Hasil untuk "Economics"

Davit Gondauri

Inflation forecasting is a core socio-economic challenge in modern macroeconomic modeling, especially when cyclical, structural, and shock factors act simultaneously. Traditional systems such as FPAS and ARIMA often struggle with cyclical asymmetry and unexpected fluctuations. This study proposes a hybrid framework (FPAS + $ζ$) that integrates a structural macro model (FPAS) with cyclical components derived from the Riemann zeta function $ζ(1/2 + i t)$. Using Georgia's macro data (2005-2024), a nonlinear argument $t$ is constructed from core variables (e.g., GDP, M3, policy rate), and the hybrid forecast is calibrated by minimizing RMSE via a modulation coefficient $α$. Fourier-based spectral analysis and a Hidden Markov Model (HMM) are employed for cycle/phase identification, and a multi-criteria AHP-TOPSIS scheme compares FPAS, FPAS + $ζ$, and ARIMA. Results show lower RMSE and superior cyclical responsiveness for FPAS + $ζ$, along with early-warning capability for shocks and regime shifts, indicating practical value for policy institutions.

Hugo Spring-Ragain

This paper introduces the concept of an economic action constant, denoted ___ E , as a structural analogue to Planck's reduced constant ___ in quantum mechanics. Building on canonical quantization, we define ___ E as the fundamental scale of irreducible uncertainty in macroeconomic dynamics through non-commuting observables ( X, PX ), derive uncertainty relations and a semi-classical limit, and study spectral properties under a double-well economic potential. Numerical simulations show that ___ E governs regime transitions between deterministic, probabilistic, and highly unstable dynamics, with topological changes in phase-space and bifurcations emerging under harmonic modulation of ___ E . We propose a systemic economic interpretation linking the magnitude of ___ E to expectation coordination, institutional stability, and structural volatility, and provide historical analogies (post-war reconstruction, speculative bubbles, systemic crises). We finally outline an empirical strategy to estimate ___ E from macro time series and agent-based simulations, opening a path toward a taxonomy of economic regimes under radical uncertainty.

Alessandro V. M. Oliveira, Moises D. Vassallo

This study investigates how the layout and density of seats in aircraft cabins influence the pricing of airline tickets on domestic flights. The analysis is based on microdata from boarding passes linked to face-to-face interviews with passengers, allowing us to relate the price paid to the location on the aircraft seat map, as well as market characteristics and flight operations. Econometric models were estimated using the Post-Double-Selection LASSO (PDS-LASSO) procedure, which selects numerous controls for unobservable factors linked to commercial and operational aspects, thus enabling better identification of the effect of variables such as advance purchase, reason for travel, fuel price, market structure, and load factor, among others. The results suggest that a higher density of seat rows is associated with lower prices, reflecting economies of scale with the increase in aircraft size and gains in operational efficiency. An unexpected result was also obtained: in situations where there was no seat selection fee, passengers with more expensive tickets were often allocated middle seats due to purchasing at short notice, when the side alternatives were no longer available. This behavior helps explain the economic logic behind one of the main ancillary revenues of airlines. In addition to quantitative analysis, the study incorporates an exploratory approach to innovative cabin concepts and their possible effects on density and comfort on board.

Jafer Mume Ahmed, Jema Haji, Moti Jaleta et al.

Abstract The aims of this study are to analysis adoption and impacts of conservation agriculture on poverty and resilience to drought in Eastern Ethiopia. Multi-stage sampling method was employed and four districts were randomly selected from Eastern Hararghe Zone. Using cross-sectional survey data collected in 2023 from 430 households, multinomial endogenous switching regression was applied in impact evaluation. The result shows farm experience, education, climatic-shocks experience, access to climate information, access to extension, number of oxen, farm size, labor force, livestock and distance to market are determinants of conservation agriculture (CA) adoption in terms of inter-cropping, crop rotation and crop residue retention or mulch practices in its single and combination of practices. Poverty was evaluated based on cost-of-basic-need and resilience to drought is in its index. Accordingly, the food poverty line and total consumption expenditure or total poverty line was found to be 6582.7ETB and 8620.70ETB per Adult/Equivalent per year in the study area. Results of average treatment effect on treated shows adoption is significantly reduced poverty and increased resilience to drought condition in the area. The study also shows importance role of extension service in adoption of CA practices. There is a need to encourage extension facilities and awareness to promote better adoption of CA particularly, in its combination. The combination of practices increased consumption expenditure of adopters by 73.3 percent compared to non-adopters, given its significant impact on households’ poverty and resilience to drought in moisture stress area of eastern Ethiopia. So, it is crucial to more advances farmers and experts’ information on climate shocks and conservation agricultural practices adoption. The policymakers ought to develop and encourage farmers’ asset building plan including livestock, adult education, market linkage facilities, extension and weather related information delivery service to enhance adoption of conservation agricultural practices to combat the current and future drought.

