In Search of Complementarity: Insights from an Exercise in Quantifying Qualitative Energy Futures
Abstract
:1. Introduction
- What are the challenges and benefits in achieving complementarity of qualitative and quantitative research?
- How suitable are tools from Bayesian statistics as a bridging strategy, i.e., for conducting the translation, and what useful qualitative information can be learned in the process?
- What are the recommendations for similar studies?
2. Integrating Qualitative and Quantitative Energy Research
2.1. Integration Approaches
2.2. Hybrid Research Projects
2.3. Translation Methods
3. Materials and Methods
3.1. Expert Elicitation
- Uncertainty distributions are created for each expert individually and these are then combined using some mathematical rule to obtain a single uncertainty distribution.
- Experts are brought together to agree on some “consensus” uncertainty distribution.
- Combination or “hybrid” of the above two approaches, where there are some elements of the mathematical rule and group consensus approaches.
- Selection of experts: selected to span all areas of expertise relevant to the elicitation.
- Arranging the workshop: this could be in person and all together (e.g., SHELF) or one-on-one (e.g., Classical method), virtually or via email or online questionnaire (e.g., Delphi).
- Putting together an evidence dossier that contains the quantitative information available about the unknowns of interest. Much of this information may be collected from the experts.
- The experts are given time to review the evidence dossier.
- Statistical training for the experts: they have expertise in the quantities of interest but may not have much knowledge about probability and statistics. Some training is then provided so that the experts are clear about what they are being asked to do.
- Feedback and revision: the fitted uncertainty distributions are fed back to the experts to check whether these represent their beliefs and uncertainty with subsequent rounds until agreement is reached.
- Documenting: the arguments used to justify the choices of the experts need to be documented in an anonymous fashion (to encourage honesty). Good practices in recording who attended and in what capacity, etc. should be followed.
3.2. Case Study
3.2.1. Overview
3.2.2. Qualitative Energy Futures
3.2.3. Quantitative Energy Model
4. Results
4.1. Elicitation Event
4.2. Quantitative Results
4.3. Qualitative Observations
- A content analysis of audio recordings of the elicitation event
- A methodological evaluation of the expert elicitation process
“For us, we are worrying about peak demand, because that is about how much we’re going to have to invest in infrastructure to be able to support electric vehicles and heat pumps and so on, and you have chosen total (annual) electricity… if we, as a society, can bring the peak down, we can reduce investment. So, I am wondering why you have left peak demand out?”
“EVs is a big story for us, and it is the area of most uncertainty for us because that is when you start to get into behavioural shifts and that is really hard and getting it wrong, the consequences become big”
- Changes to transport and heating demand, notably hydrogen availability, will significantly impact electricity demand
- Demand reduction through technological change is unlikely to continue at the rate it has in recent years (this is important, as it shows how problematic it is to use historical data for future scenarios)
- Increased decentralisation will lead to increased electricity demand
“I was trying to balance between reduction of demand through energy efficiency and an increase in demand from EV transport”
“I was thinking about how the demand for electricity might be influenced by the shift to decarbonised generation”
“We have kind of gone as far as we can on energy efficiency and we’re unlikely to get much more from solar because that kind of reached its Zenith and tailed off”
“There probably isn’t going to be much change in electricity until EVs become bigger—diesel ban is not till 2030 so until then there might not be much change”
“I was thinking about localism [outlined as part of the narrative scenario] and producing things locally thinking does that create a boom in the industrial sector? If you don’t want to be shipping stuff in from China and creating pollution elsewhere then you see a return to more being produced locally… Localism is very carbon intensive”
5. Discussion
- What were the final numbers a representation of?
- What uses do these numbers have?
- Did the experts have expertise in the questions they were asked?
6. Conclusions
- What are the challenges and benefits in achieving complementarity of qualitative and quantitative research?
- How suitable are tools from Bayesian statistics as a bridging strategy, i.e., for conducting the translation, and what useful qualitative information can be learned in the process?
- What are the recommendations for similar studies?
