A central socioeconomic concern about Artificial Intelligence is that it will lower wages by depressing the labor share - the fraction of economic output paid to labor. We show that declining labor share is more likely to raise wages. In a competitive economy with constant returns to scale, we prove that the wage-maximizing labor share depends only on the capital-to-labor ratio, implying a non-monotonic relationship between labor share and wages. When labor share exceeds this wage-maximizing level, further automation increases wages even while reducing labor's output share. Using data from the United States and eleven other industrialized countries, we estimate that labor share is too high in all twelve, implying that further automation should raise wages. Moreover, we find that falling labor share accounted for 16\% of U.S. real wage growth between 1954 and 2019. These wage gains notwithstanding, automation-driven shifts in labor share are likely to pose significant social and political challenges.
The HCI community has called for renewed attention to labor issues and the political economy of computing. Yet much work remains in engaging with labor theory to better understand modern work and workers. This article traces the development of Labor Process Theory (LPT) -- from Karl Marx and Harry Braverman to Michael Burawoy and beyond -- and introduces it as an essential yet underutilized resource for structural analysis of work under capitalism and the design of computing systems. We examine HCI literature on labor, investigating focal themes and conceptual, empirical, and design approaches. Drawing from LPT, we offer directions for HCI research and practice: distinguish labor from work, link work practice to value production, study up the management, analyze consent and legitimacy, move beyond the point of production, design alternative institutions, and unnaturalize bourgeois designs. These directions can deepen analyses of tech-mediated workplace regimes, inform critical and normative designs, and strengthen the field's connection to broader political economic critique.
This paper investigates land-use as the cornerstone of spatial planning in rapidly urbanising contexts, focusing on the critical gaps at the mesoscale between centralised vision and local implementation. By exploring Java’s complex desakota landscapes, this study employs an innovative GIS-based land-use cluster analysis using multidimensional parameters—including slope, population density, agricultural land, forest cover, and surface water—to categorise land-use patterns. The resulting mesoscale clusters reveal cohesive functional territories that transcend traditional political boundaries, articulating distinctive ‘mixtures’ of urbanity within Java’s rural-urban continuum. This approach not only captures socio-environmental dynamics across administrative silos but also establishes a new strategic framework for regional planning challenges. By advancing boundary-making beyond mere political convention to reflect on-the-ground ecological and functional coherence, this framework responds to the urgent global challenge of reconciling accelerating suburban and regional development pressures with the preservation of local communities, agricultural systems, and natural landscapes.
Land cover and land use (LULC) changes are key applications of satellite imagery, and they have critical roles in resource management, urbanization, protection of soils and the environment, and enhancing sustainable development. The literature has heavily utilized multispectral spatiotemporal satellite data alongside advanced machine learning algorithms to monitor and predict LULC changes. This study analyzes and compares LULC changes across various governorates (provinces) of the Sultanate of Oman from 2016 to 2021 using annual time steps. For the chosen region, multispectral spatiotemporal data were acquired from the open-source Sentinel-2 satellite dataset. Supervised machine learning algorithms were used to train and classify different land covers, such as water bodies, crops, urban, etc. The constructed model was subsequently applied within the study region, allowing for an effective comparative evaluation of LULC changes within the given timeframe.
Aristides Moustakas, Irene Christoforidi, George Zittis
et al.
To promote climate adaptation and mitigation, it is crucial to understand stakeholder perspectives and knowledge gaps on land use and climate changes. Stakeholders across 21 European islands were consulted on climate and land use change issues affecting ecosystem services. Climate change perceptions included temperature, precipitation, humidity, extremes, and wind. Land use change perceptions included deforestation, coastal degradation, habitat protection, renewable energy facilities, wetlands, and others. Additional concerns such as invasive species, water or energy scarcity, infrastructure problems, and austerity were also considered. Climate and land use change impact perceptions were analysed with machine learning to quantify their influence. The predominant climatic characteristic is temperature, and the predominant land use characteristic is deforestation. Water-related problems are top priorities for stakeholders. Energy-related problems, including energy deficiency and issues with wind and solar facilities, rank high as combined climate and land use risks. Stakeholders generally perceive climate change impacts on ecosystem services as negative, with natural habitat destruction and biodiversity loss identified as top issues. Land use change impacts are also negative but more complex, with more explanatory variables. Stakeholders share common perceptions on biodiversity impacts despite geographic disparity, but they differentiate between climate and land use impacts. Water, energy, and renewable energy issues pose serious concerns, requiring management measures.
Aristides Moustakas, Shiri Zemah-Shamir, Mirela Tase
et al.
