Evidence map›Paper›PMID 35250143›Full record

ArticleEconomic modelling2022

Modeling economic losses and greenhouse gas emissions reduction during the COVID-19 pandemic: Past, present, and future scenarios for Italy.

Dario Cottafava, Michele Gastaldo, Francesco Quatraro, Cristina Santhiá

Abstract read
In one paragraph

Article in Economic modelling, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Dario CottafavaUniversity of Turin, Department of Management, Corso Unione Sovietica 218 bis, 10134, Turin, Italy.
Michele GastaldoCzech Academy of Sciences, J. HeyrovskýÌ Institute of Physical Chemistry, 182 23, Prague, Czech Republic.
Francesco QuatraroUniversity of Turin, Department of Economics and Statistics, Lungo Dora Siena, 100A, 10153, Torino, Italy.
Cristina SanthiáUniversity of Turin, Department of Economics and Statistics, Lungo Dora Siena, 100A, 10153, Torino, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

COVID-19Dynamic input outputGreenhouse gasInoperabilityInput outputMacroeconomic industrial structure

Identifiers

PMID35250143
PMCPMC8885618

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.