- Email: [email protected]
Supporting Low-Carbon Transitions
1. Kim, J.H., Hansora, D., Sharma, P., Jang, J.-W., and Lee, J.S. (2019). Toward practical solar hydrogen production - an artificial photosynthetic leaf-to-farm challenge. Chem. Soc. Rev. 48, 1908–1971. 2. Jia, J., Seitz, L.C., Benck, J.D., Huo, Y., Chen, Y., Ng, J.W.D., Bilir, T., Harris, J.S., and Jaramillo, T.F. (2016). Solar water splitting by photovoltaic-electrolysis with a solar-tohydrogen efficiency over 30. Nat. Commun. 7, 13237. 3. Schu¨ttauf, J.-W., Modestino, M.A., Chinello, E., Lambelet, D., Delfino, A., Domine´, D., Faes, A., Despeisse, M., Bailat, J., Psaltis, D., et al. (2016). Solar-to-Hydrogen Production at 14.2% Efficiency with Silicon Photovoltaics and EarthAbundant Electrocatalysts. J. Electrochem. Soc. 163, F1177–F1181.
4. Luo, J., Im, J.-H., Mayer, M.T., Schreier, M., Nazeeruddin, M.K., Park, N.-G., Tilley, S.D., Fan, H.J., and Gra¨tzel, M. (2014). Water photolysis at 12.3% efficiency via perovskite photovoltaics and Earth-abundant catalysts. Science 345, 1593–1596. 5. Gao, J., Sahli, F., Liu, C., Ren, D., Guo, X., Werner, J., Jeangros, Q., Zakeeruddin, S.M., Ballif, C., Gra¨tzel, M., et al. (2019). Solar Water Splitting with Perovskite/Silicon Tandem Cell and TiC-Supported Pt Nanocluster Electrocatalyst. Joule, this issue, 2930–2941. 6. Sahli, F., Werner, J., Kamino, B.A., Bra¨uninger, M., Monnard, R., Paviet-Salomon, B., Barraud, L., Ding, L., Diaz Leon, J.J., Sacchetto, D., et al. (2018). Fully textured monolithic perovskite/silicon tandem solar cells with
25.2% power conversion efficiency. Nat. Mater. 17, 820–826. 7. Kibsgaard, J., and Chorkendorff, I. (2019). Considerations for the scaling-up of water splitting catalysts. Nat. Energy 4, 430–433. 8. Pinaud, B.A., Benck, J.D., Seitz, L.C., Forman, A.J., Chen, Z., Deutsch, T.G., James, B.D., Baum, K.N., Baum, G.N., Ardo, S., et al. (2013). Technical and economic feasibility of centralized facilities for solar hydrogen production via photocatalysis and photoelectrochemistry. Energy Environ. Sci. 6, 1983–2002. 9. Wang, R., Mujahid, M., Duan, Y., Wang, Z.-K., Xue, J., and Yang, Y. (2019). A Review of Perovskites Solar Cell Stability. Adv Funct Mater, 1808843.
Preview
Supporting Low-Carbon Transitions Brandon R. Sutherland1,* Decarbonizing the electricity sector is an urgent global energy challenge. Recently in Energy Research and Social Science, Barido et al. used data mining approaches on a large set of factors that influence decarbonization progress across 130 countries. Their findings led them to propose a new framework to determine an effective set of region-specific support mechanisms to sustain a long-term clean energy transition.
