VGTU Journals (Vilnius Gediminas Technical University - Vilnius Tech)
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Existential risk from transformative AI: an economic perspective
No full textThe prospective arrival of transformative artificial intelligence (TAI) will be a filter for the human civilization – a threshold beyond which it will either strongly accelerate its growth, or vanish. Historical evidence on technological progress in AI capabilities and economic incentives to pursue it suggest that TAI will most likely be developed in just one to four decades. In contrast, theoretical problems of AI alignment, needed to be solved in order for TAI to be “friendly” towards humans rather than cause our extinction, appear difficult and impossible to solve by mechanically increasing the amount of compute. This means that transformative AI poses an imminent existential risk to the humankind which ought to be urgently addressed. Starting from this premise, this paper provides new economic perspectives on discussions surrounding the issue: whether addressing existential risks is cost effective and fair towards the contemporary poor, whether it constitutes “Pascal’s mugging”, how to quantify risks that have never materialized in the past, how discounting affects our assessment of existential risk, and how to include the prospects of upcoming singularity in economic forecasts. The paper also suggests possible policy actions, such as ramping up public funding on research on existential risks and AI safety, and improving regulation of the AI sector, preferably within a global policy framework.
First published online 10 July 202
Land use land cover change mapping from Sentinel 1B & 2A imagery using random forest algorithm in Côte d’Ivoire
No full textMonitoring crop condition, soil properties, and mapping tillage activities can be used to assess land use, forecast crops, monitor seasonal changes, and contribute to the implementation of sustainable development policy. Agricultural maps can provide independent and objective estimates of the extent of crops in a given area or growing season, which can be used to support efforts to ensure food security in vulnerable areas. Satellite data can help detect and classify different types of soil. The evolution of satellite remote sensing technologies has transformed techniques for monitoring the Earth’s surface over the last several decades. The European Space Agency (ESA) and the European Union (EU) created the Copernicus program, which resulted in the European satellites Sentinel-1B (S1B) and Sentinel-2A (S2A), which allow the collection of multi-temporal, spatial, and highly repeatable data, providing an excellent opportunity for the study of land use, land cover, and change. The goal of this study is to map the land cover of Côte d’Ivoire’s West Central Soubre area (5°47′1′′ North, 6°35′38′′ West) between 2014 and 2020. The method is based on a combination of S1B and S2A imagery data, as well as three types of predictors: the biophysical indices Normalized Difference Vegetation Index “(NDVI)”, Modified Normalized Difference Water Index “(MNDWI)”, Normalized Difference Urbanization Index “(NDBI)”, and Normalized Difference Water Index “(NDWI)”, as well as spectral bands (B1, B11, B2, B3, B4, B6, B7, B8) and polarization coefficients VV. For the period 2014–2020, six land classifications have been established: Thick_Forest, Clear_Drill, Urban, Water, Palm_Oil, Bareland, and Cacao_Land. The Random Forest (RF) algorithm with 60 numberOfTrees was the primary categorization approach used in the Google Earth Engine (GEE) platform. The results show that the RF classification performed well, with outOfBagErrorEstimates of 0.0314 and 0.0498 for 2014 and 2020, respectively. The classification accuracy values for the kappa coefficients were above 95%: 96.42% in 2014 and 95.28% in 2020, with an overall accuracy of 96.97% in 2014 and 96 % in 2020. Furthermore, the User Accuracy (UA) and Producer Accuracy (PA) values for the classes were frequently above 80%, with the exception of the Bareland class in 2020, which achieved 79.20%. The backscatter coefficients of the S1B polarization variables had higher GINI significance in 2014: VH (70.80) compared to VH (50.37) in 2020; and VV (57.11) in 2014 compared to VV (46.17) in 2020. Polarization coefficients had higher values than the other spectral and biophysical variables of the three predictor variables. During the study period, the Thick_Forest (35.90% ± 1.17), Palm_Oil (57.59% ± 1.48), and Water (5.90% ± 0.47) classes experienced a regression in area, while the Clear_Drill (16.96% ± 0.80), Urban (2.32% ± 0.29), Bareland (83.54% ± 1.79), and Cacao_Land (35.14% ± 1.16) classes experienced an increase. The approach used is regarded as excellent based on the results obtained
Investigation of the effects of Kahramanmaraş earthquake series on Cyprus Arc, Dead Sea fault, Hatay regions and stations close to two earthquakes epicenters
