- Email: [email protected]
Life-cycle consequences of internalising socio-environmental externalities of power generation
Science of the Total Environment 612 (2018) 386–391
Contents lists available at ScienceDirect
Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv
Short Communication
Life-cycle consequences of internalising socio-environmental externalities of power generation Diego García-Gusano, I. Robert Istrate, Diego Iribarren ⁎ Instituto IMDEA Energía, Systems Analysis Unit, Av. Ramón de la Sagra 3, 28935 Móstoles, Spain
H I G H L I G H T S
G R A P H I C A L
A B S T R A C T
• Calculation of external costs of power generation technologies in Spain. • Integration of externalities and lifecycle indicators into an energy systems model. • Internalisation of externalities hastens the decarbonisation of the electricity mix. • Internalisation of externalities effectively leads to a decrease in climate change. • Internalisation of externalities effectively leads to reduced human health impact.
a r t i c l e
i n f o
Article history: Received 10 July 2017 Received in revised form 21 August 2017 Accepted 22 August 2017 Available online xxxx Editor: D. Barcelo Keywords: Climate change Electricity Energy systems modelling Externalities Human health Life cycle assessment
a b s t r a c t Current national energy sectors are generally unsustainable. Within this context, energy policy-makers face the need to move from economy- to sustainability-oriented schemes. Beyond the integration of the sustainability concept into energy policies through the implementation of techno-economic, environmental and/or social restrictions, other approaches propose the use of externalities –based on life-cycle emissions– to deeply take into account sustainability in the design of the future energy system. In this sense, this work evaluates the consequences of internalising socio-environmental externalities associated with power generation. Besides the calculation of external costs of power generation technologies and their implementation in an energy systems optimisation model for Spain, the life-cycle consequences of this internalisation are explored. This involves the prospective analysis of the evolution of the sustainability indicators on which the externalities are founded, i.e. climate change and human health. For the first time, this is done by endogenously integrating the life-cycle indicators into the energy systems optimisation model. The results show that the internalisation of externalities highly influences the evolution of the electricity production mix as well as the corresponding life-cycle profile, hastening the decarbonisation of the power generation system and thus leading to a significant decrease in life-cycle impacts. This effect is observed both when internalising only climate change externalities and when internalising additionally human health external costs. © 2017 Elsevier B.V. All rights reserved.
1. Introduction and motivation
⁎ Corresponding author. E-mail address: [email protected] (D. Iribarren).
http://dx.doi.org/10.1016/j.scitotenv.2017.08.231 0048-9697/© 2017 Elsevier B.V. All rights reserved.
National energy sectors are currently unsustainable in most of the countries. For instance, at the global level, electricity production from fossil fuels still represents around 67% (IEA, 2016). Despite the growing
D. García-Gusano et al. / Science of the Total Environment 612 (2018) 386–391
387
Fig. 1. Methodological approach to enhanced energy systems optimisation modelling through endogenous integration of external costs and life-cycle sustainability indicators. (For interpretation of the references to colour in this figure, the reader is referred to the web version of this article.)
role of renewable energy technologies, their emergence rate is low when compared to previous energy transitions (Sovacool, 2016). In this respect, new technologies do not yet arise as the perfect solution for the future, nor are fossil-based power generation technologies a
vestige of the past. Within this context, energy policy-makers face the challenging need to move from economy- to sustainability-oriented policies actually responding to societal concerns.
Table 1 Climate change-related external costs of power generation technologies in Spain [BWR: boiling water reactor; NGCC: natural gas combined cycle; PV: photovoltaics; PWR: pressurised water reactor; RoR: run-of-river; SOFC: solid oxide fuel cells].
Table 2 Human health-related external costs of power generation technologies in Spain [BWR: boiling water reactor; NGCC: natural gas combined cycle; PV: photovoltaics; PWR: pressurised water reactor; RoR: run-of-river; SOFC: solid oxide fuel cells].
Power generation technology Existing coal thermal Existing NGCC Existing cogeneration Existing oil combustion engine Existing nuclear BWR Existing nuclear PWR Existing hydropower – dam Existing hydropower – RoR Existing wind onshore Existing solar PV Existing biomass power Existing waste-to-energy power Existing biogas power New NGCC New cogeneration New NGCC with CO2 capture New wind onshore New wind offshore New solar PV – plant New solar PV – roof New solar thermal (with storage) New solar thermal (without storage) New biomass power New waste-to-energy power New biogas power New geothermal power New wave power New SOFC
External cost due to climate change (€2013/MWh) 2015 2020 2025 2030 2035 2040 2045 2050 26.7 12.5 14.1 23.6
28.9 13.5 15.3 25.6
32.2 15.1 17.0 28.5
34.4 16.1 18.2 30.5
36.6 17.1 19.4 32.5
49.9 23.4 26.4 44.2
65.5 30.6 34.6 58.0
76.6 35.8 40.5 67.8
0.18 0.18 0.13
0.19 0.20 0.14
0.21 0.22 0.16
0.23 0.24 0.17
0.24 0.25 0.18
0.33 0.35 0.24
0.43 0.45 0.32
0.51 0.53 0.37
0.09
0.10
0.11
0.12
0.12
0.17
0.22
0.26
0.27 1.14 2.29 0.00
0.29 1.24 2.47 0.00
0.33 1.38 2.73 0.00
0.35 1.48 2.90 0.00
0.37 1.57 3.14 0.00
0.51 2.14 4.26 0.00
0.66 2.81 5.61 0.00
0.78 3.29 6.55 0.00
2.62 11.8 12.2 6.48
2.84 12.8 13.2 7.02
3.15 14.2 14.7 7.82
3.37 15.2 15.7 8.35
3.60 16.2 16.8 8.91
4.90 22.1 22.9 12.1
6.44 28.9 30.0 15.9
7.53 33.8 35.0 18.6
0.18 0.38 0.68 0.55 1.03
0.20 0.42 0.73 0.60 1.12
0.22 0.46 0.82 0.67 1.25
0.23 0.49 0.87 0.71 1.33
0.25 0.53 0.93 0.76 1.42
0.34 0.72 1.27 1.04 1.93
0.44 0.94 1.66 1.36 2.54
0.52 1.10 1.95 1.59 2.97
0.86
0.93
1.04
1.11
1.19
1.62
2.12
2.48
0.09 0.00
0.09 0.00
0.10 0.00
0.10 0.00
0.12 0.00
0.16 0.00
0.21 0.00
0.24 0.00
2.01 0.10 0.64 10.3
2.17 0.11 0.69 11.1
2.41 0.12 0.77 12.4
2.56 0.13 0.82 13.3
2.76 0.13 0.87 14.1
