Translating Global Integrated Assessment Model Output into Lifestyle Change Pathways at the Country and Household Level
Abstract
:1. Introduction
- To develop and test a methodology for translating global IAM output into lifestyle change implications for households;
- To recognise variation between countries and between different households in the effort required to reach net-zero targets;
- To reveal differences between mobility and heating-related lifestyle changes within 1.5 °C scenario pathways.
2. Literature Review
2.1. The Demand Reduction Challenge
2.2. Analysing Energy Demand
2.3. Extending Global IAM Analysis of Low-Carbon Lifestyles
3. Generalisable Method for Translating IAM Regional Output into Household-Level Lifestyle Change
3.1. Overview and Principles
3.2. Detailed Steps in 5Ds Method, with Illustration of Each Step for Mobility
3.2.1. Step 1 Disaggregate
Illustration for Mobility
3.2.2. Step 2 Decompose for Region
Illustration for Mobility
3.2.3. Step 3 Downscale
- follow the same trends as the region as a whole (apply Algorithm 1);
- converge on the regional mean (apply Algorithm 2);
- diverge or otherwise follow irregular trajectories (apply Algorithm 3 by using external input from a higher resolution analysis of the specific energy service).
Illustration for Mobility
3.2.4. Step 4 Decompose for Country
Illustration for Mobility
3.2.5. Step 5 Differentiate
Illustration for Mobility
3.2.6. Step 6 Describe
- avoid activity by reducing how much service is used;
- shift within structure by choosing a lower energy form of service provision;
- improve intensity by using a more efficient technology.
Illustration for Mobility
3.3. Application of 5Ds Method to Heating
3.4. Uncertainties in Results
4. Discussion
4.1. General Applicability
4.2. Benefits for National Policy Analysis
4.3. Lifestyle Change
4.4. Limitations of the Method
5. Conclusions
5.1. Practical Implications of the Research
5.2. Future Research Directions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. An Overview of Integrated Assessment Models
Appendix B. Principles of 5Ds Calculations
a | floor area |
A | activity |
B | final energy for bus |
C | final energy for LDV |
E | final energy |
F | final energy for freight transport |
H | building heating required per unit area |
I | Intensity |
L | total final energy from ‘liquid’ fuels (incorporates gaseous fuels) |
P | total final energy from electricity |
Q | useful space heat energy |
R | final energy for passenger rail |
T | total final energy for residential and commercial |
S | Structure |
V | final energy for (passenger) aviation |
ε | electrification ratio–ratio of distance travelled by mode using electricity to total distance travelled by mode |
ζ | ratio of total energy for transport from IAM to total from detailed model |
η | efficiency of heating technology / vehicle |
subscripts | |
C | country |
e | electric |
f | fuel |
g | fuel (excluding electricity) |
I | derived from IAM |
k | convergence year |
l | liquid |
M | derived from detailed scenario model |
p | electric heat pump |
r | electric resistive |
R | region |
T | total |
α | archetype |
Appendix B.1. Technology and Fuel Combinations
- LDV (light duty vehicles, predominantly cars)
- Bus
- Rail
- Aviation
- Electric resistance heater
- Electric heat pump
- Gas boiler
- Heat from district heating network
- Hydrogen boiler
- Oil boiler
- Biomass boiler
- Coal boiler
Appendix B.2. Base Year Selection
Appendix B.3. Calculation Algorithms
Appendix B.4. ASI Contributions to Change in Final Energy
Appendix C. Additional Details of 5Ds Method Applied to Mobility
Appendix C.1. Disaggregation
Appendix C.2. Decomposition
- The ratio of distance travelled by bus to distance travelled by LDVs data is the same as that derived from the detailed sector model.
- The electrification ratio ε (of distance travelled using electric fuel to total distance travelled) is the same for LDV and for bus.
Appendix D. 5Ds Method Applied to Heating
Appendix D.1. Downscaling
Appendix D.2. Decomposition
- Fossil fuels (gas, coal, oil). Find the ratio of fraction of floor area heated by the fossil fuel in country to the fraction of floor area heated in the region by the fuel in the base year. Apply this ratio to the target year regional proportion. This linear rather than convergence relationship is based on the assumption that the existing infrastructure and installed equipment base will influence the share of future fossil fuel use for an extended period in the future.
- Hydrogen. It is assumed that uptake of hydrogen will involve a conversion of a similar proportion of the existing natural gas infrastructure in each country. The area heated by hydrogen is derived by multiplying the area heated by gas in country by the regional ratio of area heated by hydrogen to area heated by gas.
- 3.
- Bioenergy. If the current proportion is sustainable and economically likely to continue, assume bioenergy share of floor area heated is same as base year. If it is not, reduce in line with national policy forecasts.
- 4.
- District heating. If there are national policy targets to increase district heating, estimate the share in the target year based on these national ambitions. Otherwise, keep the current proportion constant.
Appendix D.3. Differentiation
- Allocate country total for each fossil fuel pro rata to existing archetypes, which use that fuel in the base year (assume no fossil fuels are used in newbuild archetypes).