6.1. Achieving Complementarity of Qualitative and Quantitative Research
6.2. Suitability of Bayesian Tools for the Translation Process, and the Value of Qualitative Data
6.3. Recommendations
Supplementary Materials
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Climate Change Committee. Net Zero: The UK’s Contribution to Stopping Global Warming. 2019. Available online: https://www.theccc.org.uk/wp-content/uploads/2019/05/Net-Zero-The-UKs-contribution-to-stopping-global-warming.pdf (accessed on 16 June 2021).
- O’Brien, G.; Hope, A. Localism and energy: Negotiating approaches to embedding resilience in energy systems. Energy Policy 2010, 38, 7550–7558. [Google Scholar] [CrossRef] [Green Version]
- McDowall, W.; Trutnevyte, E.; Tomei, J.; Keppo, I. UKERC Energy Systems Theme: Reflecting on Scenarios. In UKERC Report UKERC/WP/ESY/2014/002; UK Energy Research Centre (UKERC): London, UK, 2014. [Google Scholar]
- Holtz, G.; Alkemade, F.; de Haan, F.; Köhler, J.; Trutnevyte, E.; Luthe, T.; Halbe, J.; Papachristos, G.; Chappin, E.; Kwakkel, J.; et al. Prospects of modelling societal transitions: Position paper of an emerging community. Environ. Innov. Soc. Transit. 2015, 17, 41–58. [Google Scholar] [CrossRef]
- Pfenninger, S.; Hawkes, A.; Keirstead, J. Energy systems modeling for twenty-first century energy challenges. Renew. Sustain. Energy Rev. 2014, 33, 74–86. [Google Scholar] [CrossRef]
- Di Silvestre, M.L.; Favuzza, S.; Sanseverino, E.R.; Zizzo, G. How Decarbonization, Digitalization and Decentralization are changing key power infrastructures. Renew. Sustain. Energy Rev. 2018, 93, 483–498. [Google Scholar] [CrossRef]
- McDowall, W.; Geels, F. Ten challenges for computer models in transitions research: Commentary on Holtz et al. Environ. Innov. Soc. Transit. 2017, 22, 41–49. [Google Scholar] [CrossRef]
- Hargreaves, T.; Burgess, J. Pathways to Interdisciplinarity: A Technical Report Exploring Collaborating Interdisciplinary Working in the Transition Pathways Consortium; University of East Anglia: Norwich, UK, 2009. [Google Scholar]
- Mittlefehldt, S.; Bunting, E.; Huff, E.; Welsh, J.; Goodwin, R. New Methods for Assessing Sustainability of Wood-Burning Energy Facilities: Combining Historical and Spatial Approaches. Energies 2021, 14, 7841. [Google Scholar] [CrossRef]
- Li, F.; Trutnevyte, E.; Strachan, N. A review of socio-technical energy transition (STET) models. Technol. Forecast. Soc. Chang. 2015, 100, 290–305. [Google Scholar] [CrossRef]
- Yilmaz, K. Comparison of Quantitative and Qualitative Research Traditions: Epistemological, theoretical, and methodological differences. Eur. J. Educ. 2013, 48, 311–325. [Google Scholar] [CrossRef]
- Barry, A.; Born, G.; Weszkalnys, G. Logics of interdisciplinarity. Econ. Soc. 2008, 37, 20–49. [Google Scholar] [CrossRef]
- Wilhite, H.; Shove, E.; Lutzenhiser, L.; Kempton, W. The legacy of twenty years of energy demand management: We know more about individual behaviour but next to nothing about demand. In Society, Behaviour, and Climate Change Mitigation; Springer: Dordrecht, The Netherlands, 2000; pp. 109–126. [Google Scholar]
- Henning, A. Climate change and energy use: The role for anthropological research. Anthropol. Today 2005, 21, 8–12. [Google Scholar] [CrossRef]
