Search Results (7,207)

Understanding how energy use and economic activity shape carbon emissions is pivotal for achieving global climate targets. This study quantifies the dynamic nexus between disaggregated energy consumption, economic growth, and CO₂ emissions in India and China—two economies that together account for more than one-third of global emissions. Using annual data from 1990 to 2021, we implement Long Short-Term Memory (LSTM) neural networks, which outperform traditional linear models in capturing nonlinearities and lagged effects. The dataset is split into training (1990–2013) and testing (2014–2021) intervals to ensure rigorous out-of-sample validation. Results reveal stark national differences. For India, coal, natural gas consumption, and economic growth are the strongest positive drivers of emissions, whereas renewable energy exerts a significant mitigating effect, and nuclear energy is negligible. In China, emissions are dominated by coal and petroleum use and by economic growth, while renewable and nuclear sources show weak, inconsistent impacts. We recommend retrofitting India’s coal- and gas-plants with carbon capture and storage, doubling clean-tech subsidies, and tripling annual solar-plus-storage auctions to displace fossil baseload. For China, priorities include ultra-supercritical upgrades with carbon capture, utilisation, and storage, green-bond-financed solar–wind buildouts, grid-scale storage deployments, and hydrogen-electric freight corridors. These data-driven pathways simultaneously cut flagship emitters, decouple GDP from carbon, provide replicable models for global net-zero research, and advance climate-resilient economic growth worldwide. Full article

Kiwifruit (Actinidia spp.) is a globally important economic fruit with high nutritional value. Fruit peelability, defined as the mechanical ease of separating the peel from the fruit flesh, is a critical quality trait influencing consumer experience and market competitiveness and has emerged as a critical breeding target in fruit crop improvement programs. The present review systematically synthesized existing studies on kiwifruit peelability, and focused on its evolutionary trajectory, genotypic divergence, quantitative evaluation, possible underlying mechanisms, and artificial manipulation strategies. Kiwifruit peelability research has advanced from early exploratory studies in New Zealand (2010s) to systematic investigations in China (2020s), with milestones including the development of evaluation metrics and the identification of genetic resources. Genotypic variation exists among kiwifruit genera. Several Actinidia eriantha accessions and the novel Actinidia longicarpa cultivar ‘Guifei’ exhibit superior peelability, whereas most commercial Actinidia chinensis and Actinidia deliciosa cultivars exhibit poor peelability. Quantitative evaluation highlights the need for standardized metrics, with “skin-flesh adhesion force” and “peel toughness” proposed as robust, instrument-quantifiable indicators to minimize operational variability. Mechanistically, peelability is speculated to be governed by cell wall polysaccharide metabolism and phytohormone signaling networks. Pectin degradation and differential distribution during fruit development form critical “peeling zones”, whereas ethylene, abscisic acid, and indoleacetic acid may regulate cell wall remodeling and softening, collectively influencing skin-flesh adhesion. Owing to the scarcity of easy-to-peel kiwifruit cultivars, artificial manipulation methods, including manual peeling benchmarking, lye treatment, and thermal peeling, can be employed to further optimize kiwifruit peelability. Currently, shortcomings include incomplete genotype-phenotype characterization, limited availability of easy-peeling germplasms, and a fragmented understanding of the underlying mechanisms. Future research should focus on methodological innovation, germplasm development, and the elucidation of relevant mechanisms. Full article

This article aims to analyze the role of migration in the process of the third demographic transition (TDT) in the context of key mediating determinants, such as migrants’ religiosity and economic conditions in the countries of origin and settlement. TDT refers to population changes resulting from migration as a demographic compensatory mechanism in countries with a low total fertility rate (TFR). The study is based on a network analysis of keywords in the scientific literature using the Scopus database and VOSviewer. The results point to three main research approaches to TDT—investigating quantitative population changes, the sociodemographic consequences of migration, and its effect on urbanization—and to the fact that economic and axionormative determinants are under-researched. This article contributes to TDT theory, pointing to the need for that theory to include cultural, economic, and axiological factors as key determinants influencing the permanence of TDT. Full article

