Hasil untuk "Industries. Land use. Labor"

Ronja Hotz, Calum Brown, Yongchao Zeng et al.

Land management intensity shapes ecosystem service provision, socio-ecological resilience and is central to sustainable transformation. Yet most land use models emphasise economic and biophysical drivers, while socio-psychological factors influencing land managers' decisions remain underrepresented despite increasing evidence that they shape land management choices. To address this gap, we develop a generic behavioural extension for agent-based land use models, guided by the Theory of Planned Behaviour as an overarching conceptual framework. The extension integrates environmental attitudes, descriptive social norms and behavioural inertia into land managers' decisions on land management intensity. To demonstrate applicability, the extension is coupled to an existing land use modelling framework and explored in stylised settings to isolate behavioural mechanisms. Results show that socio-psychological drivers can significantly alter land management intensity shares, landscape configuration, and ecosystem service provision. Nonlinear feedbacks between these drivers, spatial resource heterogeneity, and ecosystem service demand lead to emergent dynamics that are sometimes counter-intuitive and can diverge from the agent-level decision rules. Increasing the influence of social norms generates spatial clustering and higher landscape connectivity, while feedbacks between behavioural factors can lead to path dependence, lock-in effects, and the emergence of multiple stable regimes with sharp transitions. The proposed framework demonstrates how even low levels of behavioural diversity and social interactions can reshape system-level land use outcomes and provides a reusable modelling component for incorporating socio-psychological processes into land use simulations. The approach can be integrated into other agent-based land use models and parameterised empirically in future work.

Xiangdong Xu, Lin Chen, Hewei Meng et al.

To explore the vibration transmission characteristics of jujube mechanical harvesting, and optimize the relationship between vibration input and dynamic response of jujube branches, the vibration characteristics simulation and layered vibration test of jujube branches were carried out. The jujube branch model was established by means of three-dimensional scanning and reverse reconstruction. The natural frequency and suitable vibration parameter range of the jujube branch model were obtained by simulation. Finally, the stratified vibration field experiment of jujube branch was carried out. The results show that there are multi-order natural frequencies of jujube branch in the range of 0–30 Hz. The typical vibration modes include the overall deformation of jujube branch, the deformation of unilateral branch and the deformation of the end of twigs. The resonance frequencies of the measuring points on different branches are mostly close, but the frequencies of the maximum peaks on different paths are different, which is often related to the branch path. The optimal working parameter combination under layered vibration is: the lower layer excitation frequency and amplitude are 5.80 Hz and 7.00 mm, the upper layer excitation frequency and amplitude are 15.60 Hz and 8.50 mm. Under this parameter combination, the acceleration of the measuring point on the fine branch is closest to the separation acceleration. Under this parameter combination, the average harvest rate is 88.74 %. The research can provide reference for the development of forest fruit vibration harvesting machinery.

Lin Che, Yizi Chen, Tanhua Jin et al.

Urban land use classification and mapping are critical for urban planning, resource management, and environmental monitoring. Existing remote sensing techniques often lack precision in complex urban environments due to the absence of ground-level details. Unlike aerial perspectives, street view images provide a ground-level view that captures more human and social activities relevant to land use in complex urban scenes. Existing street view-based methods primarily rely on supervised classification, which is challenged by the scarcity of high-quality labeled data and the difficulty of generalizing across diverse urban landscapes. This study introduces an unsupervised contrastive clustering model for street view images with a built-in geographical prior, to enhance clustering performance. When combined with a simple visual assignment of the clusters, our approach offers a flexible and customizable solution to land use mapping, tailored to the specific needs of urban planners. We experimentally show that our method can generate land use maps from geotagged street view image datasets of two cities. As our methodology relies on the universal spatial coherence of geospatial data ("Tobler's law"), it can be adapted to various settings where street view images are available, to enable scalable, unsupervised land use mapping and updating. The code will be available at https://github.com/lin102/CCGP.

Thomas Lechner, Ben Watson, Uri Wilenski et al.

