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Historical and potential future contributions of power technologies to global warming


Using the mathematical formalism of the Brazilian Proposal to the IPCC, we analyse eight power technologies with regard to their past and potential future contributions to global warming. Taking into account detailed bottom-up technology characteristics we define the mitigation potential of each technology in terms of avoided temperature increase by comparing a “coal-only” reference scenario and an alternative low-carbon scenario. Future mitigation potentials are mainly determined by the magnitude of installed capacity and the temporal deployment profile. A general conclusion is that early technology deployment matters, at least within a period of 50–100 years. Our results conclusively show that avoided temperature increase is a better proxy for comparing technologies with regard to their impact on climate change, and that numerous short-term comparisons based on annual or even cumulative emissions may be misleading. Thus, our results support and extend the policy relevance of the Brazilian Proposal in the sense that not only comparisons between countries, but also comparisons between technologies could be undertaken on the basis of avoided temperature increase rather than on the basis of annual emissions as is practiced today.

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  1. Life-cycle assessment (LCA) uses the terms “mid-points” and “end-points” in order to characterise the causal distance of measured and reported quantities to the question asked (Bare et al. 2000; Hertwich and Hammitt 2001; Heijungs et al. 2003).

  2. The uncertainties of impacts of climate change are noted for example in National Research Council (2011).

  3. This point was made by an anonymous referee.

  4. The B1 future is characterised by a high level of environmental and social awareness and a globally coherent approach to sustainable development. Technological change and resource efficiency play an important role. Incentive systems and strong international institutions permit the rapid diffusion of cleaner technology. As a consequence, B1 is a low-carbon emission scenario.

  5. The A2 scenario represents a differentiated world, consolidated into distinct, self-reliant regions, and characterised by relatively low trade flows, slow capital stock turnover, and slow technological change. Economic, social, and cultural interactions between regions are weak, economic growth is uneven and the income gap between now-industrialised and developing parts of the world does not narrow. As a consequence, A2 is a high-carbon emission scenario.

  6. We define the load factor or capacity factor of an energy supply system as the equivalent percentage of time over one year during which the system supplies electricity at 100% load, that is supplies electricity at its nominal power rating. For example, a 1000 MW power plant running constantly at 800 MW power output has a capacity factor of 80%. Equally, a 1000 MW power plant running for 292 days (80%) of a year at the full 1000 MW load has a capacity factor of 80%.

  7. For an overview of consequential Life-Cycle Assessment, see Finnveden et al. 2009. See Pehnt et al. 2008 for an interesting study about the effects of variability and limited predictability of wind power on increased need for balancing reserves and efficiency penalties for the remaining conventional power plants.

  8. A geometric progression provides for a smoother transition of growth rates, but an arithmetic progression yields a smoother transition of deployment. On a cumulative basis, an arithmetic progression of growth rates leads to a slightly higher electricity production.

  9. CH4 emissions were converted into CO2-equivalent emissions using a Global Warming Potential for a 100-year time horizon.

  10. UNFCCC 2009a,b. \( {\tau_{{{\text{C}}{{\text{O}}_2},1}}} = \infty \), \( {\tau_{{{\text{C}}{{\text{O}}_2},2}}} = 171{\text{y}} \), \( {\tau_{{{\text{C}}{{\text{O}}_2},3}}} = 18{\text{y}} \), \( {\tau_{{{\text{C}}{{\text{O}}_2},4}}} = 2.6 {\rm y} \); \( {f_{{{\text{C}}{{\text{O}}_2},1}}} = 15.2\% \), \( {f_{{{\text{C}}{{\text{O}}_2},2}}} = 25.3\% \), \( {f_{{{\text{C}}{{\text{O}}_2},3}}} = 27.9\% \), \( {f_{{{\text{C}}{{\text{O}}_2},3}}} = 31.6\% \); \( {\tau_{{{\text{C}},3}}} = 8.4y \), \( {\tau_{{{\text{C}},2}}} = 410{\text{y}} \); \( {l_{{{\text{C}},1}}} = 59.6\% \), \( {l_{{{\text{C}},2}}} = 40.4\% \).

  11. Rosa et al. 2004; \( {\tau_{{{\text{C}}{{\text{O}}_2},1}}} = 330{\text{y}} \), \( {\tau_{{{\text{C}}{{\text{O}}_2},2}}} = 80{\text{y}} \), \( {\tau_{{{\text{C}}{{\text{O}}_2},3}}} = 20{\text{y}} \), \( {\tau_{{{\text{C}}{{\text{O}}_2},4}}} = 1.6{\text{y}} \); \( {f_{{{\text{C}}{{\text{O}}_2},1}}} = 21.6\% \), \( {f_{{{\text{C}}{{\text{O}}_2},2}}} = 39.2\% \), \( {f_{{{\text{C}}{{\text{O}}_2},3}}} = 29.4\% \), \( {f_{{{\text{C}}{{\text{O}}_2},3}}} = 9.8\% \); \( {\tau_{{{\text{C}},3}}} = 20{\text{y}} \), \( {\tau_{{{\text{C}},2}}} = {\text{990y}} \); \( {l_{{{\text{C}},1}}} = 63.4\% \), \( {l_{{{\text{C}},2}}} = 36.6\% \).

