Urban land use and building intensity are often planned without a direct, auditable link to network accessibility, limiting ex-ante policy evaluation. This study asks whether multi-radius street centralities can be elevated from diagnosis to design lever to allocate land use and floor area in a transparent, optimization-ready workflow. We introduce a three-stage pipeline that connects configuration to program and intensity. First, multi-radius accessibility is computed on the street network and translated to blocks to provide scale-legible measures of reach. Second, these measures structure nested service basins that guide a rule-based placement of land uses with explicit priorities and minimum parcel footprints, ensuring reproducibility. Third, within each use, floor-area ratio (FAR) is assigned by an accessibility-weighted linear model that satisfies global construction totals while anchoring the average FAR, thereby tilting height toward better-connected blocks without pathological extremes. The framework supports multi-objective policy search via sampling and Pareto screening. Applied to a real urban district, the workflow reproduces corridor-biased commercial siting and industrial belts while concentrating intensity on highly connected blocks. Policy sampling via multi-objective screening yields Pareto-efficient plans that reconcile accessibility gains with deviations from target land-share and construction-share structures. The contribution is twofold: methodologically, it translates familiar space-syntax measures into cluster-aware, rule-governed land-use and FAR assignment with explicit guarantees (scale-legible radii, parcel minima, and an average-FAR anchor). Practically, it offers planners a transparent instrument for counterfactual testing and negotiated trade-offs at neighborhood/district/city scales.
Maintaining stability in rural labor markets and enhancing labor employment stickiness (RLFS) are essential for alleviating the persistent outflow of rural labor. Based on data from the 2014–2022 China Family Panel Studies and Treating whether digital rural development plans were issued as a quasi-natural experiment, we employ a staggered difference-in-differences model to evaluate the impact of digital rural development policy implementation on RLFS. Meanwhile, we also explore the potential mechanisms through which the policy affects RLFS by combining the analysis with Order Logit model. The results show that the implementation of the digital rural development policy significantly increases RLFS, and these findings remain robust after a series of checks. Mechanism analysis indicates that the policy improves RLFS by strengthening rural workers’ embeddedness in local social networks, enhancing digital literacy and physical health, reducing speculative motives, and expanding local labor demand. Heterogeneity analyses reveal that the policy has stronger positive effects on RLFS among younger and middle-aged individuals, those with lower levels of human capital, and those engaged in agricultural work and that it is more effective in regions with diminishing demographic dividends and weaker land resource endowments. Further analysis suggests that although the policy increases the employment stickiness of younger rural workers and agricultural laborers, it does not improve the efficiency of rural land use. Therefore, the government should continue expanding the coverage of the digital rural development policy to fully leverage its positive effects on rural labor markets while also adjusting existing policy instruments to identify the key channels through which digital technologies can enhance land use efficiency.
Platform laborers play an indispensable yet hidden role in building and sustaining AI systems. Drawing on an eight-month ethnography of Bangladesh's platform labor industry and inspired by Gray and Suri, we conceptualize Ghostcrafting AI to describe how workers materially enable AI while remaining invisible or erased from recognition. Workers pursue platform labor as a path to prestige and mobility but sustain themselves through resourceful, situated learning - renting cyber-cafe computers, copying gig templates, following tutorials in unfamiliar languages, and relying on peer networks. At the same time, they face exploitative wages, unreliable payments, biased algorithms, and governance structures that make their labor precarious and invisible. To cope, they develop tactical repertoires such as identity masking, bypassing platform fees, and pirated tools. These practices reveal both AI's dependency on ghostcrafted labor and the urgent need for design, policy, and governance interventions that ensure fairness, recognition, and sustainability in platform futures.
The global decline in the labor income share has challenged the classical Kaldor facts; however, the macroeconomic aggregation mechanism -- namely, how aggregate factor shares emerge from firm-level heterogeneity -- remains underexplored. This paper bridges this gap by constructing a theoretical framework that links firm size distribution to aggregate factor shares. We extend Houthakker's aggregation theory and formalize the \textit{weighting effect}: when large firms are systematically more capital-intensive than small firms, a shift in market structure toward larger firms mechanically reduces the aggregate labor share. Using comprehensive firm-level data from Chinese manufacturing (2001--2015), we empirically validate this mechanism. First, we estimate production function parameters and confirm that capital elasticity significantly exceeds labor elasticity, implying a negative relationship between firm size and labor share. Second, we find that the negative effect of firm size on labor share is significant only in industries with high technological heterogeneity. Counterfactual decomposition reveals that the shift in the size distribution toward ``superstar firms'' during 2001--2015 constitutes the primary driver of the labor share decline. Our findings provide a technical micro-foundation for the ``superstar firm'' hypothesis and highlight the distributional consequences of ``winner-take-all'' market structures.