No full textIn various parts of the globe, there have been several earthquakes of a modest size. Monitoring the change of the points over time is a key component of typical techniques for extracting dynamic responses. This technique was unable to completely extract all of the earthquake’s dynamic properties. The GNSS precise point positioning (PPP) may be a useful tool for obtaining values of the point’s displacement that are more exact up to millimeters, which can help to overcome these flaws and evaluate the seismic wave of such earthquakes. Ultimately, PPP is a crucial tool for getting the precise observations. In this study, Canadian Spatial Reference System Precise Point Positioning (CSRS-PPP) approach to analyze the station’s displacement components and the station’s heights in periods from the two Kahramanmaraş earthquakes. The earthquake sequences that occurred in Turkey’s Kahramanmaraş in 2023 is an example of complicated faulting brought on by interactions between three plates close to the Hatay Triple Junction (HTJ). While the relative plate movements in this area are minimal (usually less than 10 mm/year), even sluggish plate motion zones may nevertheless see earthquakes that are quite destructive. Due to the three-plate system’s unusual geometry, a number of large earthquakes with very varied fault orientations were active throughout this series. A 7.8-magnitude earthquake happened on February 6, 2023 in southern Turkey, close to Syria’s northern border. A magnitude 7.5 earthquake, situated about 95 kilometers to the southwest, was occurred nine hours after the first one. The first earthquake was as big as the most powerful one ever recorded there in 1939 and was the most catastrophic to strike earthquake-prone Turkey in more than 20 years. In this study, the effects of two earthquakes in Kahramanmaraş were investigated on the Cyprus Arc, the Dead Sea fault, Hatay and the points close to two earthquakes zone. In the obtained results, it was computed that the greatest horizontal displacement occurred at the HAT2 station with 68.97 cm
Surface runoff estimation at the Densu River Basin using geographic information system (GIS) and remote sensing (RS)
No full textAccurate estimation of runoff depth and volume is essential for effective watershed management. Runoff, resulting from rainfall, is influenced by numerous factors, including soil type, vegetation, land use patterns, and rainfall characteristics. The Densu River Basin, located in the Greater Accra region of Ghana, has experienced flooding incidents, partly attributed to changes in land use and land cover. To address these challenges and facilitate proper flood management, drainage network design, hydropower generation, and other applications, this study aims to estimate surface runoff depth in the Densu River Basin, Ghana. The Natural Resources Conservation Services Curve Number (NRCS-CN) method, combined with Geographic Information System (GIS) and Remote Sensing (RS), is employed for runoff depth estimation. The research involves supervised classification of Landsat images from 2001, 2011, and 2022 to determine land use patterns, calculate grass cover percentages, identify hydrologic soil categories, extract rainfall intensity data, compute maximum soil storage, and estimate runoff depths for 10 year, 25 year, and 50 year return periods. The study reveals a significant increase in direct surface runoff depth, from 138.29 mm to 144.70 mm, for soil type D (Clay loam), the dominant soil type in the basin, during the 10 year return period, attributed to changes in land use and climate within the basin. The findings from this study hold valuable insights for mitigating environmental hazards in the area and improving water resource management
Impact of data structure types and spatial resolution on landslide volumetric change measurements
No full textTerrain is a dynamic component of the landscape, subject to rapid changes, particularly in scenarios such as landslides. This study investigates how the spatial resolution and data structure of digital terrain models (DTMs) influence the estimation of landslide volume changes. We selected a landslide formed by the undercutting action of the Belá River in Slovakia as our research site. Our findings indicate that raster data structures, across various spatial resolutions, generally yield more consistent volume estimates compared to 3D mesh data structures. Nonetheless, at higher spatial resolutions (0.1 m and 0.25 m), the 3D mesh data structure demonstrates superior capability in capturing detailed terrain features, resulting in more precise volume estimations of the landslide
The incentive effects of project governance elements on agents in agent-led construction of social security housing projects
No full textThe agent-led construction system is a construction mode that emphasizes the introduction of professional management teams instead of government departments to develop public projects in China, including social security housing. In reality, the problem in practice is that the government owners’ management system of the agent market is not sound enough, and it cannot effectively motivate the agents. Existing research has not paid enough attention to this agent construction market. To reveal the effective incentives of agent developers, using the stimulus–organism–response theory, this study constructed a structural equation model and proposed a research hypothesis about the effect of project governance elements on project performance. The study found that governance elements including internal contractual governance, external contractual governance, and relational governance, had a positive incentivizing effect on the project performance. The psychology and behavior of the construction agent played a partial mediating role. The results provide a policy implication for city government to improve the incentive system for agent construction of security housing projects