- Allocate hydrogen in proportion to gas use.
- Allocate district heating equally across small home archetypes based on its suitability for high density housing.
- Allocate biomass pro-rata based on initial proportions for each archetype in the base year.
Appendix D.4. Description
Appendix E. Data Sources for Illustrations
Data | Level | Source | Notes |
---|---|---|---|
IAM passenger transport final energy by fuel | Region (WEU) | IMAGE Scenario LOWTOT_19 | This is the “all” scenario in van Vuuren et al. [16] |
Population, number of households | Region and country (UK/SE) | Eurostat [94] for base year. ONS [95] and Statistics [96] for target year | Target year scaled for population increases from IMAGE IAM output |
Intensities for mode and fuel | Region | Derived from IMAGE data | Regional figures also applied for country |
Comparator scenario with country data | Country | ASTRA Directed Vision scenario [67] | |
Archetype household and distance travelled data | UK | National Travel Survey (NTS) 2002–19 [72] | Large: more than two people High income: >GBP 25,000 household income. Rural or urban based on NTS settlement classification |
Archetype household and distance travelled data | SE | Swedish National Travel Survey 2011–16 [97] | Large: more than two people High income > SEK 500,000 annual income |
Data | Level | Source | Notes |
---|---|---|---|
IAM residential space heating final energy by fuel | Region (WEU) | IMAGE Scenario LOWTOT_19 | This is the “all” scenario in [16] |
Residential floor area, population, number of households | Region and country (UK/SE) | Eurostat [94] ONS [95] | Target year scaled for regional floor area and population increases from IMAGE IAM output |
Space heating final energy by fuel (calibration data) | Region and country | Odyssee-mure [98] | Data for 14 countries available–scaled by population to match WEU region in IAM |
Heating technology conversion efficiencies | Region and country | Compilation from the literature [19,99,100,101,102] | Regional figures also applied for country |
Archetype heat loss rate | UK and SE | National typology brochures [103] | |
Archetype floor area and fuel use | UK | English Housing Survey 2011–12 [104] | Separate analysis of survey dataset to derive mean for each archetype Old: built before 1980 New: built after 1980 Small: flat and terrace Large: Detached and semi detached |
Archetype floor area and fuel use | SE | National building statistics [103,105,106] | Old: built before 1980 New: built after 1980 Small: flerbostadshus (multi-family home) Large: småhus (one and two family dwelling) |
Appendix F. Generalizing the 5Ds Method to Other Energy Services
References
- Rose, S.K.; Richels, R.; Blanford, G.; Rutherford, T. The Paris Agreement and next Steps in Limiting Global Warming. Clim. Chang. 2017, 142, 255–270. [Google Scholar] [CrossRef] [Green Version]
- IPPC. Global Warming of 1.5 °C. An IPCC Special Report on the Impacts of Global Warming of 1.5 °C above Pre-Industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change, Sustainable Development, and Efforts to Eradicate Poverty. 2018. Available online: https://www.ipcc.ch/sr15/ (accessed on 11 February 2022).
- Luderer, G.; Vrontisi, Z.; Bertram, C. Residual Fossil CO2 Emissions in 1.5–2 °C Pathways. Nat. Clim. Chang. 2018, 8, 626–633. [Google Scholar] [CrossRef] [Green Version]
- Mundaca, L.; Ürge-Vorsatz, D.; Wilson, C. Demand-Side Approaches for Limiting Global Warming to 1.5 °C. Energy Effic. 2019, 12, 343–362. [Google Scholar] [CrossRef] [Green Version]
- Grubler, 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. A Low Energy Demand Scenario for Meeting the 1.5 °C Target and Sustainable Development Goals without Negative Emission Technologies. Nat. Energy 2018, 3, 515–527. [Google Scholar] [CrossRef]
