Search Results (118)

Accurate assessments of aboveground biomass (AGB) stocks and their changes in extensive Miombo forests are challenging due to the lack of site-specific allometric equations (AEs). Terrestrial Laser Scanning (TLS) is a non-destructive method that enables the calibration of AEs and has recently been validated by the IPCC guidelines for carbon accounting within the REDD+ framework. TLS surveys were carried out in five non-contiguous 1-ha plots in two study sites in the wet Miombo forest of Katanga, in the Democratic Republic Congo. Local wood densities (WD) were determined from wood cores taken from 619 trees on the sites. After a careful checking of Quantitative Structure Models (QSMs) output, the individual volumes of 213 trees derived from TLS data processing were converted to AGB using WD. Four AEs were calibrated using different predictors, and all presented strong performance metrics (e.g., R² ranging from 90 to 93%), low relative bias and relative individual mean error (11.73 to 16.34%). Multivariate analyses performed on plot floristic and structural data showed a strong contrast in terms of composition and structure between sites and between plots within sites. Even though the whole variability of the biome has not been sampled, we were thus able to confirm the transposability of results within the wet Miombo forests through two cross-validation approaches. The AGB predictions obtained with our best AE were also compared with AEs found in the literature. Overall, an underestimation of tree AGB varying from −35.04 to −19.97% was observed when AEs from the literature were used for predicting AGB in the Miombo of Katanga. Full article

This article investigates the political cost of poverty in democracies marked by deep social divisions. We develop a probabilistic voting model that incorporates clientelism as a strategic tool employed by elite political parties to secure electoral support from non-elite voters. Unlike models based on ideological proximity, our framework conceptualizes party competition as structured by the socioeconomic composition of their constituencies. We demonstrate that in contexts of high inequality and widespread poverty, elite parties face structural incentives to deploy clientelistic strategies rather than universalistic policy agendas. Our model predicts that clientelistic expenditures by elite parties increase proportionally with both inequality (GINI index) and poverty levels, rendering clientelism a rational and cost-effective mechanism of political control. Empirical evidence from a cross-national panel (2013–2019) confirms the theoretical predictions: an increase of the 1 percent in the GINI index increase a 1.3 percent in the clientelism, even after accounting for endogeneity and dynamic effects. These findings suggest that in divided democracies, poverty is not merely a condition to be alleviated, but a political resource that elites strategically exploit. Consequently, clientelism persists not as a cultural residue or institutional failure, but as a rational response to inequality-driven constraints within democratic competition. Full article

The historical peak in CO₂ emissions has intensified global environmental concerns, urging the identification of key determinants. While economic drivers are well-documented, political dimensions—especially democracy and institutional quality—are increasingly emphasized. However, the role of freedom of association and organization (AOF), a core democratic element, remains largely unexamined in this context. This study fills this gap by analyzing the impact of AOF on CO₂ emissions in the top 20 emitter countries from 2006 to 2022. The selection of these countries enables a focused assessment of the world’s primary polluters, ensuring high policy relevance. Using second-generation panel estimators, the Augmented Mean Group and the Common Correlated Effects Mean Group estimators, the analysis accounts for heterogeneity and cross-sectional dependence. Robustness is tested using the CS-ARDL method, confirming the stability of results. Empirical findings show that higher levels of AOF significantly reduce CO₂ emissions. Income and energy consumption increase emissions, while the effect of trade openness is statistically insignificant. These results suggest that strengthening associational freedoms can offer a dual benefit: advancing democratic norms and achieving environmental goals. Full article

Urban systems are transforming as artificial intelligence (AI) evolves from automation to Agentic Urban AI (AI systems with autonomous goal-setting and decision-making capabilities), which independently define and pursue urban objectives. This shift necessitates reassessing governance, planning, and ethics. Using a conceptual-methodological approach, this study integrates urban studies, AI ethics, and governance theory. Through a literature review and case studies of platforms like Alibaba’s City Brain and CityMind AI Agent, it identifies early agency indicators, such as strategic adaptation and goal re-prioritisation. A typology distinguishing automation, autonomy, and agency clarifies AI-driven urban decision-making. Three trajectories are proposed: fully autonomous Agentic AI, collaborative Hybrid Urban Agency, and constrained Non-Agentic AI to mitigate ethical risks. The findings highlight the need for participatory, transparent governance to ensure democratic accountability and social equity in cognitive urban ecosystems. Full article

