Transition to low-carbon economy: Assessing cumulative impacts of individual behavioral changes

Energy Policy

Volumn 118, Issue , 2018, Pages 325-345

  Niamir, Leila ^a   Filatova, Tatiana ^a,b   Voinov, Alexey ^b   Bressers, Hans ^a  

^a UNIVERSITY OF TWENTE   (Netherlands)

^b UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

Author keywords

Agent based modeling; Behavior change; Low carbon economy; Norm Activation Theory; Residential energy

Indexed keywords

ACTIVATION ENERGY; AUTONOMOUS AGENTS; BEHAVIORAL RESEARCH; COMPUTATIONAL METHODS; EMISSION CONTROL; ENERGY MANAGEMENT; ENERGY UTILIZATION; ENVIRONMENTAL MANAGEMENT; HOUSING; RENEWABLE ENERGY RESOURCES; SIMULATION PLATFORM;

AGENT-BASED MODEL; BEHAVIORAL CHANGES; BEHAVIOUR CHANGES; CUMULATIVE IMPACTS; ENERGY USE; LOW CARBON ECONOMY; NORM ACTIVATION THEORY; REGIONAL LEVELS; RESIDENTIAL ENERGY; RESIDENTIAL ENERGY DEMAND;

CARBON;

CARBON EMISSION; EMISSION CONTROL; ENERGY USE; MODELING; RESIDENTIAL ENERGY;

EID: 85045127758     PISSN: 03014215     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.enpol.2018.03.045     Document Type: Article

References (102)