  12. Increasing \( \eta_{{{\text{nucl}}{\text{.C}}{{\text{O}}_2}}}^{\text{ind}}\left( {2100} \right) \) from 135 g CO2/kWh to 530 g CO2/kWh.

  13. The B2 world features concern for environmental and social sustainability, combined with a trend toward local self-reliance and stronger communities. Decision-making lies more with local and regional than with international institutions. Energy systems develop specific to locally available natural resources. Less carbon-intensive technology is advanced in some regions.

  14. The A1 storyline sees rapid and successful economic development and converging regional average per-capita incomes. Abundant energy and mineral resources coupled with rapid technical progress reduces the resource intensity of production, and increases economically recoverable reserves.

  15. The period between the end of our historical time series (2006) and the start of our future scenario (2009) is not covered in our analysis because, on one hand, capacity and generation statistics are not yet available for many of the technologies here considered and, on the other hand, this period is past and, as such, cannot be part of a future scenario. Hence, some scenario parameters for 2009 (Tables 1, 2 and 3) had to be modelled based on 2006 data.

  16. The energy penalty is quantified here exclusive of life-cycle components (compare with a definition in Rubin et al. 2007, p. 4451 and footnote 3).

  17. For example, the emissions from 1 kWh generated in a pulverised-coal power plant with CCS are composed of 880 g (combustion) +88 g (10% power plant life cycle) +79 g (9% efficiency penalty) +141 g (16% remaining energy penalty)—935 g (85% capture of 880 + 79 + 141 g) + 20 g (remaining CCS life cycle) = 273 g.


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The authors thank Maria Cecilia Pinto de Moura for help with the manuscript and an anonymous referee for valuable comments.

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Correspondence to Manfred Lenzen.


Appendix A: Data sources

1.1 A.1 Emissions data and global warming parameters

The parameters \( {\overline \sigma_g} \) and β g in Equation 2 were parametrised using the RBP values for the fractions f and l and their corresponding lifetimes τ, as listed in Rosa et al. 2004 and UNFCCC 2009a,b. Values for the β and σ were obtained from Meira 2009. C was calculated according to Equation 28 in Meira and Miguez 2000, using a climate sensitivity of 3°C. The model was calibrated and fine-tuned (Fig. 8) against historical measurements of atmospheric concentrations (CO2 Keeling et al. 2008; CH4 Steele et al. 2003) and ice core samples (CO2 Neftel et al. 1994; CH4 Etheridge et al. 2002), as well as against historical measurements of global temperature anomalies (Jones et al. 2009).

Fig. 8
figure 8

Calibration of the RBP (grey curves) in terms of atmospheric concentrations of two greenhouse gases (left) and temperature anomaly (right) against measurements (left: markers; right: dashed curve)

Present mean radiative forcing and global warming are a function of GHG emissions reaching back into the past as far as 300 years. Therefore, the calibration and fine-tuning of the RBP model requires historical emissions data starting 1750. CDIAC data on global CO2 emissions from fossil fuel usage, cement production and gas flaring between 1750 and 2005 were taken from Marland et al. 2008, on CO2 emissions resulting from land use change between 1850 and 2005 from Houghton 2008, on CH4 emissions between 1860 and 1994 from Stern and Kaufmann 1998. N2O emissions between 1890 and 1995 were taken from the EDGAR-HYDE model, documented in Van Aardenne et al. 2001. Values prior to these periods were extrapolated using pre-1890 growth rates. These extrapolations are not expected to exert major influence on the results obtained here, since pre-1890 emissions are small compared to post-1890 emissions.

Historical mitigation potentials \( M_{{{\text{hist}},i}}^{\text{coal}}\left( {t\prime } \right) \) are based on historical data on electricity generation and consumption (Fig. 1 in the main text), collated mostly from IEA 2008b (post-1971) and Energy Information Administration 2008 (post-1980), but complemented by data on renewable technologies from various industry sources (Brakmann et al. 2005; DiPippo 2008b; IEA-PVPS 2008; WWEA 2008), and historical data from Darmstadter 1971 (post-1925) and Etemad et al. 1991 (post-1900), the latter two sources downloaded from the HYDE database (MNP 2008).

