Economic Model Research Articles

Factors Influencing Income Tax Compliance Decisions: A Review of the Literature

The payment of income tax to the state by its citizens who have an income that is liable to tax is a mandatory legal requirement. In every country in the world, however, there exists a phenomenon known as a tax gap; i.e., not all of the people who are supposed to pay tax on their income are doing so. Why some pay taxes while others decide not to comply with the mandatory legal requirements to do so remains a question that has not yet been answered in full. Income tax is multidisciplinary in nature: economics, psychology, law, accounting, political economy, sociology, public sector management, etc; all come into play to explain why some people pay their taxes while others decide not to. The economic approach attempts to understand tax compliance from the perspective of the tax gap, while the psychological approach is based on exploring the citizens’ voluntary willingness to act not only withing the letter of the income tax law but also within its spirit. This paper attempts to provide an overview of the current ‘Tax Compliance’ literature, from the neo-classical economic deterrence models to the behavioural economics models and demonstrate the limitations of the tax compliance literature from the perspective of developing countries.

Integrated deviance information criterion for spatial autoregressive models with heteroskedasticity

In this study, we introduce the integrated deviance information criterion (DIC) for nested and non-nested model selection problems in heteroskedastic spatial autoregressive models. In a Bayesian estimation setting, we assume that the idiosyncratic error terms of our spatial autoregressive model have a scale mixture of normal distributions, where the scale mixture variables are latent variables that induce heteroskedasticity. We first derive the integrated likelihood function by analytically integrating out the scale mixture variables from the complete-data likelihood function. We then use the integrated likelihood function to formulate the integrated DIC measure. We investigate the finite sample performance of the integrated DIC in selecting the true model in a simulation study. The simulation results show that the integrated DIC performs satisfactorily and can be useful for selecting the correct model in specification search exercises. Finally, in a spatially augmented economic growth model, we use the integrated DIC to choose the spatial weights matrix that leads to better predictive accuracy.

A Very British industrial policy: Green finance and the City-Bank-Treasury control of Net Zero

In 2017, the UK’s Industrial Strategy was thought to have marked an unconventional moment in British politics, as the state began to explicitly ‘pick the winners’ necessary to both grow and decarbonise the economy. This article demonstrates that a more conventional view of this moment can be found in the pages of the Green Finance Strategies where, contrary to the assumption that a green transformation requires developing domestic capacity in emergent technologies, the UK has instead chosen to be the financier of them. It draws on a novel dataset to find another instance of ‘Treasury Control’, in which large scale investments in low carbon sectors was eschewed to instead 'green' the financial expertise located in the City of London, thereby making green finance British industrial policy. Instead of any industrial transformation of the UK political economy, the pursuit of green finance belies the fact that any such transition, and by extension Net Zero, is shaped by the City-Bank-Treasury nexus' desire to preserve the prevailing economic model with as few adjustments as possible.

Planned expansion of transportation infrastructure in Brazil has implications for the pattern of agricultural production and carbon emissions

High transportation costs have been a barrier to the expansion of agriculture in the interior of Brazil. To reduce transportation costs, Brazil launched the National Logistics Plan, aiming to expand its railway network by up to 91 % by 2035. Such a large-scale infrastructure investment raises concerns about its economic and environmental consequences. By combining geospatial estimation of transportation cost with a grid-resolving, multi-scale economic model that bridges fine-scale crop production with its trade and demand from national and global perspectives, we explore impacts of transportation infrastructure expansion on agricultural production, land use changes, and carbon emissions both locally and nationally in Brazil. We find that globally, the impacts on output and land use changes are small. However, within Brazil, the plan's primary impacts are impressive. PNL2035 results in the reduction of transportation costs by 8–23 % across states (depending on expansion's extent) in the interior Cerrado biome. This results in cropland expansion and increases in terrestrial carbon emissions in the Cerrado region. However, the increase in terrestrial carbon emissions in the Cerrado is offset by spillover effects elsewhere in Brazil, as crop production shifts away from the Southeast-South regions and accompanying change in the mix of transportation mode for farm products from roadway to more emission-efficient railway. Furthermore, we argue that the transportation infrastructure's impact on the enhanced mobility of labor and other agricultural inputs would further accentuate the regional shift in agricultural production and contribute to carbon emission mitigation. Upon its completion, PNL2035 is expected to result in the reduction of net national emissions by 1.8–30.7 million metric ton of CO2-equivalent, depending on the impacts on labor and purchased input mobility. We conclude that the omission of spillover effects due to infrastructure expansion can lead to misleading assessments of transport policies.

