Renewable energy consumption and industrial production: A disaggregated time-frequency analysis for the U.S.

ENEECO-04433; No of Pages 24 Energy Economics xxx (xxxx) xxx

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Renewable energy consumption and industrial production: A disaggregated time-frequency analysis for the U.S.☆ Thi Hong Van Hoang a,⁎, Syed Jawad Hussain Shahzad a, Robert L. Czudaj b a b

Montpellier Business School, Montpellier, France Chemnitz University of Technology, Chemnitz, Germany

a r t i c l e

i n f o

Article history: Received 19 November 2017 Received in revised form 24 May 2019 Accepted 19 June 2019 Available online xxxx JEL classification: Q2 Keywords: Renewable energies Disaggregated sources Industrial production U.S. Wavelet Granger causality Rolling-window

a b s t r a c t This paper aims to analyze the interaction between renewable energy consumption and industrial production for the U.S. over the 1981–2018 period. We contribute to the existing literature by the disaggregation of renewable energy sources (hydropower, geothermal, and biomass), by the consideration of time and frequency through wavelet techniques and the time-varying Granger causality test recently proposed by Shi et al. (2018). Based on monthly data, wavelet results show a positive co-movement between industrial production and biomass energy consumption at low frequencies, meaning in the long term only. The bootstrap rolling-window Granger causality test indicates a bi-directional predictability between renewable energy consumption and industrial production in crisis and turmoil periods only. These results are found to be robust to the inclusion of control variables (non-renewable energy consumption and crude oil prices) and to the selection of the lag length for the corresponding VAR model. © 2019 Elsevier B.V. All rights reserved.

1. Introduction The important role of energy in economic activities has attracted the attention of policymakers and academics since the 1950s with studies of Mason (1955), Warren (1964), Solow (1974), Rasche and Tatom (1977), among others. Most of the early studies focused on the U.S., like that of Kraft and Kraft (1978), which is the first academic study using econometric techniques to analyze the nexus between energy consumption and economic growth. Renewable energies were introduced in the debate more recently, and mostly after the first climate ☆ Acknowledgments: We would like to thank the Editor-in-chief, Professor R.S.J. Tol (University of Sussex, Falmer, Brighton, UK), the Guest-Editor, Professor C. Weber (University of Duisburg-Essen, Duisburg, Germany), and three anonymous Referees during the two revisions for their careful reading, comments and suggestions that helped us improve the paper significantly. We would like to thank the members of Montpellier Research in Management for their valuable comments during the seminar of July 2017. We are grateful to Professor Kevin F. Forbes (The Catholic University of America, Washington, USA) for his encouragement. Montpellier Business School (MBS) is a founding member of the public research center Montpellier Research in Management, MRM (EA 4557, Univ. Montpellier). Any errors or shortcomings remain the authors' responsibility. ⁎ Corresponding author at: Montpellier Business School, 2300 avenue des Moulins, 34185 Montpellier, France. E-mail addresses: [email protected] (T.H.V. Hoang), [email protected] (R.L. Czudaj).

meeting initiated by the United Nations in Rio de Janeiro in 1992 (see for example Silverman and Worthman (1995)). With data made available by the World Bank from 2010, there have been numerous studies investigating the linkage between renewable energies and economic growth (e.g., Apergis and Payne, 2011; Bloch et al., 2015; Inglesi-Lotz, 2016; Koçak and Sarkgünesi, 2017; Dong et al., 2018). In this context, the objective of this paper is to investigate the relationship between the consumption of renewable energies and industrial production in the U.S. over the 1981–2018 period. We choose to study the U.S. because it is the second most attractive country for renewable energies following the “Renewable Energy Country Attractiveness Index” of March 2018. Furthermore, according to the REmap-2030 report of the IRENA in 2015,1 the U.S. can lead the global transition to renewable energy with its rich resources (wind, solar, geothermal, hydro and biomass), its innovation culture, its high financing opportunities and its skilled workforce. The report forecasted that the share of renewable energy would reach 10% by 2030, compared to 7.5% in 2010. On the other hand, we choose to consider the industrial production specifically because this sector represents the highest part in the U.S. economic growth, nearly 19% in 2016 (World Bank). It is also the most energy-consuming sector following the Energy Information 1 International Renewable Energy Agency. 2015. A renewable energy roadmap – REmap 2030.

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Please cite this article as: T.H.V. Hoang, S.J.H. Shahzad and R.L. Czudaj, Renewable energy consumption and industrial production: A disaggregated time-frequency analysis for ..., Energy Economics, https://doi.org/10.1016/j.eneco.2019.06.018

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Administration (EIA hereafter). Indeed, over the 1974–2018 period, the industrial sector accounts for 91% of the total non-renewable energy consumption and for 63% of the total renewable energy consumption (more information is provided in the Appendix). Thus, understanding the interaction between renewable energy consumption and industrial production is of great importance for policymakers and firms in their decisions on the production and consumption of renewable energy. We contribute to the literature on the energy-growth nexus in different ways. First, we distinguish between different renewable energy sources, i.e., hydropower, geothermal, and biomass energy (from wood, waste, and biofuel).2 This disaggregation is very important since each energy source behaves differently to economic and political events, and this potentially affects its relationship with industrial production (Hoang et al., 2019). Only few previous papers have considered this disaggregation for the U.S. (e.g., Ewing et al., 2007; Sari et al., 2008; Yildirim et al., 2012; Ben-Salha et al., 2018), to the best of our knowledge. Among these studies, only Ewing et al. (2007) and Sari et al. (2008) considered industrial production specifically. However, the latter studies did not consider the impact of both time and frequency in their empirical analysis. We fill this gap in this study by relying on wavelet techniques (i.e., wavelet squared coherence and partial wavelet coherence) and bootstrap rolling-window Granger causality tests recently proposed by Shi et al. (2018). The former allows us to distinguish between the short, medium and long run, while the latter allows us to consider the time-varying character of the Granger causal relationship between renewable energy consumption and industrial production. Third, to check the robustness of the empirical results, we further include control variables, such as non-renewable energy consumption and crude oil prices that may impact the relationship between renewable energy consumption and industrial production within a multivariate framework. To the best of our knowledge, none of the previously-mentioned studies has checked the robustness of the results by considering the multivariate framework. To fill this gap, we compare the results achieved within bivariate and multivariate models, between various sub-periods and between two criteria to select the optimal lag length in the VAR model used for the Granger causality testing (Akaike Information Criterion, AIC, and Bayesian Information Criterion, BIC.3 The results show that the renewable energy source matters and it is important to take time and frequency into account. Indeed, wavelet results indicate that there is a positive comovement between industrial production and biomass energy only at low frequencies, which can be characterized as long-term components. This finding implies that this relationship mostly holds for the long run and policymakers should take this time constraint into account. Furthermore, the time rollingwindow Granger causality test shows that there is a bidirectional predictability between renewable energy consumption and industrial production only in crisis and turmoil periods. This finding implies that renewable energy plays an important role for industrial production in crisis periods in the U.S. In addition, the Granger causality from biomass energy consumption to industrial production is stronger than from other renewable energies. This finding confirms the wavelet results and shows that biomass energy should be further developed in the U.S. to optimize the energy mix in the future. It also implies that except for biomass energy, the impact of renewable energy consumption on industrial production (and vice versa) is still small, compared to non2 Hydropower denotes electricity produced from falling water. Geothermal denotes energy from heat stored under the earth's surface in hydrothermal reservoirs, geopressured brine zones, hot dry rocks or magmas. Biomass denotes energy derived from combustion, fermentation, gasification or anaerobic digestion of plant or animal matters, including wood, waste or agricultural waste-burning plants (Silverman and Worthman, 1995, p. 14). 3 We would like to thank an anonymous referee for his/her suggestion to consider a multivariate analysis framework and to consider two different criteria to select the optimal lag length in the VAR model for the Granger causality test for the robustness check of the main results.

renewable energy consumption, to be a strong predictive element of industrial production. These results are found to be robust to the inclusion of control variables within a multivariate framework and to the lag length selection in the VAR model. Finally, crude oil prices can impact the relationship between industrial production and renewable energy consumption to some extent. The rest of the paper is organized as follows. Section 2 presents the literature review on the nexus between energy consumption and economic growth. Section 3 presents the data sample and explains the empirical methodology. The results of the wavelet method are analyzed in Section 4 while those on the Granger causality test are detailed in Section 5. Section 6 concludes the paper with a focus on policy implications. 2. Literature review The literature review is divided into two different parts. The first one focuses on the general nexus between energy consumption (EC hereafter) and economic growth (EG hereafter). The second part concentrates on the U.S. while highlighting the contributions of the present study and the formulated hypotheses. Since our focus is renewable energy consumption (REC hereafter), most of the reviewed studies cover this aspect while some studies also cover non-renewable energy consumption (NREC hereafter). 2.1. Energy consumption and economic growth: a review of academic studies The first studies that consider renewable energies appeared in the 1990s with, for example, the work of Silverman and Worthman (1995) showing the importance and potential of the renewable energy industry. From then, the number of studies analyzing the role of renewable energies has increased significantly, particularly since 2010 when access to data was opened publicly by the World Bank. Sebri (2015) made a meta-analysis of the academic literature on the nexus between REC and EG. Based on 40 empirical studies published between 2009 and 2013, the author concluded that, as for non-renewable energies, the results can be summarized following four main hypotheses: feedback hypothesis (bidirectional linkage), conservation hypothesis (EG leads REC), growth hypothesis (REC leads EG) or neutrality (no interaction between them). Overall, Sebri (2015) showed that the results vary in function of the model specification, data characteristics, estimation techniques and the development level of the country under study. In the same vein, Adewuyi and Awodumi (2017) conducted a survey of the literature about the linkage between REC, NREC and EG. They confirmed the conclusion of Sebri (2015). In the next part of this section, we will detail some recent studies that demonstrate these conclusions. Bildirici (2012) focused on biomass EC in Argentina, Bolivia, Brazil, Chile, Colombia, Guatemala and Jamaica over the 1980–2009 period. The ARDL method shows that there is no cointegration between the biomass EC and EG in Argentina and Jamaica while cointegration can be found for the other countries in the sample. Ocal and Aslan (2013) showed that REC has a negative impact on EG in Turkey over the 1990–2010 period. Salim et al. (2014) found that there is a bidirectional causality between both REC as well as NREC and industrial output in the short run in OECD countries over the 1980–2011 period. However, the result is not the same for EG. This study thus shows the importance to distinguish between industrial production and EG when investigating the corresponding relationship. Some other studies compared the impact of REC and NREC on EG. Bloch et al. (2015) distinguished between coal, oil and REC to study their interaction with EG in China over the 1977–2013 period. Through ARDL and VECM methods, EG in China was found to be led by all three energy sources. Bhattacharya et al. (2016) investigated the effect of REC on EG with a panel dataset of 38 top countries following the Renewable Energy Country Attractiveness index over the 1991–2012

Please cite this article as: T.H.V. Hoang, S.J.H. Shahzad and R.L. Czudaj, Renewable energy consumption and industrial production: A disaggregated time-frequency analysis for ..., Energy Economics, https://doi.org/10.1016/j.eneco.2019.06.018