1. Abrahamse, W., Steg, L., How do socio-demographic and psychological factors relate to households’ direct and indirect energy use and savings?. J. Econ. Psychol. 30 (2009), 711–720.
2. Abrahamse, W., Steg, L., Factors related to household energy use and intention to reduce It: the role of psychological and socio-demographic variables. Hum. Ecol. Rev. 18 (2011), 30–40.
3. Acemoglu, D., Ozdaglar, A., Opinion dynamics and learning in social networks. Dyn. Games Appl. 1 (2011), 3–49.
4. Adnana, N., Nordina, S.M., Rahmanb, I., Adoption of PHEV/EV in Malaysia: a critical review on predicting consumer behaviour. Renew. Sustain. Energy Rev., 72, 2017.
5. Ajzen, I., 1980. Understanding Events - Affect and the Construction of Social-Action - Heise, Dr. Contemp Psychol 25, pp. 775–776.
6. Aliabadi, D.E., Kaya, M., Sahin, G., Competition, risk and learning in electricity markets: an agent-based simulation study. Appl. Energy, 195, 2017.
7. Amouroux, E., Huraux, T., Sempé F., Sabouret, N., Haradji, Y., Simulating human activities to investigate household energy consumption. ICAART, 2013, 2013.
8. Anatolitis, V., Welisch, M., Putting renewable energy auctions into action – An agent-based model of onshore wind power auctions in Germany. Energy Policy 110 (2017), 394–402.
9. Armitage, C.J., Conner, M., Efficacy of the theory of planned behaviour: a meta-analytic review. Br. J. Soc. Psychol. 40 (2001), 471–499.
10. Bamberg, S., Hunecke, M., Blobaum, A., Social context, personal norms and the use of public transportation: two field studies. J. Environ. Psychol. 27 (2007), 190–203.
11. Bamberg, S., Rees, J., Seebauer, S., Collective climate action: determinants of participation intention in community-based pro-environmental initiatives. J. Environ. Psychol. 43 (2015), 155–165.
12. Bhattacharyya, S.C., Energy Economics : Concepts, Issues, Markets and Governance, 2011, Springer, London.
13. Bhushan, N., Albers, C., Steg, L., 2016. Detecting patterns in household electricity consumption after behavioural interventions, In: Proceedings of the 4th European Conference on Behaviour and Energy Efficiency, Coimbra, Portugal.
14. Bravo, G., Vallino, E., Cerutti, A.K., Pairotti, M.B., Alternative scenarios of green consumption in Italy: an empirically grounded model. Environ. Modell. Softw. 47 (2013), 225–234.
15. Carley, K.M., 1996. Validating Computational Models, in: University, C.M. (Ed.).
16. Chappin, E., Afman, M.R., An agent-based model of transitions in consumer lighting: policy impacts from the E.U. phase-out of incandescents. Environ. Innov. Soc. Transit., 7, 2013.
17. Chappin, E., Dijkema, G., van Dam, K., Lukszo, Z., 2007. Modeling strategic and operational decision-making - An agent-based model of electricity producers. In: Proceedings of the European Simulation and Modelling Conference 2007, pp. 356–363.
18. Ciarreta, A., Espinosa, M.P., Pizarro-Irizar, C., Is green energy expensive? Empirical evidence from the Spanish electricity market. Energy Policy 69 (2014), 205–215.
19. Ciarreta, A., Espinosa, M.P., Pizarro-Irizar, C., Has renewable energy induced competitive behavior in the Spanish electricity market?. Energy Policy 104 (2017), 171–182.
20. Cleveland, C.J., Ayres, R.U., Encyclopedia of energy. 2004, Elsevier Academic Press, Amsterdam; Boston.
21. De Groot, J.I.M., Steg, L., Morality and prosocial behavior: the role of awareness, responsibility, and norms in the norm activation model. J. Soc. Psychol. 149 (2009), 425–449.
22. Degroot, M.H., Reaching a Consensus. J. Am. Stat. Assoc. 69 (1974), 118–121.
23. del Río, P., Unruh, G., Overcoming the lock-out of renewable energy technologies in Spain: the cases of wind and solar electricity. Renew. Sustain. Energy Rev. 11 (2007), 1498–1513.
24. EC, 2017. Consumer conditions scoreboard, Consumers at home in the single market : 2017 edition. EU Publication.
25. Faber, J., Schoroten, A., Bles, M., Sevenster, M., Markowska, A., Smit, M., Rohde, C., Dütschke, E., Köhler, J., Gigli, M., Zimmermann, K., Soboh, R., Riet, J., 2012. Behavioural Climate Change Mitigation Options and Their Appropriate Inclusion in Quantitative Longer Term Policy Scenarios, Delft.
26. Federico, G., Vives, X., Competition and Regulation in the Spanish Gas and Electricity Markets. 2008, Public-Ptivate Sector Research Center, IESE Bussiness School, Madrid, Spain.
27. Frederiks, E.R., Stennerl, K., Hobman, E.V., Household energy use: applying behavioural economics to understand consumer decision-making and behaviour. Renew. Sust. Energ. Rev. 41 (2015), 1385–1394.
28. Gerst, M.D., Wang, P., Roventini, A., Fagiolo, G., Dosi, G., Howarth, R.B., Borsuk, M.E., Agent-based modeling of climate policy: an introduction to the ENGAGE multi-level model framework. Environ. Modell. Softw. 44 (2013), 62–75.
