Quantifying the impact of low carbon transition scenarios at regional level through soft-linked energy and economy models: The case of South-Tyrol Province in Italy

Highlights: • A soft-linked model combining EPLANopt with Hybrid Input-Output model is presented. • Feasible energy transition scenarios for the South-Tyrol Province are simulated. • Related economic/environmental impacts are assessed in a comparative-static way. • Potential of the model in deriving insights to support low carbon policies is shown. Low carbon transition agendas imposed by climate change claim for ambitious strategies grounded on non-biased information provided by energy and economic models. This is particularly relevant at local and regional scales, where environmental policies are practically implemented. To this end, this paper proposes a soft-linked model combining the EPLANopt energy simulation model with an empirical Input-Output model and applies it to assess the prospective impact of future scenarios in the Italian region of South-Tyrol. The proposed approach allows to perform scenario analysis of regional contexts with an accurate and physically detailed representation of the energy supply chain, while keeping track of the indirect and sector-specific economic and environmental impacts induced by a prospective policy action: the features and capabilities of the proposed approach are not fully covered by currently available methods and models. The EPLANopt model was setup and run for a selection of prescriptive scenarios, identifying optimal Pareto solutions in terms of minimization of economic cost of energy and carbon dioxide emissions. The results were then processed and incorporated into the Input-Output model, which allowed to evaluate the economy-wide economic and environmental impacts of such scenarios for different sectors. Results of the soft-linked model complement those of the EPLANopt model alone, as they reveal significant economic and environmental indirect effects that characterize the analysed scenario and are thus important to support decision-making.