Analysis of Model Predictive Control-Based Energy Management System Performance to Enhance Energy Transmission

A supervisory control system using Model Predictive Control (MPC) has been designed to evaluate the efficiency of wind and solar power and is consistent with the cost function in the supervisory MPC optimization problem. A two-layer Economic Model Predictive Control (EMPC) framework has been developed and has improved results such as cost reductions compared to recent advanced methods. A speed Generalized Predictive Control (GPC) scheme intended for wind energy conversion systems was developed last year, with simulation results indicating superior performance over previous models. A Hierarchical Distributed Model Predictive Control (HDMPC) can work under different weather conditions with improved economic performance and keep a good balance between power delivery and load demand. An energy management system (EMS), built on the basis of MPC, can be quite lucrative for the sphere in the present climate scenario, with the selection and testing of suitable algorithms, controlled processes, cost functions, and a set of constraints as well as with proper optimizations carried out. Previous research indicates that an MPC-based EMS has the potential to be a good solution to manage energy well and also introduced it to the world experimentally. The key intention of this research study is to explore the existing advances that have been introduced and to analyze their performance in terms of cost function, different sets of constraints, variant conversion processes, and scalability to achieve more optimized operation of MPC-based EMS.

INVITED PAPER: EMOTION EXPRESSION AND INDIRECT RECIPROCITY

This study explores how emotion expression (EE) can help promote positive indirect reciprocity while also breaking chains of negative indirect reciprocity. We report data from a two-stage dictator game and compare the decisions subjects make towards a third party both with and without the chance to express emotion. Standard economic models suggest opportunities to express emotion should have no impact on behavior. In sharp contrast to these predictions, we find that subjects who are treated unkindly behave more generously towards others in subsequent interactions if they have the opportunity to convey their emotions to a third party. Our findings have important welfare implications. In particular, they suggest that opportunities to express emotion can break negative reciprocity chains and promote generosity and social well-being even among those who have not themselves been previously well-treated. We argue that reference-dependent valuations can help to explain our results.

Pakistan's path to sustainability: Advancements in cleaner production, a circular economy, and climate-smart solutions

Pakistan is at a critical juncture in its sustainability journey, a moment characterized by diverse land­scapes and resilient communities. This commentary explores the multifaceted approach necessary to tackle the nation’s environmental, economic, and societal challenges within the framework of cleaner production, a circular economy, and climate-smart solutions. Environmental issues loom large, with rising air and water pollution, land degradation, and the increasing threat of climate change. To combat these challenges, adopting cleaner production tech­nologies and circular economy practices across industries is imperative. Pakistan is embracing cir­cular principles to mitigate pollution, reduce waste, and enhance resource efficiency, particularly in the agricultural sector, where circular agriculture prac­tices are ensuring food security and sustainability. The circular economy is an economic model that aims to minimize waste and make the most effi­cient use of resources by keeping products and materials in use for as long as possible through reuse, repair, remanufacturing, and recycling. It seeks to design out waste and pollution, regenerate natural systems, and transition toward renewable energy sources (Bourguignon, 2016). In Pakistan, the nation’s broad aspirations and goals regarding the circular economy are centered around sustaina­ble development and environmental stewardship. The government is committed to promoting prin­ciples of a circular economy to address environ­mental challenges, enhance resource efficiency, and promote economic growth. . . .

Advancements and Prospects of Electronic Nose in Various Applications: A Comprehensive Review

An electronic nose, designed to replicate human olfaction, captures distinctive ‘fingerprint’ data from mixed gases or odors. Comprising a gas sensing system and an information processing unit, electronic noses have evolved significantly since their inception in the 1980s. They have transitioned from bulky, costly, and energy-intensive devices to today’s streamlined, economical models with minimal power requirements. This paper presents a comprehensive and systematic review of the electronic nose technology domain, with a special focus on advancements over the last five years. It highlights emerging applications, innovative methodologies, and potential future directions that have not been extensively covered in previous reviews. The review explores the application of electronic noses across diverse fields such as food analysis, environmental monitoring, and medical diagnostics, including new domains like veterinary pathology and pest detection. This work aims to underline the adaptability of electronic noses and contribute to their continued development and application in various industries, thereby addressing gaps in current literature and suggesting avenues for future research.

