Hasil untuk "Economic geography of the oceans (General)"

Balkar Yildirim, Alan Albert Coley, Diego Fernando López

We investigate whether black holes can persist through the bounce with a minimal scale factor in a non-singular cosmology, whereby black holes from a previous contracting phase survive into the current expanding one. We do so by studying a generalized McVittie spacetime which embeds a spherically symmetric black hole in a positive spatial curvature bouncing FLRW cosmological background within the modified theory of teleparallel new general relativity. There are no further assumptions on the spacetime (e.g., on the form of the scale factor) initially, and the local evolution is derived from the field equations of the theory, utilizing a perturbative scheme which is valid ``near the bounce". To leading order we obtain a simple bounce solution similar to that in general relativity for a closed FLRW model with a positive cosmological constant, but in which the curvature term in the Friedmann equation is re-normalized within new general relativity. Qualitatively the minimum of the bounce at $t=0$ changes, but near the bounce the evolution remains symmetric. The central inhomogeneity evolves at higher perturbative orders, where the details depend on the arbitrary constants of the perturbative solution. Hence the evolution of the local horizon during the bounce changes qualitatively, where the effects depend on the signs of the perturbation, and the symmetry across the bounce is disrupted due to a linear term.

Krzysztof Janowicz, Gengchen Mai, Rui Zhu et al.

Understanding how AI will represent and reason about geography should be a key concern for all of us, as the broader public increasingly interacts with spaces and places through these systems. Similarly, in line with the nature of foundation models, our own research often relies on pre-trained models. Hence, understanding what world AI systems construct is as important as evaluating their accuracy, including factual recall. To motivate the need for such studies, we provide three illustrative vignettes, i.e., exploratory probes, in the hope that they will spark lively discussions and follow-up work: (1) Do models form strong defaults, and how brittle are model outputs to minute syntactic variations? (2) Can distributional shifts resurface from the composition of individually benign tasks, e.g., when using AI systems to create personas? (3) Do we overlook deeper questions of understanding when solely focusing on the ability of systems to recall facts such as geographic principles?

Magdalena Nowak, Tomasz Kamizela, Małgorzata Worwąg

The article discusses issues related to the pollution of watercourses, and an analysis of various aspects of this topic has been carried out: sources of pollution, types, effects and methods of counteracting. The issue of watercourse pollution is described on the example of the situation on the Oder in 2022. Rivers play a key role for both the natural and anthropogenic environment. Used by industrial sectors that heavily exploit the aquatic environment, they are exposed to the emission of pollutants in many forms. The deterioration of water parameters affects the development of harmful microorganisms, the presence of which puts an additional burden on watercourses. In addition, the natural migration of water on the surface of the Earth's crust facilitates the free migration of low-quality water along with harmful substances. In Poland, between 2014 and 2019, more than half of the rivers studied, i.e. as much as 63.1%, were classified as waters of moderate quality class, thus not allowing them to be fit for consumption. The analysis was carried out on the basis of a literature review and reports published by the Institute of Environmental Protection and the Chief Inspectorate of Environmental Protection, which show that one of the main problems of the environmental disaster on the Oder was the increased electrical conductivity of water, exceeding 7000 μS/cm, and the increased pH, which was the result of contamination of the river with poorly treated post-mining wastewater. The persistently high salinity of the water created conditions conducive to the bloom of Prymnesium parvum algae  capable of producing a strong toxin called primnesin. The release of primnesin into the aquatic environment as a result of the death of microorganisms and the associated reduction in oxygen content in the water led to the mass death of fish, estimated at 360 tonnes, including protected species.

Justyna Berniak-Woźny

Purpose – This study explores the evolving role of artificial intelligence (AI) in sustainability and ESG reporting, mapping research trends, contributions, and future directions. Methodology/Approach – A two-stage methodology combining bibliometric analysis (304 SCOPUS-indexed documents from 2015–2025) with qualitative content analysis of 18 key publications was applied. Findings—AI enhances ESG reporting through automation, real-time monitoring, predictive analytics, and improved data integrity. These capabilities support organisations in aligning with international standards and responding to growing stakeholder demands for transparency and accountability.  Research Implications – While conceptual frameworks are emerging, empirical validations and sectoral comparisons remain limited. Practical Implications – AI supports more efficient, transparent, and standards-aligned reporting processes, enabling better decision-making and risk mitigation. Social Implications – Ethical and inclusive design of AI systems is crucial to prevent bias and enhance stakeholder trust. Originality/Value – This study offers a comprehensive bibliometric perspective, identifies key AI-enabled advancements, and proposes future research avenues.