Emanuele Possas de Souza, Sheury Celante Marques, Flávia Mendes dos Santos Lourenço et al.

The relation between seed vigor and field performance is not yet fully understood, and it is questionable whether these effects extend to more advanced phenological stages and if they affect crop production. In front of that, the objective of this study was to evaluate the effects on the seedlings and plant production of cauliflower using doses of product based on Bacillus subtilis in seed treatment. The study was conducted in Ilha Solteira city, São Paulo State. The experimental design was a complete randomized design for laboratory analysis and complete randomized blocks for the field stage. Ten treatments were studied in a 2 x 5 factorial scheme with four replications. The treatments consisted of seed treatment of cultivars Sharon and Barcelona with Bacillus subtilis-based product (strain FMT001 containing 3x108 cells cm-3) in five doses (0, 100, 200, 300 and 400 mL per 10 kg of seeds). Seed vigor and crop yield (shoot, root and inflorescence weight, leaf number, inflorescence diameter and yield) characteristics were evaluated. Cultivars averages were compared by Tukey test (p <0.05) and regression for the doses. The results showed that doses of 200 and 400 mL per 10 kg of seeds increased the percentage of strong seedlings of cultivars Barcelona and Sharon, respectively.

Gabriele Campanelli, Margherita Amenta, Luana Bontempo et al.

Different research works have been carried out over the years to investigate new and reliable systems to test the authenticity of products obtained using organic cultivation methods. Based on a previously proposed integrated approach for discriminating organic from conventional products through the acquisition of isotopic data and other chemical and biochemical parameters, we herein report the results of an open-field cultivation case study for cauliflower crop. Experiments were carried out on soil, leaves, and corymb samples of cauliflowers grown using six different nitrogen fertilization treatments (organic, conventional, and mixed at different % of mineral fertilizers). The results of this study have shown that a multivariate analysis of isotopic data (¹³C/¹²C; ¹⁵N/¹⁴N, ³⁴S/³²S, ²H/¹H, and ¹⁸O/¹⁶O isotopic ratios) combined with other parameters (fresh weight, total soluble solids, total acidity, cut resistance, CIE L*, a*, b* color indices, head height, head diameter, ascorbic acid content, total polyphenols, and ORAC units) performed using the linear discriminant analysis method gives researchers the possibility to discriminate organic products from conventional ones. Our study highlighted that the different isotopic signatures impressed on the cauliflowers by the different nitrogenous sources combined with the qualitative pattern of the crop, significantly affected by the different treatments, could effectively be jointly used to trace the organic origin of the crop.

Yue Guo, Yi Yang

Large Language Models (LLMs) are widely used for writing economic analysis reports or providing financial advice, but their ability to understand economic knowledge and reason about potential results of specific economic events lacks systematic evaluation. To address this gap, we propose a new dataset, natural language inference on economic events (EconNLI), to evaluate LLMs' knowledge and reasoning abilities in the economic domain. We evaluate LLMs on (1) their ability to correctly classify whether a premise event will cause a hypothesis event and (2) their ability to generate reasonable events resulting from a given premise. Our experiments reveal that LLMs are not sophisticated in economic reasoning and may generate wrong or hallucinated answers. Our study raises awareness of the limitations of using LLMs for critical decision-making involving economic reasoning and analysis. The dataset and codes are available at https://github.com/Irenehere/EconNLI.

Azizul Hakim Rafi, Abdullah Al Abrar Chowdhury, Adita Sultana et al.