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period. Their results showed that REC has a significantly positive impact on the economic output for 57%. Inglesi-Lotz (2016) studied the impact of REC on EG through a panel data analysis for 34 OECD countries from 1990 to 2010. The results showed that a 1% increase of REC will lead to a 0.0105% rise in EG. Shahbaz et al. (2016) showed that biomass EC stimulates EG in BRICS countries over the 1991–2015 period. Tang et al. (2016) found that there is a unidirectional causality from EC to EG in Vietnam over the 1971–2011 period. More recently, Destek and Aslan (2017) used the bootstrap panel causality test to examine the relationship between REC, NREC and EG in 17 emerging countries from 1980 to 2012. For REC, their results revealed that the growth hypothesis holds only in Peru, the conservation hypothesis is validated for Colombia and Thailand, while the feedback hypothesis was found for South Korea and the neutrality hypothesis was valid for the remaining 12 countries. Rafindadi and Ozturk (2017) were interested in the case of Germany over the 1971–2013 period using a cointegration approach. Their results reported that a 1% increase in REC leads to a rise of 0.2194% in EG. For 24 MENA countries from 1980 to 2012, Kahia et al. (2017) found that the treatment effect of renewable energy policies has a significantly positive impact on EG. However, renewable energy policies need to have the commitment of all private and public stakeholders. Koçak and Sarkgünesi (2017) confirmed the growth hypothesis for Bulgaria, Greece, Macedonia, Russia and Ukraine; the feedback hypothesis for Albania, Georgia and Romania; and the neutrality hypothesis for Turkey over the 1990–2012 period. Narayan and Doytch (2017) examined a panel of countries over the 1971–2011 period and found that the neutrality hypothesis holds for REC. Only in lowand middle-income countries, REC leads EG while the feedback, growth and conservative hypotheses are validated for NREC. Aydin and Esen (2018) found that above the 44% threshold level of energy intensity, EC retards EG in 12 Commonwealth of Independent States countries over the 1991–2013 period. Barreto (2018) found that the dynamic substitution of depleting fossil fuel with renewable alternative energy can mitigate the negative consequences on growth and welfare. Gozgor et al. (2018) found that both NREC and REC are positively associated with a higher rate of EG in 29 OECD countries over the 1990–2013 period. Kibria et al. (2018) showed that there might exist a polynomial relationship between fossil fuel share and income, called the Energy Mix Kuznets Curve (EMKC), based on a panel data sample of 151 countries over the 1971–2013 period. Pradhan et al. (2018) indicated that both EC and financial development are long-term determinants of EG in 35 Financial Action Task Force countries over the 1961–2015 period. Finally, Tugcu and Topcu (2018) found that there is an asymmetric relationship between EC and EG in the long run in G7 countries over the 1980–2014 period. In the next subsection, we will detail the review of previous studies focusing on the U.S. 2.2. Energy consumption and economic growth in the U.S. Ewing et al. (2007) investigated the nexus between industrial production and EC from 2001 to 2005. They based on various sources of energy such as total energy, total renewable energy, coal, fossil fuels, hydroelectricity, solar energy, wood energy, gas, alcohol, geothermal and waste energies. The variance decomposition results showed that shocks occurring in the NREC from coal, gas and fossil fuels explain shocks in the industrial output better than REC (except for waste EC). Sari et al. (2008) reexamined the linkage between EC and EG in the U.S. from 2001 to 2005 by applying the bounds testing approach for cointegration. They found that coal EC is negatively linked to industrial production, but industrial production leads fossil fuels, hydroelectricity, waste, wind and wood EC. Furthermore, industrial production declines the demand for solar and natural gas energies. Menyah and WoldeRufael (2010) investigated the Granger causal relationship between REC and nuclear EC, real GDP and CO2 emissions in the U.S. from 1960

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to 2007. Their results showed that there is a unidirectional causality from EG to REC in the U.S. The results of our study will show that these findings cannot always be verified when time and frequency are considered (see Sections 4 and 5). Yildirim et al. (2012) applied Toda-Yamamoto and bootstrapcorrected Granger causality tests to show the neutral effect between EG and REC in the U.S. from 1949 to 2010. Tiwari (2014) tested the linkage between EG and coal, natural gas, primary, and REC for the U.S. over the 1973–2011 period. An asymmetric Granger causality test showed that there is a significant difference in the way that positive and negative variations of EC impact EG. Bilgili (2015) used monthly U.S. data from 1981 to 2013 and analyzed the relationship between EC and industrial production. The energies considered are total renewable energy, coal, natural gas, and petroleum consumption. Using wavelet coherence and wavelet partial coherence analysis, the author reported that REC has a positive impact on industrial production. Even though this study also considers the wavelet approach, it does not distinguish among different sources of renewable energy like in the present study. Furthermore, it did not consider the time-varying Granger causality. On the other hand, Carmona et al. (2017) showed that there is a nonlinear relationship between EC and EG with a structural break in the U.S. over the 1973–2015 period. This thus shows the necessity to consider time and frequency in the analysis. Furthermore, Hoang et al. (2019) demonstrated the importance to disaggregate energy sources while studying the U.S. as they found that oil supply and demand shocks spillover the most to hydropower energy consumption. At the sectoral level, Lin et al. (2013) examined the linear and nonlinear causality between REC and real GDP in the U.S. from 1989 to 2008. The investigated sectors are industrial, residential, commercial, transportation, and electric power. The results displayed that encouraging REC in the industrial sector can help achieving EG. In the same vein, Ben-Salha et al. (2018) studied the lead-lag relationship between aggregate and sectoral EC (by source) and the output in the U.S. economy using the wavelet power spectrum and cross wavelet over the 2005– 2015 period (quarterly data). Their results showed that the industrial sector exhibits the highest intensity of wavelet coherence with the sectorial output. This result confirms the interest to study the relationship between disaggregated EC and industrial production as done in the present article. Our added-value compared to the study by Ben-Salha et al. (2018) is related to the consideration of control variables in a multivariate framework and to the time-varying Granger causality testing approach. Bruns et al. (2018) concluded that it is important to account for changes in the energy mix in time series modeling of the energyGDP relationship and control for other factors of production. Cai et al. (2018) found that REC Granger causes EG in Canada, Germany and the U.S. Troster et al. (2018) analyzed the relationship between REC, oil prices and economic activity in the U.S. over the 1989–2016 period considering different quantiles of the distribution. They found that there is a bidirectional Granger causality between REC and EG only at the lowest tail of the distribution. There is evidence of lower-tail dependence between oil prices and REC. Shahbaz et al. (2018) showed a positive association between EC and EG in a panel of top-ten energy-consuming countries, including the U.S. However, this result differs across quantiles. This again shows the existence of nonlinearity in the relationship between REC and economic activities. The literature also provides other panel data studies including the U.S. such as Apergis et al. (2010), Apergis and Payne (2011), Halkos and Tzeremes (2014), Cho et al. (2015), Bhattacharya et al. (2016), or Bildirici and Gökmenoglu (2017). Overall, the review of previous studies focusing on the U.S. shows that even though the results diverge in function of the periods considered and the methods used, most of them showed that there is an interaction between REC and economic activities in the U.S. (e.g., Ewing et al., 2007; Sari et al., 2008; Menyah and Wolde-Rufael, 2010; Tiwari, 2014; Bilgili, 2015; Ben-Salha et al., 2018). However, the direction of this interaction seems to be unclear since Ewing et al. (2007) found that shocks in NREC can better explain shocks in industrial output

Please cite this article as: T.H.V. Hoang, S.J.H. Shahzad and R.L. Czudaj, Renewable energy consumption and industrial production: A disaggregated time-frequency analysis for ..., Energy Economics, https://doi.org/10.1016/j.eneco.2019.06.018

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over the 2001–2005 period. However, Sari et al. (2008) showed that industrial production leads REC, from hydropower, waste, wind and wood but declines EC of solar and natural gas. In contrast, Menyah and Wolde-Rufael (2010) reported a Granger causality running from EG to REC and Bilgili (2015) demonstrated that REC has a positive impact on industrial production. Among these inconclusive findings, our study proposes to decompose the relationship between disaggregated REC (wind, earth heat and biomass) and industrial production into time and frequency domains through a wavelet decomposition technique. Furthermore, we also decompose the Granger causality analysis into multiple sub-periods through the time-rolling window approach. Therefore, we can show that the divergence in the results of previous studies is due to nonlinearity and variation in time and data frequency of the relationship between REC and economic activities (see Section 4 for more details). From the literature review above and the statistics provided by the EIA, we formulate the following hypotheses4 about the relationship between REC and industrial production in the U.S.: H1. REC and industrial production have a positive co-movement (Bilgili, 2015; Ben-Salha et al., 2018). This is because renewable energy is the most used in the industrial sector while the industrial sector is the most energy-consuming (Source: EIA). Thus, using more renewable energy would increase the industrial production in the U.S. and vice versa. However, this impact can change following the energy source over time and can be different between long-term and short-term horizons (Carmona et al., 2017; Troster et al., 2018). This hypothesis can be tested by the wavelet method, which accounts for the impact of time and frequency. H2. REC does not Granger cause industrial production (Ewing et al., 2007) due to the fact that the part of REC is still small compared to NREC (9% against 91% over the 1974–2018 period, source: EIA).5 However, industrial production can Granger cause REC because this sector is the most energy-consuming and its variations can help predict future variations of REC (Sari et al., 2008). The conservative hypothesis should therefore be validated for the U.S. This relationship can vary in function of the energy source and can be more intense in crisis periods because REC can help maintain the energy independence of the country. This hypothesis is tested through the time rolling-window Granger causality test proposed by Shi et al. (2016) and Shi et al. (2018). H3. Crude oil prices have an impact on the relationship between REC and industrial production since oil prices determine the price of nonrenewable energy and its variations can affect the balance between REC and NREC for industrial production (Troster et al., 2018). A rise in oil prices should decrease NREC and thus increase REC and vice versa. This hypothesis is tested by using the multivariate approach in which oil prices and NREC are used as control variables.

3. Data and empirical methodology 3.1. Data The data sample is composed of monthly time series of U.S industrial production measured by the Industrial Production Index (IPI) and REC from different sources, namely: Hydroelectric Power Consumption (HPC), Geothermal Energy Consumption (GEC), Wood Energy Consumption (WEC), Waste Energy Consumption (WaEC), Biofuel Energy Consumption (BiEC), Total Biomass Energy Consumption (BEC), and Total REC (REC). The study period runs from January 1981 to June 2018. The IPI data is extracted from International Financial Statistics (CD-ROM, 2018, meaning the World Development Indicators (WDI) database of 4 5

We would like to thank an anonymous Referee for this valuable suggestion. Refer to Fig. A1 in the Appendix for more details.

the World Bank for 2018) and data on REC (in Trillion BTU6) is obtained from the U.S. Energy Information Administration (EIA). In addition to this main data sample, we also include two control variables for robustness checks, i.e., U.S. total fossil fuel consumption and WTI crude oil prices with monthly data over the study period from January 1981 to June 2018. The total fuel consumption is the sum of coal, natural gas and petroleum consumption, which represents the total NREC in the U.S. (also collected from the EIA and measured in Trillion BTU). West Texas Intermediate (WTI) crude oil prices are taken from the website of the Federal Reserve Bank of St. Louis database. It represents the spot7 crude oil price in USD per barrel and is a proxy for energy prices. This variable is important following the findings of Belke et al. (2011), Masih and Masih (1997), Asafu-Adjaye (2000), and Mahadevan and Asafu-Adjaye (2007). It also allows us to account for a potential effect of common factors as shown by Belke et al. (2013) and Beckmann et al. (2014a). These two variables, NREC and oil prices,8 are used to check our bivariate results for sensitivity within a multivariate framework. Finally, it should be noted that before obtaining the final empirical results, we first correct the raw data from a seasonal effect by employing X-13ARIMA-SEATS Seasonal Adjustment Program.9 Table 1 reports the main descriptive statistics for the time series of interest. Total biomass and hydroelectric power EC show the highest average, 284.54 and 237.08 trillion BTU per month, respectively. The monthly variations are the highest for biofuel EC, as shown by the standard deviation (67.42 trillion BTU) and by Fig. A2 in the Appendix. Total REC was 583.51 trillion BTU per month on average over the sample period. All the time series are non-normal as the null hypothesis of normality is rejected at least at the 5% level of significance, following the Jarque-Bera test. For further empirical analyses, we transformed the series into logarithmic form. Fig. A2 in the Appendix shows the time series pattern of REC and industrial production in the U.S. over the 1981–2018 period. Overall, it displays that there is an important structural change in the REC in the U.S. in the 1980s. This may be explained by the timeline of renewable energy policies in the U.S. which started with the PURPA era (Public Utilities Regulatory Policy Act) from 1978 to 1990 during which there was an increasing tendency in the renewable energy sector with pioneering states such as California, New York and Maine (Martinot et al., 2005, p. 4). The PURPA era was then followed by a stagnation period starting in the 1990s. In the late 1990s, an increasing trend of state-level policy was implemented thanks to the Production Tax Credit (PTC), ethanol tax credits and the reduction of renewable energy cost due to technology advances and economies of scale in production and learning. From the information provided by IEA and IRENA,10 we also notice that there was an increasing number of renewable energy policies between 2001 and 2009 with numerous policy tools such as 6 BTU stands for British Thermal Units, which is the amount of heat required to raise the temperature of 1 pound of liquid water by 1-degree Fahrenheit at a constant pressure of one atmosphere. 7 Indeed, there are arguments to focus on futures prices instead of spot prices in the context of an accelerator-based explanation of industrial production. However, our focus is related to energy consumption, and not energy prices. Therefore, we do not include futures prices in our data sample. See also Beckmann et al. (2014b) for an analysis of the relationship between spot and futures prices for energy commodities. We would like to thank an anonymous Referee for raising this aspect. 8 Our study focuses on the relationship between REC and industrial production in only one country, the U.S. In such case, no trading with other countries is directly involved. That is why we do not focus on prices in our study but on energy consumption (except for the oil price in the robustness check within a multivariate framework). Therefore, we do not include variables such as exchange rates or global liquidity in the present mono-country study. We would to thank an anonymous Referee for raising this aspect. 9 X-13ARIMA-SEATS is a seasonal adjustment software produced, distributed, and maintained by the U.S. Census Bureau. Further details of the software and program are available at: https://www.census.gov/srd/www/x13as/. 10 IEA = International Energy Agency, IRENA = International Renewable Energy Agency. The related website is: https://www.iea.org/policiesandmeasures/renewableenergy/? country=United%20States.