3.75 0.18 1.19 19.3
4.93 0.24 1.56 25.3
5.76 0.28 1.83 29.6
Power generation technology Existing coal thermal Existing NGCC Existing cogeneration Existing oil combustion engine Existing nuclear BWR Existing nuclear PWR Existing hydropower – dam Existing hydropower – RoR Existing wind onshore Existing solar PV Existing biomass power Existing waste-toenergy power Existing biogas power New NGCC New cogeneration New NGCC with CO2 capture New wind onshore New wind offshore New solar PV – plant New solar PV – roof New solar thermal (with storage) New solar thermal (without storage) New biomass power New waste-to-energy power New biogas power New geothermal power New wave power New SOFC
External cost due to human health impact (€2013/MWh) 2015 2020 2025 2030 2035 2040 2045
2050
68.6 2.01 5.09 35.6
74.7 2.19 5.54 38.7
81.3 2.38 6.03 42.1
88.4 2.59 6.56 45.8
92.2 2.71 6.84 47.8
96.2 2.82 7.14 49.8
100.4 2.95 7.45 52.0
104.7 3.07 7.77 54.2
2.57 2.63 0.41
2.79 2.87 0.44
3.04 3.12 0.48
3.31 3.39 0.52
3.45 3.54 0.55
3.60 3.69 0.57
3.75 3.85 0.60
3.92 4.02 0.62
0.32
0.35
0.38
0.41
0.43
0.45
0.47
0.49
2.04 3.30 4.49 0.00
2.22 3.59 4.89 0.00
2.42 3.91 5.32 0.00
2.63 4.25 5.79 0.00
2.74 4.44 6.04 0.00
2.86 4.63 6.30 0.00
2.99 4.83 6.57 0.00
3.11 5.04 6.85 0.00
4.34 0.93 1.73 2.21
4.73 1.01 1.88 2.40
5.14 1.10 2.05 2.61
5.59 1.19 2.23 2.84
5.84 1.24 2.33 2.97
6.09 1.30 2.43 3.09
6.35 1.35 2.53 3.23
6.63 1.41 2.64 3.37
0.72 1.98 1.54 1.04 1.84
0.79 2.16 1.68 1.13 2.00
0.86 2.35 1.82 1.23 2.17
0.93 2.55 1.99 1.34 2.37
0.97 2.66 2.07 1.40 2.47
1.01 2.78 2.16 1.46 2.58
1.06 2.90 2.25 1.52 2.69
1.10 3.02 2.35 1.58 2.80
1.27
1.38
1.50
1.63
1.70
1.78
1.86
1.94
2.25 0.00
2.45 0.00
2.67 0.00
2.90 0.00
3.03 0.00
3.16 0.00
3.30 0.00
3.44 0.00
4.77 0.19 3.76 3.03
5.18 0.21 4.09 3.30
5.64 0.23 4.44 3.59
6.14 0.25 4.84 3.90
6.40 0.26 5.04 4.07
6.68 0.27 5.26 4.25
6.97 0.28 5.49 4.43
7.27 0.29 5.73 4.62
388
D. García-Gusano et al. / Science of the Total Environment 612 (2018) 386–391
Fig. 2. Evolution of electricity production in Spain (2015–2050) under the three scenarios considered.
The integration of the sustainability concept into energy policies is often performed through the implementation of technical, economic, environmental and/or social restrictions (e.g., emission ceilings). On the other hand, in the power generation sector, relevant projects such as NEEDS (www.needs-project.org) and CASES (www.feem-project. net/cases) have gone beyond this approach, proposing the use of externalities –based on life-cycle emissions– to strongly take into account sustainability in the design of the future energy system. The rationale behind externalities is the Pigouvian taxes concept, a specific type of tax associated with the activity production –power generation in this article– in order to compensate the damage caused by the corresponding pollution, thereby correcting an inefficient market outcome. Thus, the internalisation of external costs in energy prices arises as a relevant option to mitigate energy-related impacts on both environment and human health. This internalisation can be done through taxes on pollution and/or emission allowances systems in addition to the removal of subsidies for fossil fuels. Krewitt (2002) highlighted the importance of considering external costs notwithstanding their limitations (e.g., the intrinsic uncertainty associated with them). In other words, energy policy-making should be guided by the precautionary principle. Since sustainability is becoming a key component of energy policies, the implementation of schemes based on external costs should not be disregarded. In this respect, the use of externalities concerns not only energy policy-makers at the stage of energy planning but also industry/private actors when defining business plans according to anticipated energy scenarios. Moreover, society –as the driving force motivating the implementation of externality-based policies– is also affected. However, to date, research efforts in the field of energy systems analysis are limited to the evaluation of the techno-economic consequences of the internalisation of externalities, rather than prospectively evaluating environmental and social consequences (Lechón et al., 2002; Pietrapertosa
et al., 2009; Rentizelas and Georgakellos, 2014). Hence, in order to fill this gap, this article focuses on the evaluation of the life-cycle consequences linked to the internalisation of socio-environmental externalities of power generation. 2. Methodological framework The goal of this study is to evaluate the evolution (time frame 2015– 2050) of climate change and human health indicators under three alternative scenarios of the Spanish power generation sector: (i) businessas-usual (BaU) scenario, in which all current policies in force are considered; (ii) internalisation of climate change externalities (CC scenario); and (iii) internalisation of climate change and human health externalities (CC & HH scenario). The Spanish electricity production mix arises as a relevant case study due to its high diversification (García-Gusano et al., 2017). Fig. 1 presents the methodological framework of the study, which is based on the novel combination of life cycle assessment (LCA), external costs calculation, and energy systems modelling (ESM). LCA (ISO, 2006a, 2006b) is used herein to evaluate the potential environmental impacts of the existing and new power generation technologies relevant to the Spanish electricity production mix. In particular, climate change (CC) and human health (HH) are the selected damage categories, which are evaluated using the IMPACT 2002+ method (Jolliet et al., 2003). Further information on the LCA component of the study is fully available in García-Gusano et al. (2016a). Regarding the calculation of externalities, while some authors use tailor-made methods (Macías and Islas, 2010), this article makes use of the CASES project database (Porchia and Bigano, 2008). According to this database and the life-cycle emissions calculated in the LCA study, the resultant external costs of power generation technologies in Spain are those originally presented in Tables 1 and 2 regarding climate change and
D. García-Gusano et al. / Science of the Total Environment 612 (2018) 386–391
389
Fig. 3. Evolution of climate change under the three scenarios considered for power generation in Spain. (For interpretation of the references to colour in this figure, the reader is referred to the web version of this article.)