- Saujot, M.; Gallic, T.L.; Waisman, H. Lifestyle Changes in Mitigation Pathways: Policy and Scientific Insights. Environ. Res. Lett. 2020, 16, 015005. [Google Scholar] [CrossRef]
- van Beek, L.; Hajer, M.; Pelzer, P.; van Vuuren, D.; Cassen, C. Anticipating Futures through Models: The Rise of Integrated Assessment Modelling in the Climate Science-Policy Interface since 1970. Glob. Environ. Chang. 2020, 65, 102191. [Google Scholar] [CrossRef]
- Weyant, J. Some Contributions of Integrated Assessment Models of Global Climate Change. Rev. Environ. Econ. Policy 2017, 11, 115–137. [Google Scholar] [CrossRef] [Green Version]
- Huppmann, D.; Kriegler, E.; Krey, V.; Riahi, K.; Rogelj, J.; Rose, S.K.; Weyant, J.; Bauer, N.; Bertram, C.; Bosetti, V.; et al. IAMC 1.5 °C Scenario Explorer and Data Hosted by IIASA; Integrated Assessment Modeling Consortium & International Institute for Applied Systems Analysis: Laxenburg, Austria, 2018. [Google Scholar]
- Strachan, N. UK Energy Policy Ambition and UK Energy Modelling—Fit for Purpose? Energy Policy 2011, 39, 1037–1040. [Google Scholar] [CrossRef]
- Süsser, D.; Ceglarz, A.; Gaschnig, H.; Stavrakas, V.; Flamos, A.; Giannakidis, G.; Lilliestam, J. Model-Based Policymaking or Policy-Based Modelling? How Energy Models and Energy Policy Interact. Energy Res. Soc. Sci. 2021, 75, 101984. [Google Scholar] [CrossRef]
- Fragkos, P.; Laura van Soest, H.; Schaeffer, R.; Reedman, L.; Köberle, A.C.; Macaluso, N.; Evangelopoulou, S.; De Vita, A.; Sha, F.; Qimin, C.; et al. Energy System Transitions and Low-Carbon Pathways in Australia, Brazil, Canada, China, EU-28, India, Indonesia, Japan, Republic of Korea, Russia and the United States. Energy 2021, 216, 119385. [Google Scholar] [CrossRef]
- Arias, P.; Bellouin, N.; Coppola, E.; Jones, R.; Krinner, G.; Marotzke, J.; Naik, V.; Palmer, M.; Plattner, G.-K.; Rogelj, J.; et al. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Technical Summary; Cambridge University Press: Cambridge, UK, 2021. [Google Scholar]
- Cartwright, E.D. “Code Red”—Recent IPCC Report Warns Time Is Running Out on Climate Change. Clim. Energy 2021, 38, 11–12. [Google Scholar] [CrossRef]
- IEA Emissions by Sector–Greenhouse Gas Emissions from Energy: Overview–Analysis. Available online: https://www.iea.org/reports/greenhouse-gas-emissions-from-energy-overview/emissions-by-sector (accessed on 29 December 2021).
- van Vuuren, D.P.; Stehfest, E.; Gernaat, D.E.H.J.; van den Berg, M.; Bijl, D.L.; de Boer, H.S.; Daioglou, V.; Doelman, J.C.; Edelenbosch, O.Y.; Harmsen, M.; et al. Alternative Pathways to the 1.5 °C Target Reduce the Need for Negative Emission Technologies. Nat. Clim. Chang. 2018, 8, 391–397. [Google Scholar] [CrossRef]
- Lettenmeier, L.; Koide, R.; Toivo, V.; Amellina, A.; Akenji, L. 1.5-Degree Lifestyles: Targets and Options for Reducing Lifestyle Carbon Footprints; Technical Report; Institute for Global Environmental Strategies: Hayama, Japan, 2019. [Google Scholar]
- Goldstein, B.; Gounaridis, D.; Newell, J.P. The Carbon Footprint of Household Energy Use in the United States. Proc. Natl. Acad. Sci. USA 2020, 117, 19122–19130. [Google Scholar] [CrossRef] [PubMed]
- Committee on Climate Change (CCC). The Sixth Carbon Budget: The UK’s Path to Net Zero; Committee on Climate Change (CCC): London, UK, 2020. [Google Scholar]
- Cherry, C.; Scott, K.; Barrett, J.; Pidgeon, N. Public Acceptance of Resource-Efficiency Strategies to Mitigate Climate Change. Nat. Clim. Chang. 2018, 8, 1007–1012. [Google Scholar] [CrossRef]
- Capstick, S.; Demski, C.; Cherry, C.; Verfuerth, C.; Steentjes, K. Climate Change Citizens’ Assemblies: CAST Briefing Paper 03. Available online: https://cast.ac.uk/wp-content/uploads/2020/03/CAST-Briefing-03-Climate-Change-Citizens-Assemblies.pdf (accessed on 11 February 2022).