1.2 A.2 Specific emissions coefficients η

Specific emissions coefficients η for the various technologies were sourced from a wide range of recent assessments (Table 7). Note that in virtually every life-cycle study, technologies are appraised in isolation, leading to an overestimation of life-cycle emissions due to double-counting (Lenzen 2008a). For example, the manufacture of a wind turbine requires electricity from fossil, nuclear or hydropower plants, so that the life-cycle emissions from those plants are also counted in the life-cycle inventory of the wind turbine. At present, there exist no comprehensive studies on the degree of double-counting. However, for the purpose of this work, life-cycle emissions of low-carbon power technologies are small compared to the emissions their deployment avoids, so that the error due to double-counting is unlikely to have a significant influence on our results.Footnote 15

Table 7 On-site and indirect GHG emissions \( \eta_{{i,g}}^{\text{ons}} \) and \( \eta_{{i,g}}^{\text{ind}} \)

Indirect life-cycle emissions for natural gas are higher (≈20% of direct emissions) than for coal (≈10%) because of fugitive emissions during venting and flaring, and leakage (Lenzen 2001; Foran et al. 2005; Meier et al. 2005; Weisser 2007; Odeh and Cockerill 2008). Negative net emissions of carbon capture and storage technologies represent avoided emissions, as defined in Fig. TS. 11 in IPCC 2005. This includes the so-called energy penalty resulting from: a) the additional energy requirements for capture, and b) conversion efficiency decreases. Energy penalties (see Tab. TS. 10 in IPCC 2005; Rubin et al. 2007; Odeh and Cockerill 2008; and Davison 2007) are typically 25% in post-combustion systems (due to an 8–10% efficiency decrease, and scrubbing agent regeneration), and 15% in pre-combustion (due to a 6–8% efficiency decrease, and to the water-gas shift reaction).Footnote 16 The life-cycle component represents CO2 transport and injection.Footnote 17

Emissions from construction and maintenance of hydroelectric plants amount to about 40 g CO2/kWh, however in addition, average CO2 and CH4 emissions from the anaerobic decay of organic matter submerged by the reservoir have been measured to be in the order of 3 g CH4/kWh and 150 g CO2/kWh (Dos Santos et al. 2006).

Emissions from the nuclear fuel cycle include mining, milling, decommissioning and waste disposal. Roth et al. 2005 and Pehnt et al. 2008 take the reduced capacity credit of wind into account in their systems LCA, and conclude that CO2 emissions arising from the need of additional spinning and non-spinning reserves add between 35 and 75 g CO2/kWh, thus outweighing CO2 emissions from the turbine life cycle. If reserves were provided using low-carbon technologies, future life-cycle emissions for wind energy could be as low as 10 g CO2/kWh (Lenzen and Munksgaard 2002).

In a case study of a hypothetical 100-MW PV plant (crystalline silicon, module efficiency 13%, system efficiency 80%) operating under Australian conditions (average capacity factor 20%, and coal-based background economy), Lenzen et al. 2006 (work undertaken by author Wood) arrive at life-cycle GHG emissions of about 100 g CO2/kWh. In a dynamic LCA, Pehnt 2006 projects future life-cycle impacts of PV to decrease by about 40% until 2030. Here, we assume 50% reductions in life-cycle emissions for both PV and CSP.

Ármannsson et al. 2005 conduct a survey of CO2 emissions from geothermal power plants, yielding a large range of 4–740 g CO2/kWh, with a weighted average of about 120 g CO2/kWh (excluding life-cycle emissions). Future emissions may be as low as 25 g CO2/kWh, if only binary-cycle plants are utilised, and life-cycle emissions are halved.

Biomass is assumed to undergo a slight shift from mainly residue and waste utilisation in boilers and steam turbines, to a higher proportion of dedicated energy crops, and overall more efficient combustion in biomass integrated-gasifier combined-cycle (BIGCC) plants (IEA 2007). The more intensive energy crop production slightly outpaces efficiency gains in terms of GHG emissions (JEC 2008).

1.3 A.3 Average capacity factors λ (Table 8)

Reduction rates of CCS are modelled to reduce from 85% under current technology to 90% using oxyfuel combustion (Viebahn et al. 2007). Average capacity factors for hydropower are determined by the demand segment (base or peak), so that this technology occupies an intermediate position at 40%. Whilst this factor may increase in principle as hydropower plants are increasingly used for balancing variable renewable power sources, increased water shortages may be a limiting factor (Lucena et al. 2009). Therefore, the capacity factor for hydropower was assumed constant. Future capacity credits for wind power are subject to counteracting trends. Increasing geographical dispersion tends to smoothen output and decrease variability (Østergaard 2008; Oswald et al. 2008). Increasing penetration leads to more wind energy that has to be discarded (Hoogwijk et al. 2007).

