In conclusion, the most attractive aspect of the presented process is, in the author’s opinion, a 2-fold uncoupling: (1) in space, as we are disconnecting the ‘‘dirty’’ waste conversion from the ‘‘clean’’ bioproduction, which principally can also be a catalytic production, and (2) in time, as the redox cathode is presenting a buffer. Given adequate control the system can deal with, on the one hand, discontinuous availability of waste organics to transform, and on the other hand, a discontinuous need for production. Both aspects are easier to achieve at small scale. Evidently, the current MB process is only a proof of concept. The current densities, below 1 mA cm 2, are lower than those achieved in some of the more recent studies on bioanodes. The
authors already get quite a promising energetic efficiency of organics to formic acid, 38% G 6%; this only includes the electrochemical reactions as such. The efficiency will be lower on real wastewater, with low conductivity (~1 mS cm 1 typically), and other factors such as pumping energy need to be taken into account. Nevertheless, the process enables harvesting the complexity of dilute waste organics and converting it to the simplicity of one outcome product all enabled by an innovative use of the redox cathode.
ACKNOWLEDGMENTS K.R. is supported by the Ghent University Bijzonder Onderzoeksfonds via GOA grant BOF2019/GOA/026/L. Tim Lacoere prepared Figure 1.
1. Dubrawski, K.L., Shao, X., Milton, R.D., Deutzmann, J.S., Spormann, A.M., and Criddle, C.S. (2019). Microbial battery powered enzymatic electrosynthesis for carbon capture and generation of hydrogen and formate from dilute organics. ACS Energy Lett. 4, 2929– 2936. 2. Rabaey, K., Rodrı´guez, J., Blackall, L.L., Keller, J., Gross, P., Batstone, D., Verstraete, W., and Nealson, K.H. (2007). Microbial ecology meets electrochemistry: electricity-driven and driving communities. ISME J. 1, 9–18. 3. Logan, B.E. (2010). Scaling up microbial fuel cells and other bioelectrochemical systems. Appl. Microbiol. Biotechnol. 85, 1665–1671. 4. Clauwaert, P., Rabaey, K., Aelterman, P., de Schamphelaire, L., Pham, T.H., Boeckx, P., Boon, N., and Verstraete, W. (2007). Biological denitrification in microbial fuel cells. Environ. Sci. Technol. 41, 3354– 3360. 5. Rabaey, K., and Rozendal, R.A. (2010). Microbial electrosynthesis - revisiting the electrical route for microbial production. Nat. Rev. Microbiol. 8, 706–716.
Changing the Climate Change Discourse Frank W. Geels1,* Despite rising greenhouse gas emissions, the rapid diffusion of solar-PV, wind turbines, and light-emitting diodes (LEDs) is beginning to change the climate change discourse from one that focuses on collective action problems, freeriding concerns, and zero-sum games to one that focuses on economic opportunities, innovation races, and win-win solutions.1 In a rich and interesting article, titled ‘‘Recalibrating climate prospects,’’ Lovins et al.2 make three major contributions to this changing discourse.
