Identifying the research advances on COVID-19, the economy and the environment: network- bibliometric analysis and statistical validation


 The COVID-19 outbreak dictates urgent research responses. The corpus of scientific publications on COVID- 19 is rapidly growing. Differently from health and technical sciences, social and environmental sciences risk to be neglected in this process. Similarly, Environmental Economics falls behind in terms of COVID-19 scholarship. The research note in hand examines and maps up-to-date research progress on the occurring Coronavirus disease, the economy and the environment. To this end, a bibliometric analysis of these three intertwined areas is performed. We constructed a database of the key publications and extracted the keywords co-occurrence network characterising each work. Thus, we studied the structural characteristics – i.e. the density and the centre – sorting from the co-occurrence network. This exercise identifies four communities of relevant keywords, including environmental health, economic impact and lifestyle, which present the maximal mutual interconnection. It is discovered that relevant possibilities and urgency to examine the relationship between the Coronavirus, the economy and the environment exist on the issues and, broadly, in the field of Environmental Economics. The study of environmental facets and environment-economy interplay within the current Coronavirus pandemic claims a larger academic production, resulting not yet relevant and scarcely explored and signals the need to boost public and environmental health scholarship response.JEL codes: C02, I15, I18, O13, Q56


Introduction
COVID-19 pandemic has highly influenced every aspect of life worldwide. Several research 28 activities have been undertaken to better analyse and explore the causes, the treatment, but also the 29 prevention of the new Coronavirus as the epidemic continues to develop (Acter et al. 2020). One of 30 the main research objectives is to analyse the effects of the relationships between the COVID-19, 31 the economy and the environment. In this context, urban dynamics are determinant. In fact, the 32 effects of the pandemic on global cities need to be clarified and some relevant lessons need to be 33 learned on urban planning and design for post-COVID (Sharifi and Khavarian-Garmsir, 2020). 34 Early studies on the effects of the COVID-19 on urban growth consider primarily four main themes: 35 the quality of the environment, the social and the economic impacts, the urban designs related to 36 transport, and governance and management issues (Sharifi and Khavarian-Garmsir, 2020). 37 Also, water-related impacts are evolving as effects related to the COVID-19 pandemic. These corpusnamely, the deterioration of the environment and COVID-19, the pandemic and air quality, -finally, from the sorting network, we are able to decompose the network by identifying the 123 "communities" of keywords that are maximally connected.

125
More particularly, an exploratory data analysis is performed to examine the structure of the existing 126 literature on these topics at the date. From the extraction, we obtain 15 articles that are simultaneously 127 relevant for the "COVID", or "Coronavirus" or "Covid-19", the "economy" and the "environment". 128 The outputs from the resulting bibliometric and statistical analysis (see Aria and Cuccurullo 2017) 129 show a fragmented scenario. All the sorting articles were published in 2020, and each author shows

144
The final network is based on 94 vertices or nodes and 1674 edges or links. Where a co-occurrence 145 of a keyword on the article exists, we can observe a single link between the two keywords. The first step is to analyse the network structure. Communities are groups of keywords that are highly connected to each otherso they are many 170 times co-occurrent on the same papersand weakly connected with the keywords which are part of 171 other communities (Fortunato 2010).

172
The identified communities are groups of co-occurrent keywords that can be found in different 173 articles. These keywords show a unique relevant "semantic core" albeit being part of different articles.

174
Analysing the diverse keywords that are part of these communities, it is possible to retrieve and 175 extract some related and important concepts which allow summarising the literature. This way, it is 176 possible to conclude that communities allow interpreting the hot themes and topics in the literature 177 thanks to their logical interconnections. 178 We detect the different communities assessing the "walktrap" algorithm (Pons Latapy 2005). This 179 algorithm identifies the nodes that are part of the same group of maximally connected nodes. Short 180 random walks in the network tend to remain on the same group of nodes or in the same community 181 (Csardi Nepusz 2006). The walktrap algorithm identifies the nodes in which these random walks tend 182 to stay.

183
The community detection approach diverges from the cluster analysis because, making use of the 184 former, we are explicitly partitioning the network to identify the nodes which show mutually a higher 185 level of connectivity as a group. The cluster analysis does not necessarily involve mutually 186 interconnected nodes. That is why the membership can be interpreted as relevant groups of nodes

217
The result is confirmed from the publishing journals evaluation. All the journals scrutinised have 218 published only one article on this literature.

219
The higher number of published works in a journal on a topic is an important signal for the researcher:  The nodes which show the highest centrality are the following keywords: "Coronavirus disease 250 2019", "pandemic", "public health", "article", "economic aspect" and finally "human". These nodes can have some great potentialities to increase.

294
All the findings are sketched in in the communities related with the first analysis and there was associated with aspects as "chronic 313 disease" and "immune-deficiency", "immune system" and "vulnerable population" (community 2).

314
These aspects link relevant concepts as vulnerability to the lifestyles.

315
It is possible to investigate more in detail the aspects of lifestyle which are investigated in this 316 literature. On the second community detection performed using the second bibliometric analysis, we 317 found two communities: in the first one we found concepts as "oxygen therapy", "quarantine" and 318 "virus transmission", whereas in the second one we found as relevant words "pandemic", 319 "coronavirus infections" and "disease transmission" "infection control" and "practice guideline". interconnections of the co-occurrences, has been identified.
We found a set of co-occurrence communities that allow network decomposition. The first one relates The finding signals a major gap in the literature that needs to be considered for the future. The 483 usefulness of the results is that these signals allow to summarise the most relevant core themes in 484 literature but also to identify the most relevant relationships between the different considered themes.

485
Future international research can usefully explore additional key-themes departing from the relevant 486 semantic-cores, which are the most structured publications on the topic.