- Silvast, A.; Laes, E.; Abram, S.; Bombaerts, G. What do energy modellers know? An ethnography of epistemic values and knowledge models. Energy Res. Soc. Sci. 2020, 66, 101495. [Google Scholar] [CrossRef]
- Barton, J.; Davies, L.; Dooley, B.; Foxon, T.J.; Galloway, S.; Hammond, G.P.; O’Grady, Á.; Robertson, E.; Thomson, M. Transition pathways for a UK low-carbon electricity system: Comparing scenarios and technology implications. Renew. Sustain. Energy Rev. 2018, 82, 2779–2790. [Google Scholar] [CrossRef] [Green Version]
- Chilvers, J.; Foxon, T.J.; Galloway, S.; Hammond, G.P.; Infield, D.; Leach, M.; Pearson, P.J.; Strachan, N.; Strbac, G.; Thomson, M. Realising transition pathways for a more electric, low-carbon energy system in the United Kingdom: Challenges, insights and opportunities. SAGE J. 2017, 231, 440–477. [Google Scholar] [CrossRef] [Green Version]
- Turnheim, B.; Berkhout, F.; Geels, F.; Hof, A.; McMeekin, A.; Nykvist, B.; van Vuuren, D. Evaluating sustainability transitions pathways: Bridging analytical approaches to address governance challenges. Glob. Environ. Chang. 2015, 35, 239–253. [Google Scholar] [CrossRef] [Green Version]
- Li, F.; Pye, S.; Strachan, N. Regional winners and losers in future UK energy system transitions. Energy Strategy Rev. 2016, 13, 11–31. [Google Scholar] [CrossRef]
- Alcamo, J. Chapter Six The SAS Approach: Combining Qualitative and Quantitative Knowledge in Environmental Scenarios. In Developments in Integrated Environmental Assessment; Alcamo, J., Ed.; Environmental Futures; Elsevier: Amsterdam, The Netherlands, 2008; Volume 2, pp. 123–150. [Google Scholar]
- Hof, A.F.; van Vuuren, D.P.; Berkhout, F.; Geels, F.W. Understanding transition pathways by bridging modelling, transition and practice-based studies: Editorial introduction to the special issue. Technol. Forecast. Soc. Chang. 2020, 151, 119665. [Google Scholar] [CrossRef]
- De Cian, E.; Dasgupta, S.; Hof, A.F.; van Sluisveld, M.A.E.; Köhler, J.; Pfluger, B.; van Vuuren, D.P. Actors, decision-making, and institutions in quantitative system modelling. Technol. Forecast. Soc. Chang. 2020, 151, 119480. [Google Scholar] [CrossRef] [Green Version]
- Cat, J. Fuzzy Empiricism and Fuzzy-Set Causality: What Is All the Fuzz About? Philos. Sci. 2006, 73, 26–41. Available online: https://www.journals.uchicago.edu/doi/abs/10.1086/510173 (accessed on 26 July 2021). [CrossRef]
- Cooke, R.M. The anatomy of the squizzel: The role of operational definitions in representing uncertainty. Reliab. Eng. Syst. Saf. 2004, 85, 313–319. [Google Scholar] [CrossRef]
- Varho, V.; Tapio, P. Combining the qualitative and quantitative with the Q2 scenario technique—The case of transport and climate. Technol. Forecast. Soc. Chang. 2013, 80, 611–630. [Google Scholar] [CrossRef]
- Watson, J.; Ketsopoulou, I.; Dodds, P.; Chaudry, M.; Tindemans, S.; Woolf, M.; Strbac, G. The Security of UK Energy Futures; UKERC: London, UK, 2018. [Google Scholar]
- Ritchey, T. Futures Studies Using Morphological Analysis. In Futures Research Methodology Series Version 3.0; Swedish Morphological Society: Stockholm, Sweden, 2009; Available online: https://www.swemorph.com/pdf/futures.pdf (accessed on 26 July 2021).