Predictive Modeling of Default Scenarios in the CMBS Market: A Macroeconomic Approach

This study analyzes potential default risks in Mortgage-Backed Securities (MBS) using time series data from 2010 to 2023. Employing Vector Autoregression (VAR) models to forecast economic indicators and calculate DTI and LTV ratios, which are crucial for predicting defaults in the MBS market. Our analysis, based on two economic models, examines the influence of macroeconomic factors and credit default risks on residential and commercial properties, such as Fixed-Rate and Adjustable-Rate Mortgages. The findings suggest that despite the significant impact of the pandemic, the likelihood of default in the American MBS market over the next 15 years remains relatively low. The study emphasizes the significance of comprehending the relationship between macroeconomic conditions and MBS default risks, providing a comprehensive methodology for future research.

Unraveling opportunism in platform-mediated work within the Nordic working life model: An institutional complexity perspective

The Nordic working life model (NWLM) promotes ‘good work’ on societal and workplace levels. However, this model is now challenged by emerging business models in the platform economy. This study investigates how digital labor platforms respond to conflicting institutional logics and how platform-mediated work intervenes with the inherent logic of the NWLM. The authors examine platform business strategies and their implications for working environment regulations, co-determination, and collective bargaining. Empirical data comprising 50 interviews with food delivery workers, platform managers, union representatives, employer association representatives, and occupational health and safety regulators from the Norwegian Labor Inspection Authority were analyzed by applying institutional complexity as a theoretical framework. The findings illustrate that a high degree of institutional complexity provides companies with discretionary space, which they utilize to achieve legitimacy and competitive advantages. The authors introduce the term institutional opportunism to describe how adaptation is performed. The study reveals that the platform economy, characterized by workers with limited experience of and knowledge about working life and strong market pressures, poses a challenge to the NWLM.

Digital Nomad and Analysis of Regional Economic Resilience of Tourism Sector in Bali Province After Covid-19 Pandemic

This study aims to analyze the impact of the pandemic on Bali's regional economic resilience and how the workcation trend brought by digital nomad tourists able to encourage tourism recovery in Bali. The research method used is descriptive-analytical, including examining documents and statements from experts that explained the response to the economic shocks that occurred in Bali Province. Data sources were obtained from tourism ministry performance reports, BPS data, news articles and accredited journals. The study revealed that the resistance phase is demonstrated by the disruption of the tourism sector when the COVID-19 pandemic broke out, causing economic Bali to decline. The reorientation phase is demonstrated by the ability of government and business actors to adjust and adapt from shocks. The workcation trend brought by digital nomads is suitable for application during the adaptation period. The recovery phase is demonstrated by the revival of tourism in Bali Province with an increase in the number of foreign tourists. One of them comes from digital nomad tourists who increase regional income. Based on analysis using the regional economic resilience model, Bali Province in 2023 will approach economically resilient status.

Construction and application of economic resilience evaluation model for megacities.

Economic resilience provides a new perspective for megacities to achieve sustainable development when facing multiple shocks, and its accurate evaluation is an essential prerequisite for optimizing urban governance. There are currently no generally accepted methods for empirical evaluation or measuring economic resilience, and the present study aims to contribute to in both the research field and methodology. The present study sets dimensions and indicators based on economic resilience's theoretical and empirical research and used Decision Making Trial and Evaluation Laboratory (DEMATEL) and Interactive Structural Modeling (ISM) methods to exclude the effect indicators and divide the indicator hierarchy, respectively. Subsequently, the present study conducts model validation using Chinese megacities as a case study. The game theory weighting method, which combines the Analytic Hierarchy Process (AHP) and Entropy methods, is used to calculate indicator weights, and the VIKOR (VIseKriterijumska Optimizacija i KOmpromisno Resenje) method is used to evaluate and compare economic resilience of megacities. The research findings indicate that the evaluation model constructed in the present study included 15 indicators (after excluding three effect indicators) divided into four levels. After merging the levels, they correspond to three dimensions: resistance, recoverability, and adaptability. In addition, using Chinese megacities as a case study, the evaluation results found that Beijing, Shanghai, and Shenzhen have high economic resilience, Tianjin and Guangzhou have moderate economic resilience, Chengdu has low economic resilience, and Chongqing has the lowest economic resilience. This result is consistent with previous studies and verifies the model's effectiveness. The present study also found that megacities with lower levels of economic resilience exhibit a more significant upward trend, as well as the highest and higher proportion of economic resilience in Chinese megacities depending on time passes, indicating that megacities' economic resilience is weakening. The evaluation result obtained in the present study is more specific, precise, and focused on depicting the distribution differences and development trends of economic resilience at the urban level.

