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The effects of different CCS technological scenarios on EU low-carbon generation mix

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Abstract

Carbon capture and storage technology (CCS), a technology to reduce the emissions in coal and gas power generation plants, will play an important role in the achievement of the European Union emissions reduction objective. In the European Union, energy policies are articulated around three different elements: measures to promote renewable energy technologies, the emissions certificates system and both energy-saving and energy-efficiency policies. The succession of directives and communications from the EU Commission on renewable technology generation share targets and the implementation of the European Emissions Market exemplify the serious EU commitment to a more environmentally friendly future. CCS technologies—together with RES technologies—are thus key to achieve the European emissions reduction target. Although the CCS commercial availability is not guaranteed—due to a slow technological development—some institutions, such as the Institute for Prospective Technological Studies, assume, for 2030 horizon, a quick development of this technology, growing until a maximum participation of an 18 % over the fossil fuels total generation. An eventual non-availability of these technologies in 2030 could increase the cost of this objective in a 70 %. Therefore, the achievement of pollutant emissions reduction targets depends on a correct design of the European generation technologies mix, which should include CCS technologies. Nevertheless, the uncertainty about the final costs and economic risk of these technologies makes a question about their future role to arise. This paper analyses the effects of different variations in the cost and risk of the CCS technologies (scenarios) over the European power technologies mix. The results confirm the need of the availability of these technologies in 2030, beyond the potential costs and risks of both options. The reason lies in the methodological approach of portfolio theory, which allows an analysis from an efficient portfolio point of view.

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Fig. 1

Source: Own author's elaboration with data collected from Eurostat (tsdcc310)

Fig. 2

Source: Own author's elaboration with data collected from Eurostat (tsdcc320)

Fig. 3

Source: Own authors' calculation

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Source: Own authors' calculation

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Source: Own authors' calculation

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Source: Own authors' calculation

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Source Own authors' calculation

Fig. 8

Source: Own authors' calculation

Fig. 9

Source: Own authors' calculation

Fig. 10

Source: Own authors' calculation

Fig. 11

Source: Own authors' calculation

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Notes

  1. In this study, we propose an analysis of the CCS technologies share behaviour in the efficient portfolios of De-Llano et al. (2014) and DeLlano-Paz et al. (2015) model. Thus, our aim is to see how far the cost or the risk of these CCS technologies—based on fossil fuels but with lower emissions than the traditional carbon and gas technologies—can affect their share in the power generation mix. We do not want to put priority on them over RES technologies. In fact, we firmly think that both types of technologies are key to achieve the European emissions reduction target.

  2. SD stands for standard deviation. The standard deviation informs about the possible variation of the asset return or cost.

  3. By doing so, and assuming normality in the CCS technologies cost, our analysis covers more than 95 %—two standard deviations up and down from the cost expected value—of the expected cost variability. The assumption of normality in the distribution of the CCS technologies cost is not a strong one from our point of view and we can see it in Awerbuch and Berger (2003), for instance.

  4. 2030 Horizon considers a CO2 Emissions reduction goal between −54 and −68 % for electricity sector (EC 2011) and a maximum limit share for the sum of the CCS coal and the CCS Natural Gas participations of 18 % of the fossil fuel total portfolio participation (Russ et al. 2009).

  5. Energy intensity was reduced in a 15 % from 2000 to 2011 (Eurostat: tsdec 360). The energy intensity is calculated as net energy imports divided by the sum of gross inland energy consumption plus bunkers.

  6. The IEA 450 scenario considers—with 50 per cent probability—the achievement of the rise limit of 2 degrees Celsius in average global temperatures—when compared to preindustrial levels. The “450” comes from the long-term concentration of greenhouse gas emissions limit of 450 ppm CO2 eq.

  7. The externalities costs are those costs related to the potential damage to ecosystems and to the society (Wesselink et al. 2010; IPCC 2005).

  8. We are assuming the hypothesis of no-correlation except for the fuel costs and the CO2 emission prices (Jansen et al. 2006).

  9. For CCS Coal the Standard Deviation is 8.59 €/MWh and for CCS Natural Gas is 8.48 €/MWh (De-Llano et al. 2014).

  10. For CCS Coal the Variance is 73.74 €/MWh and for CCS Natural Gas is 71.89 €/MWh (De-Llano et al. 2014).

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Authors and Affiliations

  1. Department of Financial Economics and Accounting, Faculty of Economics and Business, University of A Coruña, Elviña Campus, 15071, A Coruña, Spain

    Fernando deLlano-Paz & Paulino Martinez Fernandez

  2. Economic Scientific Group and CEF-UP, Faculty of Economics, University of Porto, Rua Dr. Roberto Frias, 4200-464, Porto, Portugal

    Isabel Soares

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  1. Fernando deLlano-Paz
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  2. Paulino Martinez Fernandez
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  3. Isabel Soares
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Correspondence to Fernando deLlano-Paz.

Appendix

Appendix

See Tables 7 and 8.

Table 7 Expected costs by technology
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Table 8 Risk by Technology
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deLlano-Paz, F., Martinez Fernandez, P. & Soares, I. The effects of different CCS technological scenarios on EU low-carbon generation mix. Environ Dev Sustain 18, 1477–1500 (2016). https://doi.org/10.1007/s10668-016-9809-4

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