- Chen, H.-H.; Hof, A.F.; Daioglou, V.; de Boer, H.S.; Edelenbosch, O.Y.; van den Berg, M.; van der Wijst, K.-I.; van Vuuren, D.P. Using Decomposition Analysis to Determine the Main Contributing Factors to Carbon Neutrality across Sectors. Energies 2022, 15, 132. [Google Scholar] [CrossRef]
- Cullen, J.M.; Allwood, J.M. Theoretical Efficiency Limits for Energy Conversion Devices. Energy 2010, 35, 2059–2069. [Google Scholar] [CrossRef]
- Grubler, A.; Johansson, L.; Mundaca, L.; Nakicenovic, N.; Pachauri, S.; Riahi, K.; Rogner, H.-H.; Strupeit, L. Chapter 1-Energy Primer. In Global Energy Assessment—Toward a Sustainable Future; Cambridge University Press: Cambridge, UK, 2012. [Google Scholar]
- TWI2050 Innovations for Sustainability. Pathways to an Efficient and Post-Pandemic Future. Report Prepared by the World in 2050 Initiative; International Institute for Applied Systems Analysis (IIASA): Laxenburg, Austria, 2020. [Google Scholar]
- Hoskins, A.J.; Bush, A.; Gilmore, J.; Harwood, T.; Hudson, L.N.; Ware, C.; Williams, K.J.; Ferrier, S. Downscaling Land-Use Data to Provide Global 30″ Estimates of Five Land-Use Classes. Ecol. Evol. 2016, 6, 3040–3055. [Google Scholar] [CrossRef] [Green Version]
- Byers, E.; Gidden, M.; Leclère, D.; Balkovic, J.; Burek, P.; Ebi, K.; Greve, P.; Grey, D.; Havlik, P.; Hillers, A.; et al. Global Exposure and Vulnerability to Multi-Sector Development and Climate Change Hotspots. Environ. Res. Lett. 2018, 13, 055012. [Google Scholar] [CrossRef] [Green Version]
- Creutzig, F.; Jochem, P.; Edelenbosch, O.Y.; Mattauch, L.; van Vuuren, D.P.; McCollum, D.; Minx, J. Transport: A Roadblock to Climate Change Mitigation? Science 2015, 350, 911–912. [Google Scholar] [CrossRef] [Green Version]
- Schipper, L.; Saenger, C.; Sudardshan, A. Transport and Carbon Emissions in the United States: The Long View. Energies 2011, 4, 563–581. [Google Scholar] [CrossRef]
- Ang, B.W.; Liu, N. Energy Decomposition Analysis: IEA Model versus Other Methods. Energy Policy 2007, 35, 1426–1432. [Google Scholar] [CrossRef]
- IEA. Energy Efficiency Indicators 2020; International Energy Agency: Paris, France, 2020. [Google Scholar]
- IEA. The Future of Cooling in China; International Energy Agency: Paris, France, 2019. [Google Scholar]
- IEA. Energy Efficiency Market Report; International Energy Agency: Paris, France, 2018. [Google Scholar]
- Marrero, G.A.; Ramos-Real, F.J. Activity Sectors and Energy Intensity: Decomposition Analysis and Policy Implications for European Countries (1991–2005). Energies 2013, 6, 2521–2540. [Google Scholar] [CrossRef]
- Famuyibo, A.A.; Duffy, A.; Strachan, P. Developing Archetypes for Domestic Dwellings—An Irish Case Study. Energy Build. 2012, 50, 150–157. [Google Scholar] [CrossRef] [Green Version]
- Kavgic, M.; Mavrogianni, A.; Mumovic, D.; Summerfield, A.; Stevanovic, Z.; Djurovic-Petrovic, M. A Review of Bottom-up Building Stock Models for Energy Consumption in the Residential Sector. Build. Environ. 2010, 45, 1683–1697. [Google Scholar] [CrossRef]
- Mata, É.; Sasic Kalagasidis, A.; Johnsson, F. Building-Stock Aggregation through Archetype Buildings: France, Germany, Spain and the UK. Build. Environ. 2014, 81, 270–282. [Google Scholar] [CrossRef] [Green Version]
- Brand, C.; Tran, M.; Anable, J. The UK Transport Carbon Model: An Integrated Life Cycle Approach to Explore Low Carbon Futures. Energy Policy 2012, 41, 107–124. [Google Scholar] [CrossRef] [Green Version]
- Milne, S.; Chambers, K.; Elks, S.; Hussain, B.; McKinnon, S. Living Carbon Free; Energy Systems Catapult: Birmingham, UK, 2019. [Google Scholar]
- ADEME. We Demain Objectif 2030 10 Familles, 10 Scénarios Pour Un Mode de Vie Plus Durable; ADEME: Paris, France, 2015. [Google Scholar]
- Akenji, L.; Chen, H. Framework for Shaping Sustainable Lifestyles: Determinants and Strategies; United Nations Environment Programme: Nairobi, Kenya, 2016. [Google Scholar]
- Clarke, L.; Jiang, K. Chapter 6: Assessing Transformation Pathways. In Working Group III contribution to the IPCC 5th Assessment Report, Climate Change 2014: Mitigation of Climate Change; Cambridge University Press: Cambridge, UK, 2014. [Google Scholar]
- Gernaat, D.E.H.J.; Van Vuuren, D.P.; Van Vliet, J.; Sullivan, P.; Arent, D.J. Global Long-Term Cost Dynamics of Offshore Wind Electricity Generation. Energy 2014, 76, 663–672. [Google Scholar] [CrossRef]
- de Boer, H.S.; van Vuuren, D. Representation of Variable Renewable Energy Sources in TIMER, an Aggregated Energy System Simulation Model. Energy Econ. 2017, 64, 600–611. [Google Scholar] [CrossRef]
- Sathaye, J.; Shukla, P.R. Methods and Models for Costing Carbon Mitigation. Annu. Rev. Environ. Resour. 2013, 38, 137–168. [Google Scholar] [CrossRef]