Upward emission of artificial light has been investigated by researchers since the commissioning of the Visible/Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) in 2011, with applications ranging from night time light mapping to quantifying socio-economical development. The wide swath of the VIIRS-DNB sensor enables detection of artificial light at multiple angles and was utilized to study emission of artificial light from cities at different angles as well as atmospheric properties. Existing studies of the relationship between the directionality and land surface features are not available for most of the Earth's surface due to the use of space-borne LiDAR as a source of proxy. To solve this problem, we compared the land use data published under the Coordination of Information on the Environment (CORINE) against the fit parameters of radiance of upward artificial light. In general, the quadratic term of the fit, which quantifies how the brightness changes when viewing closer from the horizon at a point on the Earth, is negative when the area is "Continuous urban fabric" or "Sparsely vegetated areas", and vice versa for all other investigated land use classes. However the quadratic term shifts towards negative values for brighter areas. These results indicate that while densely built areas emit more light towards the zenith than sideways, the VIIRS-DNB is unable to distinguish small densely built areas scattered around larger unbuilt areas. Therefore, sensors with higher spatial resolution will be required to resolve the light emission patterns of areas with complicated combinations of land uses.
Land Use and Land Cover (LULC) mapping is a fundamental task in Earth Observation (EO). However, current LULC models are typically developed for a specific modality and a fixed class taxonomy, limiting their generability and broader applicability. Recent advances in foundation models (FMs) offer promising opportunities for building universal models. Yet, task-agnostic FMs often require fine-tuning for downstream applications, whereas task-specific FMs rely on massive amounts of labeled data for training, which is costly and impractical in the remote sensing (RS) domain. To address these challenges, we propose LandSegmenter, an LULC FM framework that resolves three-stage challenges at the input, model, and output levels. From the input side, to alleviate the heavy demand on labeled data for FM training, we introduce LAnd Segment (LAS), a large-scale, multi-modal, multi-source dataset built primarily with globally sampled weak labels from existing LULC products. LAS provides a scalable, cost-effective alternative to manual annotation, enabling large-scale FM training across diverse LULC domains. For model architecture, LandSegmenter integrates an RS-specific adapter for cross-modal feature extraction and a text encoder for semantic awareness enhancement. At the output stage, we introduce a class-wise confidence-guided fusion strategy to mitigate semantic omissions and further improve LandSegmenter's zero-shot performance. We evaluate LandSegmenter on six precisely annotated LULC datasets spanning diverse modalities and class taxonomies. Extensive transfer learning and zero-shot experiments demonstrate that LandSegmenter achieves competitive or superior performance, particularly in zero-shot settings when transferred to unseen datasets. These results highlight the efficacy of our proposed framework and the utility of weak supervision for building task-specific FMs.
Giulio Attenni, Youssef Moawad, Novella Bartolini
et al.
In this paper, we investigate the potential of spatial and temporal cloud workload shifting to reduce carbon, water, and land use footprints. Specifically, we perform a simulation study leveraging publicly available data on the cloud infrastructure of major providers (AWS and Azure) as well as real-world workload traces (big data analytics and FaaS) and grid mix data to consider two different scenarios. Our simulation results indicate that spatial shifting can substantially lower carbon, water, and land use footprints. In the FaaS applications, shifting the spatiotemporal workload achieves carbon savings of up to 85%, water savings of around 50%, and reductions in land use of up to 45%, all while optimizing for the respective factors. Mixed optimization yields results comparable to those of land use alone. For big data workloads, spatiotemporal shifting delivers reductions of up to 45% in carbon emissions, 40% in water consumption, and nearly 40% in land use when optimized for the respective factors. Temporal shifting also decreases the footprint, though to a lesser extent. When applied together, the two strategies yield the greatest overall reduction, driven mainly by spatial shifting with temporal adjustments providing an additional, incremental benefit. Sensitivity analysis demonstrates that such shifting is robust to prediction errors in grid mix data and to variations across different seasons.