- Ritchey, T. Modeling Alternative Futures with General Morphological Analysis. World Future Rev. 2011, 3, 83–94. [Google Scholar] [CrossRef] [Green Version]
- de Waal, A.; Ritchey, T. Combining morphological analysis and Bayesian networks for strategic decision support. ORiON 2007, 23, 105–121. [Google Scholar]
- Bauer, N.; Calvin, K.; Emmerling, J.; Fricko, O.; Fujimori, S.; Hilaire, J.; Eom, J.; Krey, V.; Kriegler, E.; Mouratiadou, I.; et al. Shared Socio-Economic Pathways of the Energy Sector—Quantifying the Narratives. Glob. Environ. Chang. 2017, 42, 316–330. [Google Scholar] [CrossRef] [Green Version]
- O’Neill, B.C.; Kriegler, E.; Ebi, K.L.; Kemp-Benedict, E.; Riahi, K.; Rothman, D.S.; van Ruijven, B.J.; van Vuuren, D.P.; Birkmann, J.; Kok, K.; et al. The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century. Glob. Environ. Chang. 2017, 42, 169–180. [Google Scholar] [CrossRef] [Green Version]
- Riahi, K.; van Vuuren, D.P.; Kriegler, E.; Edmonds, J.; O’Neill, B.C.; Fujimori, S.; Bauer, N.; Calvin, K.; Dellink, R.; Fricko, O.; et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Glob. Environ. Chang. 2017, 42, 153–168. [Google Scholar] [CrossRef] [Green Version]
- Oakley, J.E.; O’Hagan, A. SHELF: The Sheffield Elicitation Framework (Version 2.0); School of Mathematics & Statistics, University of Sheffield: Sheffield, UK, 2010; Available online: http://www.tonyohagan.co.uk/shelf/ (accessed on 1 April 2019).
- Lavine, M. Frequentist, Bayes, or Other? Am. Stat. 2019, 73, 312–318. [Google Scholar] [CrossRef] [Green Version]
- Garthwaite, P.H.; Kadane, J.B.; O’Hagan, A. Statistical methods for eliciting probability distributions. J. Am. Stat. Assoc. 2005, 100, 680–701. [Google Scholar] [CrossRef]
- O’Hagan, A.; Buck, C.E.; Daneshkhah, A.; Eiser, J.; Garthwaite, P.; Jenkinson, D.; Oakley, J.; Rakow, T. Uncertain Judgements: Eliciting Experts’ Probabilities. Statistics in Practice; Wiley: Hoboken, NJ, USA, 2006; ISBN 1-280-64887-2. [Google Scholar]
- Jackman, S. Bayesian Analysis for the Social Sciences; John Wiley & Sons: Hoboken, NJ, USA, 2009; Volume 846, ISBN 0-470-68663-4. [Google Scholar]
- Cooke, R.M. Validation in the classical model. In Elicitation; Springer: Berlin/Heidelberg, Germany, 2018; pp. 37–59. [Google Scholar]
- Gosling, J.P. SHELF: The Sheffield elicitation framework. In Elicitation; Springer: Berlin/Heidelberg, Germany, 2018; pp. 61–93. [Google Scholar]
- EFSA Guidance on expert knowledge elicitation in food and feed safety risk assessment. EFSA J. 2014, 12, 3734.
- Stirling, A. “Opening up” and “closing down” power, participation, and pluralism in the social appraisal of technology. Sci. Technol. Hum. Values 2008, 33, 262–294. [Google Scholar] [CrossRef]
- Hanea, A.M.; Burgman, M.; Hemming, V. IDEA for uncertainty quantification. In Elicitation; Springer: Berlin/Heidelberg, Germany, 2018; pp. 95–117. [Google Scholar]
- Loo, R. The Delphi method: A powerful tool for strategic management. Polic. Int. J. Police Strateg. Manag. 2002, 25, 762–769. [Google Scholar] [CrossRef]
- Christensen, R.; Johnson, W.; Branscum, A.; Hanson, T.E. Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians; CRC Press: Boca Raton, FL, USA, 2010; ISBN 978-0-429-11177-8. [Google Scholar]
- Copeland, C.; MacKerron, G.; Foxon, T.J. Regional energy futures as decision support in the transition to net zero emissions: North of Tyne case study. Local Environ. 2022, 27, 747–766. [Google Scholar] [CrossRef]
- Mazzi, N. Benders with Adaptive Oracles. 2020. Available online: https://github.com/nimazzi/Stand_and_Adapt_Bend (accessed on 2 September 2019).