Given the fact that climate change has become one of the most pressing problems in many countries in recent years, specialized research on how to mitigate climate change has been adopted by many countries. Within this discussion, the influence of advanced technologies in achieving carbon neutrality has been discussed. While several studies investigated how AI and Digital innovations could be used to reduce the environmental footprint, the actual influence of AI in reducing CO2 emissions (a proxy measuring carbon footprint) has yet to be investigated. This paper studies the role of advanced technologies in general, and Artificial Intelligence (AI) and ICT use in particular, in advancing carbon neutrality in the United States, between 2021. Secondly, this paper examines how Stock Market Growth, ICT use, Gross Domestic Product (GDP), and Population affect CO2 emissions using the STIRPAT model. After examining stationarity among the variables using a variety of unit root tests, this study concluded that there are no unit root problems across all the variables, with a mixed order of integration. The ARDL bounds test for cointegration revealed that variables in this study have a long-run relationship. Moreover, the estimates revealed from the ARDL model in the short- and long-run indicated that economic growth, stock market capitalization, and population significantly contributed to the carbon emissions in both the short-run and long-run. Conversely, AI and ICT use significantly reduced carbon emissions over both periods. Furthermore, findings were confirmed to be robust using FMOLS, DOLS, and CCR estimations. Furthermore, diagnostic tests indicated the absence of serial correlation, heteroscedasticity, and specification errors and, thus, the model was robust.

Shangmin Guo, Haoran Bu, Haochuan Wang et al.

Large language models (LLMs) have been extensively used as the backbones for general-purpose agents, and some economics literature suggest that LLMs are capable of playing various types of economics games. Following these works, to overcome the limitation of evaluating LLMs using static benchmarks, we propose to explore competitive games as an evaluation for LLMs to incorporate multi-players and dynamicise the environment. By varying the game history revealed to LLMs-based players, we find that most of LLMs are rational in that they play strategies that can increase their payoffs, but not as rational as indicated by Nash Equilibria (NEs). Moreover, when game history are available, certain types of LLMs, such as GPT-4, can converge faster to the NE strategies, which suggests higher rationality level in comparison to other models. In the meantime, certain types of LLMs can win more often when game history are available, and we argue that the winning rate reflects the reasoning ability with respect to the strategies of other players. Throughout all our experiments, we observe that the ability to strictly follow the game rules described by natural languages also vary among the LLMs we tested. In this work, we provide an economics arena for the LLMs research community as a dynamic simulation to test the above-mentioned abilities of LLMs, i.e. rationality, strategic reasoning ability, and instruction-following capability.

Habib Ullah Khan, Shah Nazir

We know that in today’s advanced world, artificial intelligence (AI) and machine learning (ML)-grounded methodologies are playing a very optimistic role in performing difficult and time-consuming activities very conveniently and quickly. However, for the training and testing of these procedures, the main factor is the availability of a huge amount of data, called big data. With the emerging techniques of the Internet of Everything (IoE) and the Internet of Things (IoT), it is very feasible to collect a large volume of data with the help of smart and intelligent sensors. Based on these smart sensing devices, very innovative and intelligent hardware components can be made for prediction and recognition purposes. A detailed discussion was carried out on the development and employment of various detectors for providing people with effective services, especially in the case of smart cities. With these devices, a very healthy and intelligent environment can be created for people to live in safely and happily. With the use of modern technologies in integration with smart sensors, it is possible to use energy resources very productively. Smart vehicles can be developed to sense any emergency, to avoid injuries and fatal accidents. These sensors can be very helpful in management and monitoring activities for the enhancement of productivity. Several significant aspects are obtained from the available literature, and significant articles are selected from the literature to properly examine the uses of sensor technology for the development of smart infrastructure. The analytical hierarchy process (AHP) is used to give these attributes weights. Finally, the weights are used with the multi-objective optimization on the basis of ratio analysis (MOORA) technique to provide the different options in their order of importance.

A. Sobri Jaafar

Malaysia is taking steps to transform the economy from being production-based to being knowledge driven (K-economy). In line with this objective, information and communication technologies (ICT) have been identified as the strategic enabling tools that will support the growth of the Malaysian economy as well as enhance the living standard of the population. Hence, in the past decade various initiatives have been taken by the government to promote the use and development of ICT. However, there are many issues and challenges that need to be addressed by the country before a successful transformation to a K-economy can be made. One of the issues is ICT utilization for the development of an information society and economy in the country. The paper assesses the current state of ICT utilization in Malaysia based on secondary data. The result indicates that the level of ICT utilization in the country is still low compared to selected countries and there exist wide disparities among states in Malaysia in terms of accessibility to ICT.

