Hasil untuk "Environmental law"

James B. Garvin, Connor J. Anderson, Katherine A. Melocik et al.

At least one large-body (diameter > 1.1 km) hypervelocity cratering event occurred during ~ 0.8-0.90 Ma (Zhamanshin, Kazakhstan) in the Middle Pleistocene Transition period. Analysis designed to reduce uncertainty in the dimensions of the Zhamanshin structure employing high resolution topography demonstrated that it likely generated a ~ 26.5 km diameter multi-ring crater. This is at least two times larger than the current best estimates. Using a range of accepted impactor sizes, velocities, compositions, and angles of impact, such impacts typically yield kinetic energies of impact over 240,000 Megatons (TNT). Explosive energetic events of this magnitude (e.g., Yellowstone Caldera) at other times (K-Pg) have created global environmental effects. The factor of two discrepancy in the dimensions of Zhamanshin increases the kinetic energy yield by factors of 7-10, with significantly larger environmental consequences. This justifies examination of rapid climate transitions linked to biological consequences, including those related to environmental perturbations, at ~0.9 Ma.

Gianfranco Giulioni, Edmondo Di Giuseppe, Arianna Di Paola

This paper presents a modeling framework for simulating the decision-making processes of artificial farms populating an agent-based model for the Italian wheat production system. The decision process is based on a mathematical programming model with which farms (i.e., agents) decide the target yield (production per hectare) and the mix of inputs needed to obtain such production, namely 1) fertilizers, 2) herbicides, and 3) insecticides. The environmental impacts of conventional production practices are assessed through a Life Cycle Assessment (LCA), using the ReCiPe 2016 methodology at the Endpoint level. Agents are made aware of the environmental consequences of their choices through two indicators: Disability-Adjusted Life Years (DALYs), which capture human health impacts, and the number of species lost per year, reflecting impacts on ecosystems. By internalizing this information, agents can make more balanced and sustainable production decisions.

Chang-hsien Tsai, Yi-Kai Wang, Shih-Ying Wu

This study examines the causal impact of Taiwan’s 2014 mandatory corporate social responsibility (CSR) reporting regulation on corporate environmental, social, and governance (ESG) performance. Drawing on stakeholder and legitimacy theories, we argue that disclosure requirements enhance transparency, increase reputational pressure, and strengthen accountability, thereby motivating firms to reduce ESG-related controversies. Using panel data of approximately 1,500 publicly listed firms over 2010–2019, we exploit the staggered implementation of disclosure rules as a quasi-experiment. The results show that mandatory CSR reporting significantly reduces law violations, fines, and ESG incidents, supporting the view that disclosure-based regulation can improve ESG outcomes even without mandated CSR expenditures. The mechanisms appear to operate through transparency-driven monitoring, stakeholder pressure, and benchmarking against peers. However, the effects of mandatory external assurance are inconclusive, likely reflecting limited coverage and potential confounding from concurrent regulatory responses in high-risk industries. While these findings contribute to ongoing debates about whether disclosure mandates induce substantive behavioral change or symbolic compliance, generalizability may be limited given Taiwan’s specific institutional and cultural context.

Bim Gustavsson, Stacey Law

Let $p$ be any prime. We determine precisely those irreducible characters of symmetric groups which contain at most $p$ distinct linear constituents in their restriction to a Sylow $p$-subgroup, answering a question of Giannelli and Navarro. Moreover, we identify all of the linear constituents of such characters, and in the case $p = 2$ explicitly calculate a new class of Sylow branching coefficients for symmetric groups indexed by so-called almost hook partitions.