Time Series Analysis of Market Dynamics within Top NFT Collection

The current investigation delves into the valuation trends of prominent Non-Fungible Token (NFT) collections, entities at the forefront of the digital economy that are redefining concepts of asset ownership and artistic appreciation. The academic import of this study is anchored in the emergent nature of NFTs and their paradigmatic shift from traditional economic models. This research aims to decipher the market value fluctuations of top NFT collections through a comprehensive time-series analysis, the application of the ARIMA model. Methodologically, the study adheres to established time-series analytical procedures, with a focus on identifying and interpreting patterns and trends within the market data. The approach synthesizes a broad spectrum of economic and cultural variables that potentially exert influence over the NFT marketplace. Results gleaned from the analysis yield insights into the price movements of NFTs, offering a juxtaposition of predicted and actual market data to deepen the understanding of this unique market sector. The implications of this research extend beyond academic interest, offering a vital resource for stakeholders within the digital economy. The discerned patterns and dynamics of NFTs, as revealed through the study, contribute to a granular understanding of digital assets, providing a scaffold for future research endeavors and practical guidance for participants in the digital art and ownership space.

Green Entrepreneurship Integration in Maoming City’s Universities Curriculum

Nowadays, petrochemical cities are all facing environmental problems, which is also a problem that Maoming, Guangdong Province, China, has been trying to solve. Maoming is a key petrochemical city in Guangdong Province, and solving environmental problems is a necessary measure to develop an economic model in the future. Maoming has unique conditions there are three colleges in Maoming City, such as Guangdong University of Petrochemical Technology, Guangdong Maoming Preschool Normal College, and Guangdong Maoming Agriculture & Forestry Technical College. The current research strategy involves combining green development with university students’ research topics and integrating development into these research topics. Through the output of some problems, corresponding feedback can be obtained and solutions can be made.

Does the emissions trading system promote regional green innovation? – Evidence from Chinese prefecture-level cities

ABSTRACT The previous economic development model based on scale expansion has caused serious environmental pollution and resource constraints. Therefore, the development of green innovation with clean production and environmental protection as the core is particularly important. The emissions trading system (ETS) has been introduced in the process of market-oriented environmental regulation, and its impact on green innovation is of great significance. This paper examines the impact of the ETS on regional green innovation and its mechanism of action using the PSM-DID method based on data from 154 prefecture-level cities in China. The study finds that the ETS has a positive effect on improving green innovation, and this effect gradually increases over time. The impact mechanism test shows that the ETS promotes green innovation by increasing regional innovation R&D investment, and the regional business environment plays a positive role in this process. Heterogeneity tests show that the effects of ETS are more pronounced in more polluted regions and in regions with stricter environmental regulations. This paper provides theoretical support for the implementation of ETS in China and some developing countries, and empirical evidence and policy implications for the green innovation effect of ETS.

Public Health Impact and Cost-Effectiveness Analysis of 4-Component Meningococcal Serotype B Vaccination for Infants in France.

In France, meningococcal serogroup B (MenB) is the most common serogroup causing invasive meningococcal disease (IMD) in infants and young children. Our objective was to illustrate the impact of model choices on health outcomes and the cost-effectiveness of infant vaccination with the multicomponent meningococcal serogroup B vaccine (4CMenB) versus no vaccine in France. A previously published dynamic transmission-based cost-effectiveness model was adapted for the French context using updated, French-specific demographic, epidemiological, and cost data. IMD incidence and long-term sequelae were derived through analysis of French healthcare and surveillance databases. A collective perspective over a 100-year time horizon was adopted, with a discount rate of 2.5%, reduced to 1.5% after the first 30 years. Deterministic and probabilistic sensitivity and scenario analyses were performed. In the base case analysis, infant vaccination with 4CMenB avoided 3101 MenB IMD cases in infants aged <1 year (-54%) and 6845 cases in all age groups (-21%). The estimated incremental cost-effectiveness ratio was €316,272/quality-adjusted life-year (QALY) but was highly sensitive to the types of sequelae included, MenB incidence, vaccine effectiveness parameters, and consideration of life-expectancy in IMD survivors (range: €65,272/QALY to €493,218/QALY). Using economic models compliant with French methodology guidelines, 4CMenB does not seem cost-effective; however, results are sensitive to model choices and 4CMenB immunization is an effective strategy to prevent MenB IMD cases and to improve quality of life and economic burden associated with MenB IMD treatment, especially with regard to long-term sequelae.