human health externalities, respectively. In line with the technology costs retrieved from Carlsson et al. (2014), monetary values are reported in €2013. The main novelty in Fig. 1 (highlighted in blue) is the endogenous integration of both external costs and life-cycle indicators into an energy systems optimisation model for power generation in Spain. This goes beyond the current state-of-the-art in the synergistic combination of LCA and ESM (García-Gusano et al., 2016b; Portugal-Pereira et al., 2016) thanks to the consideration of scenarios with internalisation of externalities. Furthermore, when compared to conventional ESM practices (DeCarolis et al., 2017), the prospective analysis is not limited to the evolution of the electricity production mix but it rather focuses on the evolution of the life-cycle indicators endogenously integrated into the model (i.e., CC and HH). With regard to the ESM component of the study, the model for power generation in Spain developed by García-Gusano et al. (2016a) is used. It is based on the Long-range Energy Alternatives Planning (LEAP) System modelling framework (Heaps, 2016) combined with the OSeMOSYS optimisation module (Howells et al., 2011). As further detailed in García-Gusano et al. (2016a), techno-economic data (costs, efficiencies, availability factors, emission factors, etc.) for the list of technologies are based mainly on Carlsson et al. (2014). 3. Results and discussion 3.1. Electricity production mix Fig. 2 shows the evolution of the Spanish electricity production mix under the three scenarios considered. When compared to the BaU scenario, the introduction of CC externalities leads to a faster retirement of coal and natural gas combined cycle (NGCC) power plants as well
as to reduced contributions from new natural gas cogeneration plants and NGCC plants with CO2 capture. This results in a faster penetration of renewable options, mainly wind onshore and biomass gasification plants. When adding HH external costs besides CC externalities, there is an even higher reduction in the use of natural gas technologies with respect to the CC scenario. A significant growth of rooftop photovoltaic systems and waste incineration plants is observed (the latter mainly due to the absence of life-cycle burdens allocated to the energy production function of the waste management system). Internalising external costs associated with power generation technologies favours the emergence of renewable energy technologies with a good socio-environmental performance. Looking at the long term, this involves business opportunities for technologies such as onshore (repowering) and offshore (large-scale deployment) wind, biomass gasification, and concentrated solar thermal with storage (its high dispatchability may assist grid operators in dealing with base load requirements). 3.2. Climate change and human health The origin of the monetisation of externalities is based on the consideration of the life-cycle emissions and impacts of power generation technologies (Porchia and Bigano, 2008). However, beyond the internalisation of externalities in the core of the objective function to be minimised, the endogenisation of the life-cycle indicators on which externalities are founded had not yet been addressed. Taking advantage of the work carried out by García-Gusano et al. (2016a), the evolution of both CC and HH life-cycle indicators is assessed herein for the three scenarios under study.
390
D. García-Gusano et al. / Science of the Total Environment 612 (2018) 386–391
Fig. 4. Evolution of the human health impact under the three scenarios considered for power generation in Spain. (For interpretation of the references to colour in this figure, the reader is referred to the web version of this article.)