Mpox, caused by the Mpox virus (MPXV), is a re-emerging zoonotic disease in the Poxviridae family. Since 2022, sub-Saharan Africa has experienced recurrent outbreaks, with the Democratic Republic of the Congo (DRC) accounting for 96% of the 567 confirmed cases reported in the African region by the World Health Organization as of June 2024. Despite MPXV’s endemic presence, its genomic diversity and evolutionary dynamics remain poorly characterized. We analyzed 270 MPXV genomes from 13 sub-Saharan African countries (2022–2024), representing the most geographically comprehensive regional dataset from the outbreak period. Phylogenetic analysis identified two geographically distinct clades: Clade I (East/Central Africa) and Clade II (West/Southern Africa). A marked disparity in APOBEC3-associated mutations was observed, with Clade IIb exhibiting significantly higher enrichment than Clade I, suggesting clade-specific host adaptation pressures. These mutations predominantly target genes involved in immune evasion and replication. Preliminary functional predictions indicated that selected missense mutations may impact on protein stability, underscoring the need for further experimental validation. Our findings provide the first pan–sub-Saharan analysis of MPXV clade divergence and reinforce the importance of sustained, regionally informed genomic surveillance to monitor viral evolution and guide outbreak response strategies across Africa. Full article

Social media refers to digital platforms that enable users to create, share, and engage with content within virtual communities. Platforms like Facebook, X, Instagram, and TikTok have democratized content creation, allowing individuals to share ideas, opinions, and experiences with global audiences. Social media has revolutionized the way information is shared and consumed, offering unprecedented opportunities for learning, engagement, and democratic participation. However, this accessibility comes with significant ethical challenges, particularly centered around the paradox of freedom versus harm—the tension between upholding freedom of expression and mitigating the harms of misinformation, privacy violations, and AI-driven bias. This entry explores the dilemmas and opportunities associated with social media, examining how these platforms shape public discourse, influence consumer behavior, and challenge traditional notions of truth and accountability. It aims to provide policymakers, educators, and platform designers with actionable insights to foster ethical social media environments. Full article

This study applies the technology hype cycle model to explore the interplay between political communication and digital technology from the mid-1990s to the present. It identifies four stages that reflect the hype cycle’s trajectory: initial optimism about the Internet’s democratic potential, subsequent disillusionment as limitations surfaced, renewed enthusiasm with the emergence of social media, and a current measured understanding of associated risks. The evolution of the digital political landscape is elucidated by synthesizing scholarly literature and documented historical accounts to trace key innovations and their impact on integrating the Internet and social media into political communication strategies. The findings highlight the critical need for enhanced media literacy and careful evaluation of technologies to uphold democratic ideals. This research contributes to the literature by providing a comprehensive perspective on the dynamic relationships between politics, communication, and emerging digital platforms, offering valuable insights for both practitioners and scholars. Full article

In this study, we develop a fractional-order mathematical model for investigating the transmission dynamics of monkeypox (Mpox), accounting for interactions between humans, rodents, and environmental reservoirs. The model uniquely integrates two key control strategies—public health awareness and environmental sanitation—often overlooked in previous models. We analyze the model’s well-posedness by establishing the existence, uniqueness, and positivity of solutions using the fixed-point theorem. Using data from the Democratic Republic of Congo, we estimate the model parameters and demonstrate that the fractional-order model ($\varphi =0.5$) fits real-world data more accurately than its integer-order counterpart ($\varphi =1$). The sensitivity analysis using partial rank correlation coefficients highlights the key drivers of disease spread. Numerical simulations reveal that the memory effects inherent in fractional derivatives significantly influence the epidemic’s trajectory. Importantly, our results show that increasing awareness ($ϵ$) and sanitation efforts ($\eta$) can substantially reduce transmission, with sustained suppression of Mpox when both parameters exceed $90\%$. These findings highlight the synergistic impact of behavioral and environmental interventions in controlling emerging zoonotic diseases. Full article

