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Environmental Policy and the International Diffusion of Cleaner Energy Technologies

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Abstract

Technology transfer is an important channel of technological change and sustainable development for countries with less innovative ability than technological leaders. This paper studies whether domestic environmental policies affect the inward technology transfer of cleaner innovation from abroad. We focus specifically on the power sector, for its important role in the decarbonization process, by looking at zero-carbon (renewable) and carbon-saving (efficient fossil) technologies for energy production. Using data on cross-country patent applications, we provide evidence that environmental policy contributes to attracting foreign cleaner technology options to OECD markets but not to non-OECD markets. We show that this is due to the nature of the implemented policy instruments. Market-based approaches positively impact technology transfer to both OECD and non-OECD economies, while non-market based approaches have at best only a weak effect in OECD countries. Domestic environmental policies may provide too weak a signal for foreign innovators in countries off the technological frontier. This calls for a strengthening of policy incentives for technology transfer in light of pressing climate change objectives.

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Notes

  1. In this paper, the terms “technology diffusion” and “technology transfer” are used interchangeably and refer to making available a new technology for power production in a given market.

  2. From now on, we will use the terms power, electricity and energy interchangeably.

  3. The role of technology transfer for economic development in general has been explored by a rich literature. The flow of technology through channels such as trade, FDI or patent transfer from frontier to laggard countries is a key contributor to economic development (see for instance Keller 2004; Eaton and Kortum 2008; Hall and Rosenberg 2010 for a review of the literature). A complementary literature focuses on uncompensated knowledge spillovers, namely the benefits associated with knowledge spillovers over time and across countries (see for instance, Peri 2005; Popp 2002; Verdolini and Galeotti 2011 focus specifically on the energy sector).

  4. Dechezleprêtre et al. (2008, 2009), Haščič and Johnstone (2011) and Popp (2011) focus on the transfer of environmentally friendly technologies in the context of the CDM.

  5. Lanzi et al. (2011) show that these technologies represent roughly 20% of innovation in fossil-based power technologies.

  6. Patents are legal titles providing a temporary monopoly power in a given market. To be eligible for a patent, an invention (device, process, etc.) needs to be new, susceptible of industrial application and to involve a non-obvious inventive step. To obtain a patent, an inventor files an application to a patenting authority. The patenting office will check whether the application fulfils the relevant legal criteria and will grant or reject the patent accordingly (OECD 2009). The limitations of patent data as an indicator of innovative activity are summarized in Griliches (1990). Cross country patent filings have been widely used in the literature to proxy for both compensated and uncompensated spillovers (Eaton and Kortum 1996; Branstetter et al. 2006; Eaton and Kortum 2008).

  7. The costs associated with a patent application are high, both in terms of information disclosure (knowledge spillovers) and in terms of patent filing fees, translation fees and agent’s fees. Helfgott (1993) estimates that financial costs for patent filing in the 1990s ranged from USD 460 in India to USD 4600 at the EPO, with the majority of countries lying in the range of USD 2000–3000. Lerner (2002) estimates the full cost of patent protection (including renewal fees) in 60 major countries, with the majority of countries having fees ranging from 1000 to 15,000 USD. Berger et al. (2005) in 2004 puts the costs of a Euro-direct and a Euro-PCT patent at 37,500 and 57,000 Euros, respectively, including all in-house costs for the firm.

  8. Two alternative approaches could be implemented to this end. First, one could use information on designation or post-grant validation of EPO patents in each single country. Unfortunately, we cannot follow this route as the data downloaded from KITES (2010) does not include this information. Second, one could assume that all EPO patents are validated in all countries. This approach would however grossly overestimate transfer to EPO members, as several contributions show that EPO patents are then validated only in a handful of countries (see for instance Straathof and van Veldhuizen 2010). We therefore believe that our approach offers the best way to include EPO patent applications in the analysis and to reflect the incentives for an EPO application in all member countries. Note that the results we present are robust to using either the share of GDP or the share of energy use of a given country as weights to build the EPO variables.

  9. Based on authors’ calculation. As explained in a later section, patent data are extracted from KITeS (2010), Lissoni et al. (2006), data on trade of wind turbine technologies from UN COMTRADE (2012), as in Wind (2008), and data on installed capacity from IEA (2012).

  10. The list of innovating countries is: AR, AT, AU, BE, BG, BR, BY, CA, CH, CL, CN, CZ, DE, DK, EE, ES, FI, FR, GB, GR, HK, HR, HU, ID, IE, IL, IN, IS, IT, JP, KR, KZ, LK, LT, LU, LV, MA, MC, MD, MX, MY, NL, NO, NZ, PA, PH, PL, PT, RO, RU, SE, SG, SI, SK, TR, UA, US, ZA.

