get a clearer picture of the causality related to climate. Furthermore, the current study provides a foundation using time series analysis that will make it possible to expand the research to model future electricity demands based on relationships with climatic factors. References [1] Asensio OI, Delmas MA. Nonprice incentives and energy conservation. PNAS 2015;112(6). published online January 12, 2015. [2] Bartons MD, Chester MV. Impacts of climate change on electric power supply in the Western United States. Nat Clim Change 2015;5:748e52. [3] Biesecker M. States and industry groups sue government over new clean air rules. PBS Newshour; 2015. Retrieved 12/15/2015 from: http://www.pbs.org/ newshour/rundown/states-industry-groups-reliant-fossil-fuels-suegovernment-new-clean-air-rules/. [4] Box GEP, Jenkins GM. Time series analysis: forecasting and control. San Francisco, CA: Holden Day Inc.; 1976. [5] Brouwer AS, van den Broek M, Seebregts A, Faaij A. Operational flexibility and economics of power plants in future low-carbon power systems. Appl Energy 2015;156:107e28. [6] Clement EP. Using normalized Bayesian information criterion (Bic) to improve Box- Jenkins model building. Am J Math Stats 2014;4(5):214e21. [7] Craig CA, Feng S. Exploring utility organization electricity generation, residential electricity consumption, and energy efficiency: a climatic approach. Appl Energy 2016 [in review)]. [8] Craig CA. Energy consumption, energy efficiency, and consumer perceptions: a case study for the Southeast United States. Appl Energy 2016;165:660e9. [9] Craig CA, Allen MW. Enhanced understanding of energy ratepayers: factors influencing perceptions of government energy efficiency subsidies and utility alternative energy use. Energy Policy 2014;66:224e33. [10] Davis LW, Fuchs A, Gertler P. Cash for coolers: evaluating a large-scale appliance replacement program in Mexico. Am Econ J Econ Policy 2014;6(4):207e38. [11] Di Luzio M, Johnson GL, Daly C, Eischeid JK, Arnold JG. Constructing retrospective gridded daily precipitation and temperature datasets for the conterminous United States. J Appl Meteorol Climatol 2008;47:475e97. [12] Fielder K. Construal level theory as an integrative framework for behavioral decision-making research and consumer psychology. J Consumer Psychol 2007;17(2):101e6. [13] Fraas AG. The treatment of uncertainty in EPA's analysis of air pollution rules: a status report. J Benefit-Cost Anal 2011;2(2):1e27. [14] Graves PE. Benefit-cost analysis of environmental projects: a plethora of biases understating net benefits. J Benefit-Cost Anal 2012;3(3):1e25. [15] Hanak DP, Biliyok C, Manovic V. Efficiency improvement for the coal-fired power plant retrofit with CO2 capture plant using chilled ammonia process. Appl Energy 2015;151:259e72.
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