29. Glynn, P.D., Voinov, A.A., Shapiro, C.D., White, P.A., From data to decisions: processing information, biases, and beliefs for improved management of natural resources and environments. Earth's Future 5 (2017), 356–378.
30. Groeneveld, J., Muller, B., Buchmann, C.M., Dressler, G., Guo, C., Hase, N., Hoffmann, F., John, F., Klassert, C., Lauf, T., Liebelt, V., Nolzen, H., Pannicke, N., Schulze, J., Weise, H., Schwarz, N., Theoretical foundations of human decision-making in agent-based land use models - A review. Environ. Modell. Softw. 87 (2017), 39–48.
31. Hegselmann, R., Krause, U., Opinion dynamics and bounded confidence, models, analysis and simulation. J. Artif. Soc. Social. Simul., 5, 2002.
32. Hunt, L.C., Evans, J., International Handbook on the Economics of Energy. 2009, Edward Elgar, Cheltenham, UK; Northampton, MA.
33. Iychettira, K.K., Hakvoort, R.A., Linares, P., de Jeu, R., Towards a comprehensive policy for electricity from renewable energy: designing for social welfare. Appl. Energy 187 (2017), 228–242.
34. Jackson, J., Improving energy efficiency and smart grid program analysis with agent-based end-use forecasting models. Energy Policy 38 (2010), 3771–3780.
35. Kahneman, D., A psychological perspective on economics. Am. Econ. Rev. 93 (2003), 162–168.
36. Kancs, A., Predicting European enlargement impacts - A framework of interregional general equilibrium. East. Eur. Econ. 39 (2001), 31–63.
37. Karatasou, S., Santamouris, M., Detection of low-dimensional chaos in buildings energy consumption time series. Commun. Nonlinear Sci. 15 (2010), 1603–1612.
38. Kowalska-Pyzalska, A., Maciejowska, K., Suszczyński, K., Sznajd-Weron, K., Weron, R., Turning green: agent-based modeling of the adoption of dynamic electricity tariffs. Energy Policy 72 (2014), 164–174.
39. Kumar, S., Assessment of renewables for energy security and carbon mitigation in Southeast Asia: the case of Indonesia and Thailand. Appl. Energy 163 (2016), 63–70.
40. Lee, J.-S., Filatova, T., Ligmann-Zielinska, A., Hassani-Mahmooei, B., Stonedahl, F., Lorscheid, I., Voinov, A., Polhill, J.G., Sun, Z., Parker, D.C., The complexities of agent-based modeling output analysis. J. Artif. Soc. Social. Simul., 18, 2015, 4.
41. Malama, A., Makashini, L., Abanda, H., Ng'ombe, A., Mudenda, P., 2015. A Comparative Analysis of Energy Usage and Energy Efficiency Behavior in Low- and High-Income Households: The Case of Kitwe, Zambia. Resources 4.
42. McKinsey, 2009. Pathways to a Low-Carbon Economy, 2 ed.
43. Moussaid, M., Brighton, H., Gaissmaier, W., The amplification of risk in experimental diffusion chains. Proc. Natl. Acad. Sci. USA 112 (2015), 5631–5636.
44. Niamir, L., Filatova, T., 2015. Linking Agent-based energy market with Computable General Equilibrium Model: an Integrated Approach to Climate-Economy-Energy System, In: Proceedings of the 20th WEHIA Conference, Sophia Antipolis, France.
45. Niamir, L., Filatova, T., 2016. From Climate Change Awareness to Energy Efficient Behaviour, In: Proceedings of the 8th International Congress on Environmental Modelling and Software, Toulouse, France.
46. Niamir, L., Filatova, T., 2017. Transition to Low-carbon Economy: Simulating Nonlinearities in the Electricity Market, Navarre Region-Spain, Advances in Social Simulation 2015. Springer.
47. Nyborg, K., Anderies, J.M., Dannenberg, A., Lindahl, T., Schill, C., Schlüter, M., Adger, W.N., Arrow, K.J., Barrett, S., Carpenter, S., Chapin, F.S., Crépin, A.-S., Daily, G., Ehrlich, P., Folke, C., Jager, W., Kautsky, N., Levin, S.A., Madsen, O.J., Polasky, S., Scheffer, M., Walker, B., Weber, E.U., Wilen, J., Xepapadeas, A., de Zeeuw, A., Social norms as solutions. Science 354 (2016), 42–43.
48. Onwezen, M.C., Antonides, G., Bartels, J., The norm activation model: an exploration of the functions of anticipated pride and guilt in pro-environmental behaviour. J. Econ. Psychol. 39 (2013), 141–153.
49. Palmer, J., Sorda, G., Madlener, R., Modeling the diffusion of residential photovoltaic systems in Italy: an agent-based simulation. Technol. Forecast Soc. 99 (2015), 106–131.
50. Pollitt, M.G., Shaorshadze, I., The Role of Behavioural Economics in Energy and Climate Policy. Handbook on Energy and Climate Change, 2013, Edward Elgar.
51. Poortinga, W., Steg, L., Vlek, C., Values, environmental concern, and environmental behavior - A study into household energy use. Environ. Behav. 36 (2004), 70–93.
52. Rai, V., Henry, A.D., Agent-based modelling of consumer energy choices. Nat. Clim. Change 6 (2016), 556–562.