- Edelenbosch, O.Y.; van Vuuren, D.P.; Blok, K.; Calvin, K.; Fujimori, S. Mitigating Energy Demand Sector Emissions: The Integrated Modelling Perspective. Appl. Energy 2020, 261, 114347. [Google Scholar] [CrossRef]
- Edelenbosch, O.Y.; McCollum, D.L.; van Vuuren, D.P.; Bertram, C.; Carrara, S.; Daly, H.; Fujimori, S.; Kitous, A.; Kyle, P.; Broin, E.Ó.; et al. Decomposing Passenger Transport Futures: Comparing Results of Global Integrated Assessment Models. Transp. Res. Part Transp. Environ. 2017, 55, 281–293. [Google Scholar] [CrossRef] [Green Version]
- van den Berg, N.J.; Hof, A.F.; van der Wijst, K.-I.; Akenji, L.; Daioglou, V.; Edelenbosch, O.Y.; van Sluisveld, M.A.E.; Timmer, V.J.; van Vuuren, D.P. Decomposition Analysis of per Capita Emissions: A Tool for Assessing Consumption Changes and Technology Changes within Scenarios. Environ. Res. Commun. 2021, 3, 015004. [Google Scholar] [CrossRef]
- van Vuuren, D.P.; Lucas, P.L.; Hilderink, H. Downscaling Drivers of Global Environmental Change: Enabling Use of Global SRES Scenarios at the National and Grid Levels. Glob. Environ. Chang. 2007, 17, 114–130. [Google Scholar] [CrossRef] [Green Version]
- Sferra, F.; Krapp, M.; Roming, N.; Schaeffer, M.; Malik, A.; Hare, B.; Brecha, R. Towards Optimal 1.5° and 2 °C Emission Pathways for Individual Countries: A Finland Case Study. Energy Policy 2019, 133, 110705. [Google Scholar] [CrossRef]
- Keppo, I.; Butnar, I.; Bauer, N.; Caspani, M.; Edelenbosch, O.; Emmerling, J.; Fragkos, P.; Guivarch, C.; Harmsen, M.; Lefèvre, J.; et al. Exploring the Possibility Space: Taking Stock of the Diverse Capabilities and Gaps in Integrated Assessment Models. Environ. Res. Lett. 2021, 16, 053006. [Google Scholar] [CrossRef]
- Mercure, J.-F.; Pollitt, H.; Bassi, A.M.; Viñuales, J.E.; Edwards, N.R. Modelling Complex Systems of Heterogeneous Agents to Better Design Sustainability Transitions Policy. Glob. Environ. Chang. 2016, 37, 102–115. [Google Scholar] [CrossRef] [Green Version]
- Krey, V.; O’Neill, B.C.; van Ruijven, B.; Chaturvedi, V.; Daioglou, V.; Eom, J.; Jiang, L.; Nagai, Y.; Pachauri, S.; Ren, X. Urban and Rural Energy Use and Carbon Dioxide Emissions in Asia. Energy Econ. 2012, 34, S272–S283. [Google Scholar] [CrossRef]
- Daioglou, V.; van Ruijven, B.J.; van Vuuren, D.P. Model Projections for Household Energy Use in Developing Countries. Energy 2012, 37, 601–615. [Google Scholar] [CrossRef] [Green Version]
- Jones, B.R.; Sovacool, B.K.; Sidortsov, R.V. Making the Ethical and Philosophical Case for “Energy Justice”. Environ. Ethics 2015, 37, 145–168. [Google Scholar] [CrossRef]
- Oswald, Y.; Owen, A.; Steinberger, J.K. Large Inequality in International and Intranational Energy Footprints between Income Groups and across Consumption Categories. Nat. Energy 2020, 5, 231–239. [Google Scholar] [CrossRef]
- Sovacool, B.K.; Burke, M.; Baker, L.; Kotikalapudi, C.K.; Wlokas, H. New Frontiers and Conceptual Frameworks for Energy Justice. Energy Policy 2017, 105, 677–691. [Google Scholar] [CrossRef] [Green Version]
- CD-Links CD-Links. Available online: https://www.cd-links.org/ (accessed on 12 August 2021).
- Schaeffer, R.; Bosetti, V.; Kriegler, E.; Riahi, K.; van Vuuren, D. Climatic Change: CD-Links Special Issue on National Low-Carbon Development Pathways. Clim. Chang. 2020, 162, 1779–1785. [Google Scholar] [CrossRef] [PubMed]
- COMMIT COMMIT. Available online: https://themasites.pbl.nl/commit/ (accessed on 12 August 2021).
- van den Berg, N.J.; Hof, A.F.; Akenji, L.; Edelenbosch, O.Y.; van Sluisveld, M.A.E.; Timmer, V.J.; van Vuuren, D.P. Improved Modelling of Lifestyle Changes in Integrated Assessment Models: Cross-Disciplinary Insights from Methodologies and Theories. Energy Strategy Rev. 2019, 26, 100420. [Google Scholar] [CrossRef]
- van Sluisveld, M.A.E.; Martínez, S.H.; Daioglou, V.; van Vuuren, D.P. Exploring the Implications of Lifestyle Change in 2 °C Mitigation Scenarios Using the IMAGE Integrated Assessment Model. Technol. Forecast. Soc. Chang. 2016, 102, 309–319. [Google Scholar] [CrossRef] [Green Version]
- Edelenbosch, O.Y.; McCollum, D.L.; Pettifor, H.; Wilson, C.; Vuuren, D.P. van Interactions between Social Learning and Technological Learning in Electric Vehicle Futures. Environ. Res. Lett. 2018, 13, 124004. [Google Scholar] [CrossRef]
- Li, F.G.N. Actors Behaving Badly: Exploring the Modelling of Non-Optimal Behaviour in Energy Transitions. Energy Strategy Rev. 2017, 15, 57–71. [Google Scholar] [CrossRef] [Green Version]
- Stehfest, E.; van Vuuren, D.P.; Bouwman, L.; Kram, T.; Alkemade, R.; Bakkens, M.; Biemans, H.; Bouwman, A.; den Elzen, M.G.J.; Janse, J.; et al. IMAGE 3.0; PBL: Piscataway, NJ, USA, 2014. [Google Scholar]
- IIASA IAMC Documentation. Available online: https://data.ene.iiasa.ac.at/database/ (accessed on 12 February 2021).