Techno-Economic, Environmental, and Policy Perspectives of Carbon Capture to Fuel Technologies

This research paper provides a comprehensive exploration of carbon capture to fuel technologies, covering various capture methodologies and conversion processes. The analysis begins by dissecting post-combustion, pre-combustion, and direct air capture technologies, elucidating their principles, advantages, and limitations. A focus on the conversion of captured carbon into usable fuels delves into synthetic fuels and hydrogen production methods, detailing chemical processes, catalysts, and energy requirements. Moving beyond technical aspects, the paper critically analyzes the efficiency and viability of carbon capture to fuel processes, employing case studies and real-world examples to illustrate the practical application of techno-economic assessments and life cycle analyses. Economic considerations further assess implementation costs, operational expenses, and potential revenue streams, drawing insights from existing economic models and case studies. Environmental impact and benefits take center stage, evaluating potential reductions in greenhouse gas emissions, resource efficiency, and ecological considerations associated with converting captured carbon to fuel. A comparative analysis with other carbon capture applications offers a holistic perspective on the environmental footprint. The regulatory landscape is thoroughly examined, encompassing existing policies, government incentives, and international agreements influencing the development and deployment of carbon capture to fuel technologies. The research concludes with reflections on the current status, challenges, and a roadmap for future advancements, serving as a comprehensive guide for researchers, policymakers, and industry stakeholders in the pursuit of sustainable energy solutions.

College rankings, labor market outcomes, and alumni satisfaction

ABSTRACT State and federal governments invest millions of dollars in providing accurate and relevant information on expected outcomes to students pursuing higher education, but whether such information targets what students value about college is unclear. We use new survey data to identify the extent to which conventional indicators for college quality and returns correlate with collegegoers’ satisfaction with their education. We find that alumni are on average very satisfied with their education, even among those with poor labor market outcomes. Our results suggest the consumptive value of higher education is much higher than economic models of college choices typically assume.

Qualitative assessment of optimizing the well spacings based on the economic analysis

The design of well spacing significantly influences the sustainability and economic benefit of geothermal energy extraction. However, most studies have predominantly employed heat production-related parameters as indicators of well spacing, and a comprehensive analysis of well spacing design based on an economic model is necessary for practical implementation. In this study, an economic indicator considering the benefits derived from heat production and operating costs is proposed and applied in the Caofeidian, a typical abandoned oilfield in the Bohai Bay Basin. It offers a refined portrayal of directional wells, moving beyond rudimentary representations, to capture their appropriate degree of complexity and behavior in drilling configurations. First, by integrating thermophysical information and site investigation data from previous oil investigations, a heterogeneous 3D model is constructed to forecast the 30-year temperature and pressure evolution. Then, a modified levelized cost of heat (LCOH-HT) is proposed to perform economic analysis in optimizing the well spacing, revealing an optimal range of 300–600 m for the different selected wells. In comparison with results derived solely from heat production considerations, drilling and pumping costs contribute to a 300 m reduction in the optimal well spacing based on the proposed approach, as a larger well spacing leads to increased hydraulic losses and drilling cost, necessitating greater pumping efforts and costs. This finding underscores the need to balance economic and thermal considerations. In addition, we found the difference in the optimal well spacing in space is also caused by the porosity variations. Porosity affects fluid temperature and pressure, leading to changes in the benefits and costs associated with pressure fluctuations. Notably, this novel economic analysis method is not limited to spacing optimization; it can also be used to optimize operating parameters, such as the flow rate, which could provide practical strategies for geothermal energy extraction.

The economic production quantity model for an imperfect production process by control chart design

The Economic Production Quantity (EPQ) plays a crucial role in the manufacturing process. The traditional EPQ model operates under the assumption that Non-Conforming (NC) items are not produced, and the process remains under control. However, real-world scenarios often involve the production of NC products, leading to process deviations. This study introduces a model that optimizes the design of a control chart to incorporate NC products into the EPQ model. The model integrates inventory and quality-related costs to minimize the expected total cost by determining EPQ, sample size, and control limit. The solution methodology employs direct search techniques to solve this problem. The sensitivity analysis results demonstrate the significant cost reduction achieved by the proposed model compared to the classical EPQ model. The numerical examples presented in the study show an average cost reduction of approximately 21 percent, highlighting the potential effectiveness of the model in cost reduction efforts.