20253 research outputs found
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The Globalization of International Migration? A Conceptual and Data‐Driven Synthesis
Full text linkAlthough the globalization of international migration is commonly accepted as a general tendency in contemporary migration patterns (de Haas, Castles, and Miller 2020, 9), the corresponding body of empirical evidence is mixed and fragmented. Our review of global migration patterns over the past half‐century highlights how the theories, expectations, and ultimately findings may vary depending on the specific definitions, vantage points, and measures being used. In this paper, we provide a simpler and integrated account of the globalization of international migration that includes a corresponding empirical template to quantify the relative importance of two processes at work: the intensity and connectivity of international migration. Using recent estimates of country‐to‐country migration flows every five years from 1990–1995 to 2015–2020, our analysis using demographic decomposition and group‐based multitrajectory modeling highlights the dynamic relationship between intensity and connectivity from both the global and country vantage points. Our work in this paper provides a starting point in the form of a much‐needed empirical template, one that is also highly flexible and customizable, for future research on the globalization of international migration to coalesce around and use going forward
A neural network architecture for disaggregating age-specific population projections to the sub-national level
Full text linkImproving our understanding of future risk from climate change requires realistic projections of future
populations, both in their size and distribution. Distribution refers not only to geographic breakdowns but also to the breakdown by important characteristics, such as age. While the location where people will live may determine future exposure to hazards, population characteristics also co-determine the degree of vulnerability and the capacity to adapt to changing environmental conditions. Despite the importance of these factors, there remains a paucity of population projections (or disaggregations thereof) at the sub-national level. We develop a machine learning-based model to disaggregate age-specific population projections based on the Shared Socioeconomic Pathways (SSPs) to the sub-national NUTS-2 level for 34 European countries. Our focus on Europe is driven by its high degree of spatial variability, both in terms of climatic conditions and population structure, as well as the rapid pace of climate change and population aging there
Identifying spatial drivers of soil heavy metal pollution risk integrating positive matrix factorization, machine learning, and multi-scale geographically weighted regression
No full textSoil heavy metal (HMs) contamination poses significant ecological and health risks, yet the spatial drivers of HMs pollution remain poorly understood. This study integrates pollution risk assessment, positive matrix factorization, machine learning, and multi-scale geographically weighted regression to develop a framework for identifying the spatial drivers of soil HMs contamination risk in Yangtze River New City, China. Analysis of 7152 samples revealed that although average HMs concentrations were below national standards, As, Cd, Cr, Cu, Hg, and Ni exceeded local background levels. Four key factors were identified as drivers of HMs contamination: natural sources (30.36 %, influenced by soil type), mixed agricultural and transportation sources (29.56 %, driven by cropland, aquaculture, and road density), human activities (12.68 %, including population density and community activities), and industrial sources (27.42 %, linked to factories and enterprises). Regional variations indicated that industrial activities, transportation, and human activities primarily influenced health risks, while agriculture and natural factors had a greater impact on ecological and environmental capacity risks. These findings underscore the importance of considering spatial heterogeneity in HMs pollution risk assessments and offer insights for developing targeted, region-specific policies to mitigate pollution risks of soil HMs
Optimizing hydropower generation with reservoir level management in humid regions
Full text linkIt is well known that water management practices can have a significant impact on the climate and hydrology of a region. As a rule, the average flow downstream decreases due to the construction of new hydropower plants and the operation of new dams, as evaporation increases in the upstream dams. However, this is not the case in every situation. This study shows that dams in humid areas such as Brazil can help to increase river flow. This phenomenon occurs due to the high humidity and low wind conditions in the region, which leads to low evaporation in the reservoirs. At the same time, full reservoirs help to maintain high humidity around the reservoirs, which increases precipitation in the catchment. To test this hypothesis, water storage and hydropower generation data from Brazilian catchments in the Southeast region were used. Reservoir data are compared with future hydropower generation to investigate the correlation between the two variables. We find that the operation of reservoirs has a significant impact on Brazilian river flows. On average, the annual hydropower potential of a catchment with a full reservoir is 111 % higher than with empty reservoirs. To increase the flow of the river, the study proposes solutions to fill the reservoirs after an energy crisis and keep the reservoirs at full capacity
