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Rising temperatures, increasingly frequent extreme weather events and sea level rise are playing a growing role in shaping displacement, rural-urban migration, circular movements but also voluntary and forced immobilities. The debate on climate mobilities – the interplay between climate change and human mobility – is currently moving centre stage in migration studies. Migration theory, on the other hand, does not necessarily reflect sufficiently on the role of the environment in mobility patterns. Including an understanding of the complex nature of climate mobilities avoids assuming climate change related mobility as new or exceptional per se, and offers understanding of the plural, multi-causal, and political ways in which climate change and human im/mobilities intersect.

Optimal power flow (OPF) plays a crucial role in addressing asymmetric operational problems of bipolar direct current distribution networks (DCDNs). However, the effectiveness of existing OPF models applied to bipolar DCDNs exhibits sensitivity to the problem type, underlying assumptions, and choice of objective functions. Hence, this paper proposes a dynamic decoupling-based convex-concave programming (CCP) framework to formulate an OPF model, aiming for a more rational impact across diverse applications in asymmetric operational bipolar DCDNs. More specifically, the coupled power between poles and ports of bipolar DCDNs is analyzed in detail, leading to the derivation of decoupled pole-to-ground equivalent circuits without relying on any preliminary assumption. Subsequently, the OPF problem of the equivalent circuits is formulated via CCP, involving the introduction of a cutting plane through difference-of-convex inequalities to limit the feasible region of the traditional OPF based on second-order cone programming, thereby diminishing dependence on exact relaxation conditions. Finally, a solution method is proposed to dynamically correct both the decoupled equivalent circuits and the cutting plane. The numerical results indicate that this method effectively addresses power optimization problems with a non-strictly monotonic objective function. Furthermore, it exhibits scalability, showcasing its applicability to large-scale radial bipolar DCDNs.

ABSTRACTMedia coverage is a key factor in increasing the flow and accessibility of information among the firm's stakeholders. Drawing on the socioemotional wealth (SEW) perspective, we investigate the antecedents of the level of media coverage of family firms' activism in sustainability. Using hand‐collected data from a sample of public Italian family firms, we provide several novel contributions to the academic debate. First, we find that family involvement on the board of directors is negatively associated with the level of media coverage of the family firm's engagement in sustainability. Conversely, we find that later generational stages of the family business are positively associated with the level of corporate media coverage on sustainability. Our study advances the literature on both corporate sustainability issues and media coverage in the context of family businesses.

This study aims to review the literature on environmental disclosure and firm performance in order to show current trends, challenges, and adventures for future research. We attempted a systematic and structured literature review of 67 studies published in 2001-2023 using the accounting journal in the Chartered Accounting Business School (CABS) ranking. The analysis revealed three main research streams: 1) firm performance, CSR and sustainability; 2) accounting and environmental disclosure; 3) board of directors and decision-making process in the field environment and firm performance. The relevance of this literature review is due to understanding the state of the art between environmental disclosure and firm performance. Through our work, we highlight the current and future issues in the firm performance and environmental disclosure. This paper supports future research and it is directed to academics, practitioners, policy makers and decision makers.

Understanding the causes of past atmospheric methane (CH4) variability is important for characterizing the relationship between CH4, global climate and terrestrial biogeochemical cycling. Ice core records of atmospheric CH4 contain rapid variations linked to abrupt climate changes of the last glacial period known as Dansgaard-Oeschger (DO) events and Heinrich events (HE)1,2. The drivers of these CH4 variations remain unknown but can be constrained with ice core measurements of the stable isotopic composition of atmospheric CH4, which is sensitive to the strength of different isotopically distinguishable emission categories (microbial, pyrogenic and geologic)3-5. Here we present multi-decadal-scale measurements of δ13C-CH4 and δD-CH4 from the WAIS Divide and Talos Dome ice cores and identify abrupt 1‰ enrichments in δ13C-CH4 synchronous with HE CH4 pulses and 0.5‰ δ13C-CH4 enrichments synchronous with DO CH4 increases. δD-CH4 varied little across the abrupt CH4 changes. Using box models to interpret these isotopic shifts6 and assuming a constant δ13C-CH4 of microbial emissions, we propose that abrupt shifts in tropical rainfall associated with HEs and DO events enhanced 13C-enriched pyrogenic CH4 emissions, and by extension global wildfire extent, by 90-150%. Carbon cycle box modelling experiments7 suggest that the resulting released terrestrial carbon could have caused from one-third to all of the abrupt CO2 increases associated with HEs. These findings suggest that fire regimes and the terrestrial carbon cycle varied contemporaneously and substantially with past abrupt climate changes of the last glacial period.

In floating photovoltaics (FPV), modules are installed on bodies of water to alleviate the land competition arising from the growing deployment of photovoltaics (PV). However, the installation of artificial structures on water basins can affect the landscape and raise concerns even among those in favor of renewables. This work specifically investigates the public acceptance of FPV among renewable energy supporters to identify its main drivers and barriers. The investigation was conducted through a public survey, which counted more than 300 respondents favorable to renewable energies. The findings reveal that, while public acceptance of FPV remains positive, it is lower than that experienced by renewable energy systems in general. This disparity is mainly due to concerns surrounding the landscape and fauna impacts of this novel technology. In particular, an inverse relationship between FPV acceptance and its perceived alteration of the landscape beauty is found. Notably, respondents expressing a negative opinion on FPV are also those most concerned by the landscape impact of traditional PV. The investigation also proposes some preliminary solutions for enhancing FPV's social acceptance. The effectiveness of these potential measures is evaluated, providing valuable insights for stakeholders and policymakers in the renewable energy sector.

The training process of learning-based distribution system state estimation (DSSE) methods relies on accurate state variables, which typically contain unknown noise and outliers in practice. To this end, this paper proposes an adaptive noise-resistant graphical learning-based DSSE method considering the impact of inaccurate state variables. Specifically, two global-scanning graph jumping connection networks are first developed to capture the regression rules between measurements and state variables considering the structure constraints. To mitigate the negative impact caused by inaccurate labels, a collaborative learning framework is further developed, within which Gaussian mixture model-based discriminators are employed to adaptively select clean samples in each mini-batch. These allow the method to obtain robustness against noisy state labels in historical data, as well as anomalous measurements during online operations. Comparative tests show the superiority of the proposed method in tackling abnormal data in both the training and test phases.