First, interpreting quantitative data from BP, the World Bank, and IEA, they suggest that the 2010–2018 years experienced a pattern break in which primary energy intensity decreases of the Gross World Product accelerated to an average of 2.03% per year. For recent years (2015–2018), they further find that the reduced energy intensity plus the increased share of decarbonized
final energy supply were 3.4% per year, which is a rate that IPCC AR5 found necessary for a 2 C trajectory. Drawing on these figures, the article expresses a qualified message of hope, suggesting that ‘‘belatedly, haltingly, yet with gathering focus and force, humanity is responding to an existential threat’’ (p. 3). They attribute these improvements to increases in renewable elec-
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tricity (especially solar-PV and wind), modern renewable heat, and gradual energy efficiency improvements. Second, for the future they suggest that ‘‘whole system’’ energy-efficiency solutions offer major opportunities for demand-side mitigation that, like modern renewables, could benefit from declining costs and rapid growth. This contribution resonates with other recent publications showing that the demand-side solution space is much larger than climate models have hitherto suggested.3–5 The authors argue that the drivers for the diffusion of these ‘‘whole system’’ demand-side solutions are less about scale economies, fastlearning, short lead-times, and innovative business and finance models (which
Manchester Business School, The University of Manchester, Booth Street West, Manchester M15 6PB, UK *Correspondence: [email protected]
drove renewables) and more about integrative design methods and engineering synergies, as a result of which total system costs are less than the sum of the individual parts. They claim, for example, that the costs of whole-building, passive-design optimizations are lower than the combined costs of individual components (like insulation, double or triple glazing, air-tightening, and furnace and air-conditioner efficiency). Referring to Lovins,6 they claim that similar technical synergies are possible in whole-vehicle integrative designs and in heavy and high-tech industrial energy use. Third, they argue that Integrated Assessment Models (IAMs), which still dominate climate mitigation research, systematically overlook or underestimate these demand-side mitigation opportunities for several reasons: (1) they use old datasets (which do not accommodate the post2010 trend breaks) for model calibration and inputs, (2) they have limited granularity and technical detail for demandside options (compared to very detailed supply-side data), and (3) they struggle to model whole system change because of their focus on separate components rather than on new combinations and technical synergies. To better analyze the demand-side solution space, the authors therefore suggest that energy and climate modeling should ‘‘fundamentally modernize its methods and assumptions’’ (p. 5) by more explicitly representing specific end-use sectors, using more recent datasets, and analyzing more granular energy efficiency options and their synergetic interactions, for instance by integrating engineering-based sub-models into bigger IAMs. While I welcome and agree with all three contributions, the article also has a few limitations that offer scope for further elaboration. First, while the article’s focus on technological efficiency improvements is important, it may underestimate the demand-side solution space by not addressing behavioral and busi-
ness model changes. For demand sectors like transport, buildings, and food, Creutzig et al.4 distinguish not only ‘‘improve’’ options (which include technical efficiency measures), but also behavioral ‘‘shift’’ options (like modal shifts from cars to public transport or dietary shifts from meat to other protein sources) and ‘‘avoid’’ options that reduce the demand for particular services (e.g., tele-working, compact cities, smart logistics, sharing economy). Although shift and avoid options are difficult to model and depend on a wide range of contextual factors, they further broaden the demand-side solution space beyond the options identified by Lovins et al. Second, while the article’s focus on the demand-side mitigation potential is very welcome, it does not say much about the factors that influence the actual use of this potential. The article acknowledges that the global scale-up of energy efficiency measures ‘‘faces many major and richly documented obstacles’’ (p. 5), and it mentions institutional and cognitive barriers such as engineering mindsets, textbooks, curricula, and deeply embedded assumptions. But it does not discuss sunk investments in machines and factories, which make companies reluctant to completely redesign cars or industrial production processes. It also does not mention that whole house retrofit designs may cost many tens of thousands of dollars. Even if technical synergies make integrated whole-building designs cheaper than the sum of their parts, such high upfront costs will still hamper adoption in many countries. Furthermore, the technical skills, building standards, and administrative enforcement capacities that accompany the construction of highly efficient buildings may not exist in developing countries and emerging economies, where the majority of new buildings will arise in the coming decades.7 Given the article’s emphasis on ‘‘whole system’’ design options,
