An Empirically Grounded Model of Green Electricity Adoption in Germany: Calibration, Validation and Insights into Patterns of Diffusion

Spatially explicit agent-based models (ABM) of innovation diffusion have experienced growing attention over the last few years. The ABM presented in this paper investigates the adoption of green electricity tariffs by German households. The model represents empirically characterised household types as agent types which differ in their decision preferences regarding green electricity and other psychological properties. Agent populations are initialised based on spatially explicit socio demographic data describing the sociological lifestyles found in Germany. For model calibration and validation we use historical data on the German green electricity market including a rich dataset of spatially explicit customer data of one of the major providers of green electricity. In order to assess the similarity of the simulation results to historical observations we introduce two validation measures which capture different aspects of the green electricity diffusion. One measure is based on the residuals of spatially-aggregated time series of model indicators and the other measure considers a temporally aggregated but spatially disaggregated indicator of spatial spread. Finally, we demonstrate the descriptive richness of the model by investigating simulation outputs of the calibrated model in more detail. In particular, the results provide insights into the dynamics of the spatial and lifestyle heterogeneity “underneath†the diffusion curve of green electricity in Germany.

[1]  M. Janssen,et al.  Learning, Signaling, and Social Preferences in Public-Good Games , 2006 .

[2]  Noah J. Goldstein,et al.  Descriptive normative beliefs and conservation behavior: The moderating roles of personal involvement and injunctive normative beliefs , 2009 .

[3]  Mathematisch-Naturwissenschaftlichen Fakultät,et al.  Urban segregation as a complex system: an agent-based simulation , 2010 .

[4]  Jacob Cohen A Coefficient of Agreement for Nominal Scales , 1960 .

[5]  Hans Spada,et al.  Modeling Actors in a Resource Dilemma: A Computerized Social Learning Environment , 1993 .

[6]  Andreas Ernst Using Spatially Explicit Marketing Data to Build Social Simulations , 2014 .

[7]  J. Baron Thinking and deciding, 3rd ed. , 2000 .

[8]  Giangiacomo Bravo,et al.  Alternative scenarios of green consumption in Italy: An empirically grounded model , 2013, Environ. Model. Softw..

[9]  Forrest Stonedahl,et al.  The Complexities of Agent-Based Modeling Output Analysis , 2015, J. Artif. Soc. Soc. Simul..

[10]  Andreas Ernst,et al.  Modelling the Role of Neighbourhood Support in Regional Climate Change Adaptation , 2013 .

[11]  Tim Verwaart,et al.  How social unrest started innovations in a food supply chain , 2017 .

[12]  Michael J. Seiler,et al.  Applying Latin hypercube sampling to agent-based models: Understanding foreclosure contagion effects , 2013 .

[13]  Joshua M. Epstein,et al.  Growing Artificial Societies: Social Science from the Bottom Up , 1996 .

[14]  Georg Holtz,et al.  Exploring Homeowners' Insulation Activity , 2016, J. Artif. Soc. Soc. Simul..

[15]  Blanca Gallego,et al.  Diffusion of Competing Innovations: The Effects of Network Structure on the Provision of Healthcare , 2010, J. Artif. Soc. Soc. Simul..

[16]  Tao Zhang,et al.  Evaluating Government's Policies on Promoting Smart Metering Diffusion in Retail Electricity Markets via Agent-Based Simulation* , 2011, Journal of Product Innovation Management.

[17]  Icek Ajzen,et al.  From Intentions to Actions: A Theory of Planned Behavior , 1985 .

[18]  J. Cacioppo,et al.  The need for cognition. , 1982 .

[19]  Bridget Rosewell,et al.  Validation and Verification of Agent-Based Models in the Social Sciences , 2009, EPOS.

[20]  Paul Windrum,et al.  Empirical Validation of Agent-Based Models: Alternatives and Prospects , 2007, J. Artif. Soc. Soc. Simul..