Aadi Patel, Nikhil Mahalingam, Rusheen Patel

The rapid expansion of artificial intelligence has significantly increased the electricity, water, and carbon demands of modern data centers, raising sustainability concerns. This study evaluates the environmental footprint of AI server operations and examines feasible technological and infrastructural strategies to mitigate these impacts. Using a literature-based methodology supported by quantitative projections and case-study analysis, we assessed trends in global electricity consumption, cooling-related water use, and carbon emissions. Projections indicate that global data center electricity demand may increase from approximately 415 TWh in 2024 to nearly 945 TWh by 2030, with AI workloads accounting for a disproportionate share of this growth. In the United States alone, AI servers are expected to drive annual increases in water consumption of 200--300 billion gallons and add 24--44 million metric tons of CO2 quivalent emissions by 2030. The results show that the design of the cooling system and the geographic location influence the environmental impact as strongly as the efficiency of the hardware. Advanced cooling technologies can reduce cooling energy by up to 50%, while location in low-carbon and water-secure regions can cut combined footprints by nearly half. In general, the study concludes that sustainable AI expansion requires coordinated improvements in cooling efficiency, renewable energy integration, and strategic deployment decisions.

Sivan Ding, Julia Wilkins, Magdalena Fuentes et al.

Self-supervised learning (SSL) approaches, such as contrastive and generative methods, have advanced environmental sound representation learning using unlabeled data. However, how these approaches can complement each other within a unified framework remains relatively underexplored. In this work, we propose a multi-view learning framework that integrates contrastive principles into a generative pipeline to capture sound source and device information. Our method encodes compressed audio latents into view-specific and view-common subspaces, guided by two self-supervised objectives: contrastive learning for targeted information flow between subspaces, and reconstruction for overall information preservation. We evaluate our method on an urban sound sensor network dataset for sound source and sensor classification, demonstrating improved downstream performance over traditional SSL techniques. Additionally, we investigate the model's potential to disentangle environmental sound attributes within the structured latent space under varied training configurations.

Gabriel Spadon, Vaishnav Vaidheeswaran, Claudio DiBacco

Marine invasive species spread through global shipping and generate substantial ecological and economic impacts. Traditional risk assessments require detailed records of ballast water and traffic patterns, which are often incomplete, limiting global coverage. This work advances a theoretical framework that quantifies invasion risk by combining environmental similarity across ports with observed and forecasted maritime mobility. Climate-based feature representations characterize each port's marine conditions, while mobility networks derived from Automatic Identification System data capture vessel flows and potential transfer pathways. Clustering and metric learning reveal climate analogues and enable the estimation of species survival likelihood along shipping routes. A temporal link prediction model captures how traffic patterns may change under shifting environmental conditions. The resulting fusion of environmental similarity and predicted mobility provides exposure estimates at the port and voyage levels, supporting targeted monitoring, routing adjustments, and management interventions.

Md Ashiqur Rahman, Mustofa Tanbir Kuhel, Clara Novoa

The risk of oil spills in the Alaskan Arctic has become an urgent environmental and logistical concern as maritime traffic increases under climate driven sea ice retreat. Traditional deterministic response planning models fail to represent key uncertainties, including variable spill magnitudes, changing environmental sensitivity, and infrastructure limitations. This study develops a two-stage stochastic mixed integer linear programming framework that jointly optimizes the location of oil spill response stations and the allocation of heterogeneous resources across multiple probabilistic spill scenarios. The model integrates a weighted objective that combines spill volume, environmental sensitivity index (ESI), response time, and costs for station setup, deployment, and inter station transfer. Separate importance weights for coverage and cost, together with internal ecological weights, allow decision makers to balance ecological protection and operational efficiency. Data was compiled from Alaska Department of Environmental Conservation spill records and National Oceanic and Atmospheric Administration ESI layers and are converted into model ready scenarios through harmonization and sampling. The model is solved with the Gurobi optimizer, and sensitivity analysis is performed over 324 combinations of importance and ecological weights. Results show about a 35.45% percent improvement in response effectiveness over deterministic methods, as confirmed by the value of the stochastic solution, and reveal clear tradeoffs between cost and ecological coverage. The framework provides a data driven decision support tool for Arctic emergency planners that simultaneously accounts for uncertainty, environmental sensitivity, and realistic logistical constraints.

Jacqueline Hilgendorf, Harmony Lautrette-Quinveros, Wing Sze Chan et al.