Please cite this article as: T.H.V. Hoang, S.J.H. Shahzad and R.L. Czudaj, Renewable energy consumption and industrial production: A disaggregated time-frequency analysis for ..., Energy Economics, https://doi.org/10.1016/j.eneco.2019.06.018

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Table 1 Descriptive statistics. Mean IPI HPC GEC WEC WaEC BiEC BEC REC TFFC Oil

82.16 237.08 13.64 190.44 34.26 59.91 284.54 583.51 6419.95 37.94

Minimum

Maximum

Std. Dev.

Skewness

Kurtosis

48.24 143.11 3.12 145.35 6.50 0.97 193.60 404.12 5115.26 8.03

107.65 333.29 18.94 240.39 54.03 196.33 425.05 966.54 7400.39 128.08

18.81 34.26 3.92 19.63 10.39 67.42 66.25 132.21 570.94 28.06

−0.33 0.32 −1.03 0.30 −0.98 0.94 0.97 1.25 −0.52 1.16

1.55 2.85 3.03 2.13 3.29 2.16 2.42 3.49 2.27 3.21

J-B 47.54⁎⁎⁎ 8.06⁎⁎ 79.33⁎⁎⁎ 20.83⁎⁎⁎ 73.19⁎⁎⁎ 79.51⁎⁎⁎ 77.03⁎⁎⁎ 122.21⁎⁎⁎ 30.66⁎⁎⁎ 101.35⁎⁎⁎

Note. Std. Dev. stands for standard deviation. J-B stands for the Jarque-Bera normality test. IPI = Industrial Production Index, HPC = Hydroelectric Power Consumption, GEC = Geothermal Energy Consumption, WEC = Wood Energy Consumption, WaEC = Waste Energy Consumption, BiEC = Biofuel Energy Consumption, BEC = Total Biomass Energy Consumption, REC = Total REC, TFFC = Total Fossil Fuels Consumption, Oil = WTI crude oil prices. ⁎⁎⁎ Indicates the rejection of the null hypothesis of normality at the 1% level of significance. ⁎⁎ Indicates the rejection of the null hypothesis of normality at the 5% level of significance.

research and development, fiscal and financial incentives, information and education, advice and aid in implementation, etc. It is however important to note that the withdrawal of the U.S. from the COP21 Paris Agreement on June 1, 2017, has had high impacts on the research and development in renewable energies in the U.S. due to budget cuts (Zhang et al., 2017, p. 216). The implementation of different renewable energy policies over the sample period indicates the necessity to consider the time-varying character in the relationship between renewable energy consumption and industrial production. 3.2. Methodology With the objective to investigate the relationship between REC and industrial production in the U.S., we perform the following steps: Step 1: Wavelet estimation in a bivariate framework with wavelet squared coherence. Step 1*: Wavelet estimation in a multivariate framework with partial wavelet coherence including control variables (NREC and oil prices) (Robustness check n° 1). Step 2: Testing the full-sample Granger causality in a bivariate framework based on the AIC. Step 2*: Testing the full-sample Granger causality in a multivariate framework including control variables (NREC and oil prices) based on the AIC (Robustness check n° 2). Step 2**: Testing the Granger causality by the method of Shi et al. (2018) in a time-varying context in both a bivariate and multivariate framework using three different time window sizes based on the AIC (Robustness check n° 3). Step 2***: Testing the Granger causality by the method of Shi et al. (2018) in a time-varying context in both a bivariate and a multivariate framework using three different window sizes based on the BIC (Robustness check n° 4). The interpretations of our results follow these steps in Section 4. It is important to note that a preliminary analysis is also performed to test the stationarity and linearity properties of the considered time series. To test for stationarity, we apply the unit root test suggested by Narayan and Popp (2010) that accounts for two structural breaks at unknown dates. To test the potential existence of nonlinearity, we use the BDS test by Brock et al. (1996). To save space, we do not present these tests in detail in the main text. However, they are available in Appendix 3. The next three sub-sections will detail the principal methods used in this study: wavelet square coherence, partial wavelet coherence and the rolling-window Granger causality test proposed by Shi et al. (2016) and Shi et al., 2018).

3.2.1. Wavelet squared coherence (bivariate framework) Wavelet measures offer a unique opportunity to study the relationship between different variables over time and across different frequencies. This allows us to provide a broader picture than time-domain methods, which aggregate all time horizons together. Furthermore, the wavelet analysis accommodates both structural breaks and asymmetries of the time series under study. In this article, we use the wavelet squared coherence proposed by Torrence and Webster (1999) for a bivariate analysis. Before detailing its calculation, we remind first the general notions related to the wavelet method. Wavelets are ‘small waves’ that grow and decay in a limited period. The results from a mother wavelet, i.e. ψ(t), can be expressed as a function of two parameters: the first one shows where the wavelet is centered (τ: translation parameter), while the second indicates the analysis resolution (s: dilation parameter). Formally, wavelets are defined as:   1 t−τ ψs;t ðt Þ ¼ pffiffiffiffiffi ψ s with τ ∈ ℜ; s≠0 s jsj

ð1Þ − Z∞

To be a mother wavelet, ψs, t(t) must have a zero mean,

ψðtÞdt ¼ 0, þ∞

2 while its square must integrate to unity, ∫−∞ +∞ψ (t)dt = 1. This condition implies that ψ(t) is limited to an interval of time. Furthermore, the continuous wavelet transform (hereafter CWT) has the aptitude to decompose and reconstruct a given time series x(t) — the admissibility condition — based on the following formula:

2

1 xðt Þ ¼ Cψ

− Z∞ − Z∞

4

þ∞

w = x ðs; τÞψðs;τÞ ðt Þdτ þ∞

ds : s2

ð2Þ

The CWT (denoted by Cψ) also preserves time series characteristics, therefore: 2

kxk2 ¼

1 Cψ

3 2 5ds : w ð s; τ Þ dτ j x j s2

− Z∞ − Z∞

4

þ∞

þ∞

ð3Þ

To study the relationship between two variables, the cross-wavelet transform can be defined as the ratio of cross-spectrum to the product of the spectrum for each series and can be viewed as a local correlation between two time-series in the time-frequency dimension (Aguiar-Conraria et al., 2008). Thus, a cross-wavelet transform value close to one shows a high degree of synchronization between two time-series while a value close to zero implies no relationship. The cross-wavelet power of two

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time-series depicts the covariance between time series at each scale or frequency. The cross-wavelet transform isolates regions in the timefrequency domain where the stated time series co-move, even if they may not exhibit a common high power. Following Goupillaud et al. (1984), the cross-wavelet transform of two time-series x(t) and y(t) is defined as follows: Wxy ðτ; sÞ ¼ Wx ðτ; sÞ W y ðτ; sÞ

ð4Þ

where Wx(τ, s) and Wy(τ, s) designate the cross-wavelet transform of x(t) and y(t), respectively. τ is a position index indicating the scale and the symbol ⁎ refers to a complex conjugate. The crosswavelet power can easily be calculated using the cross-wavelet transform as |Wxy(u, s)|. One of the cross-wavelet transforms is wavelet squared coherence (hereafter WSC), defined by Torrence and Webster (1999) as follows:

R2t;YX ðsÞ

  2   S s−1 W XY t ðsÞ  ¼   2    2      S s−1 W Xt ðsÞ S s−1 W Yt ðsÞ

ð5Þ

p

yt ¼ Φ0 þ ∑i¼1 Φi yt−i þ εt ;

where S is a smoothing operator. WSC can be considered as a correlation coefficient localized in the time-frequency domain with a value that ranges between 0 and 1 (see for example Grinsted et al., 2004). Since the WSC allows capturing the dynamic correlation between two time-series in the time and frequency domains, we decided to use this method in our analysis. Furthermore, the WSC can show the direction of impact through phase angle or arrows (more details are provided in Section 4). 3.2.2. Partial wavelet coherence (multivariate framework) To account for the impact of other factors such as NREC and prices of crude oil on the relationship between REC and industrial production, we also rely on the partial wavelet coherence (PWC). PWC basically follows the same idea as the partial correlation coefficient and gives the WSC provided in Eq. (5) between REC and industrial production after eliminating the influence of an additional factor (NREC or oil prices in our case). Like the WSC, the PWC takes values ranging between 0 and 1 and is defined by:

PR2t;YXZ ðsÞ

 2   Rt;YX ðsÞ−Rt;YZ ðsÞRt;YX ðsÞ ¼ 2  2 1−Rt;YZ ðsÞ 1−Rt;XZ ðsÞ

context, to accommodate structural breaks and shifts in parameters, we use the Shi et al. (2016) and Shi et al., 2018) rolling-window Granger causality approach. Shi et al. (2016) and Shi et al. (2018)) showed that the rolling-window approach yields the best results in comparison with the forward and recursive rolling approaches for stationary and possibly integrated systems. Shi et al. (2016) and Shi et al., 2018) also examined the lag-augmented VAR (LA-VAR) rolling-window approach following Toda and Yamamoto (1995). To investigate the Granger causal flows between IPI and REC, we thus apply the bootstrapped rolling -window procedure to overcome the above highlighted issues. Zapata and Rambaldi (1997) noted that this approach is more convenient and provides efficient results not only for large samples but also for small samples due to its good size and power properties. Furthermore, Mantalos (2000) shows that the bootstrap test exhibits the highest accuracy in all estimates, regardless of the cointegration properties. These pioneering findings motivate our choice of the bootstrap Granger causality test, which relies on the following bivariate VAR(p) specification:

ð6Þ

where Z denotes the additional factor (i.e., NREC or oil prices in our case). 3.2.3. The bootstrap rolling-window Granger causality test (Shi et al., 2018) To further examine the relationship between disaggregated REC and industrial production, we further study the Granger causality between them. The industrial production index (IPI hereafter) is said to Granger cause REC if and only if including lags of the IPI in the information set improves the forecast of REC (Granger, 1969). The same principle is applied for the reverse causality from REC to IPI. Granger causality tests are typically performed by examining the joint significance of the lagged values of a variable in a predictive model of another variable. Both symmetric (Toda and Yamamoto, 1995) and asymmetric (Hatemi-J, 2012) Granger causality approaches are not free from criticism. For instance, Mantalos (2000) demonstrated that the Toda and Yamamoto (1995) test has low power and thus can provide misleading results for small and medium sample sizes. Furthermore, Tang (2013) pointed out that such Granger causality approaches, as proposed by Toda and Yamamoto (1995) and Hatemi-J (2012), are unable to capture time-varying Granger causality features as these rely on full-sample estimates, which can show substantial instability over time. In this

t ¼ 1; …; T

ð7Þ

where yt is a K × 1 vector of K endogenous variables at time t, Φi are K × K coefficient matrices for each lag i,11 Φ0 is a K × 1 vector of constants and εt denotes a K × 1 vector of i.i.d. error terms with zero-mean and covariance matrix ∑. We use a bivariate setting, where yt includes the IPI and one REC. In this setting, we test the null hypothesis that REC does not Granger-cause the IPI by imposing zero restrictions for all coefficients referring to REC in the first equation of the VAR system provided by Eq. (7).12 It is important to note that the inclusion of two control variables, non-renewable energy consumption and oil prices, does not alter the estimation procedure because VAR models usually do not rely on any restrictions about the exogeneity of the included variables. Each variable is considered as endogenous.13 To overcome the issue of structural instability resulting from structural breaks, we then employ the rolling-window Granger causality approach proposed by Shi et al. (2016) and Shi et al., 2018). For that, we first fix the rollingwindow size before estimating the test statistics for each time window, or subsample. This procedure is repeated in the next rolling window by moving forward until the last observation is reached. The Wald statistic obtained for each subsample regression is denoted by Wf2 (f1), and the sup-Wald statistic is defined as:

 sup W f ð f 0 Þ ¼ sup W f 2 ð f 1 Þ : f 1 ∈ ½0; f 2 −f 0 ; f 2 ¼ f

ð8Þ

where f1 and f2 are the starting and end points of the regression sample as outlined by Shi et al. (2016) and Shi et al., 2018).