- Crespo del Granado, P.; Resch, G.; Holz, F.; Welisch, M.; Geipel, J.; Hartner, M.; Forthuber, S.; Sensfuss, F.; Olmos, L.; Brenath, C.; et al. Energy Transition Pathways to a Low-Carbon Europe in 2050: The Degree of Cooperation and the Level of Decentralization. Econ. Energy Environ. Policy 2020, 9. [Google Scholar] [CrossRef]
- Fouquet, R. Path Dependence in Energy Systems and Economic Development. Nat. Energy 2016, 1, 16098. [Google Scholar] [CrossRef] [Green Version]
- Unruh, G.C. Escaping Carbon Lock-In. Energy Policy 2002, 30, 317–325. [Google Scholar] [CrossRef]
- Ang, B.W. Decomposition Analysis for Policymaking in Energy: Which Is the Preferred Method? Energy Policy 2004, 32, 1131–1139. [Google Scholar] [CrossRef]
- Zhang, R.; Fujimori, S.; Hanaoka, T. The Contribution of Transport Policies to the Mitigation Potential and Cost of 2 °C and 1.5 °C Goals. Environ. Res. Lett. 2018, 13, 054008. [Google Scholar] [CrossRef] [Green Version]
- Department for Transport National Travel Survey, 2002–2019, 14th ed.; Data Collection; UK Data Service: Colchester, UK, 2020.
- Creutzig, F.; Fernandez, B.; Haberl, H.; Khosla, R.; Mulugetta, Y.; Seto, K.C. Beyond Technology: Demand-Side Solutions for Climate Change Mitigation. Annu. Rev. Environ. Resour. 2016, 41, 173–198. [Google Scholar] [CrossRef] [Green Version]
- Li, W.; Long, R.; Chen, H.; Geng, J. A Review of Factors Influencing Consumer Intentions to Adopt Battery Electric Vehicles. Renew. Sustain. Energy Rev. 2017, 78, 318–328. [Google Scholar] [CrossRef]
- Cullen, J.M.; Allwood, J.M.; Borgstein, E.H. Reducing Energy Demand: What Are the Practical Limits? Environ. Sci. Technol. 2011, 45, 1711–1718. [Google Scholar] [CrossRef]
- Caird, S.; Roy, R.; Potter, S. Domestic Heat Pumps in the UK: User Behaviour, Satisfaction and Performance. Energy Effic. 2012, 5, 283–301. [Google Scholar] [CrossRef] [Green Version]
- Judson, E.P.; Bell, S.; Bulkeley, H.; Powells, G.; Lyon, S. The Co-Construction of Energy Provision and Everyday Practice: Integrating Heat Pumps in Social Housing in England. Sci. Technol. Stud. 2015, 28, 26–53. [Google Scholar] [CrossRef]
- Parrish, B.; Hielscher, S.; Foxon, T.J. Consumers or Users? The Impact of User Learning about Smart Hybrid Heat Pumps on Policy Trajectories for Heat Decarbonisation. Energy Policy 2021, 148, 112006. [Google Scholar] [CrossRef]
- Wilson, C.; Guivarch, C.; Kriegler, E.; van Ruijven, B.; van Vuuren, D.P.; Krey, V.; Schwanitz, V.J.; Thompson, E.L. Evaluating Process-Based Integrated Assessment Models of Climate Change Mitigation. Clim. Chang. 2021, 166, 3. [Google Scholar] [CrossRef]
- Shi, J.; Chen, W.; Yin, X. Modelling Building’s Decarbonization with Application of China TIMES Model. Appl. Energy 2016, 162, 1303–1312. [Google Scholar] [CrossRef]
- Urge-Vorsatz, D.; Petrichenko, K.; Staniec, M.; Eom, J. Energy Use in Buildings in a Long-Term Perspective. Curr. Opin. Environ. Sustain. 2013, 5, 141–151. [Google Scholar] [CrossRef]
- Zhou, N.; Khanna, N.; Feng, W.; Ke, J.; Levine, M. Scenarios of Energy Efficiency and CO2 Emissions Reduction Potential in the Buildings Sector in China to Year 2050. Nat. Energy 2018, 3, 978–984. [Google Scholar] [CrossRef]
- Kriegler, E.; Bauer, N.; Popp, A.; Humpenöder, F.; Leimbach, M.; Strefler, J.; Baumstark, L.; Bodirsky, B.L.; Hilaire, J.; Klein, D.; et al. Fossil-Fueled Development (SSP5): An Energy and Resource Intensive Scenario for the 21st Century. Glob. Environ. Chang. 2017, 42, 297–315. [Google Scholar] [CrossRef] [Green Version]
- Tummers, L. Public Policy and Behavior Change. Public Adm. Rev. 2019, 79, 925–930. [Google Scholar] [CrossRef]
