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With dry deposition and below-cloud scavenging, in-cloud scavenging is one of the three components of aerosol transfer from the atmosphere to the ground. There is no experimental validation of in-cloud particle scavenging models for all cloud types that is not impacted by uncertainties concerning below-cloud scavenging. In this article, the choice was made to start with a recognised and validated microphysical cloud formation model (the DEtailed SCAvenging Model, DESCAM) to extract a scheme of aerosol scavenging by clouds, valid for different cloud types. The resulting model works for the two most extreme precipitation clouds: from cumulonimbus to stratus. It is based on data accessible a priori from numerical weather prediction (NWP) outputs, i.e. the intensity of the rain and the relative humidity in the cloud. The diagnostic of the altitude of the cloud base proves to be a key parameter, and accuracy in this regard is vital. This new in-cloud scavenging scheme is intended for use in long-distance (> 100 km) atmospheric transport models (ATMs) or global climate models (GCMs).

This study examines a relationship that has been implied by years of correlational research: that natives’ support for welfare redistribution declines when benefits are allocated to immigrants rather than natives—a phenomenon known as welfare chauvinism. We conducted two online experiments ( N 1  = 273, N 2  = 1060) involving redistribution to unemployed people through real donations to charity, framed as tax compliance decisions within a simulated reporting task. We employed a between-subject design, randomly assigning participants to treatments that differed solely in the immigration status of the charity-benefit recipients. Drawing from native samples in Italy, Denmark, and the UK, we find that natives’ support for redistribution is not statistically affected by the recipients’ immigration status. This null effect holds across both studies, despite spanning a four-year period (2000–2024) marked by major global events which might have been expected to shift preferences regarding welfare state distribution and immigration, including: the COVID-19 pandemic, the Ukrainian refugee crisis, and the increasingly anti-immigrant turn in Italian, Danish, and UK politics. Our findings challenge prevailing theories of welfare chauvinism and invite both replication efforts and reconsideration of long-standing theoretical givens.

Nanotechnologies based on magnetic materials have been successfully used as efficient and reusable strategies to remove pharmaceutical residuals from water. This paper focuses on the fabrication, characterization, and application of ferrite-based magnetic nanoparticles functionalized with L-lysine as potential nanoadsorbents to remove acetylsalicylic acid (ASA) from water. The proposed nanomaterials are composed of highly magnetic and chemically stable core–shell nanoparticles covered with an adsorptive layer of L-lysine (CoFe2O4–γ-Fe2O3–Lys). The nanoadsorbents were elaborated using the coprecipitation method in an alkaline medium, leading to nanoparticles with two different mean sizes (13.5 nm and 8.5 nm). The samples were characterized by XRD, TEM, FTIR, XPS, Zetametry, BET, and SQUID magnetometry. The influence of time, pH, and pollutant concentration was evaluated from batch studies using 1.33 g/L of the nanoadsorbents. The Freundlich isotherm best adjusted the adsorption data. The adsorption process exhibited a pseudo-second-order kinetic behavior. The optimal pH for adsorption was around 4–6, with a maximum adsorption capacity of 16.4 mg/g after 150 min of contact time. Regeneration tests also showed that the proposed nanomaterials are reusable. The set of results proved that the nanoadsorbents can be potentially used to remove ASA from water and provide relevant information for their application in large-scale designs.

We use a lab experiment to examine whether and how leaders influence workers’ (un)ethical behavior through financial reporting choices. We randomly assign the role of leaders or workers to subjects, who can choose to report an outcome via automatic or self-reporting. Self-reporting allows for profitable and undetectable earnings manipulation. We vary the leaders’ ability to choose the reporting method and to punish workers. We show that workers are more likely to choose automatic reporting when their leader voluntarily does so and can assign punishment. Even workers who choose self-reporting tend to cheat less when their leader chooses automatic reporting. Nonetheless, most leaders do not opt for automatic reporting in the first place: they often choose self-reporting and punish workers who rather choose automatic reporting. Collectively, our results reveal a dual effect of leadership on ethical behaviors in organizations: workers behave more ethically if their leader makes ethical choices, but often leaders do not make ethical choices in the first place. Hence, leading by example can backfire.

