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Table 1 Evaluation results of methodologies for renewable energy policy modeling

From: Are decisions well supported for the energy transition? A review on modeling approaches for renewable energy policy evaluation

Criteria Input-output models CGE models System dynamics Agent-based modeling Theory-based evaluation Multi-criteria analysis Hybrid approaches
Spatial coverage Regional to global Regional to multi-regional Regional to global Regional Regional to multi-regional Regional to multi-regional Regional to global
Sectoral coverage Medium level of disaggregation High level of disaggregation Medium level of disaggregation High level of disaggregation High level of disaggregation High level of disaggregation High level of disaggregation
Time horizon Short term Short term to long term Midterm to long term Short term to midterm Short term to long term
Ex-post/ex-ante Ex-ante Ex-ante Ex-ante Ex-ante Ex-post Ex-post Combination possible
Quality of data sources Medium dependent on data quality Medium dependent on data quality Highly dependent on data quality to validate model   Highly dependent on data Highly dependent on data Highly dependent on data quality
Assumptions on actor behavior Combining knowledge of individual agents’ behavior to make inferences about market relationships Cooperation and an open exchange of information or competition and secrecy Dependent on the combination of approaches
Assumptions on markets and systems Constant technological coefficients and linear production functions static system Perfectly competitive markets
CGE models have an explicit representation of the microeconomic behavior of the economic agents
Single country and open economy
Equilibrium across all the markets (e.g., capital, labor, materials/services)
System behavior is dynamic system components interact with each other through feedbacks
Time dependency of system (structure)
Consistent description of the real system
In evolutionary game theory mostly limited to constant environments
Numeric simulations of multi-agent systems offer much more flexibility
Often based on the economic theory of general equilibrium
Assumptions of how an intervention is supposed to work is linked to the actual outcomes Uses a variety of different criteria rather than a single criterion to analyze options and alternatives for decision-making Dependent on the combination of approaches
Computer-aided framework Available Available Available Available Not available Available Available
References Bruckner et al. (2005) [26] Pettersson et al. (2012) [27] Liu et al. (2009) [28] Cellura et al. (2013) [29] Li and Jiang (2016) [30] Wianwiwat & Asafu-Adjaye (2013) [34] Bretschger et al. (2011) [35] Wang et al. (2009) [33] Beckman et al. (2011) [36] Kretschmer and Peterson (2010) Fortes et al. (2014) [38] Guo et al. (2014) [39] Suttles et al. (2014) [40] Barisa et al. (2015) [49] Hsu (2012) [48] Li et al. (2012) [50] Aslani et al. (2014) [43] Jeon & Shin (2014) [51] Wang et al. (2014) [52] Wu et al. (2011) [53] Chyong Chi et al. (2009) [54] Szarka et al. (2008) [42] Movilla et al. (2013) [55] Ansari & Seifi (2012) [56] Nannen et al. (2013) [59] Tang et al. (2015) [60] Gerst et al. (2013) [21] Lee et al. (2014) [58] Zhang et al. (2011) [61] Gerst et al. (2013)b [62] Ding et al. (2016) [63] Abdul-Mannan et al. (2015) [68] Harmelink et al. (2008) [66] Murphy et al. (2012) [67] Harmelink (2005) [65] Browne et al. (2010) [69] von Stechow et al. (2011) [70] Yavuz et al. (2015) [72] Cannemi et al. (2014) [73]
Clo et al (2013) [71]
Igos et al. (2015) [81] Sarica and Tyner (2013) [80] Proença and St. Aubyn (2013) [31] Strachan and Kannan (2008) [83] Jaccard et al. (2004) [84] Böhringer and Rutherford (2008) Pollitt et al. (2014)[85] Barker et al. (2010) [86] Cai et al. (2015)