Bartosz Fortuński, Arnold Bernaciak

This article analyses the impact of Russia's trade with 78 selected countries on Russia’s Actual-Open CO₂ Emissions (EAO) from 2000 to 2020, especially concerning the European Union’s CO₂ emission reduction policies active during that period. The countries selected for analysis were identified based on trade significance, emissions data availability, and consistency with BP statistical reports. The methodological approach relies on the Actual-Open CO₂ Emissions (EAO) model, which reveals a significant influence of Russia's foreign trade on its CO₂ emissions. Results indicate that Russia’s international trade substantially affected its emissions, primarily because Russia was a major CO₂ emitter and exported a considerable portion of its GDP. While the results also indicate emission shifts affecting Russia’s trade partners, the analysis does not provide full EAO calculations for EU countries or others—only trade-attributed impacts are assessed. Differences indicate the presence of emission transfers via trade, complicating efforts to achieve emission reduction targets within the EU, which aimed for a 20% reduction by 2020. The findings of this study are particularly relevant in the context of the EU’s current "Fit for 55" policy, which targets a 55% reduction in greenhouse gas emissions by 2030, underscoring the need to account for emission transfers in trade policy considerations.

Michael Koenig, Jakob Rauch, Martin Woerter

Understanding the effects of economic shocks on firms is critical for analyzing economic growth and resilience. We introduce a Web-Based Affectedness Indicator (WAI), a general-purpose tool for real-time monitoring of economic disruptions across diverse contexts. By leveraging Large Language Model (LLM) assisted classification and information extraction on texts from over five million company websites, WAI quantifies the degree and nature of firms' responses to external shocks. Using the COVID-19 pandemic as a specific application, we show that WAI is highly correlated with pandemic containment measures and reliably predicts firm performance. Unlike traditional data sources, WAI provides timely firm-level information across industries and geographies worldwide that would otherwise be unavailable due to institutional and data availability constraints. This methodology offers significant potential for monitoring and mitigating the impact of technological, political, financial, health or environmental crises, and represents a transformative tool for adaptive policy-making and economic resilience.

Louis Boucherie

Complex systems are made up of many interacting components. Network science provides the tools to analyze and understand these interactions. Community detection is a key technique in network science for uncovering the structures that shape the behavior of these networks. This thesis introduces the Adaptive Cut, a novel method that improves clustering methods by employing multi-level cuts in hierarchical dendrograms. Overcoming the limitations of traditional single-level cuts-especially in unbalanced dendrograms-the Adaptive Cut provides a multi-level cut by optimizing a Markov chain Monte Carlo with simulated annealing. In addition, we propose the Balanceness score, an information-theoretic metric that quantifies dendrogram balance and predicts the benefits of multilevel cuts. Evaluations on over 200 real and synthetic networks show significant improvements in partition density and modularity. In the second part, our analysis shows that incorporating network geometry allows redefining administrative boundaries into non-contiguous regions that better reflect social and spatial dynamics. We also discuss the representation of hierarchical data in hyperbolic space through Poincare maps, which can represent tree-like structures in low dimension. In addition, we examine how geography constrains human mobility, an aspect often overlooked in scale-free characterizations of mobility. By incorporating geography via the pair distribution function from condensed matter physics, we separate geographic constraints from mobility choices. Analyzing datasets containing millions of individual movements, we identify a universal power law that spans five orders of magnitude, thereby bridging the divide between distance-based and opportunity-driven models of human mobility.

José M. Gaspar, Minoru Osawa

We develop a Schumpeterian quality-ladder spatial model in which innovation arrivals depend on regional knowledge spillovers. A parsimonious reduced-form diffusion mechanism induces the convergence of regions' average distance to the global frontier quality. As a result, regional differences in knowledge levels stem residually from asymmetries in the spatial distribution of researchers and firms. We analytically characterize the processes of innovation and knowledge diffusion. We then explore how the weight of intra-relative to inter-regional knowledge spillovers interacts with freer trade to shape the spatial distribution of economic activities. If intra-regional spillovers are relatively stronger, a higher economic integration leads to progressive agglomeration. If inter-regional spillovers dominate, researchers and firms may re-disperse after an initial phase of agglomeration as integration increases. This happens because firms and researchers have incentives to relocate to the smaller region, where they can leverage the concentrated knowledge base of the larger region while avoiding congestion in innovation. The smoothness of the dispersion process depends on the particular weight of intra-regional spillovers. If inter-regional spillovers become stronger as trade becomes freer, then the latter induces a monotone dispersion process. When integration is high enough, stable long-run equilibria always maximize the growth rate of the global frontier quality and the average distance to the frontier, irrespective of whether spillovers are mainly local or global.

Marek Zieliński, Barbara Gołebiewska, Marcin Adamski et al.

