Search

The spiraling growth of the world's population and unregulated urbanization have resulted in many environmental problems, including poor quality of air, which is associated with a wide range of health issues. Machine learning approaches have been extensively employed to predict air quality, attracting the attention of the scientific community worldwide. Bibliometric studies provide a useful means by which to visualize and analyze published works, helping researchers to make novel scientific contributions by filling existing knowledge gaps in the research. To acquire an in-depth understanding of the topic, this paper presents a bibliometric analysis of all published articles on the use of machine learning networks to predict air quality found in the Web of Science (WoS) search engine from 1992 to 2021. S-curve analysis and social network analysis were used to identify the temporal distribution of articles, productivity by countries/continents, research institutions, and scientific metrics of journal productivity. This study indicated that maximum expansion of the literature witnessed during 2017–2021 (second phase) which represents an expansion or growth stage of machine learning and air quality prediction research. The number of published works increased significantly with 1432 articles accounting for 68.51% of all publications. As a result of the increased interest in machine learning-based prediction tools, the number of articles grew 2.17-fold compared to the 1992–2016 (first phase). In terms of international collaboration impact, Italy emerged as the most successful country (43.44), followed by Greece (31.22) and Spain (23.29). Author keywords analysis was employed to explore and evaluate the emerging research trends on the subject of air quality using machine learning models. Keywords that appear most frequently in this study are 'air pollution', 'air quality', 'machine learning', and 'forecasting'. Citation burst analysis, research productivity analysis, highly influential and highly cited works were also employed to examine various research themes and questions. In this study we also discussed how conventional methods were transformed into machine learning approaches. It is expected that this paper will provide technical guidelines, research priorities, and future opportunities for the precise prediction of air quality and emergency management of air pollution globally. • Interest in air quality prediction through machine learning model is expanding. • China and US were found productive countries in terms of countries collaboration. • Big data, Internet of Things, and Smart city are emerging topic for future research. • Significant difference between citations, funded and non-funded research. • Ensemble modeling is popular in air quality prediction research. [ABSTRACT FROM AUTHOR]

Over the last few years, much attention has been paid to the phenomenon of household food waste which largely contributes to the total amount of food waste generated in industrialised countries. The reduction of food waste has been also included within the European strategy of circular economy and ambitious objectives have been assigned to the European member states. Youths proved to be the segment of the population most inclined to waste food and therefore they need to be monitored. This exploratory study, based on a survey involving 380 youths residing in Italy and Spain, aims at investigating the complexities of food waste behaviour within the framework of the Theory of Planned Behaviour. By referring to the PLS-SEM approach, we analyse the validity of the relationships between individual habits, attitudes, addiction to marketing ploys and sales strategies, and attitudes towards food waste. It also aims at verifying the existence of mediator effects in order to better understand these relationships. The construction of the Importance Performance Matrix Analysis enables identification of the dimensions which greatly influence positive behaviour, and whether urgent attention and interventions are required. The results confirm that behavioural models are appropriate for explaining youths' behaviour towards food waste with an important role played by the perceived behavioural control. Situational factors which characterize the moment of purchase should be carefully considered and monitored. This paper concludes that marketing and sale strategies negatively influence the waste behaviour of individuals, emphasizing the important role of retailers in preventing the generation of food waste. [ABSTRACT FROM AUTHOR]

Since the enactment of the EU Habitats Directive, the conservation status of forest habitat types, habitats of species, and species has become the central concept of the Directive's nature conservation legislation. Despite its role, it has drawn relatively little attention. Within a rather short research period, a few research papers have addressed the existing definitions, indicators for the conservation status assessment, and assessment techniques. This paper attempted to complete the set of measurable indicators available in national forest inventories and connect them with the forest habitat types' conservation status components (area, function, structure, and prospects). A set of 40 indicators was defined, labelled with one or more of the four conservation status components and assessed with the quality dimensions. The analysis uncovered that five indicators could be used to assess the component of range and area, 20 that of structure, 22 that of function and 27 that of prospects. It also showed that conventional forestry indicators such as tree species, diameter at breast height, and regeneration are less sensitive regarding the data quality. Conversely, some typical biodiversity indicators lacked completeness, timeliness, and precision. In addition to this analysis, the data distributions (data for them were provided by the national forest inventories of Italy, Slovenia, and Spain) of some indicators were analysed. Based on all the results, it was also possible to conclude that there is a shortage of national forest inventory indicators for the assessments of the area and function conservation status components. While the area component should be described with the indicators of forest habitat type fragmentation, mingling and perforation with non-forest and other forest vegetation communities, the functional component is bereft of indicators describing processes such as biomass growth and carbon cycling. Future research should thus search for more indicators to represent all conservation status components in a more balanced way. More efforts should also be expended into the harmonisation of indicators. [ABSTRACT FROM AUTHOR]

The literature on Sustainable Business Models (SBMs) has burgeoned, identifying different archetypes to capture the variety of business models applied. Little is known, however, regarding to what extent such SBMs are effectively driving sustainable performance. This paper addresses this gap by exploring how SBMs relate to sustainability performance, considering both overall sustainable performance and the balance across the three dimensions – environmental, social, and economic (integrated performance). Based on original survey data on B Corps located in Italy, Spain and the United Kingdom, our findings suggest that the implementation of most SBMs results in the prioritization of one sustainability dimension over the others, especially when it comes to economically-oriented SBMs. Furthermore, our study suggests that none of the SBM archetypes considered is associated with a balanced sustainable performance, that is, none of them are inherently better able to overcome tensions across the Triple Bottom Line. • We study how SBMs archetypes relate to sustainability performance (SP). • Most SBMs prioritise a triple bottom line (TBL) axes over the others. • Economically-oriented SBMs are not connected with superior SP. • No SBM archetype drives a balanced performance across TBL axes. • A means-end divide emerges. [ABSTRACT FROM AUTHOR]

The effectiveness of Environmental Impact Assessment (EIA) in the transport field is increasingly being contested. Apart from technical issues (e.g., impacts measurements), the literature highlights process-related barriers as key obstacles to effective EIA implementation in transport projects (e.g., lack of collaborative work, transparency, etc.). Nevertheless, most academic efforts to date have focused on technical improvements, paying limited attention to the relevance of process-related barriers. To address this shortcoming, the paper aims to explore and compare how EIA is experienced and perceived by professionals in three South-European countries (Italy, Portugal, and Spain), providing additional insights into EIA process-related barriers in transport projects. The findings were obtained through an online survey of 294 professionals, representing two main stakeholder groups: environmental consultants and transport planners. The results reveal four main types of process problems shared in all three countries: (i) EIA timing, (ii) assessment of alternatives, (iii) monitoring system, and (iv) public participation. The highest divergences are seen in Spain, where 42% of identified process-related barriers are shared with the rest of countries, while Italian and Portuguese respondents agree in the perception of 68% of process-related barriers. The main differences between the barriers identified by transport planners and environmental consultants are related to their assessment of the need for more collaborative work between key actors. It is finally discussed the added value of this research to progress towards the homogenization of EIA processes across countries. • It compares EIA process barriers in transport projects in three South European countries. • Process barriers are identified though an on-line survey filled out by professionals. • The most genuine process barriers are found in the context of Spain. • Professional domains disagree on the need for more collaborative work between key actors. • General agreement on the low impact of EIA in transport decision-making [ABSTRACT FROM AUTHOR]