Islands are diversity hotspots and vulnerable to environmental degradation, climate variations, land use changes and societal crises. These factors can exhibit interactive impacts on ecosystem services. The study reviewed a large number of papers on the climate change-islands-ecosystem services topic worldwide. Potential inclusion of land use changes and other drivers of impacts on ecosystem services were sequentially also recorded. The study sought to investigate the impacts of climate change, land use change, and other non-climatic driver changes on island ecosystem services. Explanatory variables examined were divided into two categories: environmental variables and methodological ones. Environmental variables include sea zone geographic location, ecosystem, ecosystem services, climate, land use, other driver variables, Methodological variables include consideration of policy interventions, uncertainty assessment, cumulative effects of climate change, synergistic effects of climate change with land use change and other anthropogenic and environmental drivers, and the diversity of variables used in the analysis. Machine learning and statistical methods were used to analyze their effects on island ecosystem services. Negative climate change impacts on ecosystem services are better quantified by land use change or other non-climatic driver variables than by climate variables. The synergy of land use together with climate changes is modulating the impact outcome and critical for a better impact assessment. Analyzed together, there is little evidence of more pronounced for a specific sea zone, ecosystem, or ecosystem service. Climate change impacts may be underestimated due to the use of a single climate variable deployed in most studies. Policy interventions exhibit low classification accuracy in quantifying impacts indicating insufficient efficacy or integration in the studies.
We study the effectiveness of textual information in predicting the returns of crude oil futures and understanding the behavior of market participants. Using a machine learning method to extract oil market sentiment from news articles, we find that the computed sentiment is significantly effective in explaining the crude oil futures returns, while existing textual analyses based on pre-defined dictionaries may mislead the contexts in the oil market. Consistent with previous findings that returns help explain the change in traders’ positions, the sentiment scores based on the machine learning method are also useful in explaining the behavior of different types of traders. Our empirical findings underscore the fact that accurately identifying textual information can increase the accuracy of oil price predictions and explain divergent behaviors of oil traders.
Wadim Strielkowski, Oxana Mukhoryanova, Oxana Kuznetsova
et al.
This paper analyzes sustainable regional economic development and land use employing a case study of Russia. The economics of land management in Russia which is shaped by both historical legacies and contemporary policies represents an interesting conundrum. Following the dissolution of the Soviet Union, Russia embarked on a thorny and complex path towards the economic reforms and transformation characterized, among all, by the privatization and decentralization of land ownership. This transition was aimed at improving agricultural productivity and fostering sustainable regional economic development but also led to new challenges such as uneven distribution of land resources, unclear property rights, and underinvestment in rural infrastructure. However, managing all of that effectively poses significant challenges and opportunities. With the help of the comprehensive bibliographic network analysis, this study sheds some light on the current state of sustainable regional economic development and land use management in Russia. Its results and outcomes might be helpful for the researchers and stakeholders alike in devising effective strategies aimed at maximizing resources for sustainable land use, particularly within their respective regional economies.
Mustafa M. Abd Zaid, Ahmed Abed Mohammed, Putra Sumari
Satellite imagery has dramatically revolutionized the field of geography by giving academics, scientists, and policymakers unprecedented global access to spatial data. Manual methods typically require significant time and effort to detect the generic land structure in satellite images. This study can produce a set of applications such as urban planning and development, environmental monitoring, disaster management, etc. Therefore, the research presents a methodology to minimize human labor, reducing the expenses and duration needed to identify the land structure. This article developed a deep learning-based approach to automate the process of classifying geographical land structures. We used a satellite image dataset acquired from MLRSNet. The study compared the performance of three architectures, namely CNN, ResNet-50, and Inception-v3. We used three optimizers with any model: Adam, SGD, and RMSProp. We conduct the training process for a fixed number of epochs, specifically 100 epochs, with a batch size of 64. The ResNet-50 achieved an accuracy of 76.5% with the ADAM optimizer, the Inception-v3 with RMSProp achieved an accuracy of 93.8%, and the proposed approach, CNN with RMSProp optimizer, achieved the highest level of performance and an accuracy of 94.8%. Moreover, a thorough examination of the CNN model demonstrated its exceptional accuracy, recall, and F1 scores for all categories, confirming its resilience and dependability in precisely detecting various terrain formations. The results highlight the potential of deep learning models in scene understanding, as well as their significance in efficiently identifying and categorizing land structures from satellite imagery.
Mario Figueira, Carmen Guarner, David Conesa
et al.