- Bemelmans-Videc, M.-L.; Rist, R.C.; Vedung, E. Carrots, Sticks & Sermons: Policy Instruments and Their Evaluation; Comparative Policy Analysis Series; Transaction Publishers: Piscataway, NJ, USA, 1998; ISBN 978-1-56000-338-0. [Google Scholar]
- Peñasco, C.; Anadón, L.D.; Verdolini, E. Systematic Review of the Outcomes and Trade-Offs of Ten Types of Decarbonization Policy Instruments. Nat. Clim. Chang. 2021, 11, 257–265. [Google Scholar] [CrossRef]
- 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]
- von Stechow, C.; McCollum, D.; Riahi, K.; Minx, J.C.; Kriegler, E.; van Vuuren, D.P.; Jewell, J.; Robledo-Abad, C.; Hertwich, E.; Tavoni, M.; et al. Integrating Global Climate Change Mitigation Goals with Other Sustainability Objectives: A Synthesis. Annu. Rev. Environ. Resour. 2015, 40, 363–394. [Google Scholar] [CrossRef] [Green Version]
- Krey, V. Global Energy-Climate Scenarios and Models: A Review. Wiley Interdiscip. Rev. Energy Environ. 2014, 3, 363–383. [Google Scholar] [CrossRef]
- Rogelj, J.; Popp, A.; Calvin, K.V.; Luderer, G.; Emmerling, J.; Gernaat, D.; Fujimori, S.; Strefler, J.; Hasegawa, T.; Marangoni, G.; et al. Scenarios towards Limiting Global Mean Temperature Increase below 1.5 °C. Nat. Clim. Chang. 2018, 8, 325–332. [Google Scholar] [CrossRef]
- Weitzel, M.; Vandyck, T.; Keramidas, K.; Amann, M.; Capros, P.; den Elzen, M.; Frank, S.; Tchung-Ming, S.; Díaz Vázquez, A.; Saveyn, B. Model-Based Assessments for Long-Term Climate Strategies. Nat. Clim. Chang. 2019, 9, 345–347. [Google Scholar] [CrossRef]
- Reference Card-IMAGE-IAMC-Documentation. Available online: https://www.iamcdocumentation.eu/index.php/Reference_card_-_IMAGE (accessed on 7 January 2022).
- Walliser, A.; Rajkovich, N.B.; Forester, J.; Friesen, C.; Malbert, B.; Nolmark, H.; Williams, J.; Wheeler, S.M.; Segar, R.B.; Utzinger, M.; et al. Exploring the Challenges of Environmental Planning and Green Design: Cases from Europe and the USA Renovating to Passive Housing in the Swedish Million Programme. Plan. Theory Pract. 2012, 13, 113–174. [Google Scholar] [CrossRef] [Green Version]
- Eurostat Eurostat Database. Available online: https://ec.europa.eu/eurostat/web/main/data/database (accessed on 5 July 2021).
- Office for National Statistics. 2016 Based Household Projections in England; ONS: London, UK, 2021. [Google Scholar]
- Statistics Sweden. The Future Population of Sweden 2021–2070; Statistics Sweden: Stockholm, Sweden, 2021. [Google Scholar]
- RVU Sweden. Transport Analysis Swedish National Travel Survey 2011–16; RVU Sweden: Lindingö, Sweden, 2020. [Google Scholar]
- Odyssee-mure. Available online: https://www.indicators.odyssee-mure.eu/energy-efficiency-database.html (accessed on 29 January 2020).
- Dunbabin, P.; Charlick, H.; Green, R. Detailed Analysis from the Second Phase of the Energy Saving Trust’s Heat Pump Field Trial; Department of Energy and Climate Change: London, UK, 2013. [Google Scholar]
- Element Energy. UCL IEDE Analysis on Abating Direct Emissions from ‘Hard-to-Decarbonise’ Homes, with a View to Informing the UK’s Long Term Targets; A Study for the Committee on Climate Change; Element Energy: Cambridge, UK, 2019. [Google Scholar]
- HETAS. Available online: Hetas.co.uk (accessed on 12 May 2021).
- Vesterlund, M.; Sandberg, J.; Lindblom, B.; Dahl, J. Evaluation of Losses in District Heating System, a Case Study. In Proceedings of the International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, Guilin, China, 16–19 July 2013. [Google Scholar]
- Episcope-TABULA. Available online: https://episcope.eu/ (accessed on 28 January 2020).
- Department for Communities and Local Government. English Housing Survey, 2011: Housing Stock Data, 4th ed.; UK Data Service: Colchester, UK, 2017.