While existing studies document physical and economic impacts of new-build gentrification, the temporally protracted trauma of indirect displacement in communities adjacent to redeveloped areas remains understudied. Employing constructivist grounded theory, this study asks the following question: how do residents experience place attachment erosion during prolonged policy suspension in Shanghai’s new-build gentrification? Through iterative analysis of 25 interviews, we reveal a temporal vicious cycle of waiting triggered by uneven redevelopment and policy inertia. This cycle systematically dismantles belonging through several mechanisms: (1) chronic place-identity deterioration; (2) progressive social network fragmentation; (3) the collapse of imagined futures; and (4) the ambiguous loss of place attachment—where physical presence coexists with psychological disengagement. Crucially, we redefine indirect displacement as a temporal erosion of place identity and attachment, revealing a paradoxical state of physical presence coexisting with psychological disengagement. This paper provides a new perspective for better understanding the different dimensions of indirect displacement in new-build gentrification, which will help inform equitable development efforts that are more inclusive and just. Full article

In the Italian intra-day electricity market (MI-XBID), where energy positions can be adjusted up to one hour before delivery, imbalance charges due to forecast errors from non-programmable renewable sources represent a critical issue. This work focuses on photovoltaic (PV) systems, whose production variability makes them particularly sensitive to forecast accuracy. To address these challenges, a comprehensive methodology for assessing and mitigating imbalance penalties by integrating a short-term PV forecasting model with a battery energy storage system is proposed. Unlike conventional approaches that focus exclusively on improving statistical accuracy, this study emphasizes the economic and regulatory impact of forecast errors under the current Italian imbalance settlement framework. A hybrid physical-artificial neural network is developed to forecast PV power one hour in advance, combining historical production data and clear-sky irradiance estimates. The resulting imbalances are analyzed using regulatory tolerance thresholds. Simulation results show that, by adopting a control strategy aimed at maintaining the battery’s state of charge around 50%, imbalance penalties can be completely eliminated using a storage system sized for just over 2 equivalent hours of storage capacity. The methodology provides a practical tool for market participants to quantify the benefits of storage integration and can be generalized to other electricity markets where tolerance bands for imbalances are applied. Full article

Road networks are essential for access, resource distribution, and population evacuation during natural events. These challenges are pronounced in rural areas, where network redundancy is limited and communities may have social disparities. While traditional risk management systems often focus on the physical consequences of hazard events alone, specialized literature increasingly suggests the development of a more comprehensive approach for risk assessment, where not only physical aspects associated with infrastructure, such as damage level or disruptions, but also the social and economic attributes of the affected population are considered. Consequently, this paper proposes a Vulnerability Access Index (VAI) to support road network decision-making that integrates the social vulnerability of rural communities exposed to natural events, their accessibility to nearby critical infrastructure, and physical risk. The research methodology considers (i) the Social Vulnerability Index (SVI) calculation based on socioeconomic variables, (ii) Importance Index estimation (I_imp) to evaluate access to critical infrastructure, (iii) VAI calculation combining SVI and I_imp, and (iv) application to a case study in the influence area of the Villarrica volcano in southern Chile. The results show that when incorporating social variables and accessibility, infrastructure criticality varies significantly compared to the infrastructure criticality assessment based solely on physical risk, modifying the decision-making regarding road infrastructure robustness and resilience improvements. Full article

Background/Objectives: The social importance of health literacy (HL) is widely understood, and its measurement is the subject of various studies. Due to the recent pandemic, several instruments for measuring HL about COVID-19 have been proposed in different countries, including the HLS-COVID-Q22 questionnaire. The diversity of cultures and languages necessitates the cross-cultural adaptation of this instrument. Thus, the present study translates, adapts, and validates the psychometric properties of the HLS-COVID-Q22 questionnaire to provide its cross-cultural adaptation from English to Spanish (Peru). Methods: As part of ensuring that the final questionnaire accommodates the cultural nuances and idiosyncrasies of the target language, the following activities were carried out: (a) a survey of 40 respondents; and (b) a focus group with 10 participants, followed by expert approval. In addition, the validity and reliability of the health instrument have been ascertained through a further pilot test administered to 490 people in the city of Tacna in southern Peru. Results: The resulting questionnaire helps measure HL in Peru, aiding better-informed decision-making for individual health choices. Conclusions: The presence of such a tool is advantageous in case of similar global health emergencies, when the questionnaire can be made readily available to support a promotion of strategies towards better self-care. Moreover, it encourages other Latin American stakeholders to adjust the instrument to their own cultural, language, and socio-economic contexts, thus invigorating the regional and global expansion of the HL study network. Full article