Fig. 3 shows the evolution of the CC performance of the Spanish electricity production mix. In other words, the life-cycle CC consequences of the internalisation of external costs are quantified. When compared to the BaU scenario (no externalities), large CC reductions are observed when the external costs related to climate change and human health, either separately or together, are implemented. As external costs grow (see Tables 1 and 2), their effects are significantly higher and thus influence new investments (existing technologies are retired significantly by 2035–2040). Accordingly, major reductions –up to 68% with respect to the BaU scenario– take place after 2040, once the production mix is almost decarbonised. It should be noted that the blue dotted line in Fig. 3 shows the effect of a double internalisation of externalities (implementation of both CC and HH external costs) on the evolution of the CC indicator. In this regard, the CC & HH scenario (blue dotted line) hardly leads to higher CC reductions than the CC scenario (purple dashed line). The reason behind this finding lies in the values in Table 2. Once decarbonisation takes place (coal and oil especially penalised by both CC and HH external costs, and natural gas options penalised by CC external costs), the relevant values of the HH-related externalities range only from 0 to 8 €/MWh. The addition of these values to those of the CC-related externalities does not influence significantly the mathematical optimisation process with respect to the CC scenario. The effects of the internalisation of socio-environmental externalities on the HH performance are shown in Fig. 4. When compared to the BaU scenario, the scenarios with internalisation of external costs present an even more pronounced reduction in the HH impact in the first years due to a faster retirement of coal-fired power plants. In all the evaluated scenarios, after the pronounced HH impact reduction,
the life-cycle indicator keeps decreasing moderately, reaching the minimum in 2040. In the period 2030–2050, when renewables dominate the electricity production mix, the internalisation of externalities leads to HH improvements around 15–17% with respect to the BaU scenario. When looking at the long term, the shift from the application of CC external costs only (purple dashed line) to the application of CC & HH external costs together (blue dotted line) is considered to be significant in terms of HH effects (zoomed graph in Fig. 4). Hence, in a long-term decarbonised power generation system where the competition is almost completely among renewable power generation technologies, the additional implementation of HH-related external costs might be advisable. Overall, the internalisation of external costs along with the endogenisation of the corresponding life-cycle sustainability indicators in the ESM framework opens up a new field of application of combined ESM + LCA approaches: testing the validity of external costs in terms of the issues for which they are designed. Acknowledging that the calculation of socio-environmental externalities is a process associated with high uncertainty –due to the assumptions concerning mainly system boundaries and monetisation–, this type of ESM + LCA study allows discussing prospectively key concerns such as climate change and human health, and enables policy-makers to check the actual fulfilment of the goals pursued regarding the emergence of renewable energy technologies. 4. Conclusions The endogenous integration of CC- and HH-related external costs and life-cycle indicators into an energy systems optimisation model for power generation in Spain proved to be a valuable methodological advance when it comes to supporting energy policy-makers in
D. García-Gusano et al. / Science of the Total Environment 612 (2018) 386–391
decision-making processes actually oriented towards the sustainability goal. Despite this progress, further efforts are still required to strengthen the link between LCA and ESM. The internalisation of socio-environmental externalities was found to hasten the decarbonisation of the electricity production mix, favouring the penetration of renewable technologies (especially wind and biomass-based technologies). When compared to the BaU scenario, this faster decarbonisation rate of the electricity production mix would bring about significant improvements in the life-cycle CC and HH performance of the national power generation sector.
Acknowledgements This research has been partly supported by the Spanish Ministry of Economy, Industry and Competitiveness (ENE2015-74607-JIN AEI/ FEDER/UE). References Carlsson, J., Pérez Fortes, M.M., de Marco, G., Giuntoli, J., Jakubcionis, M., Jäger-Waldau, A., Lacal-Arantegui, R., Lazarou, S., Magagna, D., Moles, C., Sigfusson, B., Spisto, A., Vallei, M., Weidner, E., 2014. Energy Technology Reference Indicator (ETRI) Projections for 2010–2050. European Commission, Luxembourg. DeCarolis, J., Daly, H., Dodds, P., Keppo, I., Li, F., McDowall, W., Pye, S., Strachan, N., Trutnevyte, E., Usher, W., Winning, M., Yeh, S., Zeyringer, M., 2017. Formalizing best practice for energy system optimization modelling. Appl. Energy 194, 184–198. García-Gusano, D., Martín-Gamboa, M., Iribarren, D., Dufour, J., 2016a. Prospective analysis of life-cycle indicators through endogenous integration into a national power generation model. Resources 5, 39. García-Gusano, D., Iribarren, D., Martín-Gamboa, M., Dufour, J., Espegren, K., Lind, A., 2016b. Integration of life-cycle indicators into energy optimisation models: the case study of power generation in Norway. J. Clean. Prod. 112, 2693–2696.
391
García-Gusano, D., Iribarren, D., Garraín, D., 2017. Prospective analysis of energy security: a practical life-cycle approach focused on renewable power generation and oriented towards policy-makers. Appl. Energy 190, 891–901. Heaps, C.G., 2016. Long-range Energy Alternatives Planning (LEAP) System. Stockholm Environment Institute, Somerville. Howells, M., Rogner, H., Strachan, N., Heaps, C., Huntington, H., Kypreos, S., Hughes, A., Silveira, S., DeCarolis, J., Bazillian, M., Roehrl, A., 2011. OSeMOSYS: the open source energy modeling system: an introduction to its ethos, structure and development. Energ Policy 39, 5850–5870. IEA, 2016. Key World Energy Statistics. International Energy Agency, Paris. ISO, 2006a. ISO 14040:2006 Environmental Management – Life Cycle Assessment – Principles and Framework. International Organisation for Standardization, Geneva. ISO, 2006b. ISO 14044:2006 Environmental Management – Life Cycle Assessment – Requirements and Guidelines. International Organisation for Standardization, Geneva. Jolliet, O., Margni, M., Charles, R., Humbert, S., Payet, J., Rebitzer, G., Rosenbaum, R., 2003. IMPACT 2002+: a new life cycle impact assessment methodology. Int. J. Life Cycle Assess. 8, 324–330. Krewitt, W., 2002. External costs of energy – do the answers match the questions?: looking back at 10 years of ExternE. Energ Policy 30, 839–848. Lechón, Y., Cabal, H., Gómez, M., Sánchez, E., Sáez, R., 2002. Environmental externalities caused by SO2 and ozone pollution in the metropolitan area of Madrid. Environ. Sci. Pol. 5, 385–395. Macías, P., Islas, J., 2010. Damage costs produced by electric power plants: an externality valuation in the Mexico City Metropolitan Area. Sci. Total Environ. 408, 4511–4523. Pietrapertosa, F., Cosmi, C., Macchiato, M., Salvia, M., Cuomo, V., 2009. Life cycle assessment, ExternE and comprehensive analysis for an integrated evaluation of the environmental impact of anthropogenic activities. Renew. Sust. Energ. Rev. 13, 1039–1048. Porchia, R., Bigano, A., 2008. Development of a Set of Full Cost Estimates of the Use of Different Energy Sources and Its Comparative Assessment in EU Countries. European Commission, Brussels. Portugal-Pereira, J., Köberle, A.C., Soria, R., Lucena, A.F.P., Szklo, A., Schaeffer, R., 2016. Overlooked impacts of electricity expansion optimisation modelling: the life cycle side of the story. Energy 115, 1424–1435. Rentizelas, A., Georgakellos, D., 2014. Incorporating life cycle external cost in optimization of the electricity generation mix. Energ Policy 65, 134–149. Sovacool, B.K., 2016. How long will it take? Conceptualizing the temporal dynamics of energy transition. Energy Res. Soc. Sci. 13, 202–215.