  11. Recipient countries are AR, AT, AU, BA, BE, BG, BR, CA, CH, CL, CN, CU, CZ, DE, DK, DZ, EC, EE, EG, ES, FI, FR, GB, GR, GT, HK, HR, HU, ID, IE, IL, IN, IS, IT, JP, KR, KZ, LK, LT, LU, LV, MA, MC, MD, MX, NL, NO, NZ, PA, PH, PL, PT, RO, RU, SE, SG, SK, TR, TW, UA, US, UY, ZA, ZW plus the EPO. Same country couples are obviously excluded from our sample.

  12. EPO is included in OECD whenever the analysis is carried out separately for OECD and non-OECD countries.

  13. The quality of the policy proxy will necessarily generate measurement error and arguably give rise to a downward bias in the estimated coefficient.

  14. A full validation and comparison of our proxy with other more detailed policy indexes is not possible due to the limited geographical and time coverage of the latter, and to the fact that often indexes are sector specific (for instance the one used in Dechezleprêtre et al. (2015) for emission standards in the automotive sector).

  15. As explained in Sect. 5, our estimation method only considers those country couples for which there is at least one patent transferred over the whole sample period. We therefore present descriptive statistics on the sample of countries included in the analysis, and exclude from the calculation all the country pairs with zero transfer.

  16. Note that in our case, time invariant bilateral characteristics, such as geographical distance, cannot be included in the analysis as our estimation technique is not able to produce coefficient for time-invariant variables, as explained in Sect. 5. However, previous evidence (Verdolini and Galeotti 2011) shows that distance does not play a role in the diffusion of energy-related knowledge. In addition, results presented in Bosetti and Verdolini (2013) using a GMM estimator which allows to identify the role of time-invariant bilateral characteristics on a restricted sample, show that distance variables are not associated with significant effect on transfer.

  17. To compute the ranking, Park and Ginarte create five different categories, namely the extent of coverage, membership in international patent agreements, provisions for loss of protection, enforcement mechanisms, and duration of protection. They define several benchmark criteria, such as the patentability of pharmaceuticals for extent of coverage. Ginarte and Park (1997) compute the share of “fulfilled” criteria in each category for each country. A country’s score is the unweighted sum of these shares over all categories. See Ginarte and Park (1997) and Park (2008) for details. The index is calculated in 5-year intervals and we interpolate the missing values.

  18. The results presented here are robust to choosing different discount rates, in the range of 0.5–0.15. The initial value of the stock \(KO_{it_0 } \) is defined as: \(KO_{it_0 } =\frac{PAT_{it_0 } }{\left( {\bar{g}_{i} +\delta } \right) }\) where \(\bar{g} _i \) is the average rate of growth of patenting for the period between \(t_0 \) and \(t_0 -4\). We use \(t_0 =1975\) as the initial year to compute the knowledge stock, while the empirical analysis starts in 1990. This ensures that the choice of the initial value of the knowledge stock has a minimum impact on the variable itself.

  19. Generally, the level of IPR is also considered endogenous. In our specific context, concerns regarding the endogeneity of the IPR proxy are however low, as it is unlikely that the bilateral transfer of clean energy technologies (our dependent variable) would affect the level of IPR: energy innovation is both a small portion of overall innovation in any given country and of overall transfer between any couple of countries.

  20. Note that by doing so, the conditional fixed-effect approach drops from the analysis all country couples where no patent is transferred over the full sample period. Hence, our analysis uses information only from those country couples where there is at least one instance in which a patent is transferred.

  21. These percentages are obtained by exponentiating the coefficients. As shown in Table 1, a one standard deviation increase in the overall policy index is equal to 3.6.

  22. The full set of results is available upon request.

  23. Results are not presented for brevity, but are available upon request.

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Authors and Affiliations

  1. FEEM - Fondazione Eni Enrico Mattei - and Fondazione CMCC - Centro Euro-Mediterranneo sui Cambiamenti Climatici, Corso Magenta 63, 20123, Milan, Italy

    Elena Verdolini & Valentina Bosetti

  2. Bocconi University, Milan, Italy

    Valentina Bosetti

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  1. Elena Verdolini
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  2. Valentina Bosetti
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Correspondence to Elena Verdolini.

Additional information

The research leading to these results has received funding from the European Research Council under the European Community’s Programme “Ideas”—Call identifier: ERC-2013-StG/ERC Grant Agreement No 336703—Project RISICO “RISk and uncertainty in developing and Implementing Climate change pOlicies”. We would like to thank Bronwyn Hall, Marzio Galeotti, Lionel Nesta and Francesco Vona for comments on earlier drafts, as well as participants to the 2010 ICARUS International Workshop, the 2011 AERE Summer Conference, the 2012 Geneva Graduate Institute Research Design Workshop “Innovation, Diffusion and Green Growth”, the 2013 OFCE/SKEMA Seminar Series, and the 2013 Baffi Centre Global Challenges seminar series.

Appendix

Appendix

See Table 8.

Table 8 IPC codes for green (renewable) and brown (efficiency improving) technologies for electricity generation
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Verdolini, E., Bosetti, V. Environmental Policy and the International Diffusion of Cleaner Energy Technologies. Environ Resource Econ 66, 497–536 (2017). https://doi.org/10.1007/s10640-016-0090-7

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