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Public policies for a sustainable energy sector: regulation, diversity and fostering of innovation

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Abstract

Many industrialized countries have introduced environmental policy measures in order to reduce negative externalities linked to economic activities. These policy actions produce different effects on the economic system depending on the regulatory tools adopted and the specific objective of public intervention. The impact on innovation is particularly difficult to predict, especially with regard to the direction of technological change. As a case study, we have chosen the energy sector, in which the strong interrelations between socio-economic and technological dimensions may exacerbate the negative consequences of implementing conflicting policies. The aim of this paper is to show how the lack of strong coordination between different public policies implemented in the energy sector may lead to an incoherent policy mix with negative effects on the development and diffusion of environmentally-friendly energy technologies. We have adopted a gravity equation model based on bilateral export flows of technologies for production and consumption of renewable energies and energy-saving technologies for OECD countries. Our key findings show that alternative measures of public support in the energy sector have been producing contrasting effects on the international competitiveness of energy technologies.

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Notes

  1. We would like to thank one anonymous referee for helping us to clarify this point.

  2. On 23 January 2008, the European Commission put forward an integrated proposal for Climate Action, including a directive that sets an overall binding target for the European Union of 20% renewable energy by 2020 and a 10% minimum target for the market share of biofuels by 2020, to be observed by all Member States. Moreover, the Commission declared that further efforts to improve energy efficiency are required, reducing energy consumption by 20% by 2020. As stated in the document, the EU goal of saving 20% of energy consumption by 2020 through energy efficiency is a crucial part of the European energy and climate policy because it is one of the key ways in which CO2 emission savings can be made. This is a clear example of a multiple set of policies which could lead to conflicting goals.

  3. This aspect will be specifically addressed in the empirical section of the paper.

  4. The adaptation process for biofuels with blending shares is quite similar to the substitution between leaded and unleaded gasoline, as described in Schwoon (2006).

  5. Four points highlight the importance of the resistance term in trade flows: (1) the existence of transport costs; (2) the time elapsed during shipment, mainly for perishable goods; (3) the production costs related to the synchronization of multiple inputs in the production process; (4) the increase with distance of communication and transaction costs.

  6. For extensive discussions on this issue, see Olper and Raimondi (2008), Santos Silva and Tenreyro (2006) and Westerlund and Wilhelmsson (2009).

  7. When replacing zeroes with ones in a regression, care must be taken that units are chosen appropriately. The key is to make certain that, whatever the units of measure, the equivalent of one is added so that the log–linear transformation preserves the variance in the original data. In order to check for robustness of our results, we have compared our main model with estimations from a Heckman two-stage procedure. Our findings are consistent and robust in respect of treating zero flows in a probit equation.

  8. In this paper, we have adopted simple distances as a distance measure for which only one city is necessary to calculate international distances. The simple distances are calculated following the great circle formula, which uses latitudes and longitudes of the most important city (in terms of population) or its official capital (Mayer and Zignago 2006).

  9. As far as concerns may arise when a unilateral variable is included in a gravity context – where mainly bilateral relationships are taken into account – we have considered some proxies of the environmental regulatory system as well as the technological innovation system of the importing countries, as suggested by Costantini and Crespi (2008a, b) and Spatareanu (2007), such as the reduction in main pollutant emissions, the total RD expenditures, or the level of technological capabilities. While the j countries sample is substantially reduced, the statistical robustness of these variables is weak. As far as j countries’ fixed effects are considered in the model, we may affirm that they represent the best proxy for the environmental regulatory framework and the innovation capacity.

  10. The so-called phenomenon of the “water in tariffs” corresponds to a wide range between bind duties (those declared to WTO) and applied duties (faced by importing countries in the international trade). For further details, see Bouët et al. (2008). All tariffs are calculated as weighted averages of the ad valorem equivalent with the corresponding trade flow related to the following HS 1996 codes: 1205.00 (Rape or colza seeds, whether or not broken), 1507.10 (Crude oil, whether or not degummed), 1511.10 (Crude oil), 1512.11 (Crude oil), 1514.10 (Crude oil), 2207.10 (Ethanol), 2905.11 (Methanol).

  11. Recent studies addressing the role of environmental regulation (see Mantovani and Vancauteren 2008) in a gravity framework propose the adoption of a GSL-RE in order to correct autocorrelation and heterosckedasticity when working on general trade data. The dependent variable used in our paper is rather different from total export values and has statistical characteristics that lead to indifference when using a 2SLS or a GLS. We have computed the Hausman test on these two specifications, reaching the same conclusion as in Costantini and Crespi (2008a, b), i.e., that 2SLS is an efficient estimator with robust standard errors.

  12. As we have explained in par. 3, we have adopted an instrumental variable approach by using a 2SLS estimator in order to treat both environmental and energy regulation and public support to R&D in the energy sector as endogenous variables. The endogenous variables are included in the equation without temporal lags, while we have considered the lagged values as instruments (two periods back). We have tested other specifications where the endogenous variables are included in the gravity equation with temporal lags, since it can be argued that the response to policies in terms of export dynamics may be not contemporary. In our opinion, considering lagged values in instruments gives a good response to this issue without losing information. For the sake of simplicity, we do not report these results in the text, but they are available upon request from the authors.

  13. In order to make the model consistent with the standard gravity literature, we have added a full set of year dummies (1996–2006) which have proven to be jointly significant in order to capture the effects related to temporal shocks. We have also included country fixed effects related to trading partners. Finally, we have also included several regional dummies, but the only one with statistically robust coefficients is related to the fact that importing countries are members of the OECD.

  14. The selection of one temporal lag for all the biofuel-related variables has been validated from a comparison of endogenous vs. independently defined variables and by including zero, one and two lags for each variable. Coefficients are definitely more significant and statistically robust, with one period back exogenous specification.

  15. We have dropped the variable related to general environmental regulation from equations due to potential multicollinearity with the specific energy policy variables.

  16. We have tested several alternative specifications for this point and our findings reveal that the opposite variable is not endogenously determined and that the one lag structure seems to be statistically more robust.

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Acknowledgements

The authors gratefully acknowledge the support provided by the research network Enea-Inea-Uniroma Tre on “Integrating bottom-up and top-down energy models” of which this work is a part. The financial support from the Collegio Carlo Alberto and the Italian Ministry of Education, University and Research (Scientific Research Programme of National Relevance 2007 on European Union policies, Economic and Trade Integration Processes and WTO Negotiations) is also gratefully acknowledged. We are indebted to Jans Horbach, Ignazio Musu, Jan Nill, Augusto Ninni, Carl Pray, Klaus Rennings, Markus Wagner, David Zilberman and the participants to the 2008 DIME Conference held in Bordeaux, the 2008 EAEPE Conference held in Rome, the 2009 EMAEE Conference held in Jena, the 2009 SIE Conference held in Rome, the 2010 IEFE Seminar held in Milan, for their helpful comments and suggestions. We are also indebted to Annalisa Zezza for guiding us in the selection of biofuels policies. The usual disclaimers apply.

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  1. Department of Economics, Università degli Studi Roma Tre, via Silvio D’Amico 77, 00145, Rome, Italy

    Valeria Costantini & Francesco Crespi

  2. BRICK (Bureau of Research in Complexity, Knowledge, Innovation), Collegio Carlo Alberto, Moncalieri, Italy

    Francesco Crespi

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  1. Valeria Costantini
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Costantini, V., Crespi, F. Public policies for a sustainable energy sector: regulation, diversity and fostering of innovation. J Evol Econ 23, 401–429 (2013). https://doi.org/10.1007/s00191-010-0211-3

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