[21]  Noah J. Goldstein,et al.  Normative Social Influence is Underdetected , 2008, Personality & social psychology bulletin.

[22]  P. Burke Distinction: a social critique of the judgement of taste , 1989 .

[23]  Joseph H. A. Guillaume,et al.  Characterising performance of environmental models , 2013, Environ. Model. Softw..

[24]  B. Ryan The diffusion of hybrid seed corn in two Iowa communities , 1943 .

[25]  K. G. Troitzsch VALIDATING SIMULATION MODELS , 2004 .

[26]  Carl A. Kallgren,et al.  A Focus Theory of Normative Conduct: A Theoretical Refinement and Reevaluation of the Role of Norms in Human Behavior , 1991 .

[27]  Steven M. Manson,et al.  Agent-based modeling and genetic programming for modeling land change in the Southern Yucatán Peninsular Region of Mexico , 2005 .

[28]  J. Gareth Polhill,et al.  The ODD protocol: A review and first update , 2010, Ecological Modelling.

[29]  Birgit Müller,et al.  A standard protocol for describing individual-based and agent-based models , 2006 .

[30]  Frank M. Bass,et al.  A New Product Growth for Model Consumer Durables , 2004, Manag. Sci..

[31]  Angelika Gellrich,et al.  Von der Minderheit zur Mehrheit? Psycho-soziale Einflüsse bei der Verbreitung klima-schonender Innovationen , 2016 .

[32]  Michael J. North,et al.  Complex adaptive systems modeling with Repast Simphony , 2013, Complex Adapt. Syst. Model..

[33]  Juliette Rouchier,et al.  Assessment and validation of multi-agent models , 2007 .

[34]  Bertha Maya Sopha,et al.  Adoption and diffusion of heating systems in Norway: Coupling agent-based modeling with empirical research , 2013 .

[35]  I. Ajzen The theory of planned behavior , 1991 .

[36]  Friedrich Krebs,et al.  Heterogeneity in individual adaptation action: Modelling the provision of a climate adaptation public good in an empirically grounded synthetic population , 2017 .

[37]  Andreas Ernst,et al.  A dynamic and spatially explicit psychological model of the diffusion of green electricity across Germany , 2017 .

[38]  Nina Schwarz,et al.  Agent-based modeling of the diffusion of environmental innovations — An empirical approach , 2009 .

[39]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[40]  Andreas Ernst,et al.  Social-ecological modelling with LARA: A psychologically well-founded lightweight agent architecture , 2012 .

[41]  Varun Rai,et al.  Agent-based modelling of consumer energy choices , 2016 .

[42]  Thomas Bruckner,et al.  Lifestyles and Their Impact on Energy-Related Investment Decisions , 2011 .

[43]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[44]  Nigel Gilbert,et al.  Simulating Innovation: Computer-Based Tools for Rethinking Innovation , 2014 .

[45]  Reinhard Madlener,et al.  An agent-based spatial simulation to evaluate the promotion of electricity from agricultural biogas plants in Germany , 2013 .

[46]  Andreas Ernst,et al.  A Spatially Explicit Agent-Based Model of the Diffusion of Green Electricity: Model Setup and Retrodictive Validation , 2015, ESSA.

[47]  Andreas Ernst,et al.  Considering baseline homophily when generating spatial social networks for agent-based modelling , 2013, Comput. Math. Organ. Theory.

[48]  Sascha Holzhauer Dynamic Social Networks in Agent-based Modelling: Increasingly Detailed Approaches of Network Initialisation and Network Dynamics , 2017 .

[49]  Vijay Mahajan,et al.  Integrating time and space in technological substitution models , 1979 .

[50]  Andreas Ernst,et al.  Integrated regional modelling and scenario development to evaluate future water demand under global change conditions , 2011 .

[51]  Mark S. Granovetter The Strength of Weak Ties , 1973, American Journal of Sociology.

[52]  L. Steg,et al.  Normative, Gain and Hedonic Goal Frames Guiding Environmental Behavior , 2007 .