11 The lag length p is first selected based on the Akaike information criterion (AIC). For a robustness check, we further use the BIC to see whether the lag length selection criterion has an impact on the results (refer to Section 5). We would like to thank an anonymous Referee for this valuable suggestion. 12 In this study, we perform a bivariate VAR analysis to detect the relationship between each source of renewable energy consumption and industrial production separately. This choice can be explained by two reasons. First, it fits with our research question which is to disaggregate renewable energy consumption following its production source. Thus, a bivariate VAR estimation is appropriate to address this research question. Including all renewable energy components into one model would shift the focus of the paper to an analysis of spillovers among the renewable energy components. Second, as shown in Table A0 in the Appendix, the correlation among most of the considered renewable energy sources is rather weak. Therefore, the inclusion of all components in one model would not change our overall findings, especially when keeping in mind that we also consider other energy sources and energy prices as control variables. For these reasons, we estimate a bivariate VAR models for each renewable energy source. Please find more details in Sections 4 and 5. We would like to thank an anonymous Referee for raising the necessity to explain this methodological choice. 13 We would like to thank an anonymous referee for raising the necessity to explain this aspect.

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Let fe and ff denote the origination and termination points of the study period, respectively. These points are estimated as the first chronological observation that respectively exceeds or falls below the critical value. In a single switch case, the dating rules are given as follows: Rolling : ^f e ¼

 inf f : W f ð f − f 0 ÞNcv and f ∈ ½ f 0 ; 1

ð9Þ

 hinf i

^f ¼ f f ∈ ^f e ; 1 f : W f ð f − f 0 Þbcv where cv is the corresponding critical value of the Wf statistics. If there are multiple switches in the sample period, we denote the origination and termination of the ith Granger causality by fie and fif for successive episodes i = 1, 2, …, I. The estimations of the dates associated with the first episode (i.e., f1e and f 1f ) are exactly the same as those for the single switch case. For i ≥ 2, fie and fif are estimated as follows:  h inf i

Rolling : ^f ie ¼ f ∈ ^f f : W f ð f −f 0 ÞNcv and i−1t ; 1

ð10Þ

 hinf i

^f ¼ if f ∈ ^f ie ; 1 f : W f ð f −f 0 Þb cv The simulation results provided by Shi et al. (2018) suggest that the rolling-window approach has the most severe size distortion but also has the highest correct detection rate. In a single direction scenario, the performance of the recursive rolling approach is relatively balanced - both the test size and correct detection rates are satisfactory. Nevertheless, in a case where there are bi-directional episodes in the sample period, the recursive rolling algorithm has difficulty detecting the second shorter episode, whereas the performance of the rolling-window approach is relatively better. We utilize the framework suggested by Shi et al. (2018) and determine the optimal lag length by employing the Akaike information criterion (AIC) for each estimation window. For a robustness check, the BIC will also be used to see whether the chosen lag length has an impact on the results. Furthermore, the Wald statistic based on the LA-VAR model follows a standard chi-squared distribution (Toda and Yamamoto, 1995). However, we prefer to use the bootstrap Wald test approach to better approximate the finite sample distribution (see for example Balcilar et al., 2010). For a further robustness check, we also estimate the test statistics using three different window sizes of 50, 60 and 70 monthly observations. For example, with a window size of 60 months, the first modified bootstrap Wald Granger causality test statistic is obtained using a subsample from 01/1981 (the first date of our sample period) to 12/1985 to get 60 observations. In the same way, the second test statistic is computed by using the data from 02/1981 to 01/1986. This rolling procedure continues until the last observation is reached to examine the Granger causal relationship. The same principle is applied for the window sizes of 50 and 70 months. To save space, we only present the results for the window size of 60 in the text while those for 50 and 70 months are available upon request (more details are provided in Section 5). 4. Wavelet results and discussions 4.1. Preliminary analysis: evidence of nonlinearity As a preliminary analysis, we first test the stationarity and nonlinearity properties of the considered time series. The objective of these tests is to demonstrate the necessity to use econometric methods that allow taking nonlinearity into account. The Narayan and Popp (2010) unit root test with two endogenously determined structural

7

breaks is used and its results (Table A1 in the Appendix) show that all variables are not stationary at level, except for the variable HPC (hydroelectric power consumption) when structural breaks are considered in the intercept of the time series only (model M1). When considering breaks in the slope of the series, the results are a bit mixed. We find more evidence for stationarity but solely at the 5% and 10% levels. More importantly, the Narayan and Popp (2010) unit root test indicates the presence of structural breaks in the time series. This gives an early indication of a nonlinear linkage between the variables over time. In such case, the outcome of linear models may be biased because this nonlinearity can result in asymmetries or parametric instability. Thus, applying wavelets and rollingwindow Granger causality tests helps capture the potential instability of parameters by accounting for time-variation. We then perform the BDS test of Brock et al. (1996) to examine the nonlinearity in the residuals of the equation relating IPI and REC. The results (Table A2 in the Appendix) reject the null hypothesis of i.i.d. residuals (independent and identically distributed) for all the considered variables at different dimensions. This implies that there are remaining dependence and presence of omitted nonlinear structure, which are not captured by a linear specification. Hence, there is nonlinearity in the data. Thus, a dynamic framework that can capture structural changes and nonlinearity between IPI and REC is necessary. To conclude, the results of unit root and nonlinearity tests show that it is necessary to use appropriate econometric techniques taking the nonlinearity into account to obtain accurate results. This confirms and justifies our choice to use wavelet and time-varying Granger causality methods.

4.2. Wavelet squared coherence and partial wavelet coherence An adequate application of the wavelet decomposition approach requires a “bias correction.” This bias problem14 may arise, not only in the wavelet power spectra, but also in the wavelet cross spectrum, due to low frequency oscillations (Liu et al., 2007; Veleda et al., 2012). Therefore, we decided to use the bias corrected version of wavelets, as suggested by Ng and Chan (2012). Fig. 1 shows the wavelet squared coherence (column 1) and partial wavelet coherence plots (columns 2 and 3) for the relationship between the industrial production index (IPI) and the seven involved REC. The partial wavelet coherence is used in a multivariate framework as a robustness check by including NREC and crude oil prices as additional control variables. To facilitate the distinction between our main findings and our robustness check, the interpretation of the results is organized into two different parts. The first one focuses on the bivariate analysis with wavelet squared coherence (column 1 in Fig. 1) and the second one on the multivariate framework with partial wavelet coherence and two additional variables (columns 2 and 3 in Fig. 1, respectively).

4.2.1. Wavelet squared coherence's results (bivariate framework) Following the standard practice, we use contour plots to present the wavelet coherence spectrum (column 1 of Fig. 1). The contour plot approach involves three dimensions: frequency (in months), time and wavelet coherence power. The frequency and time are on the vertical and horizontal axes, but the level of similarity is indicated by a color coding ranging from blue (low similarity) to red (high similarity). The thick black continuous line in Fig. 1 isolates regions where the wavelet squared coherence is statistically significant at the 5% level. In these figures, arrows pointing towards

14 This bias is consisted of a time series that comprises sine waves with different periods but the same amplitude that does not produce identical peaks.

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A). IPI – HPC

B). IPI – GEC

C). IPI – WEC

D). IPI – WaEC

E). IPI – BiEC

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F). IPI – BEC

G). IPI – REC

Fig. 1. Wavelet squared coherence and partial wavelet coherence. Column 1 shows the Wavelet Squared Coherence (WSC) figures between the IPI and REC from different sources. Columns 2 and 3 show partial wavelet coherence figures where TFFC (total fossil fuel consumption) and oil prices are used as additional control variables, respectively. Time and frequency (in months) are on the horizontal and the vertical axes, respectively. The color bar on the right side shows the coherence (or the strength of the relationship). The warmer the color of a region, the greater the coherence between the variables is. The lighter shade cone shows the edge effect, also named the cone of influence (COI) which is the region where the wavelet results are interpretable. The black solid line isolates the statistically significant area at the 5% significance level. The arrows indicate the phase difference between the two series. → & ← indicate that the IPI and REC are in phase and out of phase, i.e., cyclical or counter cyclical effect on each other, respectively. ↗ & ↙ indicate IPI is leading whereas ↘ & ↖ indicate IPI is lagging. For interpretation purpose, red color at the bottom (top) of the graph indicates high interdependence at low (high) frequencies. IPI = Industrial Production Index, HPC = Hydroelectric Power Consumption, GEC = Geothermal Energy Consumption, WEC = Wood Energy Consumption, WaEC = Waste Energy Consumption, BiEC = Biofuel Energy Consumption, BEC = Total Biomass Energy Consumption, REC = Total REC, TFFC = Total Fossil Fuels Consumption, Oil = WTI crude oil prices. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

left (right) indicate a negative (positive) relationship between the variables. Throughout the visual inspection, we identify a weak relationship between IPI and REC over the whole sample period as indicated by the islands of blue color (low level of coherence). This result confirms the findings by Salim et al. (2014) and this weak relationship between IPI and REC can be explained by the fact that the part of renewable energy in the energy portfolio is still small in the U.S. (about 7.5% in 2010, according to the IRENA) though this proportion has been increasing. Additionally, we observe a quite similar co-movement pattern with the IPI across the different REC. However, significant positive co-movements are located at low frequencies since some red vortices are detected between 16 and 128 months (bottom of the graphs in red), especially in the 1990s during the PURPA era, as mentioned in Section 3.1. This result indicates that the comovement between the IPI and REC is more pronounced in the long term. This finding suggests that the impact of REC on industrial production needs time to be effective. Thus, renewable energy policies should be expected to be efficient in the long term instead of showing short-run impacts. This time constraint requires a longterm view while drawing renewable energy policies. The red areas are disconnected from each other as they are cut by the blue zones. This mainly indicates the occurrence of abrupt changes over the whole period for all pairs and suggests that the relationship between REC and the IPI is time-varying. One reason for the latter might be a dependence of this relationship on the economic and political

conditions in the country. For example, we find a higher comovement between IPI and REC in crisis periods, as shown in Section 5 below. This finding implies that IPI and REC comove at low frequencies (i.e., longer time periods) and that this relationship changes over time. In other words, these variables tend to move together in the long run and this implies that renewable energy policies show their effectiveness only in the long term. That may explain why several policies, such as the Renewable Portfolio Standard (RPS)15 initiated in 1997, are still in force today in the U.S. However, we note that there are more red vortices for biomass EC such as wood, waste and biofuel (panels C, D, and E in Fig. 1) than for hydropower and geothermal EC (panels A and B in Fig. 1). This result implies that there is a higher interaction between biomass EC and industrial production in the long run. Thus, a higher consumption of biomass energy can positively affect industrial production and vice versa. In this case, renewable energy policies should encourage further

15 The RPS is a voluntary policy, which gives the choice to each state to impose retail electricity suppliers to purchase a growing amount or percentage of renewable energy over time. Massachusetts was one of the first states to enact an RPS in 1997, followed by Connecticut and Wisconsin in 1998, Maine, New Jersey and Texas in 1999, Arizona, Hawaii and Nevada in 2001, California and New Mexico in 2002, Minnesota in 2003, Colorado, Maryland, New York, Pennsylvania and Rhode Island in 2004, District of Columbia in 2005 (Martinot et al., 2005). In 2017, 29 states plus the District of Colombia adopted an RPS, according to the report performed by Barbose (2017) from the Lawrence Berkeley National Laboratory.

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1.

Bivariate causality

A). IPI – HPC

2.