To avoid the negative economic, environmental, and social consequences associated with rising mean global temperatures, it is presently estimated that greenhouse gas emissions must rapidly decrease to zero by 2070 and proceed to be negative after that.1 Electricity and heat production account for approximately half of the world’s CO2 emissions.2 Decarbonizing the electricity sector remains one of the most urgent energy production challenges. Rallying efforts from every major emissions-producing country in the world to decarbonize is no simple feat. Each country or region has its own unique energy system forged over decades of societal change starting from the
pre-industrial era, further tied to local resources manifested after the big bang, and numerous other factors. Such circumstances determine the optimal way to decarbonize, but the journey begins with why a given region chooses to transition away from fossil fuels. The motivation behind decarbonization extends far beyond the altruistic pursuit of climate change mitigation. Even if the world unanimously agreed it is simply the right thing to do, rarely is such a realization sufficient to advance agendas. Fortunately, there are numerous reasons that have and will continue to galvanize low-carbon transitions. For example, a country that
2894 Joule 3, 2889–2896, December 18, 2019 ª 2019 Published by Elsevier Inc.
lacks local fossil fuel reserves would be reliant on energy imports. Switching to increased proportions of wind, solar, and hydro would increase its energy independence, which may be in the country’s own geopolitical benefit. Another case could be a prospering crystalline silicon manufacturing infrastructure that has historically been used for the electronics industry. This could position a country to be a technological leader in the production of state-of-the-art silicon solar photovoltaics, creating new economic opportunities. Lastly, consider the scenario of major cities with pollution problems that could be remediated with clean energy transitions. Clean air often finds itself at the top of mega-urban regional agendas. These many potential motivating factors and unique characteristics of a region’s energy system highlight the need for studies that find coherence among the chaos. Discovering commonalities that link regions with similar needs and conditions may expedite decarbonization progress. It is a big
1Joule,
Cell Press, 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, USA *Correspondence: [email protected] https://doi.org/10.1016/j.joule.2019.12.001
Figure 1. Examples of Inherent Characteristics, Enabling Environments, and Factors on the Motivation Spectrum that Could Advise Support Mechanisms for Decarbonization Adapted from Barido et al. 3
problem with lots of disjointed data that requires modern numerical solutions to resolve underlying patterns. Recently in Energy Research and Social Science, Barido et al. strived to achieve this by studying 11 different decarbonization-relevant datasets for 130 countries.3 These datasets included energy, electricity, economic growth, sustainability and social progress, quality of governance, fossil fuel subsidies, environmental policies, and other factors. To examine this expansive set of information, modern computational data mining approaches were used. The data revealed three new groupings of factors that influence decarbonization (Figure 1). First are the inherent characteristics of the country. This includes the human development index in the region, average income per capita, population
and population density, land size, local fossil fuel reserves, and other such factors that are fixed or slow to change. Second are a region’s enabling environments. These change and evolve frequently and could include elements such as the current government expenditure in clean technology research, the local cost of electricity, and subsidies for fossil fuels or renewables. The third is a decarbonization motivation spectrum that represents the reasons for (or against) moving toward clean energy. The motivation behind low-carbon transitions can fuel effective support mechanisms that can sustain long-term decarbonization when they are considered in view of existing inherent characteristics and enabling environments. When evaluating country-specific trends using this framework, a
number of propositions for policymakers, governments, financiers, and other agents of change are put forth. It was recommended that extra attention is placed on demand-side drivers, that support mechanisms are diversified and countries with common motivations learn from each other, that the decision-makers and influencers are diversified to support countries with different needs, and that they think beyond energy and recognize that an optimal pathway from a techno-economic perspective is a narrow view of a large problem. Solving the global decarbonization challenge is of pressing importance. Yet, it is an incredibly complex, multiscale, and multi-disciplinary problem. The development of big-data algorithms and methods to find patterns
Joule 3, 2889–2896, December 18, 2019 2895
where econometrics or classical mathematical approaches fall short is burgeoning. There remains a need at the research scale for more approaches that leverage big-data methods to find optimized pathways forward and for key change agents to combat the complexity of decarbonization
2896 Joule 3, 2889–2896, December 18, 2019
with diverse, instruments.
evidence-supported
1. Masson-Delmotte, V., Zhai, P., Po¨rtner, H.O., Roberts, D., Skea, J., Shukla, P.R., Pirani, A., Moufouma-Okia, W., Pe´an, C., Pidcock, R., et al. (2018). IPCC, 2018: Global warming of 1.5 C (World Meteorological Organization).