Cities are important elements of content in digital productions, but their complexity and size make them very challenging to model. Few tools exist that can help artists with this work, even as rapid improvements in graphics hardware create demand for richer content without matching increases in production cost. We propose a method for procedurally generating realistic patterns of land use in cities, automating placement of buildings and roads for artists.

Kuldeep Ray, Jérémie Vigier, Perrine Usé et al.

The writing process of SOT-MRAMs is considered deterministic when additional symmetry-breaking factors, such as the application of an external magnetic field aligned with the current, are present. Notably, the write probability exhibits a unique behavior as a function of the current: it drops to zero at high currents or even oscillates with the current. This phenomenon is attributed to back-switching, an intrinsic effect of magnetization reversal driven by spin-orbit torques. A systematic investigation of this back-switching phenomenon is conducted on sub-100 nm CoFeB magnetic pillars positioned at the center of $β$-W Hall crosses. Using a statistical approach, the study examines the impact of various parameters, including the amplitude of current pulses and the application of magnetic fields in different directions. The findings reveal that the back-switching phenomenon is not statistically random. Macrospin simulations, employing realistic magnetic parameter values, accurately replicate the experimental observations and provide insights into the underlying mechanisms of back-switching. These simulations also explore strategies to mitigate the phenomenon, such as optimizing the shape of the writing pulses. Applying this approach to complete SOT-MRAM single cells achieves a write error rate below $2 \times 10^{-6}$, demonstrating the effectiveness of this strategy in expanding the operational current range for write operations in SOT-MRAMs.

Adam Nelson-Archer, Aleia Sen, Meena Al Hasani et al.

We present a deep learning approach for forecasting short-term employment changes and assessing long-term industry health using labor market data from the U.S. Bureau of Labor Statistics. Our system leverages a Long- and Short-Term Time-series Network (LSTNet) to process multivariate time series data, including employment levels, wages, turnover rates, and job openings. The model outputs both 7-day employment forecasts and an interpretable Industry Employment Health Index (IEHI). Our approach outperforms baseline models across most sectors, particularly in stable industries, and demonstrates strong alignment between IEHI rankings and actual employment volatility. We discuss error patterns, sector-specific performance, and future directions for improving interpretability and generalization.

Ziyi Guo, Yan Wang

Natural disasters such as hurricanes and wildfires increasingly introduce unusual disturbance on economic activities, which are especially likely to reshape commercial land use pattern given their sensitive to customer visitation. However, current modeling approaches are limited in capturing such complex interplay between human activities and commercial land use change under and following disturbances. Such interactions have been more effectively captured in current resilient urban planning theories. This study designs and calibrates a Urban Theory-Informed Spatio-Temporal Attention Model for Predicting Post-Disaster Commercial Land Use Change (Urban-STA4CLC) to predict both the yearly decline and expansion of commercial land use at census block level under cumulative impact of disasters on human activities over two years. Guided by urban theories, Urban-STA4CLC integrates both spatial and temporal attention mechanisms with three theory-informed modules. Resilience theory guides a disaster-aware temporal attention module that captures visitation dynamics. Spatial economic theory informs a multi-relational spatial attention module for inter-block representation. Diffusion theory contributes a regularization term that constrains land use transitions. The model performs significantly better than non-theoretical baselines in predicting commercial land use change under the scenario of recurrent hurricanes, with around 19% improvement in F1 score (0.8763). The effectiveness of the theory-guided modules was further validated through ablation studies. The research demonstrates that embedding urban theory into commercial land use modeling models may substantially enhance the capacity to capture its gains and losses. These advances in commercial land use modeling contribute to land use research that accounts for cumulative impacts of recurrent disasters and shifts in economic activity patterns.

Angela Busheska, Vikram Iyer, Bruno Silva et al.