Electoral fraud, particularly multiple voting, undermines the integrity of democratic processes. To address this challenge, this study introduces an innovative facial recognition system that integrates an enhanced 50-layer Residual Network (ResNet50) architecture with Additive Angular Margin Loss (ArcFace) and Multi-Task Cascaded Convolutional Neural Networks (MTCNN) for face detection. Using the Mahalanobis distance, the system verifies voter identities by comparing captured facial images with previously recorded biometric features. Extensive evaluations demonstrate the methodology’s effectiveness, achieving a facial recognition accuracy of 99.85%. This significant improvement over existing baseline methods has the potential to enhance electoral transparency and prevent multiple voting. The findings contribute to developing robust biometric-based electoral systems, thereby promoting democratic trust and accountability. Full article

‘Transitional justice’ refers to the full range of efforts to deal with past human rights violations. Since its inception, the concept has undergone an immense expansion of interpretation as it has been applied to a wider range of scenarios in the field. This paper examines the dilemmas of transitional justice as a concept, showing how it has evolved over time and adapted to new contexts. After a brief conceptual history, the paper analyzes the two components of the concept, ‘justice’ and ‘transition’, followed by an examination of the contemporary challenges facing the concept. We argue that the evolving and multifaceted nature of transitional justice defies a singular definition, as neither the term ‘justice’ nor ‘transition’ fully encapsulates its scope. Although conceptual ambiguities persist and debates over its boundaries continue, the conclusion argues that the term remains analytically and normatively valuable and warrants further engagement rather than abandonment. Full article

The rapid advancement of artificial intelligence (AI) has introduced unprecedented opportunities and challenges, necessitating a robust ethical and regulatory framework to guide its development. This study reviews key ethical concerns such as algorithmic bias, transparency, accountability, and the tension between automation and human oversight. It discusses the concept of algor-ethics—a framework for embedding ethical considerations throughout the AI lifecycle—as an antidote to algocracy, where power is concentrated in those who control data and algorithms. The study also examines AI’s transformative potential in diverse sectors, including healthcare, Insurtech, environmental sustainability, and space exploration, underscoring the need for ethical alignment. Ultimately, it advocates for a global, transdisciplinary approach to AI governance that integrates legal, ethical, and technical perspectives, ensuring AI serves humanity while upholding democratic values and social justice. In the second part of the paper, the author offers a synoptic view of AI governance across six major jurisdictions—the United States, China, the European Union, Japan, Canada, and Brazil—highlighting their distinct regulatory approaches. While the EU’s AI Act as well as Japan’s and Canada’s frameworks prioritize fundamental rights and risk-based regulation, the US’s strategy leans towards fostering innovation with executive directives and sector-specific oversight. In contrast, China’s framework integrates AI governance with state-driven ideological imperatives, enforcing compliance with socialist core values, whereas Brazil’s framework is still lacking the institutional depth of the more mature ones mentioned above, despite its commitment to fairness and democratic oversight. Eventually, strategic and governance considerations that should help chief data/AI officers and AI managers are provided in order to successfully leverage the transformative potential of AI for value creation purposes, also in view of the emerging international standards in terms of AI. Full article

The acceleration of the climate crisis calls into question the effectiveness of traditional legal frameworks in addressing environmental emergencies. This article examines whether France should adopt an environmental state of exception, inspired by the legal mechanisms implemented during the COVID-19 pandemic. While such exceptional measures could enhance the State’s capacity to respond swiftly to climate threats, they also raise concerns about the concentration of executive power and potential infringements on fundamental rights. Through a comparative legal analysis, this study assesses the benefits and risks of an environmental state of exception, highlighting its potential to accelerate climate action while scrutinizing its democratic and legal implications. It also explores alternative frameworks and advocates for a regulated model of environmental emergency governance, ensuring that urgent climate interventions remain subject to the rule of law and democratic oversight. By drawing on French legal precedents, international climate commitments, and case studies, this research evaluates whether legal exceptionalism is a viable tool for addressing environmental crises or whether a more structured and accountable legal approach would better ensure long-term climate resilience. Full article