Floods are among the most devastating hydrometeorological natural disasters worldwide, causing massive infrastructure and economic loss in low-lying, flood-prone developing countries like Bangladesh. Effective disaster mitigation relies on organized and detailed flood damage information to facilitate emergency evacuation, coordinate relief distribution, and formulate an effective disaster management policy. Nevertheless, the nation confronts considerable obstacles due to insufficient historical flood damage data and the underdevelopment of near-real-time (NRT) flood monitoring systems. This study addresses this issue by developing a replicable methodology for flood damage assessment and NRT monitoring systems. Using the Google Earth Engine (GEE) platform, we analyzed flood events from 2019 to 2023, integrating geospatial layers such as roads, cropland, etc. Analysis of flood events over the five-year period revealed substantial impacts, with 21.60% of the total area experiencing inundation. This flooding affected 6.92% of cropland and 4.16% of the population. Furthermore, 18.10% of the road network, spanning over 21,000 km within the study area, was also affected. This system has the potential to enhance emergency response capabilities during flood events and inform more effective disaster mitigation policies. Full article

To address the limited flexibility of pumped storage power stations (PSPSs) under hierarchical clearing of energy and ancillary service markets, this study proposes a joint clearing mechanism for multi-level electricity markets. A bi-level optimization model based on the Stackelberg game is developed to characterize the strategic interaction between PSPSs and the market operator. Simulation results on the IEEE 30-bus system demonstrate that the proposed mechanism captures the dynamics of nodal supply and demand, as well as time-varying network congestion. It guides PSPSs to operate more flexibly and economically. Additionally, the mechanism increases PSPS profitability, reduces system costs, and improves frequency regulation performance. This game-theoretic framework offers quantitative decision support for PSPS participation in multi-level spot markets and provides insights for optimal storage deployment and market mechanism improvement. Full article

This theoretical essay aims to analyze some of the socio-economic innovations introduced by Platform Capitalism Specifically, it focuses on two main aspects: first, the digital platform as a radical organizational innovation. Digital platforms represent a structural novelty in the market economy, signaling a new organization of production and labor. Second, the essay examines the role of platforms in directly generating value through the concept of “network value”. To this end, it explores the function of “business intelligence” as a strategic and competitive tool. Finally, the paper discusses the key issues associated with platform capitalism, which could threaten its social sustainability and contribute to economic and financial instability. These issues include the increasing commodification of everyday activities, the devaluation of paid labor in favor of free production driven by platform users (the so-called prosumers), and the emergence of proprietary and financial monopolies. Hence, digital platforms do not inherently ensure comprehensive social and environmental sustainability unless supported by targeted economic policy interventions. Conclusively, it is emphasized that defining robust social welfare frameworks—which account for emerging value creation processes—is imperative. Simultaneously, policymakers must incentivize the proliferation of cooperative platforms capable of fostering experimental circular economy models aligned with ecological sustainability. Full article

Saffron (Crocus sativus L.) is a vegetatively propagated crop of high economic and cultural value, potentially affected by viral infections that may impact its productivity. Despite Iran’s dominance in global saffron production, knowledge of its virome remains limited. In this study, we conducted the first nationwide virome survey of saffron in Iran employing a high-throughput sequencing (HTS) approach on pooled samples obtained from eleven provinces in Iran and one location in Afghanistan. Members of three virus families were detected—Potyviridae (Potyvirus), Solemoviridae (Polerovirus), and Geminiviridae (Mastrevirus)—as well as one satellite from the family Alphasatellitidae (Clecrusatellite). A novel Potyvirus, tentatively named saffron Iran virus (SaIRV) and detected in three provinces, shares less than 68% nucleotide identity with known Potyvirus species, thus meeting the ICTV criteria for designation as a new species. Genetic diversity analyses revealed substantial intrapopulation SNP variation but no clear geographical clustering. Among the two wild Crocus species sampled, only Crocus speciosus harbored turnip mosaic virus. Virome network and phylogenetic analyses confirmed widespread viral circulation likely driven by corm-mediated propagation. Our findings highlight the need for targeted certification programs and biological characterization of key viruses to mitigate potential impacts on saffron yield and quality. Full article

Fuel price increases have long been a contentious issue in Indonesia, sparking intense public and political debates. This study examines how digital media, particularly Kompas.com and Tempo.co, shape public discourse on fuel price hikes through mediatization. Using discourse network analysis, this study compares the political narratives surrounding fuel price increases during the administrations of Susilo Bambang Yudhoyono (2013) and Joko Widodo (2022). The findings reveal a shift in dominant discourse—opposition to price hikes was prominent in both periods, with government authority and economic justification emphasized in 2013, whereas concerns over rising living costs and social unrest dominated in 2022. This study highlights how mediatization has transformed policymaking from deliberative discussions into fragmented media battles, where digital platforms amplify competing narratives rather than facilitating consensus. Kompas.com predominantly featured counter-discourses, while Tempo.co exhibited stronger pro-government narratives in 2013. This study suggests that while digital media plays a crucial role in shaping policy perceptions, it does not necessarily translate into policy influence. It contributes to the broader understanding of the media’s role in policy debates. It underscores the need for more strategic government communication to manage public expectations and mitigate political unrest surrounding fuel price adjustments. Full article