Changes in land use patterns have significant environmental and socioeconomic impacts, making it crucial for policymakers to understand their causes and consequences. This study, part of the European LAMASUS (Land Management for Sustainability) project, aims to support the EU's climate neutrality target by developing a governance model through collaboration between policymakers, land users, and researchers. We present a methodological synthesis for treating land use data using a Bayesian approach within spatial and spatio-temporal modeling frameworks. The study tackles the challenges of analyzing land use changes, particularly the presence of zero values and computational issues with large datasets. It introduces joint model structures to address zeros and employs sequential inference and consensus methods for Big Data problems. Spatial downscaling models approximate smaller scales from aggregated data, circumventing high-resolution data complications. We explore Beta regression and Compositional Data Analysis (CoDa) for land use data, review relevant spatial and spatio-temporal models, and present strategies for handling zeros. The paper demonstrates the implementation of key models, downscaling techniques, and solutions to Big Data challenges with examples from simulated data and the LAMASUS project, providing a comprehensive framework for understanding and managing land use changes.
Introdução
Este estudo analisou a participação social nas consultas públicas promovidas pela CONITEC (Comissão Nacional de Incorporação de Tecnologias no Sistema Único de Saúde) com o objetivo de promover a equidade no acesso a tecnologias em saúde. A pesquisa, realizada por meio de um formulário eletrônico durante junho e julho de 2023, buscou compreender a percepção da sociedade sobre as consultas públicas da CONITEC e propor melhorias na metodologia de Avaliação de Tecnologias em
Saúde (ATS).
Métodos
Foram coletadas respostas de 650 participantes por meio de um questionário eletrônico. Os dados incluíram informações demográficas, níveis de conhecimento sobre a CONITEC e ATS, percepções sobre o impacto das ATS na qualidade da assistência no SUS, viabilidade da participação pública, confiança nas decisões governamentais e a importância da participação da população na formulação de políticas de saúde.
Resultados
A pesquisa revelou que a maioria dos respondentes estava na faixa etária de 26 a 45 anos, com predomínio de mulheres (69,2%). Cerca de 47,7% dos participantes não trabalhavam na área de saúde, enquanto 52,3% estavam envolvidos de alguma forma, com destaque para aqueles que trabalhavam em estabelecimentos de saúde (26,2%). A maioria dos entrevistados (64,6%) conhecia a CONITEC e seu papel na saúde pública, mas apenas 58,5% entendiam o que era ATS. A maioria acreditava que as ATS impactavam positivamente a qualidade da assistência no SUS (75,5%) e que sua participação nas consultas públicas era viável (80%). No entanto, uma parcela
significativa (63,1%) não tinha confiança de que o governo utilizava as informações coletadas nas consultas para incorporar novas tecnologias. A participação da população nas políticas de saúde foi considerada importante por 76,9% dos entrevistados, embora apenas 60% tivessem participado de consultas públicas online, possivelmente devido à falta de conhecimento (76,9%) e confiança
(15,4%) em sua capacidade de contribuir efetivamente.
Discussão e conclusões
Os resultados destacam a necessidade de melhorias nas consultas públicas da CONITEC para aumentar a confiança da população e garantir que suas opiniões sejam consideradas. Uma sugestão apoiada por 67,7% dos participantes foi a criação de formulários específicos para diferentes doenças ou tecnologias. Essas conclusões ressaltam a importância de reformular a metodologia de ATS e promover uma participação efetiva da sociedade na tomada de decisões em saúde.
Pharmacy and materia medica, Pharmaceutical industry
Caterina Conigliani, Martina Iorio, Salvatore Monni
According to the UN's Sustainable Development Agenda, to effectively achieve sustainable development, strategies for building economic growth should also address social needs, including access to essential services. Sustainable integrated management of water resources for both primary use and energy production is crucial, especially in territories such as the Amazonian State of Pará, where a primary good like fresh water is also the main source of electricity. However, the territorial transformations occurring in Pará over installing new hydroelectric plants have jeopardised local development. This was mainly caused by the top-down approach underlying national strategic projects that have paid little attention to local needs, thus paving the way for detrimental conditions for implementing the UN's 2030 Agenda. This paper aims to analyse the relationship between a municipality's level of development and quality of life and the most relevant key determinants of sustainable development in Pará. To this end, we consider a spatial regression analysis, with particular attention devoted to the role of access to both energy and water. The presence of significant spillover effects implies that providing public services on a geographically broad basis could induce self-reinforcing benefits.