Abstract Chemical pollution is identified as a significant driver of biodiversity loss, raising concerns about the effectiveness of current environmental risk assessment (ERA) practices. Conventional ERA approaches primarily rely on the endpoints of mortality, growth, and reproduction, often failing to capture the full scope of potential effects that chemicals can have on organisms. This is potentially problematic in cases of chemicals causing neurotoxicity, immunotoxicity, and metabolic toxicity, which have recently been introduced to the discussion under REACH by the new report of the European Chemical Agency (ECHA) on Key Areas of Regulatory Challenge. For these modes of action (MoAs), which have to date been discussed primarily in the context of human toxicity, there is currently no established approach for addressing them in ERA. This is despite the fact that these chemicals often have sublethal effects on traits linked to potential effects on population-relevant endpoints (e.g., foraging behaviour). In this study, we evaluated the importance of non-conventional sublethal endpoints for hazard and risk practices. We categorised endpoints into conventional (CE; i.e., defined by standardised guidelines), semi-conventional (semi-CEs; i.e., defined by standardised guidelines but only for a limited number of species), and non-conventional endpoints (NCE; i.e., ecotoxicological measurements not defined by standardised guidelines and so going beyond conventional measurements). In this conceptual review, we selected case studies that evaluated both conventional and non-conventional endpoints to evaluate the importance of NCEs for the assessment of the emerging hazards in comparison to CEs, focusing on (1) sensitivity (effect levels), (2) mechanistic understanding, and (3) population-level effects. Our assessment shows that using NCEs can improve mechanistic understanding of chemical hazards and provide important information about the chemicals’ MoA. Comparisons between NCEs and CEs at the individual and population levels revealed that in 13% of cases, NCEs showed effects when CEs were unaffected. NCEs were generally more sensitive, being on average 56 times more sensitive than mortality, 8 times than reproduction, and 2 times than growth—in 9 cases, the NCEs were more than 1000 times more sensitive than the CE. NCEs showed unconventional links to the population level that would have gone undetected in the current ERA system (e.g., changes in boldness behaviour affecting reproduction in fish). We propose a first approach to address environmental hazard identification and risk prediction for neurotoxic, immunotoxic, and metabolic toxic compounds by organising relevant NCEs according to an Adverse-Outcome-Pathway (AOP) structure, and a MoA-based AOP framework.

Kristin Hällmark

Negative eco-emotions such as fear, anger, and anxiety are becoming increasingly widespread as the consequences of the ongoing climate crisis are getting progressively worse. Previous literature has warned that these emotions are best avoided in the public sphere, as they risk contributing to either depoliticization or divisive antagonism. In this article, I defend the political and democratic potential of eco-fear, eco-anger, and eco-anxiety. Using Chantal Mouffe's framework of agonistic politics as a lens, I examine how these emotions can be formulated to facilitate the formation of collective political identities and transform potentially destructive antagonism into democratic agonism. By bringing forward how these negative eco-emotions can enable politicization while avoiding destructive antagonism, this article contributes to the growing literature on eco-emotions and the literature on agonistic theory. Through a review of the existing literature on eco-emotions, I contend that fear can help to re-politicize environmental discourses when directed towards specific eco-political threats, such as technologies or political inaction. Anger, I argue, can strengthen collective identities through renewed agency and counter-hegemonic thinking, with “Lordean anger” mitigating some of its moralizing tendencies. Finally, I suggest that anxiety is most productive when tied to a sense of place and belonging, grounding political identities in our ecological interconnectedness.