Multivariate causality TFFC

3.

Multivariate causality – Oil

B). IPI – GEC

C). IPI – WEC

D). IPI – WaEC

Fig. 2. Results of bootstrapped rolling window Granger causality test – from IPI to REC. Fig. 2 presents the test statistic sequence (on the y-axis) of the rolling window bootstrapped Wald tests and the corresponding 5% critical values (also on the y-axis) (fixed window of 60 months). The period is on the x-axis. When the test statistic sequence curves (blue dashed lines) are above the 5% critical value sequence (black line), the Granger causality is significant. The optimal lag length is selected based on AIC. IPI = Industrial Production Index, HPC = Hydroelectric Power Consumption, GEC = Geothermal Energy Consumption, WEC = Wood Energy Consumption, WaEC = Waste Energy Consumption, BiEC = Biofuel Energy Consumption, BEC = Total Biomass Energy Consumption, REC = Total REC, TFFC = Total Fossil Fuels Consumption, Oil = WTI crude oil prices. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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E). IPI – BiEC

F). IPI – BEC

G). IPI – REC

Fig. 2 (continued).

the biomass energy industry, in addition to policies such as the Biomass Research and Development Initiative (BRDI) initiated in 2002 or the Woody Biomass Utilization Initiative launched in 2003.16 The different behavior of biomass energy compared to other renewable energy sources in the U.S. was also found by Hoang et al. (2019) who showed that biomass energy is the most sensitive renewable energy to oil shocks. This may be related to the use of crude oil in the electricity production from biomass materials, as stated in the book published in 2010 by the National Academy of Sciences.17 More precisely, the most intense positive interactions occur between IPI-WaEC (waste EC) and IPI-BiEC (biofuel EC) during the

1990s as the red color has long lasting durations. This strong relationship between IPI and biomass energy consumption in the 1990s, from waste and biofuels, may be a result of the Biomass Energy and Alcohol Fuels Act developed 1980 (according to IEA and IRENA).18 In the same vein, the IPI-BiEC and IPI-BEC (total biomass EC) pairs show high interactions during the most recent period. IPI-WEC (wood EC) also reports a significant coherence between 2001 and 2011. These pairs may exhibit a convergent pattern, meaning co-booming or co-crashing together with IPI leading since the arrows in the graph are mostly right-up. This finding is important because it indicates that an increase in the industrial production will induce an increase in the consumption of biomass energies in the long run. This confirms the finding by Hoang et al.

16

More information can be found on the website of IEA and IRENA: https://www.iea.org/policiesandmeasures/renewableenergy/?country=United% 20States. 17 National Academy of Sciences, 2010. Electricity from renewable resources. The National Academies of Sciences Engineering Medicine, Washington.

18 IEA = International Energy Agency, IRENA = International Renewable Energy Agency. See the related website: https://www.iea.org/policiesandmeasures/renewableenergy/? country=United%20States.

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(2019) who showed that biomass energy is the only renewable energy that received a significant impact from shocks in the aggregate demand of the economy, which itself depends on the industrial production. This mechanism may explain why biomass energy consumption has the highest interactions with IPI. Finally, we also notice that the relationship between total REC and industrial production is much lower (indicated by the blue color on the graph) compared to the disaggregated energy sources. It thus underlines the importance to distinguish between different energy sources while studying its relationship with economic activity. Finally, these findings achieved by the wavelet squared coherence method let us believe that biomass energies play an important role in the industrial production of the U.S. and this may incite innovative firms to develop new technologies of energy production from waste, wood or biofuel. In the meanwhile, hydropower and geothermal EC seem to have a lower interaction with industrial production in the U.S. 4.2.2. Partial wavelet coherence's results (multivariate framework) Fig. 1 also provides the partial wavelet coherence plots accounting for total NREC (TFFC, or total fossil fuel consumption, column 2) and WTI crude oil prices (Oil, column 3) as a robustness check. Although the inclusion of the two control variables generally diminishes the coherence between industrial production and the different components of REC, the regions with the strongest coherence indicated by warmer colors are roughly the same as already shown in column 1 of Fig. 1, as discussed above. The fact that the strongest coherence is observed for the relationship between biomass energy consumption and industrial production in the 1990s, and between 2001 and 2011, does not disappear after controlling for NREC and crude oil prices. Thus, we can conclude that the multivariate wavelet analysis confirms the results previously obtained within a bivariate framework. This thus confirms the interrelation between industrial production and biomass consumption in the long run. Overall, the wavelet results allow us to verify H1 following which there is a positive comovement between REC and industrial production. However, thanks to the disaggregation of the energy sources, we can further conclude that this hypothesis is mostly validated for biomass EC, as discussed above. We also validate H1 following which this relationship is time-varying. Furthermore, we also show that renewable energy policies' dates and crisis periods can have an impact on this relationship. The next section investigates the Granger causal relationship between industrial production and REC through the Shi et al. (2016) and Shi et al. (2018) rollingwindow test. 5. Rolling-window Granger causality results Shi et al. (2016) and Shi et al. (2018) The wavelet results have provided a clear picture of the lead-lag relationship between the IPI and REC, especially between IPI and biomass EC. This section focuses on the Granger causality analysis to examine the forecasting ability in a time-varying context by performing the bootstrap rolling-window test proposed by Shi et al. (2016) and Shi et al. (2018), see Section 3). This test is conducted in two steps: First, we estimate bivariate VAR models. Second, we estimate multivariate VAR models by including two control variables, as also used for the wavelet approach (total fossil fuel consumption and crude oil prices). The objective of the second step is to check whether the results within the bivariate framework are robust. As a second robustness check for the Granger causality results, we perform the VAR model estimation based on two different criteria to select the optimal lag lengths: first, we use the AIC before changing it to the BIC. To save space, the results using the BIC are presented in Appendix 5, as

well as the results for the Granger causality test applied on the whole sample period. Indeed, the results of the Andrews (1993) and Andrews and Ploberger (1994) tests show that the parameters of the VAR model estimated for the full sample period are not stable (see Tables A3 and A4 in the Appendix). Thus, the results based on the whole period are not valid. Therefore, we only present the findings for the time rolling-window Granger causality test in the main body of the paper. As mentioned in Section 3.2, we applied the MWALD test to verify the Granger causality between the IPI and REC. Furthermore, we consider three window sizes (T = 50, 60 and 70 months) to check whether the window size has an impact on the results. To save space, we only present the results for the window size of 60 months because the results with a window size of 50 or 70 are very similar (they are available upon request). The results of the test statistics are shown in Fig. 2 for testing the Granger causality from the IPI to REC while Fig. 3 refers to the Granger causality from REC to the IPI. The solid black line indicates critical values associated with the 5% level of significance while the vertical axis reflects the Wald test statistic displayed by the dashed blue lines. Thus, when the rolling-window test statistics fluctuate above this black line, a significant Granger causality is detected. In Figs. 2 and 3, column 1 presents the results of the bivariate framework while columns 2 and 3 show those of the multivariate framework, including total fossil fuel consumption (TFFC) and oil prices, respectively. Like in Section 4, we first interpret the bivariate results (column 1) before comparing it with those in a multivariate framework (columns 2 and 3). Fig. 2 reveals a great instability of the Granger causality as the amplitudes of the blue dashed lines vary widely over the sample period, regardless of the energy source. Panel A of Fig. 2 (column 1) shows two periods (1987–1990 and 2001–2007) of significant Granger causal flows from industrial production to HPC (hydroelectric power consumption), where the test statistic is above the 5% critical level. These two periods coincide with well-known extreme events: the first period corresponds to the crisis following the 1987 stock market crash; the second was marked by three extreme events in the U.S. (the dot-com bubble in 2000, the 2001 terrorist attack and the 2007–2008 global financial crisis). All the mentioned periods of turmoil seem to have an impact on Granger causality from the IPI to REC. Thus, when the U.S. economy was affected by these crises, industrial production had a much stronger impact on REC, which is thus important for energy safety. Hoang et al. (2019) indicated that in crisis periods, there are big changes in the aggregate demand of the economy, which in turn impacts oil prices and this can switch the consumption of fossil energy to renewable energy. This may explain why there is a stronger relationship from the IPI to REC in crisis periods. Figs. 2B to 2G also report a significant Granger causality running from industrial production to REC from other sources in the late 1980s and the 2000s. During these periods, the U.S. experienced its biggest historical downturn and was consuming more renewable energy to guarantee a minimum of safety for its global economy. These results also confirm those from Section 4 following which there are higher co-movements between the IPI and REC in the late 1980s and in the 2000s. These periods correspond to the PURPA era (Public Utilities Regulatory Policy Act) (see Section 4 for more details) to the launch of the Renewables Portfolio Standard (RPS) policy in 1997. The RPS consists of voluntary choice by each state in the U.S. to impose retail electricity suppliers to purchase a certain quantity from renewable energy (Martinot et al., 2005). This energy policy has been popular in the U.S. and there were 29 states and the district of Colombia that adopted the RPS until 2017 (Barbose, 2017). In addition, we notice, once again, a bigger interaction between biomass EC, especially waste EC, and industrial production. Indeed, for biomass EC (wood, waste and biofuel), the dashed blue lines are higher and are over the black line more frequently, meaning higher and more numerous significance zones than for

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hydropower and geothermal EC. This result may be explained by the launch of the “Food, Conservation and Energy Act” in 2008 which is directly related to the waste energy production and consumption (IEA and IRENA). The graphs in the second and third columns of Fig. 2 confirm the findings of the bivariate settings by augmenting the VAR models to a multivariate framework including two control variables such as total fossil fuel consumption (TFFC) and crude oil prices. The periods of significant Granger causality mentioned above (late 1980s and the 2000s) are basically unchanged, showing that the results obtained within the bivariate framework are robust. However, we observe that there are more differences when oil prices are included compared to the setting including TFFC. This suggests that oil prices may be an important determinant factor in renewable energy policies and therefore can affect the relationship between REC and industrial production to some extent. This finding makes sense since oil prices are a determinant factor of fossil fuel energy consumption, or non-renewable energy consumption. Changes in oil prices can thus have significant impacts on the balance between renewable and non-renewable energy consumption. Hoang et al. (2019) showed that oil supply and oil demand shocks have high impacts on both renewable and non-renewable energy consumption while aggregate demand shocks only affect biomass energy consumption. This important role of oil prices should thus be considered in energy policies to adjust between renewable and non-renewable energy consumption in the U.S. Fig. 3 provides the results of the bootstrapped rolling-window Granger causality tests for the opposite direction, running from REC to industrial production. Overall, the findings provide evidence for bi-directional Granger causality in crisis periods since we again find significant causality from REC to the IPI in the late 1980s (except for waste EC in Panel D of Fig. 3), in the period around 2001 (except for geothermal EC in Panel B of Fig. 3), in the 2007–2008 period (for GEC, WaEC, BiEC, BEC and REC) and the latest years (except for waste EC). This finding therefore confirms the important role of renewable energy for industrial production and vice versa in crisis periods. It allows us to conclude that there is a bidirectional causality between REC and industrial production in turmoil and crisis periods. However, we also observe more periods, excluding crisis periods, in which REC Granger causes industrial production than vice versa, especially for biomass EC in the 1990s (Panels D and E of Fig. 3). This may be explained by the presence of numerous renewable energy policies in the 1990s that caused structural changes in REC, which in turn Granger caused industrial production.19 These findings are in the same vein as those of Troster et al. (2018) following which there is a bi-directional Granger causality between REC and economic growth in the U.S. in low quantiles only, meaning in downturn or crisis periods only. On the other hand, Cai et al. (2018) found that REC Granger causes economic growth in the U.S., which confirms the result for biomass energy in our case. This result again confirms the special role of biomass EC in the U.S. Therefore, it should be considered as an important element in the country's energy mix policy. Again, the comparison between bivariate results and multivariate results (column 1 vs. columns 2 and 3 in Fig. 3) shows that the main results are confirmed regardless of the inclusion of control variables. This means that in columns 2 and 3, including NREC and oil prices respectively, we still find a bi-directional causality between REC and the IPI in crisis periods (in the 1980s and 2000s) and biomass EC also Granger causes the IPI in the 1990s. These results allow us to conclude that the previous findings are robust to

19

More information can be found on the website of IEA and IRENA: https://www.iea.org/policiesandmeasures/renewableenergy/?country=United% 20States.

