Linking of of a multi-country discrete choice experiment and an agent-based model to simulate the diffusion of smart thermostats

被引:4
作者
Chappin, Emile J. L. [1 ]
Schleich, Joachim [2 ,3 ]
Guetlein, Marie-Charlotte [2 ]
Faure, Corinne [2 ]
Bouwmans, Ivo [1 ]
机构
[1] Delft Univ Technol, Fac Technol Policy & Management, Jaffalaan 5, NL-2628 BX Delft, Netherlands
[2] Grenoble Ecole Management, 12 Rue Pierre Semard, F-38000 Grenoble, France
[3] Fraunhofer Inst Syst & Innovat Res, Breslauer Str 48, D-76139 Karlsruhe, Germany
基金
欧盟地平线“2020”;
关键词
Smart thermostats; Multi-method; Discrete choice experiment; Agent-based modelling; Multi-country; ENERGY TECHNOLOGY ADOPTION; ELECTRIC VEHICLES; HEATING-SYSTEMS; HOUSEHOLDS; BARRIERS; PREFERENCES; TRANSITIONS; EVOLUTION; CONSUMERS; BEHAVIOR;
D O I
10.1016/j.techfore.2022.121682
中图分类号
F [经济];
学科分类号
02 ;
摘要
In this paper, we link findings from a demographically representative discrete choice experiment (DCE) in eight European countries on the adoption of smart thermostats with an agent-based model (ABM) in a methodologically consistent way. We employ the ABM to simulate the diffusion pattern of smart thermostats until 2030 and to examine the effects of subsidies and recommendations by specific agents. Our findings highlight the importance of allowing for within- and across country heterogeneity in preferences for these policies and for technology attributes such as heating cost savings. Further, social interactions reinforce country differences in technology stock in the starting year of the simulations. We find that subsidies moderately accelerate the diffusion of smart thermostats, but they are less effective in countries with a large stock of smart thermostats in the starting year, strong preferences for heating cost savings, and when smart thermostats lead to a strong reduction in heating costs. For some countries, targeting subsidies at particular socio-economic groups (in our case low-income households) slightly mitigates free-riding effects. Our policy simulations further imply that recommendations by energy providers or by energy experts accelerate the diffusion of smart thermostats compared to recommendations by peers.
引用
收藏
页数:26
相关论文
共 61 条
[21]   An agent based approach to the potential for rebound resulting from evolution of residential lighting technologies [J].
Hicks, Andrea L. ;
Theis, Thomas L. .
INTERNATIONAL JOURNAL OF LIFE CYCLE ASSESSMENT, 2014, 19 (02) :370-376
[22]   Enhancing Agent-Based Models with Discrete Choice Experiments [J].
Holm, Stefan ;
Lemm, Renato ;
Thees, Oliver ;
Hilty, Lorenz M. .
JASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION, 2016, 19 (03)
[23]   Enhancing the Realism of Simulation (EROS): On Implementing and Developing Psychological Theory in Social Simulation [J].
Jager, Wander .
JASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION, 2017, 20 (03)
[24]   Agent-based assessment framework for behavior-changing feedback devices: Spreading of devices and heating behavior [J].
Jensen, Thorben ;
Holtz, Georg ;
Chappin, Emile J. L. .
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, 2015, 98 :105-119
[25]   Predicting household occupancy for smart heating control: A comparative performance analysis of state-of-the-art approaches [J].
Kleiminger, Wilhelm ;
Mattern, Friedemann ;
Santini, Silvia .
ENERGY AND BUILDINGS, 2014, 85 :493-505
[26]   A transitions model for sustainable mobility [J].
Koehler, Jonathan ;
Whitmarsh, Lorraine ;
Nykvist, Bjorn ;
Schilperoord, Michel ;
Bergman, Noam ;
Haxeltine, Alex .
ECOLOGICAL ECONOMICS, 2009, 68 (12) :2985-2995
[27]   NEW APPROACH TO CONSUMER THEORY [J].
LANCASTER, KJ .
JOURNAL OF POLITICAL ECONOMY, 1966, 74 (02) :132-157
[28]   Actors behaving badly: Exploring the modelling of non-optimal behaviour in energy transitions [J].
Li, Francis G. N. .
ENERGY STRATEGY REVIEWS, 2017, 15 :57-71
[29]   A review of socio-technical energy transition (STET) models [J].
Li, Francis G. N. ;
Trutnevyte, Evelina ;
Strachan, Neil .
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, 2015, 100 :290-305
[30]  
Li XG, 2016, ENVIRON RESOUR ECON, V63, P1, DOI 10.1007/s10640-014-9833-5