Contents lists available at ScienceDirect
Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv
Short Communication
Life-cycle consequences of internalising socio-environmental externalities of power generation Diego García-Gusano, I. Robert Istrate, Diego Iribarren ⁎ Instituto IMDEA Energía, Systems Analysis Unit, Av. Ramón de la Sagra 3, 28935 Móstoles, Spain
H I G H L I G H T S
G R A P H I C A L
A B S T R A C T
• Calculation of external costs of power generation technologies in Spain. • Integration of externalities and lifecycle indicators into an energy systems model. • Internalisation of externalities hastens the decarbonisation of the electricity mix. • Internalisation of externalities effectively leads to a decrease in climate change. • Internalisation of externalities effectively leads to reduced human health impact.
a r t i c l e
i n f o
Article history: Received 10 July 2017 Received in revised form 21 August 2017 Accepted 22 August 2017 Available online xxxx Editor: D. Barcelo Keywords: Climate change Electricity Energy systems modelling Externalities Human health Life cycle assessment
a b s t r a c t Current national energy sectors are generally unsustainable. Within this context, energy policy-makers face the need to move from economy- to sustainability-oriented schemes. Beyond the integration of the sustainability concept into energy policies through the implementation of techno-economic, environmental and/or social restrictions, other approaches propose the use of externalities –based on life-cycle emissions– to deeply take into account sustainability in the design of the future energy system. In this sense, this work evaluates the consequences of internalising socio-environmental externalities associated with power generation. Besides the calculation of external costs of power generation technologies and their implementation in an energy systems optimisation model for Spain, the life-cycle consequences of this internalisation are explored. This involves the prospective analysis of the evolution of the sustainability indicators on which the externalities are founded, i.e. climate change and human health. For the first time, this is done by endogenously integrating the life-cycle indicators into the energy systems optimisation model. The results show that the internalisation of externalities highly influences the evolution of the electricity production mix as well as the corresponding life-cycle profile, hastening the decarbonisation of the power generation system and thus leading to a significant decrease in life-cycle impacts. This effect is observed both when internalising only climate change externalities and when internalising additionally human health external costs. © 2017 Elsevier B.V. All rights reserved.
1. Introduction and motivation
⁎ Corresponding author. E-mail address: [email protected] (D. Iribarren).
http://dx.doi.org/10.1016/j.scitotenv.2017.08.231 0048-9697/© 2017 Elsevier B.V. All rights reserved.
National energy sectors are currently unsustainable in most of the countries. For instance, at the global level, electricity production from fossil fuels still represents around 67% (IEA, 2016). Despite the growing
D. García-Gusano et al. / Science of the Total Environment 612 (2018) 386–391
387
Fig. 1. Methodological approach to enhanced energy systems optimisation modelling through endogenous integration of external costs and life-cycle sustainability indicators. (For interpretation of the references to colour in this figure, the reader is referred to the web version of this article.)
role of renewable energy technologies, their emergence rate is low when compared to previous energy transitions (Sovacool, 2016). In this respect, new technologies do not yet arise as the perfect solution for the future, nor are fossil-based power generation technologies a
vestige of the past. Within this context, energy policy-makers face the challenging need to move from economy- to sustainability-oriented policies actually responding to societal concerns.
Table 1 Climate change-related external costs of power generation technologies in Spain [BWR: boiling water reactor; NGCC: natural gas combined cycle; PV: photovoltaics; PWR: pressurised water reactor; RoR: run-of-river; SOFC: solid oxide fuel cells].
Table 2 Human health-related external costs of power generation technologies in Spain [BWR: boiling water reactor; NGCC: natural gas combined cycle; PV: photovoltaics; PWR: pressurised water reactor; RoR: run-of-river; SOFC: solid oxide fuel cells].
Power generation technology Existing coal thermal Existing NGCC Existing cogeneration Existing oil combustion engine Existing nuclear BWR Existing nuclear PWR Existing hydropower – dam Existing hydropower – RoR Existing wind onshore Existing solar PV Existing biomass power Existing waste-to-energy power Existing biogas power New NGCC New cogeneration New NGCC with CO2 capture New wind onshore New wind offshore New solar PV – plant New solar PV – roof New solar thermal (with storage) New solar thermal (without storage) New biomass power New waste-to-energy power New biogas power New geothermal power New wave power New SOFC
External cost due to climate change (€2013/MWh) 2015 2020 2025 2030 2035 2040 2045 2050 26.7 12.5 14.1 23.6
28.9 13.5 15.3 25.6
32.2 15.1 17.0 28.5
34.4 16.1 18.2 30.5
36.6 17.1 19.4 32.5
49.9 23.4 26.4 44.2
65.5 30.6 34.6 58.0
76.6 35.8 40.5 67.8
0.18 0.18 0.13
0.19 0.20 0.14
0.21 0.22 0.16
0.23 0.24 0.17
0.24 0.25 0.18
0.33 0.35 0.24
0.43 0.45 0.32
0.51 0.53 0.37
0.09
0.10
0.11
0.12
0.12
0.17
0.22
0.26
0.27 1.14 2.29 0.00
0.29 1.24 2.47 0.00
0.33 1.38 2.73 0.00
0.35 1.48 2.90 0.00
0.37 1.57 3.14 0.00
0.51 2.14 4.26 0.00
0.66 2.81 5.61 0.00
0.78 3.29 6.55 0.00
2.62 11.8 12.2 6.48
2.84 12.8 13.2 7.02
3.15 14.2 14.7 7.82
3.37 15.2 15.7 8.35
3.60 16.2 16.8 8.91
4.90 22.1 22.9 12.1
6.44 28.9 30.0 15.9
7.53 33.8 35.0 18.6
0.18 0.38 0.68 0.55 1.03
0.20 0.42 0.73 0.60 1.12
0.22 0.46 0.82 0.67 1.25
0.23 0.49 0.87 0.71 1.33
0.25 0.53 0.93 0.76 1.42
0.34 0.72 1.27 1.04 1.93
0.44 0.94 1.66 1.36 2.54
0.52 1.10 1.95 1.59 2.97
0.86
0.93
1.04
1.11
1.19
1.62
2.12
2.48
0.09 0.00
0.09 0.00
0.10 0.00
0.10 0.00
0.12 0.00
0.16 0.00
0.21 0.00
0.24 0.00
2.01 0.10 0.64 10.3
2.17 0.11 0.69 11.1
2.41 0.12 0.77 12.4
2.56 0.13 0.82 13.3
2.76 0.13 0.87 14.1
3.75 0.18 1.19 19.3
4.93 0.24 1.56 25.3
5.76 0.28 1.83 29.6