Liudmyla Golovko, Viktor Ladychenko, Olena Yara

The environment is one of the casualties of armed conflict. In the context of military operations, a range of ecological impacts is observed, including but not limited to: human casualties; destruction of infrastructure; pollution of air, land and water resources; and damage to forests and nature reserves. Moreover, as military technologies develop, the situation is only getting worse. Environmental damage also has far-reaching economic consequences. All of this suggests that there is a need for high-quality international legal regulation of environmental protection during armed conflicts. The purpose of this article is twofold: firstly, to analyse the consequences of environmental damage caused by the Russian Federation's aggression for the Ukrainian economy; and secondly, to identify the existing international legal regulation of environmental protection during armed conflict. In addition, the article will propose ways to solve the identified problems. An analysis was conducted on statistical data pertaining to the environmental damage caused by various armed conflicts. The issue of international legal regulation of the assessment of damage caused to the environment during military conflicts was considered. Methodology. In the course of composing the article, the primary focus was on the utilisation of general theoretical methodologies. The analysis and synthesis of existing literature, alongside theoretical generalisation and systematic interpretation, were instrumental in evaluating individual international treaties pertinent to the research topic. Results. The article considered the economic consequences of environmental damage caused by the Russian Federation in Ukraine, as well as the content of international legal regulations on environmental protection during armed conflicts and the practice of implementing them. Practical implications. The authors have identified the main range of problems existing in the sphere of international legal regulation of environmental protection, and proposed the possible ways of their resolving, on the basis of doctrinal provisions, data from reports of the Ministry of Environment and Natural Resources of Ukraine and the UN, as well as the norms of the current international law. Value/Originality. It has been determined that a significant step in enhancing the international legal regulation of environmental protection during armed conflict is necessary. This step involves the enactment of a separate convention aimed at resolving this issue, as well as the adoption of international standards on environmental damage assessment.

Shu-Hong Lin, Yu-Cheng Wang

The Marine Pollution Control Act (MPCA) in Taiwan aims to align with international conventions such as the United Nations Convention on the Law of the Sea (UNCLOS), the International Convention for the Prevention of Pollution from Ships (MARPOL), the International Convention on Civil Liability for Oil Pollution Damage (CLC), the International Oil Pollution Compensation Funds (FUNDs), and the International Convention for the Control and Management of Ships’ Ballast Water and Sediments (BWM). However, Taiwan’s particular international status prevents formal participation in these treaties. This study evaluates Taiwan’s legal and institutional frameworks on ship emission control, pollution liability and compensation, and interagency coordination, identifying key gaps compared with global standards. By analyzing Japan’s and South Korea’s best practices in port management, cross-border pollution prevention, and vessel monitoring, this study proposes legal and policy reforms that are tailored to Taiwan. Recommendations include strengthening liability mechanisms, enhancing interagency collaboration, monitoring vessels, and fostering regional cooperation. Our findings suggest that these reforms will improve Taiwan’s marine environmental governance and contribute to regional and global ocean sustainability.

Noriyuki Tonami, Wataru Kohno, Keisuke Imoto et al.

Deep neural network (DNN)-based models for environmental sound classification are not robust against a domain to which training data do not belong, that is, out-of-distribution or unseen data. To utilize pretrained models for the unseen domain, adaptation methods, such as finetuning and transfer learning, are used with rich computing resources, e.g., the graphical processing unit (GPU). However, it is becoming more difficult to keep up with research trends for those who have poor computing resources because state-of-the-art models are becoming computationally resource-intensive. In this paper, we propose a trainingless adaptation method for pretrained models for environmental sound classification. To introduce the trainingless adaptation method, we first propose an operation of recovering time--frequency-ish (TF-ish) structures in intermediate layers of DNN models. We then propose the trainingless frequency filtering method for domain adaptation, which is not a gradient-based optimization widely used. The experiments conducted using the ESC-50 dataset show that the proposed adaptation method improves the classification accuracy by 20.40 percentage points compared with the conventional method.

M. Dolia, V. Lysenko, T. Lendiel et al.

Protecting wheat from pests directly affects the country’s food security. Therefore, the purpose of this study was to create predictive models for estimating the harmfulness of E. integriceps by years. The harmfulness of E. integriceps was considered depending on the following indicators: pest abundance, environmental index (Wolf number) and hydrothermal moisture coefficient (HTC). The study proved the existence of mathematical uncertainty of information flows in relation to the specified pest, and therefore the mathematics of artificial neural networks with the structure of “multilayer perceptron” was used for forecasting. The results of the study of the harmfulness of E. integriceps to winter wheat in Ukraine were presented, including a forecast of the phytosanitary state of agrocenoses of Ukraine and recommendations for assessing the distribution of harmfulness of E. integriceps by years of observation (1996-2023) for the Odesa Oblast. It was noted that this distribution corresponds to a normal law with a mathematical expectation of 25%, which is confirmed by the results of observations for other regions of the Steppe zone. The relationship between the number of E. integriceps, Wolf number, and the accumulated integrated temperature and humidity characteristics of the environment was analysed. It was found that the harmfulness of E. integriceps is characterised by a fading periodic component with a period of 10-12 years. This result suggests the impact of the current year’s E. integriceps damage on the next year in 10-12 years. According to the forecasting results, the dependence of the harmfulness of E. integriceps on its number and the Wolf number was presented. Therewith, the accumulated integrated temperature and humidity characteristics of the environment were considered. The obtained findings are recommended for consideration in the organisation of planned technological operations for the protection of cereal grain crops