13

the consideration of control variables. Once again, we observe that oil prices tend to produce more variations in the time rollingwindow Granger causality results than NREC. This confirms the wavelet results and suggests that oil prices affect the relationship between REC and industrial production to some extent. However, these results do not allow us to entirely validate H2 following which only the IPI Granger causes REC while we find the reverse for biomass EC only. This finding shows that it is important to disaggregate renewable energy sources when studying its relationship with economic activity. Furthermore, our results validate H3 because including oil prices leads to higher variations in the Granger causality results. The same estimations are also carried out by using the BIC to select the optimal lag length and these are reported in Appendix 5. The time rolling-window results for the Granger causality from the IPI to REC using the BIC are presented in Fig. A3 in the Appendix. Overall, our main conclusions are supported since there are only small differences compared to the results using the AIC (comparison between Fig. 2 and Fig. A3). Indeed, the patterns of the test statistics are quite similar with some small variations for certain periods. When we compare Fig. A4 (using BIC) to Fig. 3 (using AIC) for testing the rolling-window Granger causality from REC to industrial production (REC to IPI), we also find that there are small differences when changing the information criterion (from AIC to BIC). These findings confirm that our main results are robust, meaning there is a bi-directional Granger causality between REC and the IPI in crisis periods and especially a Granger causality from biomass EC to the IPI also in other periods. In addition, we still find that there are more differences between bivariate and multivariate models when oil prices are included. This result implies that oil prices should be considered as an important factor when drawing energy policies. 6. Conclusion and policy implications We have studied the interaction between REC and industrial production in the U.S. using monthly data over the 1981–2018 period. The contributions of this study rely in the distinction among different sources of renewable energies, such as wind (hydropower), heat (geothermal), and biomass (wood, waste, and biofuel). Furthermore, we investigate the industrial production, instead of economic growth used in most of previous studies. This aspect is important because the industrial sector is the most energyconsuming in the U.S. In addition, we also contribute to the literature by decomposing this relationship into time and frequency domains based on wavelet decomposition techniques (i.e., wavelet squared coherence and partial wavelet coherence). Moreover, we extend the existing literature by examining the time-varying Granger causality between renewable energy consumption and industrial production through the rolling-window test developed by Shi et al. (2016) and Shi et al. (2018). Thus, the considered time-frequency and time-variation allow us to investigate the nonlinear nature of the relationship between renewable energy consumption and industrial production. This nonlinearity may help explain inconclusive findings of previous studies. Finally, we also check the robustness of our results by performing several sensitivity analyses such as the comparison between bivariate and multivariate frameworks including control variables (such as non-renewable energy consumption and oil prices), the comparison between two criteria to select the optimal lag length (AIC vs. BIC), and the comparison between different time window sizes for the rolling-window Granger causality test. The results show that the renewable energy source matters. Indeed, there is a stronger co-movement between industrial production and biomass energy consumption (from waste, biofuel and wood) compared to hydropower and geothermal energy

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1.

Bivariate causality

A). HPC – IPI

2.

Multivariate causality – TFFC

3.

Multivariate causality Oil

B). GEC – IPI

C). WEC – IPI

D). WaEC – IPI

Fig. 3. The results of bootstrapped rolling-window Granger causality test – from REC to IPI. Fig. 3 presents the test statistic sequence (on the y-axis) of the rolling window bootstrapped Wald tests and the corresponding 5% critical values (fixed window of 60 months). The period is on the x-axis. When the test statistic sequence curves (blue dashed lines) are above the 5% critical value sequence (black line), the Granger causality is significant. The optimal lag length is selected based on AIC. IPI = Industrial Production Index, HPC = Hydroelectric Power Consumption, GEC = Geothermal Energy Consumption, WEC = Wood Energy Consumption, WaEC = Waste Energy Consumption, BiEC = Biofuel Energy Consumption, BEC = Total Biomass Energy Consumption, REC = Total REC, TFFC = Total Fossil Fuels Consumption, Oil = WTI crude oil prices. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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E). BiEC – IPI

F). BEC – IPI

G). REC – IPI

Fig. 3 (continued).

consumption. Thus, U.S. energy policymakers should pay more attention on the development of biomass energies in the future since it can have important impacts on industrial production. Furthermore, wavelet results also show that this relationship is significant at low frequencies, meaning in the long term. This finding implies that the impact of renewable energy consumption needs time to have a significant impact on industrial production. Thus, firms and policymakers should be patient and draw long-run strategies on the development of renewable energies to boost industrial production and vice versa. The rolling-window Granger causality results reveal that the U.S. industrial sector is more dependent on renewable energies during crisis periods since the bi-directional predictability between them is the most pronounced during crisis periods. This suggests that renewable energies allow the U.S. to ensure the continuum of industrial production in times of turmoil. We then conclude that the U.S. overall continuum of economic growth depends on an optimal

diversification of energy sources. Therefore, all sources of energy should be considered, and the energy mix should be further developed in the future. Furthermore, the rolling-window Granger causality results indicate that biomass energy consumption also Granger causes industrial production in periods other than crisis periods. This thus confirms the wavelet results following which biomass energy consumption has the highest correlation with industrial production. Therefore, biomass energy should be further developed in the future to optimize the energy mix in the U.S. The above-mentioned results are found to be robust to the inclusion of control variables, to the lag length selection, and to the choice of the time rolling-window size. However, we find that crude oil prices have a higher impact on the relationship between renewable energy consumption and industrial production, compared to nonrenewable energy consumption. This result suggests that oil prices should be considered as an important factor when drawing renewable energy policies. This may be explained by the fact that the

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industrial sector tends to base on oil prices to make an arbitrage between renewable and non-renewable energy use to optimize the production cost. The findings of the present study differ from previous studies focusing on the U.S. to some extent. Indeed, Sari et al. (2008) showed that industrial production leads the consumption of hydropower, waste and wood energy consumption while we find that there is a bi-directional relationship between them in only crisis periods. However, our findings are in line with those from Troster et al. (2018) following which there is a bi-directional

Granger causality between renewable energy consumption and economic growth in low quantiles, meaning in downturn and crisis periods only. It is thus important to decompose the relationship between energy consumption and economic activities in time and frequency to detect the nonlinear and complex structure of the relationship between them. The findings of this study also show that it is important to disaggregate energy sources as well as to include other common factors, such as energy prices, when studying the relationship between energy consumption and economic activities.

Appendix 1. Energy consumption in the U.S., 1974–2018

Fig. A1. Renewable and non-renewable energy consumption in the industrial sector from 1974 to 2018. This figure shows the total renewable and non-renewable energy consumption in the industrial sector in the U.S. per month from January 1974 to March, 2018, collected from the website of the U.S. Energy Information Administration EIA (https://www.eia.gov/ totalenergy/data/monthly/). The units in the y-axis show the quantity of energy consumption measured by trillion of BTU, meaning British Thermal Units. The BTU measures the amount of heat required to raise the temperature of 1 pound of liquid water by 1-degree Fahrenheit at a constant pressure of one atmosphere. Monthly data, source: EIA.

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Appendix 2. Data sample

A). Industrial Production Index (IPI)

B). Hydroelectric Power Consumption (HPC)

C). Geothermal Energy Consumption (GEC)

D). Wood Energy Consumption (WEC)

E). Waste Energy Consumption (WaEC)

F). Biofuel Energy Consumption (BiEC)

G). Total Biomass Energy Consumption (BEC)

H). Total REC (REC)

I). Total Fossil Fuels Consumption (TFFC)

J). WTI Crude Oil Price (Oil)

Fig. A2. Time trend of renewable energy consumption and industrial production in the U.S. from 1981 to – seasonally adjusted series. This figure shows the total renewable energy consumption per source of energy in the U.S. per month from January 1974 to March, 2018, from panel A to panel H. The raw data was collected from the website of the U.S. Energy Information Administration EIA (https://www.eia.gov/). Panels I and J show the data for total fossil fuels consumption (or total non-renewable energy concumption) and crude oil prices used in the robustness checks. The y-axis of panels from A to I shows the quantity of energy consumption measured by Trillion of BTU, meaning British Thermal Units. The BTU measures the amount of heat required to raise the temperature of 1 pound of liquid water by 1-degree Fahrenheit at a constant pressure of one atmosphere. The y-axis in panel J shows the price of 1 barrel in USD.

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Table A0 Correlation among the sources of renewable energy consumption. HPC HPC GEC WEC WaEC BiEC BEC

1

GEC

WEC

WaEC

BiEC

BEC

−0.29 1

0.10 −0.48 1

−0.05 0.82 −0.38 1

−0.30 0.67 −0.34 0.40 1

−0.27 0.64 −0.06 0.43 0.95 1

Notes. This table shows the correlation coefficients among sources of renewable energy consumption. HPC = Hydroelectric Power Consumption, GEC = Geothermal Energy Consumption, WEC = Wood Energy Consumption, WaEC = Waste Energy Consumption, BiEC = Biofuel Energy Consumption, BEC = Total Biomass Energy Consumption.

Appendix 3. Stationarity and nonlinearity tests

Table A1 Results of the Narayan and Popp (2010) unit root test with two structural breaks. Model M1

Model M2

Test statistics IPI HPC GEC WEC WaEC BiEC BEC REC TFFC Oil

−2.084 −4.883⁎⁎⁎ −2.635 −2.690 −3.674 −0.821 −2.316 −2.000 −2.023 −3.548

TB1

TB2

k

2008 M08 2007 M01 1988 M12 1996 M03 1995 M04 1996 M05 1989 M12 1998 M05 1989 M12 1990 M07

2008 M11 2010 M05 1995 M07 1999 M09 2000 M11 1997 M07 1999 M09 1999 M09 2005 M12 2008 M11

4 2 10 8 4 12 8 2 9 1

Test statistics −4.793⁎ −4.838⁎⁎ −1.962 −3.970 −4.752⁎⁎ −1.838 −2.906 −4.542⁎ −4.172 −5.507⁎⁎

TB1

TB2

k

1998 M07 2007 M01 1988 M12 1999 M09 1995 M04 1996 M05 1989 M12 1999 M09 1989 M12 1990 M07

2008 M08 2010 M05 1995 M07 2000 M01 2000 M11 1997 M07 1999 M09 2000 M12 2005 M12 2008 M11

6 2 10 8 4 12 8 5 9 1

Notes. This table displays the results of the Narayan and Popp (2010) unit root test for the model M1 and M2 as explained in Narayan and Popp (2010). The model M1 assumes two structural breaks at unknown dates in the level of each series. The model M2 assumes two structural breaks at unknown dates in the level as well as the slope of each series. The test statistics for the null hypothesis of a unit root are presented for both models. The critical values for model M1 are −4.67, −4.08 and −3.77 at the 1%, 5% and 10% significance levels, respectively. The critical values for model M2 are −5.29, −4.69 and −4.40 at the 1%, 5% and 10% significance levels, respectively. These critical values are collected from Narayan and Popp (2010) based on 50,000 replications for a sample size of 500 observations. TB1 and TB2 are the dates of the structural breaks determined according to the sequential procedure discussed in Narayan and Popp (2010) and k stands for the optimal lag length obtained by using the procedure suggested by Hall (1994) and Narayan and Popp (2010). Following Narayan and Popp (2010), a trimming percentage of 20 is used, meaning that breaks are only searched in the interval [0.2 T, 0.8 T]. IPI = Industrial Production Index, HPC = Hydroelectric Power Consumption, GEC = Geothermal Energy Consumption, WEC = Wood Energy Consumption, WaEC = Waste Energy Consumption, BiEC = Biofuel Energy Consumption, BEC = Total Biomass Energy Consumption, REC = Total REC, TFFC = Total Fossil Fuels Consumption, Oil = WTI crude oil price. ⁎ Indicates significance at 10% level. ⁎⁎ Indicates significance at 5% level. ⁎⁎⁎ Indicates significance at 1% level.