Power generation technology Existing coal thermal Existing NGCC Existing cogeneration Existing oil combustion engine Existing nuclear BWR Existing nuclear PWR Existing hydropower – dam Existing hydropower – RoR Existing wind onshore Existing solar PV Existing biomass power Existing waste-toenergy power Existing biogas power New NGCC New cogeneration New NGCC with CO2 capture New wind onshore New wind offshore New solar PV – plant New solar PV – roof New solar thermal (with storage) New solar thermal (without storage) New biomass power New waste-to-energy power New biogas power New geothermal power New wave power New SOFC
External cost due to human health impact (€2013/MWh) 2015 2020 2025 2030 2035 2040 2045
2050
68.6 2.01 5.09 35.6
74.7 2.19 5.54 38.7
81.3 2.38 6.03 42.1
88.4 2.59 6.56 45.8
92.2 2.71 6.84 47.8
96.2 2.82 7.14 49.8
100.4 2.95 7.45 52.0
104.7 3.07 7.77 54.2
2.57 2.63 0.41
2.79 2.87 0.44
3.04 3.12 0.48
3.31 3.39 0.52
3.45 3.54 0.55
3.60 3.69 0.57
3.75 3.85 0.60
3.92 4.02 0.62
0.32
0.35
0.38
0.41
0.43
0.45
0.47
0.49
2.04 3.30 4.49 0.00
2.22 3.59 4.89 0.00
2.42 3.91 5.32 0.00
2.63 4.25 5.79 0.00
2.74 4.44 6.04 0.00
2.86 4.63 6.30 0.00
2.99 4.83 6.57 0.00
3.11 5.04 6.85 0.00
4.34 0.93 1.73 2.21
4.73 1.01 1.88 2.40
5.14 1.10 2.05 2.61
5.59 1.19 2.23 2.84
5.84 1.24 2.33 2.97
6.09 1.30 2.43 3.09
6.35 1.35 2.53 3.23
6.63 1.41 2.64 3.37
0.72 1.98 1.54 1.04 1.84
0.79 2.16 1.68 1.13 2.00
0.86 2.35 1.82 1.23 2.17
0.93 2.55 1.99 1.34 2.37
0.97 2.66 2.07 1.40 2.47
1.01 2.78 2.16 1.46 2.58
1.06 2.90 2.25 1.52 2.69
1.10 3.02 2.35 1.58 2.80
1.27
1.38
1.50
1.63
1.70
1.78
1.86
1.94
2.25 0.00
2.45 0.00
2.67 0.00
2.90 0.00
3.03 0.00
3.16 0.00
3.30 0.00
3.44 0.00
4.77 0.19 3.76 3.03
5.18 0.21 4.09 3.30
5.64 0.23 4.44 3.59
6.14 0.25 4.84 3.90
6.40 0.26 5.04 4.07
6.68 0.27 5.26 4.25
6.97 0.28 5.49 4.43
7.27 0.29 5.73 4.62
388
D. García-Gusano et al. / Science of the Total Environment 612 (2018) 386–391
Fig. 2. Evolution of electricity production in Spain (2015–2050) under the three scenarios considered.
The integration of the sustainability concept into energy policies is often performed through the implementation of technical, economic, environmental and/or social restrictions (e.g., emission ceilings). On the other hand, in the power generation sector, relevant projects such as NEEDS (www.needs-project.org) and CASES (www.feem-project. net/cases) have gone beyond this approach, proposing the use of externalities –based on life-cycle emissions– to strongly take into account sustainability in the design of the future energy system. The rationale behind externalities is the Pigouvian taxes concept, a specific type of tax associated with the activity production –power generation in this article– in order to compensate the damage caused by the corresponding pollution, thereby correcting an inefficient market outcome. Thus, the internalisation of external costs in energy prices arises as a relevant option to mitigate energy-related impacts on both environment and human health. This internalisation can be done through taxes on pollution and/or emission allowances systems in addition to the removal of subsidies for fossil fuels. Krewitt (2002) highlighted the importance of considering external costs notwithstanding their limitations (e.g., the intrinsic uncertainty associated with them). In other words, energy policy-making should be guided by the precautionary principle. Since sustainability is becoming a key component of energy policies, the implementation of schemes based on external costs should not be disregarded. In this respect, the use of externalities concerns not only energy policy-makers at the stage of energy planning but also industry/private actors when defining business plans according to anticipated energy scenarios. Moreover, society –as the driving force motivating the implementation of externality-based policies– is also affected. However, to date, research efforts in the field of energy systems analysis are limited to the evaluation of the techno-economic consequences of the internalisation of externalities, rather than prospectively evaluating environmental and social consequences (Lechón et al., 2002; Pietrapertosa
et al., 2009; Rentizelas and Georgakellos, 2014). Hence, in order to fill this gap, this article focuses on the evaluation of the life-cycle consequences linked to the internalisation of socio-environmental externalities of power generation. 2. Methodological framework The goal of this study is to evaluate the evolution (time frame 2015– 2050) of climate change and human health indicators under three alternative scenarios of the Spanish power generation sector: (i) businessas-usual (BaU) scenario, in which all current policies in force are considered; (ii) internalisation of climate change externalities (CC scenario); and (iii) internalisation of climate change and human health externalities (CC & HH scenario). The Spanish electricity production mix arises as a relevant case study due to its high diversification (García-Gusano et al., 2017). Fig. 1 presents the methodological framework of the study, which is based on the novel combination of life cycle assessment (LCA), external costs calculation, and energy systems modelling (ESM). LCA (ISO, 2006a, 2006b) is used herein to evaluate the potential environmental impacts of the existing and new power generation technologies relevant to the Spanish electricity production mix. In particular, climate change (CC) and human health (HH) are the selected damage categories, which are evaluated using the IMPACT 2002+ method (Jolliet et al., 2003). Further information on the LCA component of the study is fully available in García-Gusano et al. (2016a). Regarding the calculation of externalities, while some authors use tailor-made methods (Macías and Islas, 2010), this article makes use of the CASES project database (Porchia and Bigano, 2008). According to this database and the life-cycle emissions calculated in the LCA study, the resultant external costs of power generation technologies in Spain are those originally presented in Tables 1 and 2 regarding climate change and
D. García-Gusano et al. / Science of the Total Environment 612 (2018) 386–391
389
Fig. 3. Evolution of climate change under the three scenarios considered for power generation in Spain. (For interpretation of the references to colour in this figure, the reader is referred to the web version of this article.)