Artificial Intelligence's (AI) rapid development and growth not only transformed industries but also fired up important debates about its impacts on employment, resource allocation, and the ethics involved in decision-making. It serves to understand how changes within an industry will be able to influence society with that change. Advancing AI technologies will create a dual paradox of efficiency, greater resource consumption, and displacement of traditional labor. In this context, we explore the impact of AI on energy consumption, human labor roles, and hybrid roles widespread human labor replacement. We used mixed methods involving qualitative and quantitative analyses of data identified from various sources. Findings suggest that AI increases energy consumption and has impacted human labor roles to a minimal extent, considering that its applicability is limited to some tasks that require human judgment. In this context, the
Maria Shams Khakwani, Anam Zafar, Gohar Mahmood
et al.
Purpose: The objective of this research is to determine the impact of green manufacturing practices and digital transformation on firm performance, with a particular focus on the mediating role of green product innovation.
Design/Methodology/Approach: The study conducted a survey of 212 employees from various businesses using a questionnaire-based method. The data is analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) to examine the connections between digital transformation, green manufacturing practices, and company performance. The study also investigated the intermediary function of green product innovation in these connections and offered valuable insights into how sustainable practices might enhance corporate performance.
Findings: This research indicates that green product innovation plays a vital role in connecting these practices to improved company success. The results suggests that organisations that have used digital transformation technologies are more inclined to foster innovation in environmental friendly goods, so enhancing sustainability and subsequently improving performance.
Implications/Originality/Value: The present research contributes to the existing understanding of the impact of digital transformation and green manufacturing techniques on enhancing company performance with a specific emphasis on sustainability. The importance of digital transformation and environmentally-conscious manufacturing methods in improving the sustainability of enterprises is emphasised.
Freydoun Sabziani, AbdulKhalegh Gholami Chenarestan Olia, Mohammad Tamimi
This research has been done with the aim of providing a sustainable development model for the Saderat Bank of Iran based on professional ethics. The current research is of the type of mixed exploratory research projects; based on this, first, using the qualitative approach, the dimensions, components, and indicators of the factors involved in the model of sustainable development based on professional ethics in the field of human resources management have been identified, and the initial research model has been designed. Next, based on the information obtained from the previous step, the model was validated (quantitative approach). The participants in the qualitative section included experts, managers, and senior experts in the development of sustainability and professional ethics in the Iran Export Bank network. The analysis of the data obtained from each in-depth interview continued using cluster and stratified random sampling and continued until the level of theoretical saturation and data sufficiency. In this way, a sample of 17 experts was invited for an interview. The population studied in the quantitative research, that is, to measure and confirm the fit of the model, their managers and deputies, and their senior experts who have years of experience, knowledge, and skills in various matters in the field of banking services, from which a sample of 400 was selected, Data collection has been done in two qualitative and quantitative parts, respectively, using semi-structured interviews and questionnaires. Data analysis has been done in the qualitative part with thematic analysis methods and MAXQUDA software, and in the quantitative part with interpretive structural modeling methods, partial least squares, and Smart PLS software. Factors including economic factors, social factors, environmental factors, strategic factors, professional ethics factors, sustainability disclosure, green human resource management, supply chain management, sustainable value creation, and psychological factors as the main themes of the sustainability development model with a professional ethics approach in the bank Exports were identified. The findings of the quantitative section, while confirming the research hypotheses, showed that the proposed model has good validity.
Daiany Alvez Araujo Moreira, Cristiane Maria Tonetto Godoy, Monica Aparecida da Rocha Silva
et al.