the argument could have been strengthened, in my view, by more deeply discussing the three examples (passive house, efficient vehicles, industrial energy use) in terms of costs, drivers, and barriers. Third, while the ‘‘whole system’’ solutions offer high mitigation potential and thus form seeds of low-carbon transitions, the article could have said more about the dynamics of transitional change needed to actualize the mitigation potential. The article rightly criticizes IAMs for their ‘‘lack of representation of innovation processes across the economy, institutions, and human behavior’’ (p. 9). But because of its engineering focus, it does not offer a better conceptualization of innovation or transitions. Perhaps this is too much to ask for a single article, but the problem is wider and also applies to many IPCC WG3 analyses, which suffer from a gap between rich sector-specific analyses of potential mitigation options and long-term explorations of transition pathways, which are generated by IAMs and thus have multiple limitations (e.g., no attention for actors, interests, institutions, power struggles, cultural meanings, societal debates). With regard to demand-side solutions, the article tantalizingly mentions the need to analyze ‘‘dynamic, complex, nonlinear, and multi-layered processes that cut across behavioral, economic, environmental, organizational, institutional, and technological layers’’ (p. 9). But it then engages with the modeling literature rather than the new field of sustainability transitions, which is a missed opportunity since this field focuses precisely on nonlinear innovation and multi-layered transition processes, especially in the socio-technical approach.8,9 Engagement with this field could lead to deeper methodological innovations in climate mitigation assessments. One option is to develop procedures for bridging and dialog between IAMs,
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socio-technical transition research, and on-the-ground implementation projects.10 Another option, pioneered in the recent IPCC 1.5 C report, is to complement IAM-generated mitigation pathways with multi-dimensional feasibility assessments that analyze the feasibility of mitigation options in technical, economic, institutional, socio-cultural, environmental, and geophysical dimensions. In conclusion, the Lovins et al. article succeeds in (1) injecting some qualified hope into the climate change debate, (2) drawing attention to the high demand-side mitigation potential, and (3) calling for methodological innovation to analyze this potential. Chapter 5 in the forthcoming IPCC AR6 report will hopefully build on these contributions in its assessment of demand-side mitigation options. But the article also has some limitations with regard to acknowledging the breadth of the
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demand-side solution space and analyzing the dynamics of change that determine if the potential will actually be used. IPCC AR6 is well placed to address these issues in its chapters on near- to mid-term pathways (4), innovation (16), and accelerated transitions (17). 1. Geels, F.W., Sovacool, B.K., Schwanen, T., and Sorrell, S. (2017). The socio-technical dynamics of low-carbon transitions. Joule 1, 463–479. 2. Lovins, A., U¨rge-Vorsatz, D., Mundaca, L., Kammen, D.M., and Glassman, J.W. (2019). Recalibrating climate prospects. Environ. Res. Lett. 14, 120201.
side solutions for mitigating climate change. Nat. Clim. Chang. 8, 268–271. 5. Mundaca, L., U¨rge-Vorsatz, D., and Wilson, C. (2019). Demand-side approaches for limiting global warming to 1.5 C. Energ. Effic. 12, 343–362. 6. Lovins, A.B. (2018). How big is the energy efficiency resource? Environ. Res. Lett. 13, 090401. 7. Thacker, S., Adshead, D., Fay, M., Hallegatte, S., Harvey, M., Meller, H., O’Regan, N., Rozenberg, J., Watkins, G., and Hall, J.W. (2019). Infrastructure for sustainable development. Nat. Sustain. 2, 324–331. 8. Geels, F.W., Sovacool, B.K., Schwanen, T., and Sorrell, S. (2017). Sociotechnical transitions for deep decarbonization. Science 357, 1242–1244.
3. Gru¨bler, A., Wilson, C., Bento, N., Boza-Kiss, B., Krey, V., McCollum, D.L., Rao, N.D., Riahi, K., Rogelj, J., De Stercke, S., et al. (2018). A low energy demand scenario for meeting the 1.5 C target and sustainable development goals without negative emission technologies. Nat. Energy 3, 515–527.
9. Ko¨hler, J., Geels, F.W., Kern, F., Markard, J., Onsongo, E., Wieczorek, A., Alkemaade, F., Avelino, F., Bergek, A., Boons, F., et al. (2019). An agenda for sustainability transitions research: state of the art and future directions. Environ. Innov. Soc. Transit. 31, 1–32.
4. Creutzig, F., Roy, J., Lamb, W.F., Azevedo, I.M.L., De Bruin, W.B., Dalkmann, H., Edelenbosch, O., Geels, F.W., Gru¨bler, A., Hepburn, C., et al. (2018). Towards demand-
10. Geels, F.W., Berkhout, F., and Van Vuuren, D. (2016). Bridging analytical approaches for low-carbon transitions. Nat. Clim. Chang. 6, 576–583.