2. Ritchie, H., and Roser, M. (2018). CO2 and Greenhouse Gas Emissions, Our World in Data. https://ourworldindata.org/co2-andother-greenhouse-gas-emissions. 3. Barido, D.P. de L., Avila, N., and Kammen, D.M. (2020). Exploring the Enabling Environments, Inherent Characteristics and Intrinsic Motivations Fostering Global Electricity Decarbonization. Energy Res. Soc. Sci. 61, 101343.
4. Luo, J., Im, J.-H., Mayer, M.T., Schreier, M., Nazeeruddin, M.K., Park, N.-G., Tilley, S.D., Fan, H.J., and Gra¨tzel, M. (2014). Water photolysis at 12.3% efficiency via perovskite photovoltaics and Earth-abundant catalysts. Science 345, 1593–1596. 5. Gao, J., Sahli, F., Liu, C., Ren, D., Guo, X., Werner, J., Jeangros, Q., Zakeeruddin, S.M., Ballif, C., Gra¨tzel, M., et al. (2019). Solar Water Splitting with Perovskite/Silicon Tandem Cell and TiC-Supported Pt Nanocluster Electrocatalyst. Joule, this issue, 2930–2941. 6. Sahli, F., Werner, J., Kamino, B.A., Bra¨uninger, M., Monnard, R., Paviet-Salomon, B., Barraud, L., Ding, L., Diaz Leon, J.J., Sacchetto, D., et al. (2018). Fully textured monolithic perovskite/silicon tandem solar cells with
25.2% power conversion efficiency. Nat. Mater. 17, 820–826. 7. Kibsgaard, J., and Chorkendorff, I. (2019). Considerations for the scaling-up of water splitting catalysts. Nat. Energy 4, 430–433. 8. Pinaud, B.A., Benck, J.D., Seitz, L.C., Forman, A.J., Chen, Z., Deutsch, T.G., James, B.D., Baum, K.N., Baum, G.N., Ardo, S., et al. (2013). Technical and economic feasibility of centralized facilities for solar hydrogen production via photocatalysis and photoelectrochemistry. Energy Environ. Sci. 6, 1983–2002. 9. Wang, R., Mujahid, M., Duan, Y., Wang, Z.-K., Xue, J., and Yang, Y. (2019). A Review of Perovskites Solar Cell Stability. Adv Funct Mater, 1808843.
Preview
Supporting Low-Carbon Transitions Brandon R. Sutherland1,* Decarbonizing the electricity sector is an urgent global energy challenge. Recently in Energy Research and Social Science, Barido et al. used data mining approaches on a large set of factors that influence decarbonization progress across 130 countries. Their findings led them to propose a new framework to determine an effective set of region-specific support mechanisms to sustain a long-term clean energy transition.
To avoid the negative economic, environmental, and social consequences associated with rising mean global temperatures, it is presently estimated that greenhouse gas emissions must rapidly decrease to zero by 2070 and proceed to be negative after that.1 Electricity and heat production account for approximately half of the world’s CO2 emissions.2 Decarbonizing the electricity sector remains one of the most urgent energy production challenges. Rallying efforts from every major emissions-producing country in the world to decarbonize is no simple feat. Each country or region has its own unique energy system forged over decades of societal change starting from the
pre-industrial era, further tied to local resources manifested after the big bang, and numerous other factors. Such circumstances determine the optimal way to decarbonize, but the journey begins with why a given region chooses to transition away from fossil fuels. The motivation behind decarbonization extends far beyond the altruistic pursuit of climate change mitigation. Even if the world unanimously agreed it is simply the right thing to do, rarely is such a realization sufficient to advance agendas. Fortunately, there are numerous reasons that have and will continue to galvanize low-carbon transitions. For example, a country that