Diante da crise provocada pela pandemia da Covid-19 a sociedade teve que se modificar, alterando as relações pessoais, prestações de serviços, atendimento, inclusive o modelo de educação. Deste modo, no Brasil as instituições educacionais de ensino superior adotaram o ensino remoto emergencial, mediado pelas tecnologias digitais para a continuidade do processo de ensino e aprendizagem durante o período pandêmico. No entanto, o país apresenta uma enorme desigualdade em relação ao acesso à internet e as Tecnologias de Informação e Comunicação (TIC), o que ficou perceptível durante a pandemia, já que os níveis de exclusão digital aumentarem ainda mais. Nesse sentindo, a presente pesquisa teve como principal objetivo apresentar e analisar quais foram as políticas públicas existentes de inclusão digital para a educação com o foco no ensino superior perante contexto pandêmico vivido a partir do ano de 2020. Trata-se de uma pesquisa documental baseada nos documentos e dados obtidos em trabalhos científicos que foram publicados sobre a temática, bem como o acesso aos sites dos órgãos governamentais e organizações que são responsáveis por essas informações. Diante dos dados levantados, pode se concluir que existe certa deficiência de políticas públicas de inclusão digital voltadas para a educação, principalmente as direcionadas para as instituições de ensino superior, o que ficou evidente durante o período de pandemia. Como consequência dessa deficiência grande parte das instituições tiveram que suspender as aulas devido à falta de infraestrutura e recursos digitais, o pode contribuir no aumento das desigualdades socioeconômicas.
Jann Weinand, Tristan Pelser, Max Kleinebrahm
et al.
Land use is a critical factor in the siting of renewable energy facilities and is often scrutinized due to perceived conflicts with other land demands. Meanwhile, substantial areas are devoted to activities such as golf, which are accessible to only a select few and have a significant land and environmental footprint. Our study shows that in countries such as the United States and the United Kingdom, far more land is allocated to golf courses than to renewable energy facilities. Areas equivalent to those currently used for golf could support the installation of up to 842 GW of solar and 659 GW of wind capacity in the top ten countries with the most golf courses. In many of these countries, this potential exceeds both current installed capacity and medium-term projections. These findings underscore the untapped potential of rethinking land use priorities to accelerate the transition to renewable energy.
Abstract Understanding the impact of rural aging on agricultural production is critical for implementing a comprehensive rural vitalization strategy in China. In this paper, the planting structure is converted into and measured by the amount of labor employing quantity in the planting industry with the 2000 and 2010 census data and crop planting area data at the county level. By establishing the elasticity coefficient of rural aging - labor quantity and taking the coupling relation between the rural aging and the change in crop production quantity in the Huang-Huai-Hai region into account, the spatio-temporal pattern of rural aging and the recessive transition of land use is analyzed. The results show that the aging level of the rural population in the Huang-Huai-Hai region has maintained an overall growth rate of 27.03% from 2000 to 2010. During the research period, the labor employing quantity in the planting industry decreased by 14.18% and, was mainly distributed among the western Shandong hilly region, the Taihang and Yanshan mountainous region, and the eastern part of the Huang-Huai-Hai Plain agricultural region. The spatial coupling relationship between the rural aging and the changes in labor employing quantity in the planting industry presented diversified patterns. From 2000–2010, the number of counties with three different coupling modes, namely, rural aging increasing and labor employing quantity decreasing, increasing of rural aging and labor employing quantity, and decreasing of rural aging and labor employing quantity, account for 84.90%, 11.11%, and 3.7%, respectively. To achieve the goal of agricultural development, the authors argue that in the dual context of rural aging and population mobilization, agricultural development must cultivate new professional farmers and improve the level of agricultural modernization in China.
Objetivo: Propuesta de acciones para potenciar la participación obrera en la Empresa Pecuaria Venegas, municipio Yaguajay, provincia Sancti Spíritus, Cuba.
Métodos: Entrevista semiestructurada a diferentes actores dentro de la vida de la organización, el análisis de documentos generados por la entidad y la observación científica.
Resultados: La propuesta de un grupo de acciones para incentivar la participación obrera y el despliegue de potencialidades, que pueden contribuir al fortalecimiento integral de esa organización.
Conclusiones: Los problemas que enfrenta esa entidad para desplegar la potencialidad de la participación de los trabajadores están determinados, en lo fundamental, por factores subjetivos, relacionados con limitaciones internas que pueden ser superadas. Se determinó que en la referida entidad existen condiciones favorables para incrementar la participación del personal.
Hazhir Aliahmadi, Maeve Beckett, Sam Connolly
et al.