human health externalities, respectively. In line with the technology costs retrieved from Carlsson et al. (2014), monetary values are reported in €2013. The main novelty in Fig. 1 (highlighted in blue) is the endogenous integration of both external costs and life-cycle indicators into an energy systems optimisation model for power generation in Spain. This goes beyond the current state-of-the-art in the synergistic combination of LCA and ESM (García-Gusano et al., 2016b; Portugal-Pereira et al., 2016) thanks to the consideration of scenarios with internalisation of externalities. Furthermore, when compared to conventional ESM practices (DeCarolis et al., 2017), the prospective analysis is not limited to the evolution of the electricity production mix but it rather focuses on the evolution of the life-cycle indicators endogenously integrated into the model (i.e., CC and HH). With regard to the ESM component of the study, the model for power generation in Spain developed by García-Gusano et al. (2016a) is used. It is based on the Long-range Energy Alternatives Planning (LEAP) System modelling framework (Heaps, 2016) combined with the OSeMOSYS optimisation module (Howells et al., 2011). As further detailed in García-Gusano et al. (2016a), techno-economic data (costs, efficiencies, availability factors, emission factors, etc.) for the list of technologies are based mainly on Carlsson et al. (2014). 3. Results and discussion 3.1. Electricity production mix Fig. 2 shows the evolution of the Spanish electricity production mix under the three scenarios considered. When compared to the BaU scenario, the introduction of CC externalities leads to a faster retirement of coal and natural gas combined cycle (NGCC) power plants as well
as to reduced contributions from new natural gas cogeneration plants and NGCC plants with CO2 capture. This results in a faster penetration of renewable options, mainly wind onshore and biomass gasification plants. When adding HH external costs besides CC externalities, there is an even higher reduction in the use of natural gas technologies with respect to the CC scenario. A significant growth of rooftop photovoltaic systems and waste incineration plants is observed (the latter mainly due to the absence of life-cycle burdens allocated to the energy production function of the waste management system). Internalising external costs associated with power generation technologies favours the emergence of renewable energy technologies with a good socio-environmental performance. Looking at the long term, this involves business opportunities for technologies such as onshore (repowering) and offshore (large-scale deployment) wind, biomass gasification, and concentrated solar thermal with storage (its high dispatchability may assist grid operators in dealing with base load requirements). 3.2. Climate change and human health The origin of the monetisation of externalities is based on the consideration of the life-cycle emissions and impacts of power generation technologies (Porchia and Bigano, 2008). However, beyond the internalisation of externalities in the core of the objective function to be minimised, the endogenisation of the life-cycle indicators on which externalities are founded had not yet been addressed. Taking advantage of the work carried out by García-Gusano et al. (2016a), the evolution of both CC and HH life-cycle indicators is assessed herein for the three scenarios under study.
390
D. García-Gusano et al. / Science of the Total Environment 612 (2018) 386–391
Fig. 4. Evolution of the human health impact under the three scenarios considered for power generation in Spain. (For interpretation of the references to colour in this figure, the reader is referred to the web version of this article.)
Fig. 3 shows the evolution of the CC performance of the Spanish electricity production mix. In other words, the life-cycle CC consequences of the internalisation of external costs are quantified. When compared to the BaU scenario (no externalities), large CC reductions are observed when the external costs related to climate change and human health, either separately or together, are implemented. As external costs grow (see Tables 1 and 2), their effects are significantly higher and thus influence new investments (existing technologies are retired significantly by 2035–2040). Accordingly, major reductions –up to 68% with respect to the BaU scenario– take place after 2040, once the production mix is almost decarbonised. It should be noted that the blue dotted line in Fig. 3 shows the effect of a double internalisation of externalities (implementation of both CC and HH external costs) on the evolution of the CC indicator. In this regard, the CC & HH scenario (blue dotted line) hardly leads to higher CC reductions than the CC scenario (purple dashed line). The reason behind this finding lies in the values in Table 2. Once decarbonisation takes place (coal and oil especially penalised by both CC and HH external costs, and natural gas options penalised by CC external costs), the relevant values of the HH-related externalities range only from 0 to 8 €/MWh. The addition of these values to those of the CC-related externalities does not influence significantly the mathematical optimisation process with respect to the CC scenario. The effects of the internalisation of socio-environmental externalities on the HH performance are shown in Fig. 4. When compared to the BaU scenario, the scenarios with internalisation of external costs present an even more pronounced reduction in the HH impact in the first years due to a faster retirement of coal-fired power plants. In all the evaluated scenarios, after the pronounced HH impact reduction,
the life-cycle indicator keeps decreasing moderately, reaching the minimum in 2040. In the period 2030–2050, when renewables dominate the electricity production mix, the internalisation of externalities leads to HH improvements around 15–17% with respect to the BaU scenario. When looking at the long term, the shift from the application of CC external costs only (purple dashed line) to the application of CC & HH external costs together (blue dotted line) is considered to be significant in terms of HH effects (zoomed graph in Fig. 4). Hence, in a long-term decarbonised power generation system where the competition is almost completely among renewable power generation technologies, the additional implementation of HH-related external costs might be advisable. Overall, the internalisation of external costs along with the endogenisation of the corresponding life-cycle sustainability indicators in the ESM framework opens up a new field of application of combined ESM + LCA approaches: testing the validity of external costs in terms of the issues for which they are designed. Acknowledging that the calculation of socio-environmental externalities is a process associated with high uncertainty –due to the assumptions concerning mainly system boundaries and monetisation–, this type of ESM + LCA study allows discussing prospectively key concerns such as climate change and human health, and enables policy-makers to check the actual fulfilment of the goals pursued regarding the emergence of renewable energy technologies. 4. Conclusions The endogenous integration of CC- and HH-related external costs and life-cycle indicators into an energy systems optimisation model for power generation in Spain proved to be a valuable methodological advance when it comes to supporting energy policy-makers in
D. García-Gusano et al. / Science of the Total Environment 612 (2018) 386–391
decision-making processes actually oriented towards the sustainability goal. Despite this progress, further efforts are still required to strengthen the link between LCA and ESM. The internalisation of socio-environmental externalities was found to hasten the decarbonisation of the electricity production mix, favouring the penetration of renewable technologies (especially wind and biomass-based technologies). When compared to the BaU scenario, this faster decarbonisation rate of the electricity production mix would bring about significant improvements in the life-cycle CC and HH performance of the national power generation sector.