A Pathways Analysis Dashboard prototype for multi-risk systems
Full text linkWith accelerating climate change, the impacts of natural hazards will compound and cascade, making them more complex to assess and manage. At the same time, tools that help decision-makers choose between different management options are limited. This study introduces a visual analytics dashboard prototype (https://www.pathways-analysis-dashboard.net/, last access: 18 October 2025) designed to support pathways analysis for multi-risk Disaster Risk Management (DRM). Developed through a systematic design approach, the dashboard employs interactive visualisations of pathways and their evaluation, including Decision Trees, Parallel Coordinates Plots, Stacked Bar Charts, Heatmaps, and Pathways Maps, to facilitate complex, multi-criteria decision-making under uncertainty. We demonstrate the utility of the dashboard through an evaluation with 54 participants at varying levels and disciplines of expertise. Depending on the expertise (non-experts, adaptation / DRM experts, pathways experts), users were able to interpret the options of the pathways, the performance of the pathways, the timing of the decisions, and perform a system analysis that accounts for interactions between the sectoral DRM pathways with precision between 71 % and 80 %. Participants particularly valued the dashboard's interactivity, which allowed for scenario exploration, added additional information on demand, or offered additional clarifying data. Although the dashboard effectively supports the comparative analysis of pathway options, the study highlights the need for additional guidance and onboarding resources to improve accessibility and opportunities to generalise the prototype developed to be applied in different case studies. Tested as a standalone tool, the dashboard may have additional value in participatory analysis and modelling. This study underscores the value of visual analytics for the DRM and Decision Making Under Deep Uncertainty (DMDU) communities, with implications for broader applications across complex and uncertain decision-making scenarios
Policy, finance, and capacity-building innovations for scaling nature-based solutions
No full textReaching the ambitious United Nations goal of tripling investments in nature-based solutions (NbS) by 2030 will require mainstreaming NbS into local, regional, and national governance regimes, including regulatory and financial procedures, as well as into land use and spatial planning strategies. While ambition is growing, NbS implementation and scaling remain problematic. Lack of expertise and knowledge, limited evidence on effectiveness and co-benefits, stakeholder conflicts, and gray measure path dependency represent some formidable obstacles. To address these and other barriers to NbS implementation, we identify some key recommendations and suggest innovations to promote NbS upscaling. We build on the results of extensive stakeholder deliberations involving over 70 NbS experts and knowledgeable stakeholders at the national, European, and international scales over 4 years. The first recommendation is the promotion of mandatory policy instruments. This includes the enforcement of legally binding targets and the simplification of NbS approval procedures. Further, measures could include fostering policy synergies, for instance, by explicitly linking NbS policies to well-being and preventative health care policies. The second recommendation is unlocking public and private funding to enable NbS investments, merging complementary funding streams into single programs that prioritize NbS, and promoting innovative financing mechanisms, such as payment for ecosystem services. Divesting from nature-negative projects is as important as enabling NbS investments. The third recommendation is strengthening capacity building through the creation of NbS project preparation facilities, accelerator programs/mentoring, user-friendly benefit/co-benefit catalogs for the private sector, and the creation of communities of practice for NbS contractors with the public, academia, and civil society. Strengthening the knowledge base is also essential to building capacities. Key actions in this regard include increased monitoring to track short- and long-term impacts, stronger evidence on the effectiveness of NbS, and co-benefit evaluation. The development of formal standards, such as insurance regulations and improved tools to compare NbS, hybrid, and conventional solutions, will further contribute to a strong knowledge base. We hope that these recommendations and suggested innovations will contribute to fostering debate and supporting the uptake of NbS as key options in fighting climate change, biodiversity loss, and reducing disaster risk