2894 Joule 3, 2889–2896, December 18, 2019 ª 2019 Published by Elsevier Inc.
lacks local fossil fuel reserves would be reliant on energy imports. Switching to increased proportions of wind, solar, and hydro would increase its energy independence, which may be in the country’s own geopolitical benefit. Another case could be a prospering crystalline silicon manufacturing infrastructure that has historically been used for the electronics industry. This could position a country to be a technological leader in the production of state-of-the-art silicon solar photovoltaics, creating new economic opportunities. Lastly, consider the scenario of major cities with pollution problems that could be remediated with clean energy transitions. Clean air often finds itself at the top of mega-urban regional agendas. These many potential motivating factors and unique characteristics of a region’s energy system highlight the need for studies that find coherence among the chaos. Discovering commonalities that link regions with similar needs and conditions may expedite decarbonization progress. It is a big
1Joule,
Cell Press, 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, USA *Correspondence: [email protected] https://doi.org/10.1016/j.joule.2019.12.001
Figure 1. Examples of Inherent Characteristics, Enabling Environments, and Factors on the Motivation Spectrum that Could Advise Support Mechanisms for Decarbonization Adapted from Barido et al. 3
problem with lots of disjointed data that requires modern numerical solutions to resolve underlying patterns. Recently in Energy Research and Social Science, Barido et al. strived to achieve this by studying 11 different decarbonization-relevant datasets for 130 countries.3 These datasets included energy, electricity, economic growth, sustainability and social progress, quality of governance, fossil fuel subsidies, environmental policies, and other factors. To examine this expansive set of information, modern computational data mining approaches were used. The data revealed three new groupings of factors that influence decarbonization (Figure 1). First are the inherent characteristics of the country. This includes the human development index in the region, average income per capita, population
and population density, land size, local fossil fuel reserves, and other such factors that are fixed or slow to change. Second are a region’s enabling environments. These change and evolve frequently and could include elements such as the current government expenditure in clean technology research, the local cost of electricity, and subsidies for fossil fuels or renewables. The third is a decarbonization motivation spectrum that represents the reasons for (or against) moving toward clean energy. The motivation behind low-carbon transitions can fuel effective support mechanisms that can sustain long-term decarbonization when they are considered in view of existing inherent characteristics and enabling environments. When evaluating country-specific trends using this framework, a
number of propositions for policymakers, governments, financiers, and other agents of change are put forth. It was recommended that extra attention is placed on demand-side drivers, that support mechanisms are diversified and countries with common motivations learn from each other, that the decision-makers and influencers are diversified to support countries with different needs, and that they think beyond energy and recognize that an optimal pathway from a techno-economic perspective is a narrow view of a large problem. Solving the global decarbonization challenge is of pressing importance. Yet, it is an incredibly complex, multiscale, and multi-disciplinary problem. The development of big-data algorithms and methods to find patterns
Joule 3, 2889–2896, December 18, 2019 2895
where econometrics or classical mathematical approaches fall short is burgeoning. There remains a need at the research scale for more approaches that leverage big-data methods to find optimized pathways forward and for key change agents to combat the complexity of decarbonization
2896 Joule 3, 2889–2896, December 18, 2019
with diverse, instruments.
evidence-supported
1. Masson-Delmotte, V., Zhai, P., Po¨rtner, H.O., Roberts, D., Skea, J., Shukla, P.R., Pirani, A., Moufouma-Okia, W., Pe´an, C., Pidcock, R., et al. (2018). IPCC, 2018: Global warming of 1.5 C (World Meteorological Organization).
2. Ritchie, H., and Roser, M. (2018). CO2 and Greenhouse Gas Emissions, Our World in Data. https://ourworldindata.org/co2-andother-greenhouse-gas-emissions. 3. Barido, D.P. de L., Avila, N., and Kammen, D.M. (2020). Exploring the Enabling Environments, Inherent Characteristics and Intrinsic Motivations Fostering Global Electricity Decarbonization. Energy Res. Soc. Sci. 61, 101343.