Exploring socio-economic externalities of development scenarios. An analysis of EU regions from 2008 to 2016.

A great debate around development scenarios has come to define conversations around the economy and the environment, two dimensions that struggle to find a proper balance. In this paper we apply unconditional growth model analyses to a new and unique dataset of European regions between 2008 and 2016 and identify four development scenarios - green growth, green de-growth, black growth and black de-growth - characterized by different relationships between CO2 emissions and economic growth. We then map European regions across these four scenarios and describe the differences that occurred among regions in terms of socio-economic externalities, mainly competences, investments and well-being. Drawing on our analyses, we contribute to the debate on development scenarios and ecological macro-economics, as well as discuss implications for sustainability policy and research.

Multilevel-growth modelling for the study of sustainability transitions.

Sustainability Transitions (ST) is a complex phenomenon, encompassing environmental, societal and economic aspects. Its study requires a proper investigation, with the identification of a robust indicator and the definition of a suitable method of analysis. To identify the most informative geographical boundaries for analysing ST pathways, we consider the Carbon Emission Intensity (CEI) and estimate a four-level growth model to study its pattern over time for all the EU regions. We apply this model to a novel longitudinal dataset that covers CEI data of European regions at four different geographical scales (state, areas, regions, and provinces) over a nine-year timespan. This approach aims at supporting the decision-makers in developing more effective sustainability transitions policies across Europe, especially focusing on regions and overcoming the well-known "one-size fits all" approach.•The unconditional growth model has been applied to a multi-level structure considering four levels, defined by three geographical scales and time.•The ideal structure of the model would have required five levels, but the sample size of the dataset made the application computationally unfeasible;•The application of the model allowed to identify patterns of stability and change over time of the variable amongst different geographical units.

Industrial carbon emission intensity: A comprehensive dataset of European regions.

The dataset has been developed within the framework of the EU EIT-Climate Kic Flagship Project "Re-Industrialise" and it includes data of Carbon Emission Intensity (CEI) from industrial sources for the European Regions. CEI is considered as a proxy for analysing the Industrial Sustainability Transition pathways and is calculated as the ratio between CO2 equivalent emissions (CO2e) and Gross Domestic Product (GDP) of the industrial sector over a nine-year timespan, i.e. from 2008 to 2016. CO2e data at plant level have been retrieved from EU Emission Trading System (EU ETS) register and aggregated at different geographical scales, corresponding to the nested structure of NUTS (Nomenclature of Territorial Units for Statistics), proposed by EUROSTAT. Industrial GDP data have been selected from EUROSTAT database to match the industrial sectors covered by EU ETS.

Indoor Climate and Air Quality in a Neonatal Intensive Care Unit.

INTRODUCTION AND OBJECTIVE: The skin and respiratory system of premature neonates are in permanent contact with indoor room air. We longitudinally analyzed the room air climate and quality in neonatal intensive care inside and outside an incubator. METHODS: Sampling was performed in 2 patient rooms and inside a neonatal incubator (Caleo, Draeger Medical, Lübeck, Germany) over 6 weeks with 5-min resolution resulting in 12,090 samples (U-Monitor, U-Earth Biotech, London, UK). Temperature, humidity, and air pollutants, including particulate matter (<1 µm [PM1] and <2.5 µm [PM2.5]), volatile organic compounds (VOC), and odorous gases (OG), were recorded. Room air parameters were analyzed using time series analysis. A linear regression model was used to check for statistically significant linear trends. Statistical analysis was performed using decompensation of time series analysis and spectral analysis by fast Fourier transformation. RESULTS: The indoor climate target values of the ward's central ventilation system for temperature and humidity were not always met. Room air parameters (PM, VOC, and OG) showed significant daytime-dependent fluctuations with different oscillation frequencies per day. The daily mean (first quartile - third quartile) concentrations of PM2.5 were significantly higher inside the incubator compared to the surrounding ambient air (2,158 [1,948-2,298] pcs/L vs. 2,018 [1,852-2,058] pcs/L; p < 0.001). OG were significantly lower inside the incubator compared to ambient air. VOC levels inside the incubator were substantially higher during the first 5 days of the observation period compared to VOC levels in the surrounding ambient air. CONCLUSIONS: The indoor climate of neonatal intensive care units should be monitored in real time to detect deviations from target parameters quickly. In our neonatal intensive care unit, indoor air quality fluctuated significantly depending on the time of day. We highly suspect that air pollutants are carried into the direct patient environment by visitors and medical staff. The incubator does not protect against PM and VOC exposure but reduces exposure to OG. Cleaning procedures may lead to substantially higher concentrations of VOC inside the incubator and may represent a potentially harmful factor for premature infants.

Indoor Air Quality Real-Time Monitoring in Airport Terminal Areas: An Opportunity for Sustainable Management of Micro-Climatic Parameters.

Indoor air quality (IAQ) management in public spaces is assuming a remarkable importance. Busy environments, like airport terminals, are currently regarded as possible hotspots and IAQ is a crucial element for passengers and staff protection, as well as a key aspect of airport passenger experience. A one-month monitoring period has been performed on IAQ in the airport of Bologna (Italy), as prototypal example of large regional airport. Four strategic areas within the airport have been equipped with electronic monitoring platforms, including different contaminants and two microclimatic sensors. Data suggest that daily variation in IAQ parameters typically follow the activity pattern of the different environments under study (i.e., passengers' flows) for gaseous contaminants, where particulate matter counts oscillate in a definite range, with a significant role played by ventilation system. Gaseous contaminants show a correlation between indoor and outdoor concentrations, mainly due to airside activities. Micro-climatic comfort parameters have been tested to match with standards for commercial environments. As results appears in line with typical households IAQ values, the current air ventilation system appears to be adequate. Nevertheless, an integrated air management system, based on real-time monitoring, would lead to optimization and improvement in environmental and economical sustainability.