Elaine Aparecida Rodrigues, Amanda Rodrigues de Carvalho, Maurício Lamano Ferreira et al.

As urban areas expand rapidly, understanding the complex interactions between human migration, climate change impacts, and biodiversity loss is crucial for effective climate policy. However, comprehensive knowledge of the simultaneous interaction of these aspects is still scarce. Thus, this paper proposes the classification of ‘Climate Emergency Coastal Cities’, with the categorization of 43 cities into four levels according to their vulnerability (extreme, very high, high, and critical). Our study contributes to evidence-based climate policy and supports efficient resource allocation and interventions for the most vulnerable coastal cities. Highly anthropogenic megacities were ranked as the most sensitive to climate emergencies (Lagos, Nigeria; Jakarta, Indonesia; Los Angeles and Houston, USA; and Hong Kong and Shenzhen, China). It is noteworthy that in countries from both the Global North and South, the entry of new populations is a critical issue, and represents a threat to urban structures and biodiversity; however, in territories with fragile economies and numerous governance challenges, the required structure is still more challenging. The study concludes that integrated urban planning policies are crucial, considering various perspectives and coordinated actions. Policies should address marginalized urban groups and include migrants, and promote human well-being, ecosystem recovery, and climate mitigation, for effective adaptation.

Iván Vargas-Chaves

El presente artículo tiene por objetivo estudiar el marco evolutivo regulatorio en la prohibición del asbesto o amianto en Colombia, desde sus impactos a la salud humana por inhalación o ingesta, hasta las implicaciones de la prohibición de su uso mediante la Ley 1968 de 2019, luego de varias décadas de estudios y denuncias por parte de la sociedad civil, así como de restricciones en su uso en otros países. La metodología utilizada fue el análisis documental de información especializada, la cual se recopiló en artículos en bases de revistas indexadas, además de jurisprudencia y normas relevantes en la materia. Como aporte se presenta una nueva aproximación a la problemática derivada de la tardía aplicación del principio de precaución en Colombia, y al marco evolutivo del tratamiento regulatorio y normativo al uso del asbesto. Por último, el artículo concluye que, aunque la prohibición del uso del asbesto se dio tardíamente en el país, fue no obstante un logro para las víctimas y la sociedad civil quienes exigían al Estado salvaguardar su derecho a la salud, a gozar de un ambiente sano y a la vida. Mientras tanto, el Estado y el sector privado se enfrentan a un reto en la sustitución progresiva del asbesto utilizado habitualmente en las edificaciones y en la construcción de ciudades.

Vittorio Ranieri, Stefano Coropulis, Veronica Fedele et al.

