Embedding sustainability in risk management: The impact of environmental, social, and governance ratings on corporate financial risk

This study investigates the effect of corporate social and environmental evaluation on investors’ risk perception to explore the potential market risk for public companies that adopt a sustainable and responsible corporate strategy. We referred to the triple corporate assessment according to environmental, social, and governance (ESG) criteria to check whether ESG factors—meant to direct firms toward social and environmental needs—improve corporate market performance or trigger, among investors, a perception of “window dressing.” In doing so, we tested the impact of corporate social performance—proxied by an ESG assessment—on corporate financial risk using double risk measurement. We conducted a five-year longitudinal study (fiscal years 2014–2018) of 222 companies listed on the Standard & Poor’s index. The empirical findings show higher investor uncertainty regarding corporate sustainability performance, probably due to the misalignment of objectives between investors and investees. Indeed, an overall ESG assessment corresponds to higher systematic risk for firms, and a corporate environmental rating has an upward effect on the same risk dimension

Web-based platforms in support of industrial symbiosis Initiatives. A bibliometric review

The main purpose of this contribution is to depict most promising web-based solutions in terms of best configuration for symbiotic network. By examining 10 existing and functioning Wb-platforms of which only some have been previously explored in the previous literature, the paper aims at underling main contributions that platforms can provide to industrial symbiosis. Thanks to a mixed research method, the paper shows that the most promising way for enhancing Wb-platforms within industrial symbiosis framework is to consider multiple platforms for integrating the results obtained from the different platforms and for assessing multi-criteria procedures

Integration of Sentinel-1 and Sentinel-2 data for Earth surface classification using Machine Learning algorithms implemented on Google Earth Engine

In this study, Synthetic Aperture Radar (SAR) and optical data are both considered for Earth surface classification. Specifically, the integration of Sentinel-1 (S-1) and Sentinel-2 (S-2) data is carried out through supervised Machine Learning (ML) algorithms implemented on the Google Earth Engine (GEE) platform for the classification of a particular region of interest. Achieved results demonstrate how in this case radar and optical remote detection provide complementary information, benefiting surface cover classification and generally leading to increased mapping accuracy. In addition, this paper works in the direction of proving the emerging role of GEE as an effective cloud-based tool for handling large amounts of satellite data.Comment: 4 pages, 7 figures, IEEE InGARSS conferenc

Multitemporal analysis in Google Earth Engine for detecting urban changes using optical data and machine learning algorithms

The aim of this work is to perform a multitemporal analysis using the Google Earth Engine (GEE) platform for the detection of changes in urban areas using optical data and specific machine learning (ML) algorithms. As a case study, Cairo City has been identified, in Egypt country, as one of the five most populous megacities of the last decade in the world. Classification and change detection analysis of the region of interest (ROI) have been carried out from July 2013 to July 2021. Results demonstrate the validity of the proposed method in identifying changed and unchanged urban areas over the selected period. Furthermore, this work aims to evidence the growing significance of GEE as an efficient cloud-based solution for managing large quantities of satellite data.Comment: 4 pages, 6 figures, 2023 InGARSS Conferenc

Search for eccentric black hole coalescences during the third observing run of LIGO and Virgo

Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass M>70 M⊙) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities 0<e≤0.3 at 0.33 Gpc−3 yr−1 at 90\% confidence level

Ultralight vector dark matter search using data from the KAGRA O3GK run

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM

Search for gravitational-lensing signatures in the full third observing run of the LIGO-Virgo network

Gravitational lensing by massive objects along the line of sight to the source causes distortions of gravitational wave-signals; such distortions may reveal information about fundamental physics, cosmology and astrophysics. In this work, we have extended the search for lensing signatures to all binary black hole events from the third observing run of the LIGO--Virgo network. We search for repeated signals from strong lensing by 1) performing targeted searches for subthreshold signals, 2) calculating the degree of overlap amongst the intrinsic parameters and sky location of pairs of signals, 3) comparing the similarities of the spectrograms amongst pairs of signals, and 4) performing dual-signal Bayesian analysis that takes into account selection effects and astrophysical knowledge. We also search for distortions to the gravitational waveform caused by 1) frequency-independent phase shifts in strongly lensed images, and 2) frequency-dependent modulation of the amplitude and phase due to point masses. None of these searches yields significant evidence for lensing. Finally, we use the non-detection of gravitational-wave lensing to constrain the lensing rate based on the latest merger-rate estimates and the fraction of dark matter composed of compact objects

Resilient Oceans: Policies and Practices to Protect Marine Ecosystems

Resilience is the capacity of an ecosystem – like marine environments – not only to persist with and adapt to sudden natural and human external perturbations but also to continue to regenerate without degrading or losing the capacity to supply its basic ecosystem goods and services. The term “resilience” was originally developed within mechanical and engineering studies for defining the structural stability or robustness of a material or a structure. From this perspective, resilience is the ability of a structure to resist and absorb perturbations and disturbances without changing (Van der Leeuw and Aschan-Leygonie 2005; Woods 2006). Further research has developed the definition of resilience within the ecology domain, as the ability of systems to absorb disturbances and persist. In this sense, resilience is a natural and emergent property of a system that allows its adaptation and persistence across dynamic environments and adversities (Holling 1973; Gunderson 2000; Klein et al. 2003; Bodin and Wiman 2004; Olsson et al. 2004)