With the increasing uncertainty introduced by the large-scale integration of renewable energy sources, traditional power dispatching methods face significant challenges, including severe frequency fluctuations, substantial forecasting deviations, and the difficulty of balancing economic efficiency with system stability. To address these issues, a deep learning-based dispatching framework is proposed, which integrates spatiotemporal feature extraction with a stability-aware mechanism. A joint forecasting model is constructed using Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to handle multi-source inputs, while a reinforcement learning-based stability-aware scheduler is developed to manage dynamic system responses. In addition, an uncertainty modeling mechanism combining Dropout and Bayesian networks is incorporated to enhance dispatch robustness. Experiments conducted on real-world power grid and renewable generation datasets demonstrate that the proposed forecasting module achieves approximately a 2.1% improvement in accuracy compared with Autoformer and reduces Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by 18.1% and 14.1%, respectively, compared with traditional LSTM models. The achieved Mean Absolute Percentage Error (MAPE) of 5.82% outperforms all baseline models. In terms of scheduling performance, the proposed method reduces the total operating cost by 5.8% relative to Autoformer, decreases the frequency deviation from 0.158 Hz to 0.129 Hz, and increases the Critical Clearing Time (CCT) to 2.74 s, significantly enhancing dynamic system stability. Ablation studies reveal that removing the uncertainty modeling module increases the frequency deviation to 0.153 Hz and raises operational costs by approximately 6.9%, confirming the critical role of this module in maintaining robustness. Furthermore, under diverse load profiles and meteorological disturbances, the proposed method maintains stable forecasting accuracy and scheduling policy outputs, demonstrating strong generalization capabilities. Overall, the proposed approach achieves a well-balanced performance in terms of forecasting precision, system stability, and economic efficiency in power grids with high renewable energy penetration, indicating substantial potential for practical deployment and further research. Full article

The advancement of Artificial Intelligence (AI) has significantly accelerated progress across various research domains, with growing interest in plant science due to its substantial economic potential. However, the integration of AI with digital vegetation analysis remains underexplored, largely due to the absence of large-scale, real-world plant datasets, which are crucial for advancing this field. To address this gap, we introduce the PP3D dataset—a meticulously labeled collection of about 500 potted plants represented as 3D point clouds, featuring fine-grained annotations for approximately 20 species. The PP3D dataset provides 3D phenotypic data for about 20 plant species spanning model organisms (e.g., Arabidopsis thaliana), potted plants (e.g., Foliage plants, Flowering plants), and horticultural plants (e.g., Solanum lycopersicum), covering most of the common important plant species. Leveraging this dataset, we propose the panoptic plant recognition task, which combines semantic segmentation (stems and leaves) with leaf instance segmentation. To tackle this challenge, we present SCNet, a novel dual-representation learning network designed specifically for plant point cloud segmentation. SCNet integrates two key branches: a cylindrical feature extraction branch for robust spatial encoding and a sequential slice feature extraction branch for detailed structural analysis. By efficiently propagating features between these representations, SCNet achieves superior flexibility and computational efficiency, establishing a new baseline for panoptic plant recognition and paving the way for future AI-driven research in plant science. Full article

European pig farmers are challenged by increasingly stringent EU regulations to protect the environment from pollution, to meet animal welfare standards and to make pig farming more sustainable. Economic sustainability is defined as the ability to achieve higher profits by respecting social and natural resources. This study is focused on the analysis of the economic resilience of intensive and extensive farming systems, based on data collected from 56 farms located in Denmark, Poland, Italy and Spain. Productive and economic performances of these farms are analyzed, and economic resilience is assessed through a survey including a selection of indicators, belonging to different themes: [i] resilience of resources, [ii] entrepreneurship, [iii] propensity to extensification. The qualitative data from the questionnaire allow for an exploration of how production systems relate to the three dimensions of resilience. Different levels of resilience were found and discussed for intensive and extensive farms. The findings suggest that intensive farms benefit from high standards and greater bargaining power within the supply chain. Extensive systems can achieve profitability through value-added strategies and generally display good resilience. Policies that support investment and risk reduction are essential for enhancing farm resilience and robustness, while strengthening farmer networks can improve adaptability. Full article