Table A2 Results of the BDS (1996) test for nonlinearity. m=2 Panel A: IPI equations HPC GEC WEC WaEC BiEC BEC REC Panel B: REC equations HPC GEC WEC WaEC BiEC BEC REC

m=3

m=4

m=5

m=6

9.983⁎⁎⁎ 9.341⁎⁎⁎ 9.051⁎⁎⁎ 8.461⁎⁎⁎ 9.740⁎⁎⁎ 9.751⁎⁎⁎ 9.785⁎⁎⁎

10.705⁎⁎⁎ 10.382⁎⁎⁎ 10.114⁎⁎⁎ 9.476⁎⁎⁎ 10.784⁎⁎⁎ 10.792⁎⁎⁎ 10.716⁎⁎⁎

10.578⁎⁎⁎ 10.479⁎⁎⁎ 10.440⁎⁎⁎ 9.596⁎⁎⁎ 10.892⁎⁎⁎ 10.893⁎⁎⁎ 10.901⁎⁎⁎

10.543⁎⁎⁎ 10.555⁎⁎⁎ 10.704⁎⁎⁎ 9.794⁎⁎⁎ 10.964⁎⁎⁎ 10.959⁎⁎⁎ 10.963⁎⁎⁎

10.224⁎⁎⁎ 10.363⁎⁎⁎ 10.631⁎⁎⁎ 9.646⁎⁎⁎ 10.791⁎⁎⁎ 10.784⁎⁎⁎ 10.799⁎⁎⁎

1.650⁎ 12.244⁎⁎⁎ 9.506⁎⁎⁎ 9.403⁎⁎⁎ 9.229⁎⁎⁎ 10.425⁎⁎⁎ 3.439⁎⁎⁎

2.201⁎⁎ 14.050⁎⁎⁎ 11.439⁎⁎⁎ 11.535⁎⁎⁎ 10.421⁎⁎⁎ 12.124⁎⁎⁎ 3.638⁎⁎⁎

2.627⁎⁎⁎ 15.420⁎⁎⁎ 13.385⁎⁎⁎ 12.646⁎⁎⁎ 11.664⁎⁎⁎ 14.161⁎⁎⁎ 3.467⁎⁎⁎

2.564⁎⁎ 16.959⁎⁎⁎ 15.202⁎⁎⁎ 13.970⁎⁎⁎ 12.758⁎⁎⁎ 16.347⁎⁎⁎ 3.379⁎⁎⁎

2.125⁎⁎ 18.950⁎⁎⁎ 17.534⁎⁎⁎ 15.602⁎⁎⁎ 13.799⁎⁎⁎ 18.606⁎⁎⁎ 3.246⁎⁎⁎

Note. The entries indicate the BDS test based on the residuals of a VAR for all selected variables. m denotes the embedding dimension of the BDS test. IPI = Industrial Production Index, HPC = Hydroelectric Power Consumption, GEC = Geothermal Energy Consumption, WEC = Wood Energy Consumption, WaEC = Waste Energy Consumption, BiEC = Biofuel Energy Consumption, BEC = Total Biomass Energy Consumption, REC = Total REC. ⁎⁎⁎ Indicates rejection of the null of residuals being i.i.d. at the 1% level of significance.

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Appendix 4. Testing the paramater stability in the causality test Before presenting the results for time rolling-window Granger causality tests, we first report results for the whole period (1981–2018) in Table A3. The first column of Table A3 shows results in the bivariate framework while columns 2 and 3 present results in the multivariate framework by including total fossil fuel consumption and oil prices, respectively. However, Table A4 shows that the parameters of the VAR models used for the whole period are not stable. Thus, the results of the Granger causality test based on the whole period are not accurate and cannot be used as valid findings. That is why we only interpret time rolling-window Granger causality results in the main text.

Table A3 Results of bootstrap Granger causality tests for the whole period. Hypothesis

1. Bivariate

2. Multivariate - TFFC

3. Multivariate - Oil

Statistic

k

Statistic

k

Statistic

k

Panel A: causality from IPI to REC IPIt − / → HPCt IPIt − / → GECt IPIt − / → WECt IPIt − / → WaECt IPIt − / → BiECt IPIt − / → BECt IPIt − / → RECt

12.875⁎⁎ 11.739 32.404⁎⁎⁎ 1.853 8.959 13.162 1.775

5 8 6 5 5 8 5

12.122 10.143 23.242⁎⁎⁎ 1.684 16.869⁎⁎⁎ 20.484⁎⁎ 4.402

8 8 11 5 5 11 5

10.667⁎ 8.676 32.289⁎⁎⁎ 1.809 8.272 12.330 2.384

5 7 10 5 5 9 5

Panel B: causality from REC to IPI HPCt − / → IPIt GECt − / → IPIt WECt − / → IPIt WaECt − / → IPIt BiECt − / → IPIt BECt − / → IPIt RECt − / → IPIt

5.883 18.076⁎⁎ 4.487 9.670⁎ 6.727 5.331 7.176

5 8 6 5 5 8 5

8.863 18.087⁎⁎ 12.641 9.733⁎ 6.858 11.103 6.253

8 8 11 5 5 11 5

6.625 13.394⁎ 8.089 7.439 15.068⁎⁎ 9.448 12.537⁎⁎

5 7 10 5 5 9 5

Notes. ⁎⁎⁎, ⁎⁎ and ⁎ denote the rejection of the null hypotheses of no Granger causality at 1%, 5% and 10% levels, respectively. The relationship “x” −/→ “y” means “x” does not Granger-cause “y”, indicating the null hypothesis. The significance level are based on the bootstrap values calculated using 2000 repetitions. The lag order “k” is selected based on the AIC. IPI = Industrial Production Index, HPC = Hydroelectric Power Consumption, GEC = Geothermal Energy Consumption, WEC = Wood Energy Consumption, WaEC = Waste Energy Consumption, BiEC = Biofuel Energy Consumption, BEC = Total Biomass Energy Consumption, REC = Total REC, TFFC = Total Fossil Fuels Consumption, Oil = WTI crude oil prices. Table A4 Results of parameter stability tests. Sup-LR

Exp-LR

Mean-LR

Panel A: IPI as the dependent variable HPC GEC WEC WaEC BiEC BEC REC

5.1549⁎⁎ 8.6845⁎⁎⁎ 4.7731⁎⁎ 6.6248⁎⁎ 15.446⁎⁎⁎ 13.594⁎⁎⁎ 8.7657⁎⁎⁎

0.7176 1.4030⁎⁎ 0.8903 1.0231 3.5685⁎⁎⁎ 2.7626⁎⁎⁎ 1.6660⁎⁎⁎

1.1973 1.5756 1.5973 1.3346 2.6561⁎⁎ 2.3505⁎⁎ 2.2333⁎⁎

Panel B: REC as the dependent variables HPC GEC WEC WaEC BiEC BEC REC

3.7364⁎ 4.6541 4.8314⁎⁎ 3.8812 20.735⁎⁎⁎ 13.1561⁎⁎⁎ 11.5781⁎⁎⁎

0.9021⁎ 1.2336⁎ 1.2186⁎⁎ 1.0224 6.0024⁎⁎⁎ 5.0123⁎⁎⁎ 2.8532⁎⁎⁎

1.6935⁎ 2.1995⁎ 2.3363⁎⁎ 1.7993 2.8386⁎ 7.0155⁎⁎⁎ 3.8801⁎⁎⁎

Notes. The null hypothesis for all tests is that the estimated parameters are constant. The Sup-LR test statistics are appropriate to examine a regime shift, while the Mean-LR and the Exp-LR tests are the optimal tests to examine whether the model captures a stable relationship over time. The significance level is based on p-values calculated using 2000 bootstrap repetitions. IPI = Industrial Production Index, HPC = Hydroelectric Power Consumption, GEC = Geothermal Energy Consumption, WEC = Wood Energy Consumption, WaEC = Waste Energy Consumption, BiEC = Biofuel Energy Consumption, BEC = Total Biomass Energy Consumption, REC = Total REC. ⁎⁎⁎ Indicates the rejection of the null hypothesis of parameter stability at the 1% significance level. ⁎⁎ Indicates the rejection of the null hypothesis of parameter stability at the 5% significance level. ⁎ Indicate the rejection of the null hypothesis of parameter stability at the 10% significance level.

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Appendix 5. Granger causality results with BIC criterion for lag length selection

1.

Bivariate causality

A). IPI – HPC

2.

Multivariate causality TFFC

3.

Multivariate causality Oil

B). IPI – GEC

C). IPI – WEC

D). IPI – WaEC

Fig. A3. The results of bootstrapped rolling window Granger causality test – from IPI to REC (lag lengths selected with the BIC criterion). Notes: These figures represent the test statistic sequence (on the y-axis) of the rolling window-based (fixed window of 60 months) bootstrapped Wald tests and the corresponding 5% critical values (also on the y-axis). The period is on the x-axis. So, when the test statistic sequence (blue dashed line) curves are above the 5% critical value sequence (black line), the causality is significant. Lag length is selected based on BIC. IPI = Industrial Production Index, HPC = Hydroelectric Power Consumption, GEC = Geothermal Energy Consumption, WEC = Wood Energy Consumption, WaEC = Waste Energy Consumption, BiEC = Biofuel Energy Consumption, BEC = Total Biomass Energy Consumption, REC = Total Renewable Energy Consumption, TFFC = Total Fossil Fuels Consumption. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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E). IPI – BiEC

F). IPI – BEC

G). IPI – REC

Fig. A3 (continued).

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1.

Bivariate causality

A). HPC – IPI

2.

Multivariate causality TFFC

3.

Multivariate causality Oil

B). GEC – IPI

C). WEC – IPI

D). WaEC – IPI

Fig. A4. The results of bootstrapped rolling window Granger causality test – from REC to IPI (lag lengths selected with the BIC criterion). Notes: These figures represent the test statistic sequence (on the y-axis) of the rolling window-based (fixed window of 60 months) bootstrapped Wald tests and the corresponding 5% critical values (also on the y-axis). The period is on the x-axis. So, when the test statistic sequence (blue dashed line) curves are above the 5% critical value sequence (black line), the causality is significant. The lag length is selected based on BIC. IPI = Industrial Production Index, HPC = Hydroelectric Power Consumption, GEC = Geothermal Energy Consumption, WEC = Wood Energy Consumption, WaEC = Waste Energy Consumption, BiEC = Biofuel Energy Consumption, BEC = Total Biomass Energy Consumption, REC = Total Renewable Energy Consumption, TFFC = Total Fossil Fuels Consumption. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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E). BiEC – IPI

F). BEC – IPI

G). REC – IPI

Fig. A4 (continued).

References CD-ROM, 2018. World Development Indicators Database for 2018. World Bank http:// datatopics.worldbank.org/world-development-indicators/. Adewuyi, A., Awodumi, O.B., 2017. Renewable and non-renewable energy-growthemissions linkages: review of emerging trends with policy implications. Renew. Sust. Energ. Rev. 69, 275–291. Aguiar-Conraria, L., Azevedo, N., Soares, M.J., 2008. Using wavelets to decompose the time-frequency effects of monetary molicy. Physica A: Statistical Mechanics and its Applications 387, 2863–2878. Andrews, D.W., 1993. Tests for parameter instability and structural change with unknown change point. Econometrica 61, 821–856. Andrews, D.W., Ploberger, W., 1994. Optimal tests when a nuisance parameter is present only under the alternative. Econometrica 62, 1383–1414. Apergis, N., Payne, J.E., 2011. On the causal dynamics between renewable and nonrenewable energy consumption and economic growth in developed and developing countries. Energy Systems 2, 299–312. Apergis, N., Payne, J.E., Menyah, K., Wolde-Rufael, Y., 2010. On the causal dynamics between emissions, nuclear energy, renewable energy, and economic growth. Ecol. Econ. 69, 2252–2260. Asafu-Adjaye, J., 2000. The relationship between energy consumption, energy prices and economic growth: time series evidence from Asian developing countries. Energy Econ. 22, 615–625. Aydin, C., Esen, O., 2018. Does the level of energy intensity matter in the effect of energy consumption on the growth of transition economies? Evidence from dynamic panel threshold analysis. Energy Econ. 69, 185–195.