We analyze optimal sentence length for recurring crimes in the face of adjudication errors. We develop an infinite-horizon model where offenders are habitual—they repeat crimes whenever free. If apprehended, criminals may be wrongfully acquitted. Similarly, innocent people may be apprehended and wrongfully convicted. The key result shows how the risks of wrongful convictions and wrongful acquittals affect optimal sentencing. For reasonable ranges of parameter values, the two types of adjudication errors have the same qualitative effect on optimal sentencing: a greater risk of any of the two adjudication errors leads to a decrease in optimal sentencing.

This paper extends the traditional rent-seeking model to consider contests in which the effects of the contestants’ efforts are externally unproductive (i.e., redistributive) but internally productive (i.e., with positive spillover effects on other contestants). Our results show that when players act sequentially, the presence of positive spillovers on other contestants may reduce, or even reverse, the first-mover’s advantage. A second-mover advantage is very likely to arise. Notably, in contests with multiple players, the second-mover advantage does not unravel into a last-mover advantage. Players want to be second, but not last. The comparative statics analysis shows how the strength of positive spillovers affects contestants’ equilibrium expenditures and payoffs, and aggregate rent dissipation.

We use experimental data to explore the conditions under which males and females may differ in their tendency to act corruptly and their tolerance of corruption. We ask if males and females respond differently to the tradeoff between the benefits accrued by corrupt actors versus the negative externality imposed on other people by corruption. Our findings reveal that neither males nor females uniformly are more likely to engage in, or be more tolerant of corruption: it depends on the exact bribery conditions—which can reduce or enhance welfare overall—and the part played in the bribery act. Females are less likely to tolerate and engage in corruption when doing so reduces overall welfare. On the other hand, males are less tolerant of bribery when it enhances welfare but confers payoff disadvantages on them relative to corrupt actors. Females’ behavior is consistent across roles when bribery reduces welfare, but apart from that, gender behavior is strongly role-dependent.

Abstract Much of the conventional wisdom of evidence law rests on the premise that the amount of evidence available in any given case is exogenously determined. With the advent of evidence technology (e.g. dashcams, black-box technology, digital data storage, surveillance cameras), the availability of evidence is substantially controlled by individuals. In this article, we show that evidence rules play an important role in determining individuals’ decisions to invest in private evidence. We compare the evidence rules adopted in the USA and Europe and analyze their relative impact on the voluntary adoption of evidence technology. We find that by making private evidence not discoverable, more rather than less evidence would be made available to courts.

In robot torts, robots carry out activities that are partially controlled by a human operator. Several legal and economic scholars across the world have argued for the need to rethink legal remedies as we apply them to robot torts. Yet, to date, there exists no general formulation of liability in case of robot accidents, and the proposed solutions differ across jurisdictions. We proceed in our research with a set of two companion papers. In this paper, we present the novel problems posed by robot accidents, and assess the legal challenges and institutional prospects that policymakers face in the regulation of robot torts. In the companion paper, we build on the present analysis and use an economic model to propose a new liability regime which blends negligence-based rules and strict manufacturer liability rules to create optimal incentives for robot torts.

This is the second of two companion papers that discuss accidents caused by robots. In the first paper (Guerra et al., 2021), we presented the novel problems posed by robot accidents, and assessed the related legal approaches and institutional opportunities. In this paper, we build on the previous analysis to consider a novel liability regime, which we refer to as ‘manufacturer residual liability’ rule. This makes operators and victims liable for accidents due to their negligence – hence, incentivizing them to act diligently; and makes manufacturers residually liable for non-negligent accidents – hence, incentivizing them to make optimal investments in R&D for robots' safety. In turn, this rule will bring down the price of safer robots, driving unsafe technology out of the market. Thanks to the percolation effect of residual liability, operators will also be incentivized to adopt optimal activity levels in robots' usage.