53. Rai, V., Robinson, S.A., Agent-based modeling of energy technology adoption: empirical integration of social, behavioral, economic, and environmental factors. Environ. Modell. Softw. 70 (2015), 163–177.
54. Richiardi, M., Leombruni, R., Saam, N.J., Sonnessa, M., A common protocol for agent-based Social simulation. J. Artif. Soc. Social. Simul., 9, 2006.
55. Salies, E., Lin, B., Jiang, Z., Designation and influence of household increasing block electricity tariffs in China. Energy Policy 42 (2012), 164–173.
56. Salies, E., Lin, B., Jiang, Z., How biased is the measurement of household's loss. Energy Policy 48 (2012), 843–845.
57. Sarkis, A.M., A comparative study of theoretical behaviour change models predicting empirical evidence for residential energy conservation behaviours. J. Clean. Prod. 141 (2017), 526–537.
58. Schwartz, S.H., Normative influences on Altruism1. Leonard, B., (eds.) Advances in Experimental Social Psychology, 1977, Academic Press, 221–279.
59. Siagian, U.W.R., Yuwono, B.B., Fujimori, S., Masui, T., Low-carbon energy development in indonesia in alignment with Intended Nationally Determined Contribution (INDC) by 2030. Energies, 10, 2017.
60. Stern, P.C., What psychology knows about energy-conservation. Am. Psychol. 47 (1992), 1224–1232.
61. Stern, P.C., Janda, K.B., Brown, M.A., Steg, L., Vine, E.L., Lutzenhiser, L., Opportunities and insights for reducing fossil fuel consumption by households and organizations. Nat. Energy, 1, 2016.
62. Tesfatsion, L., Agent-based computational economics: a constructive approache to economic theory. Tesfatsion, L., Judd, K.L., (eds.) Handbook og Computational Economics., 2006, Amsterdam, The Netherlands, North-Holland.
63. UNEP, 2017. The Emissions Gap Report 2017. United Nations Environment Programme.
64. Varian, H.R., Microeconomic Analysis, 3rd ed., 1992, Norton, New York.
65. Wall, R., Devine-Wright, P., Mill, G.A., Comparing and combining theories to explain proenvironmental intentions - The case of commuting-mode choice. Environ. Behav. 39 (2007), 731–753.
66. Wilensky, U., 1999. NetLogo.
67. Wilson, C., Dowlatabadi, H., Models of decision making and residential energy use. Annu. Rev. Environ. Resour. 32 (2007), 169–203.
68. Windrum, P., Fagiolo, G., Moneta, A., Empirical validation of agent-based models: alternatives and prospects. J. Artif. Soc. Social. Simul., 10, 2007.
69. Abdmouleh, Z., Gastli, A., Ben-Brahim, L., Survey about public perception regarding smart grid, energy efficiency & renewable energies applications in Qatar. Renewable and Sustainable Energy Reviews 82 (2018), 168–175.
70. Ameli, N., Brandt, N., Determinants of households’ investment in energy efficiency and renewables: evidence from the OECD survey on household environmental behaviour and attitudes. Environ Res Lett, 10, 2015.
71. Buchanan, K., Banks, N., Preston, I., Russo, R., The British public's perception of the UK smart metering initiative: Threats and opportunities (vol 91, pg 87, 2016). Energ Policy, 93, 2016, 149 -149.
72. Buryk, S., Mead, D., Mourato, S., Torriti, J., Investigating preferences for dynamic electricity tariffs: The effect of environmental and system benefit disclosure. Energ Policy 80 (2015), 190–195.
73. Cao, J., Choi, C.H., Zhao, F., Agent-based modeling of the adoption of high-efficiency lighting in the residential sector. Sustainable Energy Technologies and Assessments 19 (2017), 70–78.
74. Ceschi, A., Dorofeeva, K., Sartori, R., Dickert, S., Scalco, A., A Simulation of Householders’ Recycling Attitudes Based on the Theory of Planned Behavior. Trends in Practical Applications of Agents, Multi-Agent Systems and Sustainability: The Paams Collection., 372, 2015, 177–184.
75. Chappin, E., 2012. Agent-based Simulation of Energy Transition, International Engineering Systems Symposium. Delft University of Technology, Delft, the Netherlands.
76. Chappin, E.J.L., Dijkema, G.P.J., 2007. An agent based model of the system of electricity production systems: Exploring the impact of CO2 emission-trading. 2007 Ieee International Conference on System of Systems Engineering, Vols 1 and 2, 277-281.
77. de Koning, K., Filatova, T., Bin, O., Bridging the Gap Between Revealed and Stated Preferences in Flood-prone Housing Markets. Ecol Econ 136 (2017), 1–13.
78. Deng, G., Newton, P., Assessing the impact of solar PV on domestic electricity consumption: Exploring the prospect of rebound effects. Energ Policy 110 (2017), 313–324.
79. Filatova, T., Parker, D.C., van der Veen, A., The Implications of Skewed Risk Perception for a Dutch Coastal Land Market: Insights from an Agent-Based Computational Economics Model. Agricultural and Resource Economics Review 40 (2011), 405–423.