Understanding land use over time is critical to tracking events related to climate change, like deforestation. However, satellite-based remote sensing tools which are used for monitoring struggle to differentiate vegetation types in farms and orchards from forests. We observe that metrics such as the Normalized Difference Vegetation Index (NDVI), based on plant photosynthesis, have unique temporal signatures that reflect agricultural practices and seasonal cycles. We analyze yearly NDVI changes on 20 farms for 10 unique crops. Initial results show that NDVI curves are coherent with agricultural practices, are unique to each crop, consistent globally, and can differentiate farms from forests. We develop a novel longitudinal NDVI dataset for the state of California from 2020-2023 with 500~m resolution and over 70 million points. We use this to develop the TerraTrace platform, an end-to-end analytic tool that classifies land use using NDVI signatures and allows users to query the system through an LLM chatbot and graphical interface.

Shuting Liu, Junsong Jia, Han Huang et al.

Agricultural land use is an important source of CO2 emissions. Therefore, taking the CO2 emissions of China’s agricultural land use during 1995–2020 as a case, we firstly calculated its composition and analyzed the spatiotemporal evolution characteristics. Then, the Tapio decoupling model and logarithmic mean Divisia index (LMDI) were, respectively, used to identify the decoupling relationship between the CO2 emission change and economic growth, and analyze the driving factors for CO2 emissions. (1) The CO2 emissions of China’s agricultural land use were composed of two main phases (fluctuating growth phase (1995–2015) and rapid decline phase (2016–2020)). The total CO2 emissions exhibited a non-equilibrium spatial distribution. The inter-provincial CO2 emissions differences first expanded and then shrank, but the inter-provincial differences of CO2 emissions intensity continuously decreased. (2) The total CO2 emissions of China’s agricultural land use increased from 50.443 Mt in 1995 to 79.187 Mt in 2020, with an average annual growth rate of 1.82%. Fertilizer, agricultural diesel and agricultural (plastic) film were the main sources of anthropogenic agricultural-land-use CO2 emissions. Controlling the use of fertilizer and agricultural diesel and improving the utilization efficiency of agricultural (plastic) film could be an effective way to reduce CO2 emissions. (3) The Tapio decoupling relationship between the CO2 emission change and economic growth was a weak decoupling state during 1995–2015 and a strong decoupling state during 2016–2020. This result indicates that China’s agricultural land use can be effectively controlled. (4) The agricultural economic level is the decisive factor in promoting CO2 emissions increase, and its cumulative contribution was 476.09%. Inversely, the CO2 emission intensity, agricultural structure and agricultural labor force were three key factors, with cumulative contributions of −189.51%, −16.86% and −169.72%, respectively. Collectively, based on the findings obtained from the present research, we have proposed some suggestions to promote the sustainable use of agriculture lands in China.

Wei Chen, Wenjun Chen, Siyin Ning et al.

Abstract Resource-based cities (RBCs) are cities that have emerged from the utilization of natural resources and are dominated by resource-based industries. Industry is the leading economic activity of resource-based cities. However, industrial land in resource-based cities is faced with the challenges of inefficient use, to which little attention has been paid. Because RBCs are the location of leading economic activity, improving industrial land use efficiency is pivotal in these cities. This study used sub-vector Data Envelope Analysis (DEA) to calculate the industrial land use efficiency (ILUE) of China's 109 resource-based cities from 2006 to 2015. The empirical results proved that there was redundancy of industrial land in most of China's RBCs, but the overall degree of redundancy is decreasing. There were obvious differences in ILUE among different regions, different resource types and different development stages. Factors influencing ILUE were analyzed using the Tobit model. Regional economic development, industrial development and science and technology development had significant positive effects on ILUE, while the labor structure and the ownership structure of enterprises have significant negative effects. The conclusions that are drawn from the results support recommendations to improve ILUE for RBCs. The ILUE should be taken as the guidance for the layout of industrial land use in a RBC and should be incorporated into new industry spatial development strategy planning.

Bo Su, Xiaoxia Shen, Qing Wang et al.