Land-use decision-making processes have a long history of producing globally pervasive systemic equity and sustainability concerns. Quantitative, optimization-based planning approaches, e.g. Multi-Objective Land Allocation (MOLA), seemingly open the possibility to improve objectivity and transparency by explicitly evaluating planning priorities by the type, amount, and location of land uses. Here, we show that optimization-based planning approaches with generic planning criteria generate a series of unstable "flashpoints" whereby tiny changes in planning priorities produce large-scale changes in the amount of land use by type. We give quantitative arguments that the flashpoints we uncover in MOLA models are examples of a more general family of instabilities that occur whenever planning accounts for factors that coordinate use on- and between-sites, regardless of whether these planning factors are formulated explicitly or implicitly. We show that instabilities lead to regions of ambiguity in land-use type that we term "gray areas". By directly mapping gray areas between flashpoints, we show that quantitative methods retain utility by reducing combinatorially large spaces of possible land-use patterns to a small, characteristic set that can engage stakeholders to arrive at more efficient and just outcomes.
Historical trends suggest the decline in importance of land as a production factor but its continued importance as a store of value. Using an overlapping generations model with land and aggregate uncertainty, we theoretically study the long-run behavior of land prices and identify economic conditions under which land becomes overvalued on the long-run trend relative to the fundamentals defined by the present value of land rents. Unbalanced growth together with the elasticity of substitution between production factors plays a critical role. Around the trend, land prices exhibit recurrent stochastic fluctuations, with expansions and contractions in the size of land overvaluation.
Convolutional Neural Networks (CNNs) are models that are utilized extensively for the hierarchical extraction of features. Vision transformers (ViTs), through the use of a self-attention mechanism, have recently achieved superior modeling of global contextual information compared to CNNs. However, to realize their image classification strength, ViTs require substantial training datasets. Where the available training data are limited, current advanced multi-layer perceptrons (MLPs) can provide viable alternatives to both deep CNNs and ViTs. In this paper, we developed the SGU-MLP, a learning algorithm that effectively uses both MLPs and spatial gating units (SGUs) for precise land use land cover (LULC) mapping. Results illustrated the superiority of the developed SGU-MLP classification algorithm over several CNN and CNN-ViT-based models, including HybridSN, ResNet, iFormer, EfficientFormer and CoAtNet. The proposed SGU-MLP algorithm was tested through three experiments in Houston, USA, Berlin, Germany and Augsburg, Germany. The SGU-MLP classification model was found to consistently outperform the benchmark CNN and CNN-ViT-based algorithms. For example, for the Houston experiment, SGU-MLP significantly outperformed HybridSN, CoAtNet, Efficientformer, iFormer and ResNet by approximately 15%, 19%, 20%, 21%, and 25%, respectively, in terms of average accuracy. The code will be made publicly available at https://github.com/aj1365/SGUMLP
In this study, the evolutionary development of labor has been tried to be revealed based on theoretical analysis. Using the example of gdp, which is an indicator of social welfare, the economic value of the labor of housewives was tried to be measured with an empirical modeling. To this end; first of all, the concept of labor was questioned in orthodox (mainstream) economic theories; then, by abstracting from the labor-employment relationship, it was examined what effect the labor of unpaid housewives who are unemployed in the capitalist system could have on gdp. In theoretical analysis; It has been determined that the changing human profile moves away from rationality and creates limited rationality and, accordingly, a heterogeneous individual profile. Women were defined as the new example of heterogeneous individuals, as those who best fit the definition of limited rational individuals because they prefer to be housewives. In the empirical analysis of the study, housewife labor was taken into account as the main variable. In the empirical analysis of the study; In the case of Turkiye, using turkstat employment data and the atkinson inequality scale; the impact of housewife labor on gdp was calculated. The results of the theoretical and empirical analysis were evaluated in the context of labor-employment independence.
In the context of China’s agricultural labor shortage and the pressure of aging, this paper uses the land fragmentation index and the intermediary efficiency model to measure the degree of land fragmentation based on farmer-level data from the main garlic producing area in Lanling County in Shandong Province in 2020. The direct effect of labor structure on land-use efficiency and the mediating effect through land fragmentation are analyzed. The research results show that: (1) the average land-use efficiency of the sample farmers is relatively low; (2) the change in labor structure has an “inverted U”-shaped direct effect on land-use efficiency; and (3) the change in land fragmentation in the labor structure has a direct effect on land-use efficiency. The influence of land-use efficiency played a nonlinear mediating effect. The change in labor structure with the degree of land fragmentation showed an “inverted U”-shaped relationship, and the degree of land fragmentation and land-use efficiency had a “U”-shaped relationship. In order to improve land-use efficiency, two aspects of policy support should be increased: encouraging farmers to integrate land and supporting specialized and diversified planting.