This study aimed to evaluate the role of trees outside forests on agricultural land (TOF-AL) in preserving the initial aboveground biomass (AGB) of forests within the agricultural landscape of Mongala province in the Democratic Republic of Congo. In 2024, tree inventories were conducted over four months in the forests and agricultural lands of Mongala province to analyse AGB. The effects of artisanal logging and charcoal production activities on the AGB conservation rate were considered. This study indicates that 78.3% of the trees encountered in agricultural lands were large-diameter trees (diameter at breast height (DBH) ≥ 60 cm). In forest areas, large-diameter trees accounted for 55.9% of tree density. The average AGBs are 66.8 Mg ha⁻¹ for TOF-AL and 373.5 Mg ha⁻¹ for forest trees. The AGB of TOF-AL accounts for 17.9% of the AGB of the total forest trees. The AGB conservation rates vary by region, with Lisala having the highest at 22.1%, Bumba the lowest at 11.2%, and Bongandanga at 20.5%. Artisanal logging and charcoal production reduce the AGB conservation rate of TOF-AL. The AGB conservation rate is positively correlated with the distances to major cities. These results prove that conserving trees in agricultural landscapes can reduce the AGB losses associated with slash-and-burn agriculture and contribute to mitigating climate change effects. Full article

Technological developments with the rapid and significant advances related to artificial intelligence (AI) have generated a broad debate on political, social, and ethical impacts, raising important questions that require multidisciplinary analysis and investigation. One of the issues under discussion is whether the integration of AI in the political context represents a promising opportunity to improve the efficiency of democratic participation and policy-making processes, as well as increase institutional accountability. The aim of this article is to propose a theoretical reflection that allows us to fully understand the implications and potential consequences of the application of AI in the political field without neglecting its social and ethical effects: can such uses really be considered democratic, or do they represent a dangerous trend of using algorithms for manipulative purposes? To achieve this, a deductive approach will be adopted based on theories, imaginaries, and expectations concerning AI in the specific context of politics. Through this type of analysis, knowledge will contribute to the understanding of the complex dynamics related to the use of AI in politics by offering a critical perspective and a picture of the different connections. Full article

Artificial Intelligence (AI) is reshaping healthcare through advancements in clinical decision support and diagnostic capabilities. While human expertise remains foundational to medical practice, AI-powered tools are increasingly matching or exceeding specialist-level performance across multiple domains, paving the way for a new era of democratized healthcare access. These systems promise to reduce disparities in care delivery across demographic, racial, and socioeconomic boundaries by providing high-quality diagnostic support at scale. As a result, advanced healthcare services can be affordable to all populations, irrespective of demographics, race, or socioeconomic background. The democratization of such AI tools can reduce the cost of care, optimize resource allocation, and improve the quality of care. In contrast to humans, AI can potentially uncover complex relationships in the data from a large set of inputs and generate new evidence-based knowledge in medicine. However, integrating AI into healthcare raises several ethical and philosophical concerns, such as bias, transparency, autonomy, responsibility, and accountability. In this study, we examine recent advances in AI-enabled medical image analysis, current regulatory frameworks, and emerging best practices for clinical integration. We analyze both technical and ethical challenges inherent in deploying AI systems across healthcare institutions, with particular attention to data privacy, algorithmic fairness, and system transparency. Furthermore, we propose practical solutions to address key challenges, including data scarcity, racial bias in training datasets, limited model interpretability, and systematic algorithmic biases. Finally, we outline a conceptual algorithm for responsible AI implementations and identify promising future research and development directions. Full article