- Ramirez, R.; Selin, C. Plausibility and probability in scenario planning. Foresight 2014, 16, 54–74. [Google Scholar] [CrossRef] [Green Version]
- Ramirez, R.; Churchhouse, S.; Palermo, A.; Hoffmann, J. Using Scenario Planning to Reshape Strategy. MIT Sloan Manag. Rev. 2017. Available online: https://sloanreview.mit.edu/article/using-scenario-planning-to-reshape-strategy/ (accessed on 16 June 2021).
- Wilkinson, A.; Kupers, R. Living in the Futures: How scenario planning changed corporate strategy. Harv. Bus. Rev. 2013, 91, 119–127. [Google Scholar]
- Li, F.G.N.; Pye, S. Uncertainty, politics, and technology: Expert perceptions on energy transitions in the United Kingdom. Energy Res. Soc. Sci. 2018, 37, 122–132. [Google Scholar] [CrossRef] [Green Version]
- Department for Business, Energy & Industrial Strategy. Digest of UK Energy Statistics (DUKES) 2021, UK Government. 2021. Available online: https://www.gov.uk/government/statistics/energy-chapter-1-digest-of-united-kingdom-energy-statistics-dukes (accessed on 1 July 2019).
- Abram, S.; Silvast, A. Flexibility of real-time energy distribution: The changing practices of energy control rooms. J. Energy Hist. 2021. Available online: energyhistory.eu/en/node/254 (accessed on 26 July 2021).
Project | Scope | Process Steps | Qualitative & Quantitative Roles | Translation or Bridging Approach |
---|---|---|---|---|
TP/RTP [17] | UK electricity system (TP) then UK whole energy system (RTP) | Iterative | Technological aspects led by quantitative models. Spatial and actor behaviour aspects led by qualitative research. | Expert elicitation and translation following the “Story and Simulation” approach [19,20] |
Pathways [18] | Selected EU cities and regions whole energy system | Linear with dialogue openings | Feasibility checks by quantitative models. Qualitative research considerations at dialogue openings. | Qualitative research informs quantitative modelling that conducts feasibility and sense checking thereby informing qualitative research. |
Fair Bare Minimum | Minimal Change | Just and Sustainable | Draconian Decarbonisation |
---|---|---|---|
High Equity, Low Decarbonisation | Low Equity, Low Decarbonisation | High Equity, High Decarbonisation | Low Equity, High Decarbonisation |
|
|
|
|
Quantiles | Expert A | Expert B | Expert C | Expert D | Expert E | Expert F | Expert G | Expert H |
---|---|---|---|---|---|---|---|---|
LQ | 264 | 265 | 275 | 210 | 245 | 215 | 250 | 265 |
Median | 290 | 285 | 290 | 230 | 255 | 220 | 300 | 300 |
UQ | 320 | 295 | 330 | 245 | 290 | 235 | 320 | 400 |
Quantiles | Expert A | Expert B | Expert C | Expert D | Expert E | Expert F | Expert G | Expert H |
---|---|---|---|---|---|---|---|---|
LQ | 220 | 260 | 310 | 200 | 360 | 200 | 320 | 320 |
Median | 320 | 310 | 345 | 220 | 390 | 210 | 350 | 350 |
UQ | 340 | 340 | 375 | 265 | 420 | 220 | 380 | 400 |
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Copeland, C.; Turner, B.; Powells, G.; Wilson, K. In Search of Complementarity: Insights from an Exercise in Quantifying Qualitative Energy Futures. Energies 2022, 15, 5340. https://doi.org/10.3390/en15155340
Copeland C, Turner B, Powells G, Wilson K. In Search of Complementarity: Insights from an Exercise in Quantifying Qualitative Energy Futures. Energies. 2022; 15(15):5340. https://doi.org/10.3390/en15155340
Chicago/Turabian StyleCopeland, Claire, Britta Turner, Gareth Powells, and Kevin Wilson. 2022. "In Search of Complementarity: Insights from an Exercise in Quantifying Qualitative Energy Futures" Energies 15, no. 15: 5340. https://doi.org/10.3390/en15155340