The aim of this study was to assess the ability of Polish farms to absorb practices within eco-schemes in Poland in the first year of operation of the Common Agricultural Policy (CAP) 2023-2027. The type of practices used, their number and the scale of use were determined, taking into account the natural conditions in which the farms implementing them operate. The assessment was made taking into account the division of farms according to the size of utilised agricultural land (UAA) and their location in communes. The analysis concerned thirteen practices within five area eco-schemes that were available to Polish farms in the first year of the Polish Strategic Plan 2023-2027 within EU CAP 2023-2027 implementation. The data used came from the database of the Agency for Restructuring and Modernization of Agriculture. A research method was used that involved analysing the distribution and characteristics of the beneficiary farms of eco-schemes in 2023. It was found that 1/3 of farms benefitted from such support. The size of the farm and the natural management conditions played a role in eco-scheme absorption. 

Monika Paradowska, Joost (johannes) Platje, Aleksandra Suchecka

As established in the Europe 2020 Strategy and the European Green Deal, combating climate change is one of the central elements of the European Union’s policy. As a member, Poland is obliged to develop and implement climate policy in order to reduce greenhouse gas emissions so climate neutrality can be achieved by 2050. In the context of low political priority for the Polish government, the study aims to identify the level of awareness, perceptions, and attitudes of young Poles towards climate change, as well as their assessment of climate policy. To achieve this aim, a non-random sampling pilot survey was conducted among young Poles in spring 2022, using an electronic questionnaire made available via social media. While the results show respondents’ awareness of and rather high interest in the threats posed by climate change, in-depth knowledge of climate change, climate policy, and its socio-economic implications is lacking. The progress in and effectiveness of climate policy in Poland is rather assessed negatively. Based on the results, key directions for future actions are suggested for the young generation to become a force in creating a more effective climate policy.

Jolanta Pakulska, Małgorzata Rutkowska

The aim of the article is to analyse the diversity of the level of eco-innovation in the EU Member States in the field of resource efficiency. For this purpose, the resource efficiency outcomes indicator and its sub-indicators were used. The study was prepared on the basis of data from the General Directorate for the Environment. Using positional statistical measures, an analysis of the diversification of the eco-innovation level of the European Union Member States in 2012-2021 was carried out, and the trends of changes in this differentiation were identified. The analysis showed that the difference between the level of eco-innovation in the indicators studied in the EU Member States does not decrease, and there is still a very large difference between the most and the least innovative countries. To date, we have not found this kind of research, so the research carried out fills the research gap in this area.

Haicang Zhou, Weiming Huang, Yile Chen et al.

Road network representation learning aims to learn compressed and effective vectorized representations for road segments that are applicable to numerous tasks. In this paper, we identify the limitations of existing methods, particularly their overemphasis on the distance effect as outlined in the First Law of Geography. In response, we propose to endow road network representation with the principles of the recent Third Law of Geography. To this end, we propose a novel graph contrastive learning framework that employs geographic configuration-aware graph augmentation and spectral negative sampling, ensuring that road segments with similar geographic configurations yield similar representations, and vice versa, aligning with the principles stated in the Third Law. The framework further fuses the Third Law with the First Law through a dual contrastive learning objective to effectively balance the implications of both laws. We evaluate our framework on two real-world datasets across three downstream tasks. The results show that the integration of the Third Law significantly improves the performance of road segment representations in downstream tasks.

Eugeniusz Kośmicki

Margaret Rutendo Magwedere

This article offers a contribution to the understanding of the links between credit, inequality and natural resources, using panel data from 2002 to 2021 for 31 countries. A system-generalised method of moments was employed to determine the dynamic relationship between the variables of the study. The findings of the study suggest inequality and natural resources have a negative and significant relationship with credit. Higher inequality levels and natural resources rents are associated with a lower ratio of private credit to gross domestic product. The study offers an insight into the three pillars of sustainability, namely economic, social and environmental. It is essential for policymakers to integrate environmental factors such as natural resources in the relationships between inequality and the financial sector.

Muhammad Zubair Khan, Oleg E. Peil, Apoorva Sharma et al.