Historical and Future Development of Greenhouse Gas Emission and Removal from the Land Use Sector from the View of Countries
Full text linkEvaluating the progress towards global and national net-zero emissions goals requires a thorough assessment of historical emission levels and future targets. However, little attention has been paid to the actual reporting by the parties themselves. In this analysis, we examine parties reporting historical emissions and removals for Agriculture, Forestry, and Other Land Use (AFOLU) sector, as well as their commitments outlined in the Nationally Determined Contributions (NDCs) and the Long-term Low Emission Development Strategies (LT-LEDS). Our analysis reveals a worldwide decrease in historical net AFOLU emissions, spanning from 1990 to 2020. This decline primarily relates to increased removals in the LULUCF sector in non-Annex I countries. In 1990, global AFOLU emissions were recorded at 4,400 MtCO2eq, but by 2020, they had been reduced to approximately 2,200 MtCO2eq. Looking ahead, countries have committed to further reduce global net AFOLU emissions by 600–1,700 MtCO2eq by 2030 compared to 2020 levels. Moreover, fulfilment of the LT-LEDS commitment can provide an additional reduction of 2,300–3,400 MtCO2eq. By integrating these datasets, the study provides insights into the progress towards achieving climate goals, highlighting the importance of land-based mitigation strategies. The findings reveal disparities between Annex I countries and Non-Annex I countries, particularly in the ambition of the commitments and objectives. As countries begin to submit their biennial transparency reports to the United Nations Framework Convention on Climate Change (UNFCCC), our recommendation is for countries to enhance transparency in reporting and communicating their progress of implementation
Leveraging the collaborative power of AI and citizen science for sustainable development
No full textBoth artificial intelligence (AI) and citizen science hold immense potential for addressing major sustainability challenges from health to climate change. Alongside their individual benefits, when combined, they offer considerable synergies that can aid in both better monitoring of, and achieving, sustainable development. While AI has already been integrated into citizen science projects such as through automated classification and identification, the integration of citizen science approaches into AI is lacking. This integration has, however, the potential to address some of the major challenges associated with AI such as social bias, which could accelerate progress towards achieving sustainable development
Does the added worker effect matter?
No full textIn the US, the likelihood of a married woman entering the labor force in a given month increases by 60% if her husband loses his job, known as the added worker effect. However, only 1.5% to 3.5% of married women entering the labor force in a given month can be added workers. This raises the question of whether the added worker effect can significantly impact aggregate labor market outcomes. Building on Shimer (2012), we introduce a new methodology to evaluate how joint transitions of married couples across labor market states affect aggregate participation, employment, and unemployment rates. Our results show that the added worker effect significantly impacts aggregate outcomes, increasing married women's participation and employment by 0.72 and 0.65 percentage points each month. Additionally, the added worker effect reduces the cyclicality of married women's participation and unemployment, lowering the correlation between GDP's cyclical components and participation by 4.5 percentage points and unemployment by 8 percentage points
Kinematic re-envision of haptic expertise through Bernstein’s problem: A comparative analysis of stochastic and deterministic features
No full textThis study examined how skill level differences influence the interplay between deterministic and stochastic elements in motor control, focusing on Bernstein's challenge of coordinating abundant degrees of freedom. Using a custom-designed device integrated with motion-capture technology, we compared haptic accuracy and kinematic trajectories between expert and novice performers. Comprehensive statistical and frequency-domain analyses revealed that experts exhibited a broader range of frequency components, indicating a more flexible and adaptive control strategy. In contrast, novices showed narrower frequency bandwidths and more predictable trajectories, reflecting a relatively rigid approach. These findings illustrate how structured predictability (deterministic control) and adaptive variability (stochastic exploration) interact to shape skilled performance patterns. The results highlight the potential benefits of practice strategies that systematically introduce controlled variability, ultimately promoting the unfreezing of additional degrees of freedom and facilitating expert-level proficiency