## 4 Driver and Barrier Extraction  
The first challenge that needs to be addressed consists of the definition of the agents behaviour. The factors (drivers and barriers) influencing the decision-making process of the actors of the simulation model need to be identified. Drivers are those elements that lead to making a specific energy efficiency investment, while barriers are those elements against the investment. These factor were extracted from a large set of empirical data, gathered through questionnaires and interviews conducted in the Emilia-Romagna region as part of the ePolicy European research project [40]. The empirical data collection took place between March and August 2013. In this section we provide a partial overview of the methodology and the results of the surveys; for the complete and detailed discussion we refer to the work of [PERSON] and [PERSON] [41].  
The tools used to collect the data are the following:  
1. an online questionnaire concentrating on the general attitudes towards photovoltaic and its adoption (196 questionnaires were completed);
2. semi-structured interviews with both apartment block caretakers (building administrators) and employees of photovoltaic installation companies (11 interviews of average length between 60 and 90 min).  
The results of both questionnaires and interviews indicated that a series of economic and social elements come into play when deciding to install a PV panel. Some of the factors involved in the decision process were purely economical (for example the profitability of the investment) or physical (such as the type of house and/or the availability of enough roof surface). Other elements reflected personal values and motivations (such as environmental awareness) and represented the social component of the decision process (for example the diffusion of PV-related information within peer networks). A very important aspect to be considered in the simulation is that many households pondering about PV installations are not able to make a clear costs-benefits calculation, but rather act on (other) perceived benefits of photovoltaic.  
Some of the drivers identified are the following:  
* high energy costs and potential saving after the PV panel installation,* a general interest on renewable energy and sensitivity towards environmental concerns,
* the possibility to generate their own electricity,
* the knowledge regarding PV technologies (its costs, the incentives, etc.)--often acquired through social networks. Conversely, these were some of the main barriers:
* misinformation about the cost of PV panels (often perceived as higher than the real one),
* uncertainty about the bureaucratic procedures,
* poor knowledge regarding the incentive mechanisms offered by government bodies,
* fiscal limitations (for example, the up front initial price of the panels),
* trust issues such as the stability of the price for solar-produced electricity in the long run,
* personal low risk propensity.  
As illustrated in the following sections, the drivers and barriers identified via interviews and questionnaires form the basis of the decision model of each agent in our simulation. However, even when drivers and barriers have been identified, it is not clear how these parameters are weighted and which is their relative importance in understanding the decision making strategy of different house owners. For this purpose, the model parameters associated with the decision factors will undergo an empirical tuning phase exploiting the observed data, the historical installation rate of PV panels in the Emilia-Romagna region.