Current capacity factors for PV are difficult to estimate because of the dispersed deployment of many small generators. Obviously, future capacity factors are even more uncertain. The average capacity factor of the US SEGS parabolic trough CSP plant is 21%. Including storage means that the plant can also produce during extended low-radiation periods, thus significantly increasing its average capacity factor. For example the Spanish Andasol trough plants have a liquid salt storage system that allows them to operate day and night at an average capacity factor of 41% (Solar Millennium 2009). Currently, geothermal power records an average capacity factor of 71% (Gawell and Greenberg 2007). However, considering that geothermal power is the only renewable energy source that is entirely independent of seasonal or climatic changes, high capacity factors in excess of 90% may be achievable in the future (Stefánsson 2002; Sanner and Bussmann 2003). Current biomass capacity factors of 65% (IEA 2007; 2008b) are expected to increase to 80% in the future (Haq 2003).15

Table 8 Average capacity factors

1.4 A.4 Installed capacity P (Table 9)

In projecting future technology deployment, we do not aim at replicating previous projections (for example UNDP 2004; Alcamo et al. 2005; IEA 2008a), and we also do not aim at providing several future pathways, as this work is not a scenario analysis. Instead, we construct one scenario that fits well within a number of future projections published in the literature (see Appendix B). We define our scenario as a set \( \left\{ {{P_i}\left( {{t_0}} \right),{r_i}\left( {{t_0}} \right),\gamma \,{\text{or}}\,{P_i}\left( {t\prime } \right)} \right\} \) of parameters for the growth of installed capacities, and justify our choice below by showing how future deployment may be constrained by a number of technical circumstances specific to the various generation technologies.

Table 9 Present and future installed capacities and their present growth rates

CCS is not expected to become competitive before 2030, but global storage capacity of around 200 Gt CO2 appears reasonably certain. We have used twice this capacity as a constraint on cumulative ε i (t′), determining P i (t′) and γ. CCS for biomass is not expected to be economical because of the small size of biomass-fired power plants (Damen et al. 2007).

As many of the world’s large rivers are already dammed, and small hydropower is still costly, global hydropower is not expected to expand to more than twice its current capacity (IHA et al. 2000; Paish 2002).

Future development of nuclear power was taken directly from the SRES B1 scenario (Nakićenović and Swart 2000). This scenario is consistent with the amount of reasonably assured and inferred resources being sufficient for 80–100 years at current generation (OECD NEA and IAEA 2008), and also with more recent assessments (UNDP 2004; EIA 2008b).

Wind is widely regarded to face grid integration problems above 20% penetration, with the main issue being excess wind energy to be discarded (Hoogwijk et al. 2007). For example in the GWEC 2008 future wind energy outlook, wind is constrained to 17% penetration even in the advanced scenario. We have hence set \( {P_{\text{wind}}}\left( {2100} \right) = 17\% {P_{\text{total}}}\left( {2100} \right) \), determining α or γ.

Future growth of PV depends critically on the reduction of generating cost, which carries a large uncertainty (van der Zwaan and Rabl 2004). There are only few projections that attribute PV a global share of more than 5% penetration by 2050. We have therefore chosen γ so that in combination with P i (t 0) and r i (t 0), \( {E_{\text{PV}}}\left( {2050} \right) = 5\% {E_{\text{total}}}\left( {2050} \right) \). No new commercial-scale CSP plant has been commissioned until recently, so that the growth rate r i (2009) was taken from the period 1986–2003. Thoughout 2040, we assume CSP to grow above 20% per year (Schott AG 2005).

The 2050 global potential of geothermal power is estimated in the ACT and BLUE scenarios of the IEA 2008a as only about 200 GW, which was taken as a reference for our projection. However, given its potential for baseload and its significant technical potential (MIT 2006; Resch et al. 2008; Blodgett and Slack 2009), geothermal power was given a “late renaissance”, and allowed to expand to 30% penetration by 2100. This scenario also provides an interesting case for comparing traditional with new technologies in their effect on global warming.

Biomass is estimated to grow only moderately by some 2–3% per year (Haq 2003; Perlack et al. 2005). Finally, natural-gas-fired power is expected to grow twofold, and oil-fired power is expected to peak around 2030 (EIA 2008a). Coal-fired generation is reduced residually, by subtracting the generation of all other sources from total electricity demand prescribed by the SRES B1 scenario15.

A comprehensive comparison of the scenario examined here with previous scenarios is in Appendix B.

Appendix B: Comparison of our scenario with future projections in the literature

Table 10 Comparison of future capacities in our scenario (bold) with previous studies
Table 11 Comparison of future generation in our scenario (bold) with previous studies
Table 12 Comparison of future avoided emissions in our scenario (bold) with previous studies
Table 13 Comparison of future cumulative avoided emissions in our scenario (bold) with previous studies

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Lenzen, M., Schaeffer, R. Historical and potential future contributions of power technologies to global warming. Climatic Change 112, 601–632 (2012).

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