Development zones are crucial spatial carriers driving economic growth and industrial upgrading, playing a key role in China’s development. After years of expansion, these zones face significant challenges in industrial land development and performance enhancement. This paper takes Suzhou Industrial Park (SIP) as a case, which is a model of Sino–Singaporean government cooperation. Using Landsat 4–5 TM data, socioeconomic data, and industrial land use data, spatial analysis and statistical modeling were employed to examine the evolution and phased patterns of industrial land use in SIP from 1994 to 2022. A performance evaluation system encompassing economic benefits, innovation-driven growth, development intensity, green development, and social security was developed to assess land use performance and its responses to spatial transformations. The results reveal that industrial land in SIP experienced a significant change in the intensity of land expansion from 1.031 to 0.352 during 1994–2022, and the peak circle density expanded from 3 km to 15 km. The mean value of the comprehensive performance score during 2017–2022 was 42.18, with the highest economic efficiency (40.54) and a lower innovation capacity (16.98). The development of industrial land in SIP presents the stage characteristics of monocentric polarization, polycentricity, and spatial diffusion toward a generalized development zone, showing significant path dependence, and the difference in the land use performance of different industrial types is obvious. In the future, the optimization and redevelopment of the stock of land should be strengthened to promote the optimization of the spatial layout of technology-intensive industries and the technological upgrading of labor-intensive industries, as well as achieving sustainable economic growth through innovation-driven, green development and enclave economy collaboration. This study provides a reference for the industrial layout and high-quality sustainable development of development zones.

Zhan Zhang, Huimin Zhou, Shuxiang Li et al.

The Beijing–Tianjin–Hebei region (BTH) is one of the crucial areas for economic development in China. However, rapid urban expansion and industrial development in this region have severely impacted the surrounding ecological environment. The air quality, water, and soil resources face significant pressure. Due to the close relationship between land utilization, population, investment, and industry, effective land use is a key factor in the coordinated development of the region. Therefore, clarifying the patterns of urban land use change and revealing its influencing factors can provide important scientific evidence for the coordinated development of the BTH region. This study aims to improve urban land use efficiency (ULUE) in the BTH region. Firstly, based on the input and output data of land elements for the 13 cities in the BTH region, the Data Envelopment Analysis (DEA) method is used to quantify the ULUE of the BTH urban agglomeration and analyze the spatiotemporal characteristics of ULUE. Input indicators includes capital, labor, and land. Output indicators includes economy, society, and environment. The results show that the overall ULUE in the BTH region has increased, albeit with notable fluctuations. Between 2000 and 2010, ULUE rose swiftly across all cities except Beijing and Tianjin, where changes were minimal. Post-2010, cities exhibited varied trends: steady growth, slow growth, sustained growth, step-wise growth, and initial growth followed by decline. Spatially, before 2010, the BTH showed a “high in the northeast and low in the southwest” pattern, transitioning post-2010 to a smoother “core-periphery” pattern. Mid-epidemic, high ULUE values reverted to the core area, shifting southward post-epidemic. Secondly, panel data analysis is conducted to explore the factors influencing ULUE. The results indicate that fiscal balance, the level of openness, the level of digitalization, industrial structure, and the level of green development are significant factors affecting ULUE. Finally, strategies are proposed to improve ULUE in the BTH region, including national spatial planning, industrial layout, existing land use, infrastructure construction, optimization of local fiscal revenue, and improvement in the business environment, aiming to enhance ULUE and promote the coordinated development of industries in the BTH region.

Silvia Andrés González-Moralejo

Abstract This study analyzes the changes that have occurred in food logistics in the three years since the emergence of the COVID-19 pandemic and the one year since the war in Ukraine commenced. Food logistics companies are highly sensitive to demand shocks, energy prices, and staff availability. In this study, “first-hand” information was collected in the Iberian Peninsula, and it showed a process of Schumpeterian transformation. This crisis environment in which food logistics companies have been operating has opened a unique opportunity to renew operating procedures and seek new solutions, products, and markets. Therefore, food logistics companies have developed more effective communication strategies and innovative, profitable, and forward-looking commercial strategies to adapt to the new needs of their clients, applied more efficient transport planning and management methods, implemented new technologies to increase automation and digitization in warehouses, transport platforms, and trucks, and boosted market concentration and investment in infrastructure. Therefore, public authorities and top executives must focus on promoting and facilitating these improvements.