Nanang Adie Setyawan, Hadiahti Utami, Bayu Setyo Nugroho et al.

This study set out to determine what motivated SMEs in Semarang City to undertake green supply chain management during the COVID-19 and New Normal pandemics. The purposive sampling approach was used as the sampling methodology in this investigation. There are 100 respondents in the research samples. The AMOS 24.0 program's structural equation modelling (SEM) is used in this research method. According to the study's findings, the Strategic Orientation variable significantly and favourably affects the Green Supply Chain Management variable expected to have a value of 0.945, and the Government Regulation variable has a positive and strong influence on the variable Green Supply Chain Management with an estimated value of 0.070, the Green Supply Chain Management variable with an estimated value of has a positive and significant impact on the environmental performance variable. 0.504, the Strategic Orientation variable with an estimated value of has a positive and significant impact on the environmental performance variable. 0.442, The Environmental Performance variable is directly impacted positively and significantly by the Government Regulation variable, with an estimated value of 0.041. This significant positive influence is because SMEs in Semarang City have government regulations, along with government support for facilities regarding efforts to implement the concept of environmental concern, causing high environmental performance caused by the optimal implementation of Green supply chain management is built on a collaboration between the government and the supply chain's participants.

Dorin BIBICU

Nowdays the password encryption is an indispensable job used in software developing process in order to secure the important information. In this work it is proposed an original and efficient algorithm capable to encrypt a password. In this regard we considered the following terms: real password and encrypted password. The encryption algorithm is based on an original method of character processing.

Julie Roba, Tom Kuppens, Tom Kuppens et al.

The concept of the circular economy is being proposed as an alternative for the current linear economy. However, little research has been done on how to integrate this topic within education. Serious games are suggested as an appropriate way to create awareness about and stimulate behavioral change toward sustainable development. Therefore, the serious game ecoCEO has been developed to introduce the circular economy concept within upper secondary education. The game's intention is to introduce students to the challenge of resource scarcity, circular product design, sustainable entrepreneurship and circular business models. EcoCEO has been tested among 42 students, whose written reflections were qualitatively examined. EcoCEO appears successful, at least partially, in contrasting the circular economy with the linear economy and in conveying relevant concepts such as recycling and reuse. EcoCEO also illustrates the importance of the circular economy within the context of material scarcity. Moreover, most students in our case study seem to have a good impression of the role and responsibilities of a (sustainable) entrepreneur. Despite its difficulty level, the majority of the students reported having fun while playing ecoCEO.

Yeşim Erdoğdu, Filiz Koçoğlu, Celil Sevim

Objective: COVID-19, which has spread rapidly around the world since December 2019, has been defined as an infectious disease by the World Health Organization. Infection and mortality rates from COVID-19 are high and the COVID-19 pandemic has many negative impacts in health, economic and security fields. The aim of this study is therefore to examine the psychosocial and demographic determinants of anxiety and hopelessness during the COVID-19 pandemic among Turkish population. Method: The study group of the research consists of 1026 participants between the ages of 18 and 65 years. Personal Information Form, Beck Hopelessness Scale and Beck Anxiety Scale were used for data collection. Results: The majority of the participants reported that both national and global health, economic, and security precautions were not sufficient in fight against COVID-19 pandemic. About one in four participants had symptoms of moderate to severe anxiety and about one in three had symptoms of moderate to severe hopelessness. Women had significantly higher levels of anxiety compared to men. Participants who reported that the health, economics, and safety precautions were not adequate had higher levels of anxiety and hopelessness than those who reported that the precautions were adequate. Discussion: Given that the COVID-19 pandemic is associated with negative psychological and social consequences, the preventive programs for mental health should be promoted and the psychosocial support services should be made available to everyone in the society.

Javier Borquez, Hector Chavez, Karina A. Barbosa et al.

In a scenario where distributed generation infrastructure is increasing, the impact of that integration on electricity tariffs has captured particular attention. As the distribution sector is mainly regulated, tariff systems are defined by the authority. Then, tariffs must be simple, so the methodology, criteria, and procedures can be made public to ensure transparency and responsiveness of the customers to price signals. In the aim of simplicity, tariff systems in current practices mostly consist of volumetric charges. Hence, the reduction of the energy purchased from the distribution network jeopardizes the ability of the tariff system to ensure recovery of the total regulated costs. Although various works have captured this concern, most proposals present significant mathematical complexity, contrasting with the simplicity of current practices and limiting its regulatory applicability. This work develops a tariff system that captures the basic elements of distribution systems, trying to maintain the simplicity of current practices, ensuring recovery of the total regulated cost under the penetration of distributed generation, and incentivizing through price signals operational efficiency. A simulation will be presented to discuss numerical results.