Balcilar, M., Ozdemir, Z.A., Arslanktur, Y., 2010. Economic growth and energy consumption causal nexus viewed through a bootstrap rolling window. Energy Econ. 32 (6), 1398–1410. Barbose, G.L., 2017. U.S. renewables portfolio standards: 2017 annual status report. Berkeley Lab https://emp.lbl.gov/publications/us-renewables-portfolio-standards-0. Barreto, R.A., 2018. Fossil fuels, alternative energy and economic growth. Econ. Model. 75, 196–220. Beckmann, J., Belke, A., Czudaj, R., 2014a. Does global liquidity drive commodity prices? J. Bank. Financ. 48, 224–234. Beckmann, J., Belke, A., Czudaj, R., 2014b. Regime-dependent adjustment in energy spot and futures markets. Econ. Model. 40, 400–409. Belke, A., Dobnik, F., Dreger, C., 2011. Energy consumption and economic growth - new insights into the cointegration relationship. Energy Econ. 33 (5), 782–789. Belke, A., Bordon, I.G., Volz, U., 2013. Effects of global liquidity on commodity and food prices. World Dev. 44, 31–43. Ben-Salha, O., Hkiri, B., Aloui, C., 2018. Sectoral energy consumption by source and output in the U.S.: new evidence from a wavelet approach. Energy Econ. 72, 75–96. Bhattacharya, M., Paramati, S.R., Ozturk, I., Bhattacharya, S., 2016. The effect of renewable energy consumption on economic growth: evidence from top 38 countries. Appl. Energy 162, 733–741. Bildirici, M.E., 2012. The relationship between economic growth and biomass energy consumption. Journal of Renewable and Sustainable Energy 4, 023113. Bildirici, M.E., Gökmenoglu, S.M., 2017. Environmental pollution, hydropower energy consumption and economic growth: evidence from G7 countries. Renew. Sust. Energ. Rev. 75, 68–85. Bilgili, F., 2015. Business cycle co-movements between renewables consumption and industrial production: a continuous wavelet coherence approach. Renew. Sust. Energ. Rev. 52, 325–333.

Please cite this article as: T.H.V. Hoang, S.J.H. Shahzad and R.L. Czudaj, Renewable energy consumption and industrial production: A disaggregated time-frequency analysis for ..., Energy Economics, https://doi.org/10.1016/j.eneco.2019.06.018

24

T.H.V. Hoang et al. / Energy Economics xxx (xxxx) xxx

Bloch, H., Rafiq, S., Salim, R., 2015. Economic growth with coal, oil and renewable energy consumption in China: prospects for fuel substitution. Econ. Model. 44, 104–115. Brock, W.A., Scheinkman, J.A., Dechert, W.D., LeBaron, B., 1996. A test for independence based on the correlation dimension. Econ. Rev. 15 (3), 197–235. Bruns, S.B., König, J., Stern, D.I., 2018. Replication and robustness analysis of ‘energy and economic growth in the USA: a multivariate approach. Energy Econ. https://doi.org/ 10.1016/j.eneco.2018.10.007. Cai, Y., Sam, C.Y., Chang, T., 2018. Nexus between clean energy consumption, economic growth and CO2 emissions. J. Clean. Prod. 182, 1001–1011. Carmona, M., Congregado, E., Feria, J., Iglesias, J., 2017. The energy-growth nexus reconsidered: persistence and causality. Renew. Sust. Energ. Rev. 71, 342–347. Cho, S., Heo, E., Kim, J., 2015. Causal relationship between renewable energy consumption and economic growth: comparison between developed and developing countries. Geosystem Engineering 18, 284–291. Destek, M.A., Aslan, A., 2017. Renewable and non-renewable energy consumption and economic growth in emerging economies: evidence from bootstrap panel causality. Renew. Energy 111, 757–763. Dong, K., Hochman, G., Zhang, Y., Sun, R., Li, H., Liao, H., 2018. CO2 emissions, economic and population growth, and renewable energy: empirical evidence across regions. Energy Econ. 75, 180–192. Ewing, B.T., Sari, R., Soytas, U., 2007. Disaggregated energy consumption and industrial output in the United States. Energy Policy 35, 1274–1281. Goupillaud, P., Grossmann, P., Morlet, J., 1984. Cycle-octave and related transforms in seismic signal analysis. Geoxploration 23 (1), 85–102. Gozgor, G., Lau, C.K.M., Lu, Z., 2018. Energy consumption and economic growth: new evidence from the OECD countries. Energy 153, 27–34. Granger, C.W., 1969. Investigating causal relations by econometric models and crossspectral methods. Econometrica 37, 424–438. Grinsted, A., Moore, J.C., Jevrejeva, S., 2004. Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Process. Geophys. 11, 561–566. Halkos, G.E., Tzeremes, N.G., 2014. The effect of electricity consumption from renewable sources on countries' economic growth levels: evidence from advanced, emerging and developing countries. Renew. Sust. Energ. Rev. 39, 166–173. Hatemi-J, A., 2012. Asymmetric causality tests with an application. Empir. Econ. 43 (1), 447–456. Hoang, T.H.V., Shahzad, S.J.H., Czudaj, R.L., Bhat, J.A., 2019. How do oil shocks impact energy consumption? A disaggregated analysis for the U.S. Energy J. 40 (SI1), 1–44. Inglesi-Lotz, R., 2016. The impact of renewable energy consumption to economic growth: a panel data application. Energy Econ. 53, 58–63. International Renewable Energy Agency, 2015. A renewable energy roadmap – REmap 2030. IRENA Report. https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2014/IRENA_REmap_summary_findings_2014.pdf. Kahia, M., Kadria, M., Aissa, M.S.B., Lanouar, C., 2017. Modelling the treatment effect of renewable energy policies on economic growth: evaluation from MEAN countries. J. Clean. Prod. 149, 845–855. Kibria, A., Akhundjanov, S.B., Oladi, R., 2018. Fossil fuel share in the energy mix and economic growth. Int. Rev. Econ. Financ. https://doi.org/10.1016/j.iref.2018.09.002 In Press. Koçak, E., Sarkgünesi, A., 2017. The renewable energy and economic growth in Black Sea and Balkan countries. Energy Policy 100, 51–57. Kraft, J., Kraft, A., 1978. On the relationship between energy and GNP. The Journal of Energy and Development 3, 401–403. Lin, H.P., Yeh, L.T., Chien, S.C., 2013. Renewable energy distribution and economic growth in the U.S. International Journal of Green Energy 10 (7), 754–762. Liu, Y., Liang, X.S., Weisberg, R.H., 2007. Rectification of the bias in the wavelet power spectrum. J. Atmos. Ocean. Technol. 24, 2093–3012. Mahadevan, R., Asafu-Adjaye, J., 2007. Energy consumption, economic growth and prices: a reassessment using panel VECM for developed and developing countries. Energy Policy 35, 2481–2490. Mantalos, P., 2000. A graphical investigation of the size and power of the Granger-causality tests in integrated-cointegrated VAR systems. Studies in Nonlinear Dynamics & Econometrics 4 (1). https://doi.org/10.2202/1558-3708.1053. Martinot, E., Wiser, R., Hamrin, J., 2005. Renewable Energy Policies and Markets in the United States (Working Paper). Masih, A.M.M., Masih, R., 1997. On the temporal causal relationship between energy consumption, real income, and prices: some new evidence from Asian-energy dependent NICs based on a multivariate cointegration/vector error-correction approach. J. Policy Model 19, 417–440. Mason, E.S., 1955. Energy requirements and economic growth. Washington: National Planning Association. Menyah, K., Wolde-Rufael, Y., 2010. CO2 emissions, nuclear energy, renewable energy and economic growth in the US. Energy Policy 38, 2911–2915.

Narayan, S., Doytch, N., 2017. An investigation of renewable and non-renewable energy consumption and economic growth nexus using industrial and residential energy consumption. Energy Econ. 68, 160–176. Narayan, P.K., Popp, S., 2010. A new unit root test with two structural breaks in level and slope at unknown time. J. Appl. Stat. 37 (9), 1425–1438. National Academy of Sciences, 2010. Electricity from Renewable Resources. The National Academies of Sciences Engineering Medicine, Washington. Ng, E.K.W., Chan, J.C.L., 2012. Geophysical applications of partial wavelet coherence and multiple wavelet coherence. J. Atmos. Ocean. Technol. 29, 1845–1853. Ocal, O., Aslan, A., 2013. Renewable energy consumption-economic growth nexus in Turkey. Renew. Sust. Energ. Rev. 28, 494–499. Pradhan, R.P., Arvin, M.B., Nair, M., Bennett, S.E., Hall, J.H., 2018. The dynamics between energy consumption patterns, financial sector development and economic growth in financial action task force (FATF) countries. Energy 159, 42–53. Rafindadi, A.A., Ozturk, I., 2017. Impacts of renewable energy consumption on the German economic growth: evidence from combined cointegration test. Renew. Sust. Energ. Rev. 75, 1130–1141. Rasche, R., Tatom, J., 1977. Energy resources and potential GNP. Federal Reserve of St Louis Review 59 (6), 10–24. Salim, R.A., Hassan, K., Shafiei, S., 2014. Renewable and non-renewable energy consumption and economic activities: further evidence from OECD countries. Energy Econ. 44, 350–360. Sari, R., B. Ewing T., Soytas, U. 2008. The relationship between disaggregated energy consumption and industrial production in the United States: an ARDL approach. Energy Econ. 30, 2302–2313. Sebri, M., 2015. Use renewables to be cleaner: meta-analysis of the renewable energy consumption-economic growth nexus. Renew. Sust. Energ. Rev. 42, 657–665. Shahbaz, M., Rasool, G., Ahmed, K., Mahalik, M.K., 2016. Considering the effect of biomass energy consumption on economic growth: fresh evidence from BRICS region. Renew. Sust. Energ. Rev. 60, 1442–1450. Shahbaz, M., Zakaria, M., Shahzad, S.J.H., Mahalik, M.K., 2018. The energy consumption and economic growth nexus in top ten energy-consuming countries: fresh evidence from using the quantile-on-quantile approach. Energy Econ. 71, 282–301. Shi, S., Hurn, S., Phillips, P.C.B., 2016. Causal Change Detection in Possibly Integrated Systems: Revisiting the Money-Income Relationship (Cowles Foundation Discussion Paper no. 2059, 59 pages). Shi, S., Phillips, P.C.B., Hurn, S., 2018. Change detection and the causal impact of the yield curve. J. Time Ser. Anal. 39 (6), 966–987. Silverman, M., Worthman, S., 1995. The future of renewable energy industries. The Electricity Journal March 12–31. Solow, R.M., 1974. The economics of resources or the resources of economics. Am. Econ. Rev. 64, 1–14. Tang, C.F., 2013. A revisitation of the export-led growth hypothesis in Malaysia using leveraged bootstrap simulation and rolling causality techniques. J. Appl. Stat. 40 (11), 2332–2340. Tang, C.F., Tan, B.W., Ozturk, I., 2016. Energy consumption and economic growth in Vietnam. Renewable and Sustainable Energy Review 54, 1506–1514. Tiwari, A.K., 2014. The asymmetric Granger-causality analysis between energy consumption and income in the United States. Renew. Sust. Energ. Rev. 36, 362–369. Toda, H.Y., Yamamoto, T., 1995. Statistical inference in vector autoregressions with possibly integrated processes. J. Econ. 66 (1), 225–250. Torrence, C., Webster, P.J., 1999. Interdecadal changes in the ENSO-monsoon system. J. Clim. 12, 2679–2690. Troster, V., Shahbaz, M., Uddin, G.S., 2018. Renewable energy, oil prices, and economic activity: a Granger-causality in quantiles analysis. Energy Econ. 70, 440–452. Tugcu, C.T., Topcu, M., 2018. Total, renewable and non-renewable energy consumption and economic growth: revisiting the issue with an asymmetric point of view. Energy 152, 64–74. Veleda, D., Montagne, R., Araújo, M., 2012. Cross wavelet bias corrected by normalizing scales. J. Atmos. Ocean. Technol. 29, 1401–1408. Warren, J.C., 1964. Energy and economic advances. The Philippine Economic Journal https://www.tandfonline.com/doi/ref/10.1080/00220387208421399?scroll=top (First Semester). Yildirim, E., Sarac, S., Aslan, A., 2012. Energy consumption and economic growth in the USA: evidence from renewable energy. Renew. Sust. Energ. Rev. 16, 6770–6774. Zapata, H.O., Rambaldi, A.N., 1997. Monte Carlo evidence on cointegration causation. Oxf. Bull. Econ. Stat. 59 (2), 285–298. Zhang, Y.X., Chao, Q.C., Zheng, Q.H., Huang, L., 2017. The withdrawal of the U.S. from the Paris agreement and its impact on global climate change governance. Adv. Clim. Chang. Res. 8, 213–219.

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