Hasnan Baber, Muneer Shaik, Himani Gupta

The objective of the study was to examine the effects of the COVID-19 pandemic on India’s economy. The analysis focused on several economic metrics, including stock market prices, the rupee’s value in relation to the US dollar, economic activity, the unemployment rate, and the rate of inflation. Contrary to popular belief, the results demonstrate that during the first wave (25 March 2020 to 16 September 2020), the increasing number of cases had a beneficial influence on economic activity and a negative impact on the unemployment rate. The second wave, which lasted from 15 March 2021 to 17 July 2021, was considerably stronger and demonstrated how confirmed instances had a significant detrimental impact on inflation rates and stock values. Contrary to expectations, the third wave (December 28, 2021, to January 30, 2022) was found to be less intense. Overall, the report shows how the pandemic affected India’s economy during each of the three waves and notes that there have been encouraging signs of recovery during the return to normalcy phase. The government, scholars, policymakers, and economists will find this study useful in understanding how the COVID-19 Pandemic affected the Indian economy and in coming up with ideas for future risk mitigation measures. First published online 26 August 2024

To Ngoc Thinh, Bui Phuong Linh, Tran Tuan Anh et al.

Nowadays, weekend travel is gradually gaining people's attention due to societal impacts, with the desire to improve health, relax, rest, and entertain after days of exhausting work. Therefore, the development of weekend travel is a strategy of interest to managers and leaders, leading to intense competition among destinations. Although it has been long established worldwide, weekend travel in Vietnam has only recently gained popularity, primarily among young people. Therefore, researching the factors influencing the decision to choose weekend travel destinations is significant in developing strategies for this type of tourism. The research results show that the choice of weekend travel destinations by Ho Chi Minh City tourists is driven by various internal and external factors. Among them, internal motivations, income, convenience in the trip, the image of the destination, etc., are factors rated highly by tourists. There are significant differences in some internal and external factors according to age groups. Model testing and research hypotheses indicate that 66.4% of destination choices are influenced by the proposed factors in the model. Among them, the destination image has the most significant impact, followed by income, internal motivations, and distance. The remaining factors in the model have low or no impact on the satisfaction and commitment to return to weekend travel destinations for Ho Chi Minh City tourists.

Ovi Paul, Abu Bakar Siddik Nayem, Anis Sarker et al.

Land Use Land Cover (LULC) analysis on satellite images using deep learning-based methods is significantly helpful in understanding the geography, socio-economic conditions, poverty levels, and urban sprawl in developing countries. Recent works involve segmentation with LULC classes such as farmland, built-up areas, forests, meadows, water bodies, etc. Training deep learning methods on satellite images requires large sets of images annotated with LULC classes. However, annotated data for developing countries are scarce due to a lack of funding, absence of dedicated residential/industrial/economic zones, a large population, and diverse building materials. BD-SAT provides a high-resolution dataset that includes pixel-by-pixel LULC annotations for Dhaka metropolitan city and surrounding rural/urban areas. Using a strict and standardized procedure, the ground truth is created using Bing satellite imagery with a ground spatial distance of 2.22 meters per pixel. A three-stage, well-defined annotation process has been followed with support from GIS experts to ensure the reliability of the annotations. We performed several experiments to establish benchmark results. The results show that the annotated BD-SAT is sufficient to train large deep learning models with adequate accuracy for five major LULC classes: forest, farmland, built-up areas, water bodies, and meadows.

Patrick Meyfroidt, Dilini Abeygunawardane, Matthias Baumann et al.