Acknowledgements This research has been partly supported by the Spanish Ministry of Economy, Industry and Competitiveness (ENE2015-74607-JIN AEI/ FEDER/UE). References Carlsson, J., Pérez Fortes, M.M., de Marco, G., Giuntoli, J., Jakubcionis, M., Jäger-Waldau, A., Lacal-Arantegui, R., Lazarou, S., Magagna, D., Moles, C., Sigfusson, B., Spisto, A., Vallei, M., Weidner, E., 2014. Energy Technology Reference Indicator (ETRI) Projections for 2010–2050. European Commission, Luxembourg. DeCarolis, J., Daly, H., Dodds, P., Keppo, I., Li, F., McDowall, W., Pye, S., Strachan, N., Trutnevyte, E., Usher, W., Winning, M., Yeh, S., Zeyringer, M., 2017. Formalizing best practice for energy system optimization modelling. Appl. Energy 194, 184–198. García-Gusano, D., Martín-Gamboa, M., Iribarren, D., Dufour, J., 2016a. Prospective analysis of life-cycle indicators through endogenous integration into a national power generation model. Resources 5, 39. García-Gusano, D., Iribarren, D., Martín-Gamboa, M., Dufour, J., Espegren, K., Lind, A., 2016b. Integration of life-cycle indicators into energy optimisation models: the case study of power generation in Norway. J. Clean. Prod. 112, 2693–2696.
391
García-Gusano, D., Iribarren, D., Garraín, D., 2017. Prospective analysis of energy security: a practical life-cycle approach focused on renewable power generation and oriented towards policy-makers. Appl. Energy 190, 891–901. Heaps, C.G., 2016. Long-range Energy Alternatives Planning (LEAP) System. Stockholm Environment Institute, Somerville. Howells, M., Rogner, H., Strachan, N., Heaps, C., Huntington, H., Kypreos, S., Hughes, A., Silveira, S., DeCarolis, J., Bazillian, M., Roehrl, A., 2011. OSeMOSYS: the open source energy modeling system: an introduction to its ethos, structure and development. Energ Policy 39, 5850–5870. IEA, 2016. Key World Energy Statistics. International Energy Agency, Paris. ISO, 2006a. ISO 14040:2006 Environmental Management – Life Cycle Assessment – Principles and Framework. International Organisation for Standardization, Geneva. ISO, 2006b. ISO 14044:2006 Environmental Management – Life Cycle Assessment – Requirements and Guidelines. International Organisation for Standardization, Geneva. Jolliet, O., Margni, M., Charles, R., Humbert, S., Payet, J., Rebitzer, G., Rosenbaum, R., 2003. IMPACT 2002+: a new life cycle impact assessment methodology. Int. J. Life Cycle Assess. 8, 324–330. Krewitt, W., 2002. External costs of energy – do the answers match the questions?: looking back at 10 years of ExternE. Energ Policy 30, 839–848. Lechón, Y., Cabal, H., Gómez, M., Sánchez, E., Sáez, R., 2002. Environmental externalities caused by SO2 and ozone pollution in the metropolitan area of Madrid. Environ. Sci. Pol. 5, 385–395. Macías, P., Islas, J., 2010. Damage costs produced by electric power plants: an externality valuation in the Mexico City Metropolitan Area. Sci. Total Environ. 408, 4511–4523. Pietrapertosa, F., Cosmi, C., Macchiato, M., Salvia, M., Cuomo, V., 2009. Life cycle assessment, ExternE and comprehensive analysis for an integrated evaluation of the environmental impact of anthropogenic activities. Renew. Sust. Energ. Rev. 13, 1039–1048. Porchia, R., Bigano, A., 2008. Development of a Set of Full Cost Estimates of the Use of Different Energy Sources and Its Comparative Assessment in EU Countries. European Commission, Brussels. Portugal-Pereira, J., Köberle, A.C., Soria, R., Lucena, A.F.P., Szklo, A., Schaeffer, R., 2016. Overlooked impacts of electricity expansion optimisation modelling: the life cycle side of the story. Energy 115, 1424–1435. Rentizelas, A., Georgakellos, D., 2014. Incorporating life cycle external cost in optimization of the electricity generation mix. Energ Policy 65, 134–149. Sovacool, B.K., 2016. How long will it take? Conceptualizing the temporal dynamics of energy transition. Energy Res. Soc. Sci. 13, 202–215.