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Modeling economic losses and greenhouse gas emissions reduction during the COVID-19 pandemic: Past, present, and future scenarios for Italy.
Cottafava, Dario; Gastaldo, Michele; Quatraro, Francesco; Santhiá, Cristina.
  • Cottafava D; University of Turin, Department of Management, Corso Unione Sovietica 218 bis, 10134, Turin, Italy.
  • Gastaldo M; Czech Academy of Sciences, J. HeyrovskýÌ Institute of Physical Chemistry, 182 23, Prague, Czech Republic.
  • Quatraro F; University of Turin, Department of Economics and Statistics, Lungo Dora Siena, 100A, 10153, Torino, Italy.
  • Santhiá C; University of Turin, Department of Economics and Statistics, Lungo Dora Siena, 100A, 10153, Torino, Italy.
Econ Model ; 110: 105807, 2022 May.
Article in English | MEDLINE | ID: covidwho-1712576
ABSTRACT
Unprecedented nationwide lockdowns were adopted because of the COVID-19 pandemic. Understanding the socioeconomic impact of the past and future restrictions while assessing the resilience of a local economy emerged as a worldwide necessity. To predict the economic and environmental effects of the lockdowns, we propose a methodology based on the well-established input-output inoperability model, using Italy as a case study. By reconstructing the 2020 restrictions, we analyzed the economic losses and greenhouse gas emissions reductions, identifying the most economically impacted sectors because of the restrictions and the sectoral interdependencies and those avoiding most air emissions. We constructed four partial-lockdown scenarios by minimizing the economic losses for increasing restrictions to highlight the model's utility as a tool for policymaking. By revealing the most interconnected and, thus, crucial sectors, the simulated scenarios showcase how the restrictions can be selected to avoid sudden and unpredicted economic damage.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Econ Model Year: 2022 Document Type: Article Affiliation country: J.econmod.2022.105807

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Econ Model Year: 2022 Document Type: Article Affiliation country: J.econmod.2022.105807