## 2 Literature Review  
Several approaches have been implemented, in order to evaluate the impact of the deployment of renewable energies on employment, value added and/or GDP. [PERSON] et al. [2] provide an overview of the methods used in the literature. Impact assessments can be distinguished in two broad categories: gross impact studies and net impact ones. The first type simply aims at assessing how many jobs (or value added) are created as result of activities along the value chain of RE (renewable energy) generation. By definition, gross impact studies result in an expansionary outcome of RE. On the other hand, if households and enterprises finance RET promotion (e.g., through higher energy prices), this will result in a lower disposable income and higher production costs; therefore, contractionary effects will spread throughout the economy. Net impact studies should ideally take into account all sorts of direct, indirect, and induced effects (see [2]) for a detailed description of all potential effects), and possibly compare multiple scenarios of RET deployment. Nevertheless, net impact studies differ greatly in the scope of the effects considered; in fact, the outcomes of RET deployment are very complex and cannot be easily fully encompassed within one study. The particular focus on the time horizon (past, present, future), the aggregation level (sub-national, national, supranational), and the kind of impact indicator (quantity/quality of employment, value added, GDP) may shape the methodology as well. For the sake of brevity, we group methodologies into three main categories: (1) Employment factors and Value Chain Analysis, (2) Cost-based input-output (I-O) modelling, and (3) Full economic assessment (or complex macroeconomic modelling). The employment factors approach has been widely applied in the literature, since it is the simplest analytical approach. \"Green\" jobs are estimated by multiplying the installed capacity or energy generation by employment factors (e.g., jobs per MW of capacity), specific for each life cycle phase activity (direct jobs generation) and its supply chain (indirect jobs generation) of a given RE (renewable energy) technology. [PERSON] and [PERSON] [3] carry out a systematic review of the employment factors present in the literature, finding that the bulk of the studies does not estimate original coefficients, but takes them from the literature itself. On the other hand, original estimates show large differences across studies (up to 10-folds). RE jobs creation can sometimes be compared with CE (Conventional Energy) jobs reduction, so that some negative impact can be accounted for. The value chain approach, similar to employment factors, focuses on life cycle phases, and analyzes different components and/orstages within the same phase. For both approaches, a system boundary must be defined. Whereas the first approach accounts merely for job unities, the second one can account for all value added attributable to RE (labor, profit, taxes). The cost-based I-O approach can account for all direct and indirect impacts (\"ripple effect\") across all upstream industries, since there is no system boundary other than the economic system. A major limitation of this approach is that I-O sectors are assumed to be proxies of RE-related activities. Business surveys can help circumvent such limitations: data on cost structures of intermediates, import shares and labor coefficients can be combined with official statistics sectoral disaggregation. [PERSON] et al. [1] distinguish two sub-approaches to integrating RE technology-specific data: either each expenditure from RE activities is assigned to the existing I-O sectors, or a new RE sector can be created, with full forward and backward linkages. A second weakness is that standard I-O models are demand-driven, and therefore an increased exogenous demand in RE will result in a corresponding adjustment of the supply. That is, neither supply maximum capacity (capital/labor fixed supply) nor resources sectoral shift are considered.