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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)