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The impact of government ideology on energy efficiency: evidence from panel data

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Abstract

This paper investigates the impact of government ideology on energy efficiency using data for a panel of 23 OECD countries over the 1980–2013 period. With dynamic panel data method applied, our evidence suggests that government ideology is a significant determinant of energy efficiency. Specifically, we show that left-wing parties are associated with energy efficiency improvements. Overall, our results are robust to the two measures of energy efficiency variables, alternative measures of government ideology variables, using annual and 3-year averages of the data, the inclusion of economic and political variables, and to model specifications.

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Notes

  1. Also, Nelson (2002) finds that the political ideology of a senator is the leading factor in voting profiles on environmental policies.

  2. The literature has ascertained that right-wing governments favor protection of property rights and legal quality, while left-wing governments prefer government intervention in the economy (Bjørnskov 2005a). Further, Buttel and Flinn (1976) argue that environmental restructuring suggests that the government is expanding regulation in the economy.

  3. According to the statistics from International Energy Agency (IEA), the energy use (kt of oil equivalent) of OECD countries as a percentage of the world’s energy consumption is 39.17 %, and the preliminary estimates of CO2 emissions in OECD countries accounted for nearly 35 % of global emissions for at 2012 based on the data from Carbon Dioxide Information Analysis Center (CDIAC).

  4. In addition, Mukherjee (2008) finds that an increase in manufacturing output in energy-intensive sectors contribute to a reduction in energy efficiency in India.

  5. The Kyoto Protocol includes the following GHG emissions: carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulfur hexafluoride (SF6). CO2 is the most significant of GHG, accounting for more than three-fourths of the total GHG (Vinuya et al. 2010).

  6. Lean and Smyth (2013) point out that an investigation of large number of countries based on panel data approach meanwhile provides the most reliable evidence for our empirical results, especially for those energy issue usually affected by the historic shocks.

  7. For a detailed analysis and discussion of energy efficiency indicators, see Ang (2006).

  8. In accordance with Ang (2006), the time-varying energy efficiency coefficient is calculated as the following formula: \( {\mathrm{Coefficient}}_{0,n}=\frac{{\left({\mathrm{EC}}_n/{\mathrm{EC}}_0\right)}^{1/n}-1}{{\left({\mathrm{GDP}}_n/{\mathrm{GDP}}_0\right)}^{1/n}-1} \), where EC and GDP corresponds to the energy consumption as well as real GDP in a particular year n and year 0. Hence, the recursive algorithm Kalman filter is used in calculation of energy efficiency coefficient given the optimal estimate of random Gaussian variables. Hence, we use at 1980 as the initial value in the filter producer for each country in the state vector to predict covariance matrix, then we can proceed to the estimation algorithm as following steps: prediction covariance matrix, filter gain, filter covariance, optimal prediction of the state vector, and optimal estimate of the state vector for energy efficiency coefficient (EE2) at each time t. For a comprehensive econometric methodology on the Kalman filter technique, see Gastaldi and Nardecchia (2003).

  9. Berdiev et al. (2012) also follow the methodology in Bjørnskov (2005b; 2008) to construct a measure of government ideology. Berdiev et al. (2012) investigate the impact of government ideology on the choice of exchange rate regime using data for 180 countries over the period 1974–2004.

  10. For a comprehensive discussion on the government type variable, including its construction and methodology, see Armingeon et al. (2010).

  11. Kruyt et al. (2009) explain that it seems most feasible to employ net imports since “subtracting the exported energy (or oil/gas/electricity) provides a more realistic view of actual dependencies (p. 2169).” Löschel et al. (2010) note that the literature has employed the reliance on fuel imports as a proxy for energy security.

  12. Lipscy (2013) analyzes whether “electoral incentives” impacts energy efficiency in 14 OECD countries. Lipscy (2013) also investigates the impact of the 1994 electoral reform in Japan on energy efficiency.

  13. Ram (2009) examines the relationship between openness, country size, and government size using 5-year and 10-year averages of the data in a panel of 154 countries over the period 1960–2000.

  14. The bootstrapped standard errors are generated using Monte Carlo simulations.

  15. We follow Huang (2010) who calculate the long-run effect for empirical model, the formula is reported as follows: “The estimated coefficient of observed (independent) variable in LSDVC model/(1- the coefficient of lagged dependent variable).”

  16. This is the main reason of why Neumayer (2004) argues that environmental policy reforms often entail costs on business.

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Acknowledgments

The authors are grateful the comments for editor and three anonymous referee for our paper. This research is partially supported by the College of Management, National Sun Yat-sen University, Taiwan under grant CMNSYSU-SRS-2013-04.

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Authors and Affiliations

  1. Department of Marketing Management, Shih Chien University at Kaohsiung, Kaohsiung, Taiwan

    Chun-Ping Chang

  2. School of Economics and Finance, Xi’an Jiaotong University, Shaanxi, China

    Chun-Ping Chang

  3. Department of Finance, National Sun Yat-sen University, Kaohsiung, Taiwan

    Chien-Chiang Lee

  4. Department of Economics, Bryant University, Smithfield, RI, USA

    Aziz N. Berdiev

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  1. Chun-Ping Chang
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Correspondence to Chien-Chiang Lee.

Appendix

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Table 5 Data definitions, sources, and descriptive statistics
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Table 6 Energy efficiency coefficients
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Chang, CP., Lee, CC. & Berdiev, A.N. The impact of government ideology on energy efficiency: evidence from panel data. Energy Efficiency 8, 1181–1199 (2015). https://doi.org/10.1007/s12053-015-9347-1

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