Plamen Nikolov

Attitudes toward risk underlie virtually every important economic decision an individual makes. In this experimental study, I examine how introducing a time delay into the execution of an investment plan influences individuals' risk preferences. The field experiment proceeded in three stages: a decision stage, an execution stage and a payout stage. At the outset, in the Decision Stage (Stage 1), each subject was asked to make an investment plan by splitting a monetary investment amount between a risky asset and a safe asset. Subjects were informed that the investment plans they made in the Decision Stage are binding and will be executed during the Execution Stage (Stage 2). The Payout Stage (Stage 3) was the payout date. The timing of the Decision Stage and Payout Stage was the same for each subject, but the timing of the Execution Stage varied experimentally. I find that individuals who were assigned to execute their investment plans later (i.e., for whom there was a greater delay prior to the Execution Stage) invested a greater amount in the risky asset during the Decision Stage.

Yosuke A. Yamashiki, Moe Fujita, Tatsuhiko Sato et al.

We present a systematic approach to effectively evaluate potential risk cost caused by exposure to solar proton events (SPEs) from solar flares for the airline industry. We also evaluate associated health risks from radiation, to provide relevant alternative ways to minimize economic loss and opportunity. The estimated radiation dose induced by each SPE for the passengers of each flight is calculated using ExoKyoto and PHITS. We determine a few scenarios for the estimated dose limit at 1 and 20mSv, corresponding to the effective dose limit for the general public and occupational exposure, respectively, as well as a higher dose induced an extreme superflare. We set a hypothetical airline shutdown scenario at 1mSv for a single flight per passenger, due to legal restrictions under the potential radiation dose. In such a scenario, we calculate the potential loss in direct and opportunity cost under the cancelation of the flight. At the same time, we considered that, even under such a scenario, if the airplane flies at a slightly lower altitude (from 12 to 9.5km: atmospheric depth from 234 to 365g/cm$^{2}$), the total loss becomes much smaller than flight cancelation, and the estimated total dose goes down from 1.2 to 0.45mSv, which is below the effective dose limit for the general public. In case of flying at an even lower altitude (7km: atmospheric depth 484g/cm$^{2}$), the estimated total dose becomes much smaller, 0.12 mSv. If we assume the increase of fuel cost is proportional to the increase in atmospheric depth, the increase in cost becomes 1.56 and 2.07 for the case of flying at 9.5 km and at 7 km, respectively. Lower altitude flights provide more safety for the potential risk of radiation doses induced by severe SPEs. At the same time, since there is total loss caused by flight cancelation, we propose that considering lower flight altitude is the best protection against solar flares.

Lucas Marc Fuhrer, Basil Guggenheim, Matthias Jüttner

Abstract This paper sheds light on Swiss franc LIBOR futures, which are often used to derive interest rate expectations. We show that the differences between LIBOR futures and realized rates (excess returns) are, on average, positive over the last 25 years. Using interest rate surveys, we decompose excess returns into a (forward) term premium and forecast errors. The decomposition reveals that the bulk of excess returns arises from forecast errors, while the term premium is, on average, zero but time varying. We find that the term premium positively correlates with the business cycle, interest rate developments, and in absolute values increases with interest rate uncertainty.

Zura Kakushadze, Willie Yu

We discuss the idea of a purely algorithmic universal world iCurrency set forth in [Kakushadze and Liew, 2014] (https://ssrn.com/abstract=2542541) and expanded in [Kakushadze and Liew, 2017] (https://ssrn.com/abstract=3059330) in light of recent developments, including Libra. Is Libra a contender to become iCurrency? Among other things, we analyze the Libra proposal, including the stability and volatility aspects, and discuss various issues that must be addressed. For instance, one cannot expect a cryptocurrency such as Libra to trade in a narrow band without a robust monetary policy. The presentation in the main text of the paper is intentionally nontechnical. It is followed by an extensive appendix with a mathematical description of the dynamics of (crypto)currency exchange rates in target zones, mechanisms for keeping the exchange rate from breaching the band, the role of volatility, etc.