80. Gallo, G., Electricity market games: How agent-based modeling can help under high penetrations of variable generation. The Electricity. Journal, 29, 2016, 399 -346.
81. Gotts, N.M., Polhill, J.G., Experiments with a Model of Domestic Energy Demand. Journal of Artificial Societies and Social Simulation, 20, 2017, 11.
82. Gotts, N.M., Polhill, J.G., Law, A.N.R., ASPIRATION LEVELS IN A LAND USE SIMULATION. Cybernetics and Systems 34 (2003), 663–683.
83. Haer, T., Botzen, W.J.W., Aerts, J.C.J.H., The effectiveness of flood risk communication strategies and the influence of social networks-Insights from an agent-based model. Environmental Science and Policy 60 (2016), 44–52.
84. He, X., Reiner, D., Why consumers switch energy suppliers: The role of individual attitudes. Energ J 38 (2017), 25–53.
85. Jager, W., Janssen, M.A., De Vries, H.J.M., De Greef, J., Vlek, C.A.J., Behaviour in commons dilemmas: Homo economicus and Homo psychologicus in an ecological-economic model. Ecol Econ 35 (2000), 357–379.
86. Kamara-Esteban, O., Sorrosal, G., Pijoan, A., Castillo-Calzadilla, T., Iriarte-Lopez, X., Macarulla-Arenaza, A.M., Martin, C., Alonso-Vicario, A., Borges, C.E., 2016. Bridging the Gap between Real and Simulated Environments: A Hybrid Agent-Based Smart Home Simulator Architecture for Complex Systems, Proceedings - 13th IEEE International Conference on Ubiquitous Intelligence and Computing, 13th IEEE International Conference on Advanced and Trusted Computing, 16th IEEE International Conference on Scalable Computing and Communications, IEEE International Conference on Cloud and Big Data Computing, IEEE International Conference on Internet of People and IEEE Smart World Congress and Workshops, UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld 2016, pp. 220-227.
87. Kiesling, E., Gunther, M., Stummer, C., Wakolbinger, L.M., Agent-based simulation of innovation diffusion: a review. Cent Eur J Oper Res 20 (2012), 183–230.
88. Krömker, D., Eierdanz, F., Stolberg, A., Who is susceptible and why? An agent-based approach to assessing vulnerability to drought. Reg Environ Change 8 (2008), 173–185.
89. Liu, X., Li, X., Anthony, G.-O.Y., Multi-agent systems for simulating spatial decision behaviors and land-use dynamics. Science in China Series D: Earth Sciences 49 (2006), 1184–1194.
90. McDaniel, T.M., Groothuis, P.A., Retail competition in electricity supply-Survey results in North Carolina. Energ Policy 48 (2012), 315–321.
91. Mills, B., Schleich, J., Residential energy-efficient technology adoption, energy conservation, knowledge, and attitudes: An analysis of European countries. Energ Policy 49 (2012), 616–628.
92. Parker, D.C., Manson, S.M., Janssen, M.A., Hoffmann, M.J., Deadman, P., Multi-Agent Systems for the Simulation of Land-Use and Land-Cover Change: A Review. Ann Assoc Am Geogr 93 (2003), 314–337.
93. Polhill, G., Gotts, N., How precise are the specifications of a psychological theory? Comparing implementations of lindenberg and steg's goal-framing theory of everyday pro-environmental behaviour. Advances in Intelligent Systems and Computing, 2017, 341–354.
94. Rangoni, R., Jager, W., Social dynamics of littering and adaptive cleaning strategies explored using agent-based modelling. JASSS, 2017, 20.
95. Rommel, J., Sagebiel, J., Müller, J.R., Quality uncertainty and the market for renewable energy: Evidence from German consumers. Renew Energ 94 (2016), 106–113.
96. Rounsevell, M.D.A., Arneth, A., Alexander, P., Brown, D.G., de Noblet-Ducoudré N., Ellis, E., Finnigan, J., Galvin, K., Grigg, N., Harman, I., Lennox, J., Magliocca, N., Parker, D., O'Neill, B.C., Verburg, P.H., Young, O., Towards decision-based global land use models for improved understanding of the Earth system. Earth Syst. Dynam 5 (2014), 117–137.
97. Schwarz, N., Ernst, A., Agent-based modeling of the diffusion of environmental innovations - An empirical approach. Technol Forecast Soc 76 (2009), 497–511.
98. Thøgersen, J., Housing-related lifestyle and energy saving: A multi-level approach. Energ Policy 102 (2017), 73–87.
99. Tran, M., Agent-behaviour and network influence on energy innovation diffusion. Commun Nonlinear Sci 17 (2012), 3682–3695.
100. van Duinen, R., Filatova, T., Jager, W., van der Veen, A., Going beyond perfect rationality: drought risk, economic choices and the influence of social networks. Ann Regional Sci 57 (2016), 335–369.
101. Vasiljevska, J., Douw, J., Mengolini, A., Nikolic, I., An agent-based model of electricity consumer: Smart metering policy implications in Europe. JASSS, 20, 2017.
102. Weidlich, A., Veit, D., A critical survey of agent-based wholesale electricity market models. Energ Econ 30 (2008), 1728–1759.