The objective of the paper is to understand if the minimum wage plays a role for the labor share of manufacturing workers in North Macedonia. We decompose labor share movements on those along a share-capital curve, shifts of this locus, and deviations from it. We use the capital-output ratio, total factor productivity and prices of inputs to capture these factors, while the minimum wage is introduced as an element that moves the curve off. We estimate a panel of 20 manufacturing branches over the 2012-2019 period with FE, IV and system-GMM estimators. We find that the role of the minimum wage for the labor share is industry-specific. For industrial branches which are labor-intensive and low-pay, it increases workers' labor share, along a complementarity between capital and labor. For capital-intensive branches, it reduces labor share, likely through the job loss channel and along a substitutability between labor and capital. This applies to both branches where foreign investment and heavy industry are nested.
The paper examines the effects of stringent land use regulations, measured using the Wharton Residential Land Use Regulatory Index (WRLURI), on employment growth during the period 2010-2020 in the Retail, Professional, and Information sectors across 878 local jurisdictions in the United States. All the local jurisdictions exist in both (2006 and 2018) waves of the WRLURI surveys and hence constitute a unique panel data. We apply a mediation analytical framework to decompose the direct and indirect effects of land use regulation stringency on sectoral employment growth and specialization. Our analysis suggests a fully mediated pattern in the relationship between excessive land use regulations and employment growth, with housing cost burden as the mediator. Specifically, a one standard deviation increase in the WRLURI index is associated with an approximate increase of 0.8 percentage point in the proportion of cost burdened renters. Relatedly, higher prevalence of cost-burdened renters has moderate adverse effects on employment growth in two sectors. A one percentage point increase in the proportion of cost burdened renters is associated with 0.04 and 0.017 percentage point decreases in the Professional and Information sectors, respectively.
Mirtha Graciela Villagra-Ferreira, María Lourdes Falcó-de Ayala, Patricia Johanna Cabrera
The objective of this study was to measure the perception of students in the last year of the Economics and Business careers of the Universidad del Norte, about the activities developed as university extension, based on a survey applied randomly to the students of the Central, Itauguá and Caacupé headquarters, during the first semester of the 2022 school year. The work corresponds to an investigation framed in the quantitative paradigm, of a descriptive, non-experimental and cross-sectional type. In this context, it could be verified that only 23% of the students want to do volunteer work, this is due to the fact that 77% of the students of the last year have a work activity and lack time. Regarding the preferences on the activities offered in the
university extension department, they focus on consulting and social service offered to various communities as support for them, followed by participation and support for courses, seminars and congresses, then participation in general cultural activities and, as a last activity, research and scientific publications. Uninorte students from the three campuses stated that they value the activities offered by the institution and consider them an enriching experience that increases the value of the professional profile, which is a way to grow as future professionals and support some communities.
Economic growth, development, planning, Human settlements. Communities
Timely and accurate land use mapping is a long-standing problem, which is critical for effective land and space planning and management. Due to complex and mixed use, it is challenging for accurate land use mapping from widely-used remote sensing images (RSI) directly, especially for high-density cities. To address this issue, in this paper, we propose a coarse-to-fine machine learning-based approach for parcel-level urban land use mapping, integrating multisource geospatial data, including RSI, points-of-interest (POI), and area-of-interest (AOI) data. Specifically, we first divide the city into built-up and non-built-up regions based on parcels generated from road networks. Then, we adopt different classification strategies for parcels in different regions, and finally combine the classified results into an integrated land use map. The results show that the proposed approach can significantly outperform baseline method that mixes built-up and non-built-up regions, with accuracy increase of 25% and 30% for level-1 and level-2 classification, respectively. In addition, we examine the rarely explored AOI data, which can further boost the level-1 and level-2 classification accuracy by 13% and 14%. These results demonstrate the effectiveness of the proposed approach and also indicate the usefulness of AOIs for land use mapping, which are valuable for further studies.
Neha Patankar, Xiili Sarkela-Basset, Greg Schivley
et al.
Land-use conflicts may constrain the unprecedented rates of renewable energy deployment required to meet the decarbonization goals of the Inflation Reduction Act (IRA). This paper employs geospatially resolved data and a detailed electricity system capacity expansion model to generate 160 affordable, zero-carbon electricity supply portfolios for the American west and evaluates the land use impacts of each portfolio. Less than 4% of all sites suitable for solar development and 17% of all wind sites appear in this set of portfolios. Of these sites, 53% of solar and 85% of wind sites exhibit higher development risk and potential for land use conflict. We thus find that clean electricity goals cannot be achieved in an affordable manner without substantial renewable development on sites with potential for land use conflict. However, this paper identifies significant flexibility across western U.S. states to site renewable energy or alter the composition of the electricity supply portfolio to ameliorate potential conflicts.