Land use expansion is linked to major sustainability concerns including climate change, food security and biodiversity loss. This expansion is largely concentrated in so-called frontiers, defined here as places experiencing marked transformations due to rapid resource exploitation. Understanding the mechanisms shaping these frontiers is crucial for sustainability. Previous work focused mainly on explaining how active frontiers advance, in particular into tropical forests. Comparatively, our understanding of how frontiers emerge in territories considered marginal in terms of agricultural productivity and global market integration remains weak. We synthesize conceptual tools explaining resource and land-use frontiers, including theories of land rent and agglomeration economies, of frontiers as successive waves, spaces of territorialization, friction, and opportunities, anticipation and expectation. We then propose a new theory of frontier emergence, which identifies exogenous pushes, legacies of past waves, and actors anticipations as key mechanisms by which frontiers emerge. Processes of abnormal rent creation and capture and the built-up of agglomeration economies then constitute key mechanisms sustaining active frontiers. Finally, we discuss five implications for the governance of frontiers for sustainability. Our theory focuses on agriculture and deforestation frontiers in the tropics, but can be inspirational for other frontier processes including for extractive resources, such as minerals.

Jie Zhang, Yajing Wang, Jiangfeng Li

Increasing land-use eco-efficiency can alleviate human-land conflict in urban areas as well as improve regional urbanization quality to achieve sustainable development. As the central urban agglomeration in China, the Middle Reaches of Yangtze River (MRYR) has experienced rapid urbanization and huge land-use change during 2000 to 2020, which poses great threats to its ecological environment. This study adopted the Super-Slack-Based Data Envelopment Analysis (Super SBM-DEA) model to evaluate the eco-efficiency of land use in MRYR. The result shows that the average eco-efficiency value of land use is above 0.77 for each year, indicating that the general efficiency is at a middle level. The trend of the evolution of the eco-efficiency can be summarized as a “U-shape” style curve. The variance between the four urban agglomerations of the MRYR changed over time. Not all capital cities or cities with higher GDP per capita obtain higher eco-efficiency in this study. Policy intervention, population and land use, technique, and environmental pollution are influencing factors of land-use eco-efficiency. Based on slacks analysis, this study proposed the optimization of the land-use structure to improve eco-efficiency from four aspects of land-use structure, investment and labor, ecosystem services value (ESV) and environment pollution, and industry structure.

Maissam Jdid, F. Smarandache

The increase in the size of the problems facing humans, their overlap, the division of labor, the multiplicity of departments, as well as the diversity of products and commodities, led to the complexity of business and the emergence of many administrative and production problems. It was necessary to search for appropriate methods to confront these problems. The science of operations research, with its diverse methods, provided the optimal solutions. It addresses many problems and helps in making scientific and thoughtful decisions to carry out the work in the best way within the available capabilities. Operations research is one of the modern applied sciences that uses the scientific method as a basis and method in research and study, and its basic essence is to build a model that helps management in making decisions related to difficult administrative problems. For example, the military field, financial aspects, industry, in construction for building bridges and huge projects to evaluate the time taken for each project and reduce this time, financial markets and stocks and forecasting economic conditions, in hospital management and controlling the process of nutrition and medicines within the available capabilities, in agriculture Agricultural marketing and many other problems that have been addressed using classical operations research methods. We know that the agricultural sector is one of the important sectors in every country, and the agricultural production process is regulated by those responsible for securing the needs of citizens. Also, those responsible for the agricultural sector are responsible for rationalizing the agricultural process so that the surplus is saved. Due to the difficult circumstances that the country may be going through, in this research, we will reformulate the general model for the optimal distribution of agricultural lands using the concepts of neutrosophic science.

I. Säumel, J. Álvarez, L. Ramírez et al.

South American grasslands, socio-ecological systems used heavily for a long time, are currently experiencing dramatic land-use changes due to implementation of large-scale afforestation and agro-industrial cash crops. Applying the conceptual framework of “Multifunctional and sustainable productive landscapes” to Uruguay, we explored the impacts on rural ecosystems and communities based on a long-term monitoring network by assessing species richness of plant and terrestrial arthropods and socio-economic data from national census. We found that silvi- and agricultural industry established mainly at the expense of extensively grazed grasslands and local family farms with traditional techniques, accompanied by a deregulation of the rural labor market, depopulation and aging of rural society. Governmental nature protection efforts increase the native forest cover and establish nature protection areas focusing mainly on forests. We also discuss pathways of land-use change in recent decades and related discourses of local stakeholders.