- Swedish Energy Agency. Swedish Energy Agency Energy Statistics for Multi-Dwelling Buildings 2019; Swedish Energy Agency: Eskilstuna, Sweden, 2020. [Google Scholar]
- Swedish Energy Agency. Swedish Energy Agency Energy Statistics for One- and Two Dwelling Buildings 2019; Swedish Energy Agency: Eskilstuna, Sweden, 2020. [Google Scholar]
- IEA. IEA Lighting: Tracking Report; IEA: Paris, France, 2020. [Google Scholar]
- Shan, M.; Wang, P.; Li, J.; Yue, G.; Yang, X. Energy and Environment in Chinese Rural Buildings: Situations, Challenges, and Intervention Strategies. Build. Environ. 2015, 91, 271–282. [Google Scholar] [CrossRef]
- van Ruijven, B.J.; van Vuuren, D.P.; de Vries, B.J.M.; Isaac, M.; van der Sluijs, J.P.; Lucas, P.L.; Balachandra, P. Model Projections for Household Energy Use in India. Energy Policy 2011, 39, 7747–7761. [Google Scholar] [CrossRef] [Green Version]
- ITF. ITF Transport Statistics; ITF: London, UK, 2020. [Google Scholar]
- Ma, J.; Ye, X. Modeling Household Vehicle Ownership in Emerging Economies. J. Indian Inst. Sci. 2019, 99, 647–671. [Google Scholar] [CrossRef]
- Oxfam. Stockholm Environment Institute the Carbon Inequality Era: An Assessment of the Global Distribution of Consumption Emissions among Individuals from 1990 to 2015 and Beyond; Oxfam: Stockholm, Sweden, 2020. [Google Scholar]
- United Nations Environment Programme. Emissions Gap Report 2020. 2020. Available online: https://www.unep.org/emissions-gap-report-2020 (accessed on 11 February 2022).
Activity | Structure | Intensity | |
---|---|---|---|
General Definition | Amount of service used | Activity share of each form of the service | Energy use for each unit of activity |
Heating | Building floor area heated (m2) | Proportion of floor area heated by each combination of heating technology and fuel (e.g., natural gas boiler, electric heat pump, biomass boiler) | Final energy/floor area heated (MJ/m2 yr) |
Mobility | Distance travelled (passenger-km) | Proportion of distance travelled by each combination of mode and fuel (e.g., electric train, diesel bus, electric car: a switch from internal combustion engine to electric vehicles would be captured as a structural shift as fuel has changed even though mode is still private driving). | Final energy/passenger-km (MJ/p-km yr) |
Algorithm No | Description |
---|---|
1 | Linear scaling (fixed proportion of larger unit) |
2 | Convergence (converges to mean for larger unit) |
3 | External input (apply ratios derived from detailed model) |
4 | Decompose into ASI components |
5 | Apply rule-based assumptions |
Activity Effect | Structure Effect | Intensity Effect | |
---|---|---|---|
Sweden | −14% | 66% | 48% |
UK | −7% | 62% | 45% |
Step in 5Ds Method | External Data (Base Year) | External Data (Target Year) | Assumptions (Target Year) | Algorithm (Type) |
---|---|---|---|---|
Disaggregate | Calibration data for sector. | Detailed sector scenario. | Personal mobility share of final energy for transport sector is same as for detailed scenario. | External input (3) |
Decompose (regional level) | Intensity for each mode and fuel. | Intensity for each mode and fuel. | Assumptions about electrification level for each mode | Energy balance (4) |
Downscale to country | Distance by mode for country and region. | Distance by mode for country and region from higher resolution analysis. | Country-level modal shares of regional activity match external input from higher resolution analysis. | External input (3) |
Decompose (country level) | Intensity for each mode and fuel. | Intensity for each mode and fuel. | Proportion of each mode electrified for country is same as for region. Intensity same as region | Energy balance (4) |
Differentiate | National travel survey data: distance by mode for different household archetypes. | - | Ratio of archetype to national average distance travelled per mode stays constant. | Linear scaling (1) |
Activity Effect | Structure Effect | Intensity Effect | |
---|---|---|---|
Sweden | 0% | −1% | 101% |
UK | 0% | 28% | 72% |
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Hanmer, C.; Wilson, C.; Edelenbosch, O.Y.; van Vuuren, D.P. Translating Global Integrated Assessment Model Output into Lifestyle Change Pathways at the Country and Household Level. Energies 2022, 15, 1650. https://doi.org/10.3390/en15051650
Hanmer C, Wilson C, Edelenbosch OY, van Vuuren DP. Translating Global Integrated Assessment Model Output into Lifestyle Change Pathways at the Country and Household Level. Energies. 2022; 15(5):1650. https://doi.org/10.3390/en15051650
Chicago/Turabian StyleHanmer, Clare, Charlie Wilson, Oreane Y. Edelenbosch, and Detlef P. van Vuuren. 2022. "Translating Global Integrated Assessment Model Output into Lifestyle Change Pathways at the Country and Household Level" Energies 15, no. 5: 1650. https://doi.org/10.3390/en15051650