Permeable-pavement design methodologies can improve the hydrologic and therefore the environmental benefits of rural and urban roadway systems. By contrast, conventional impervious pavements perturb the hydrologic cycle, altering the relationship between the rainfall loading and runoff response. Impervious pavements create a hydraulically conductive interface for the transport of traffic-generated chemicals and particulate matter (PM), deleteriously impacting their proximate environments. Permeable-pavement systems are countermeasures to mitigate hydrologic, chemical, and PM impacts. However, permeable pavements are not always equally implementable due to costs, PM loadings, and design constraints. A potential solution to facilitate environmental benefits while meeting the traffic load capacity is the combination of two filtration systems placed at the pavement shoulders and/or pedestrian sidewalks: a bituminous-pavement open-graded friction course (BPFC) and an aggregate-filled infiltration trench. This solution is presented in this manuscript together with the methodological framework and the first results of the investigations into designing and validating such a combined system. The research was conducted at the laboratories of the Polytechnic University of Bari and the University of Florida, while an operational and full-scale physical model was constructed in Bari, Italy. The first results presented characterize the PM deposition on public roads based on granulometry (particle size distributions (PSDs) and particle number densities (PNDs)). Samples (n = 16) were collected and analyzed at eight different sites with different land uses, traffic, and pavements from different cities (Bari and Taranto, Italy). The PM analysis showed similar distributions (PSDs and PNDs), except for two samples. The gravimetric-based PSDs of the PM had granulometric distributions in the sand-size range. In contrast, the PNDs, modeled by a Power Law Model (PLM) (R² ≥ 0.92), illustrated an exponentially increasing number of particles in the fine silt and clay-size range, representing less than 10% of the PSD mass. Moreover, the results indicate that PM sourced from permeable-pavement systems has differing impacts on the pavement service life.

Alexey S. Matveev, Anna A. Semakova

A team of non-holonomic constant-speed under-actuated unmanned aerial vehicles (UAVs) with lower-limited turning radii travel in 3D. The space hosts an unknown and unpredictably varying scalar environmental field. A space direction is given; this direction and the coordinate along it are conditionally termed as the “vertical” and “altitude”, respectively. All UAVs should arrive at the moving and deforming isosurface where the field assumes a given value. They also should evenly distribute themselves over a pre-specified range of the “altitudes” and repeatedly encircle the entirety of the isosurface while remaining on it, each at its own altitude. Every UAV measures only the field intensity at the current location and both the Euclidean and altitudinal distances to the objects (including the top and bottom of the altitudinal range) within a finite range of visibility and has access to its own speed and the vertical direction. The UAVs carry no communication facilities, are anonymous to one another, and cannot play distinct roles in the team. A distributed control law is presented that solves this mission under minimal and partly inevitable assumptions. This law is justified by a mathematically rigorous global convergence result; computer simulation tests confirm its performance.

Carole-Jean Wu, Ramya Raghavendra, Udit Gupta et al.

This paper explores the environmental impact of the super-linear growth trends for AI from a holistic perspective, spanning Data, Algorithms, and System Hardware. We characterize the carbon footprint of AI computing by examining the model development cycle across industry-scale machine learning use cases and, at the same time, considering the life cycle of system hardware. Taking a step further, we capture the operational and manufacturing carbon footprint of AI computing and present an end-to-end analysis for what and how hardware-software design and at-scale optimization can help reduce the overall carbon footprint of AI. Based on the industry experience and lessons learned, we share the key challenges and chart out important development directions across the many dimensions of AI. We hope the key messages and insights presented in this paper can inspire the community to advance the field of AI in an environmentally-responsible manner.

Vincent Calvez, Benoît Henry, Sylvie Méléard et al.

We investigate a simple quantitative genetics model subjet to a gradual environmental change from the viewpoint of the phylogenies of the living individuals. We aim to understand better how the past traits of their ancestors are shaped by the adaptation to the varying environment. The individuals are characterized by a one-dimensional trait. The dynamics -- births and deaths -- depend on a time-changing mortality rate that shifts the optimal trait to the right at constant speed. The population size is regulated by a nonlinear non-local logistic competition term. The macroscopic behaviour can be described by a PDE that admits a unique positive stationary solution. In the stationary regime, the population can persist, but with a lag in the trait distribution due to the environmental change. For the microscopic (individual-based) stochastic process, the evolution of the lineages can be traced back using the historical process, that is, a measure-valued process on the set of continuous real functions of time. Assuming stationarity of the trait distribution, we describe the limiting distribution, in large populations, of the path of an individual drawn at random at a given time $T$. Freezing the non-linearity due to competition allows the use of a many-to-one identity together with Feynman-Kac's formula. This path, in reversed time, remains close to a simple Ornstein-Uhlenbeck process. It shows how the lagged bulk of the present population stems from ancestors once optimal in trait but still in the tail of the trait distribution in which they lived.