Willingness analysis of middle-aged and older people’s participation in reverse mortgage schemes
No full textEnabling older adults to age at home is an urgent issue. This study focuses on the attitudes of middle-aged and older people (MAOP) in the capital cities of Taiwan, which are characterised by expensive housing prices and living costs, to examine their preferences for reverse mortgage (RM) schemes. The stated preference method and conditional multinomial logit model are utilised for analysis. The study simulates the total payment duration (TPD) and monthly payment amounts (MPA) to determine the market share of MAOPs’ choices regarding terms. The results indicate that MAOPs tend to opt for RM schemes when they have children, partly enhancing the preference toward the long-term alternative (AL). Increasing the MPA has a positive effect on the market share of the AL scheme, but the amount must be increased to 90% to replace the market share of non-participation schemes significantly. The experimental design of this study could serve as a reference for future RM scheme designs. The findings suggest that there should be more alternative funding sources in an ageing society, particularly through revitalising housing assets, to promote ageing in place
Mapping the landscape: A systematic literature review on automated valuation models and strategic applications in real estate
No full textIn the rapidly evolving real estate industry, integrating automated valuation models (AVMs) has become critical for improving property assessment accuracy and transparency. Although there is some research on the subject, no thorough qualitative systematic review has been done in this field. This paper aims to provide an up-to-date and systematic understanding of the strategic applications of AVMs across various real estate subsectors (i.e., real estate development, real estate investment, land administration, and taxation), shedding light on their broad contributions to value enhancement, decision-making, and market insights. The systematic review is based on 97 papers selected out of 652 search results with an application of the PRISMA-based method. The findings highlight the transformative role of AVMs approaches in streamlining valuation processes, enhancing market efficiency, and supporting data-driven decision-making in the real estate industry, along with developing an original conceptual framework. Key areas of future research, including data integration, ethical implications, and the development of hybrid AVMs approaches are identified to advance the field and address emerging challenges. Ultimately, stakeholders can create new avenues for real estate valuation efficiency, accuracy, and transparency by judiciously utilizing AVMs approaches, leading to more educated real estate investment decisions
Property appraisal via lens of property registration abundance – real estate market asymmetry assessment
No full textInformation on transaction prices and the ones characterizing the property as the subject of the valuation are essential for a proper valuation process. The accuracy and completeness of the collected set of information directly affects the quality of the valuation process. When market participants operate on the basis of unequal sets of information, information asymmetry is revealed. This research investigates the effects of information asymmetry on property market from the perspective of property registration abundance and mass appraisal systems. It explores how disparities in information abundance and quality within property registration and appraisal processes can affect market fairness and transparency. Employing a mixed-methods approach, it analyses property transaction data and tries to investigate effects of information asymmetry. The findings indicate that enhanced transparency and data quality can significantly reduce valuation discrepancies and lead to a more equitable real estate market. The study concludes with recommendations aimed at justifying information asymmetry’s negative effects, supporting for policies that promote information uniformity to improve the fairness and efficiency of property registration and mass appraisal practices
Does online media attention improve China’s green fund performance?
No full textThis study investigates the relationship between online media attention and the performance of China’s green funds. The results show that increased media attention can boost the performance of green funds in the short term, however, this effect is short-lived. The mechanism of short-term positive effects may be due to increased media attention leading to larger purchases, which may undermine funds’ long-term performance. In particular, online media attention has a greater impact on larger and older funds. Moreover, it indicates that media attention reduces the returns of individual investor-dominated funds, but has little effect on institutional investor-dominated funds