In the rapidly expanding field of two-dimensional materials, magnetic monolayers show great promise for the future applications in nanoelectronics, data storage, and sensing. The research in intrinsically magnetic two-dimensional materials mainly focuses on synthetic iodide and telluride based compounds, which inherently suffer from the lack of ambient stability. So far, naturally occurring layered magnetic materials have been vastly overlooked. These minerals offer a unique opportunity to explore air-stable complex layered systems with high concentration of local moment bearing ions. We demonstrate magnetic ordering in iron-rich two-dimensional phyllosilicates, focusing on mineral species of minnesotaite, annite, and biotite. These are naturally occurring van der Waals magnetic materials which integrate local moment baring ions of iron via magnesium/aluminium substitution in their octahedral sites. Due to self-inherent capping by silicate/aluminate tetrahedral groups, ultra-thin layers are air-stable. Chemical characterization, quantitative elemental analysis, and iron oxidation states were determined via Raman spectroscopy, wavelength disperse X-ray spectroscopy, X-ray absorption spectroscopy, and X-ray photoelectron spectroscopy. Superconducting quantum interference device magnetometry measurements were performed to examine the magnetic ordering. These layered materials exhibit paramagnetic or superparamagnetic characteristics at room temperature. At low temperature ferrimagnetic or antiferromagnetic ordering occurs, with the critical ordering temperature of 38.7 K for minnesotaite, 36.1 K for annite, and 4.9 K for biotite. In-field magnetic force microscopy on iron bearing phyllosilicates confirmed the paramagnetic response at room temperature, present down to monolayers.

Zhen Bi, Ningyu Zhang, Yida Xue et al.

Ocean science, which delves into the oceans that are reservoirs of life and biodiversity, is of great significance given that oceans cover over 70% of our planet's surface. Recently, advances in Large Language Models (LLMs) have transformed the paradigm in science. Despite the success in other domains, current LLMs often fall short in catering to the needs of domain experts like oceanographers, and the potential of LLMs for ocean science is under-explored. The intrinsic reasons are the immense and intricate nature of ocean data as well as the necessity for higher granularity and richness in knowledge. To alleviate these issues, we introduce OceanGPT, the first-ever large language model in the ocean domain, which is expert in various ocean science tasks. We also propose OceanGPT, a novel framework to automatically obtain a large volume of ocean domain instruction data, which generates instructions based on multi-agent collaboration. Additionally, we construct the first oceanography benchmark, OceanBench, to evaluate the capabilities of LLMs in the ocean domain. Though comprehensive experiments, OceanGPT not only shows a higher level of knowledge expertise for oceans science tasks but also gains preliminary embodied intelligence capabilities in ocean technology.

Janusz Krupanek , Beata Michaliszyn-Gabryś, Manuel Moreno

The aim of the publication is to assess environmental performance of meat product and to point out the possibilities of improving the product quality in terms of its environmental impact. The research was performed within the CIRC4Life project on the example of ALIA company, Spain. Life Cycle Assessment method and literature study were used for this purpose. The LCA was carried out for subsystems: feed production, pig housing, slaughtering, meat processing. Two scenarios: basic and improved were compared using the ReCiPe method. In the basic scenario the highest impacts are attributed to agricultural land occupation 29%, climate change 34%, natural land transformation 11% and fossil depletion-11%. The animal feed production is the most critical phase. Comparative analysis of the scenarios showed that there is a potential for beneficial trade-offs between different impact categories by changing the processes and materials for feed production.

Artur Zajkowski, Łukasz Wysocki, Piotr Tuz et al.

Most of the small water companies supplying a small number of consumers with water are struggling with the extremely tight budget, often making any large-scale modernisation impossible. In effect network managed by these companies is often very leaky and unreliable. One possible and cheap way of leakage reduction is the reduction of average pressure in the network. Thanks to new computing technologies, the device selection process for pressure reduction is accurate and easy to do. This study uses the hydraulic model to select required pressure-reducing valves and correct locations accurately and adequately approximate the resulting absolute water loss reduction thanks to this approach.

Barbara Fura

This article presents a methodological basis of the project “Environmental Initiatives and Factors of Competitiveness in Companies” with reg. no. 2016/23/D/HS4/03007. The project is funded by the National Science Centre as a part of the SONATA 12 Contest. The aim of the project is to determine the relationship between environmental initiatives and the competitiveness of companies. Both primary and secondary statistical data was used for the project. This article describes the main project assumptions, research thesis, manner of acquiring the empirical data and statistical analysis of such data. The attention was also paid on diffi culties and limitations which occurred when conducting the research.

Arnaud Mazier, Alexandre Bilger, Antonio E. Forte et al.

In this paper, we develop a framework for solving inverse deformation problems using the FEniCS Project finite element software. We validate our approach with experimental imaging data acquired from a soft silicone beam under gravity. In contrast with inverse iterative algorithms that require multiple solutions of a standard elasticity problem, the proposed method can compute the undeformed configuration by solving only one modified elasticity problem. This modified problem has a complexity comparable to the standard one. The framework is implemented within an open-source pipeline enabling the direct and inverse deformation simulation directly from imaging data. We use the high-level Unified Form Language (UFL) of the FEniCS Project to express the finite element model in variational form and to automatically derive the consistent Jacobian. Consequently, the design of the pipeline is flexible: for example, it allows the modification of the constitutive models by changing a single line of code. We include a complete working example showing the inverse deformation of a beam deformed by gravity as supplementary material.