Economic Model Research Articles

Implications of Innovative Strategies for Sustainable Entrepreneurship—Solutions to Combat Climate Change

We face significant challenges related to sustainability and social responsibility, so sustainable entrepreneurship is a key way to help address these issues. How can sustainable entrepreneurship contribute to combating climate change? This is the question from which this research started. The paper proposes important research directions to develop innovative and sustainable solutions tailored to the problem. By investigating the impact of different innovation strategies, the study aims to provide concrete recommendations to all stakeholders. Recognizing that effective climate action requires collaboration among government, businesses, non-governmental organizations, and citizens, the study emphasizes the importance of raising awareness, education, and access to information. Using fsQCA software version 4.1, the research analyzes various innovation strategies to develop tailored, sustainable solutions. The findings offer concrete recommendations for stakeholders, aiming to facilitate the implementation of sustainable practices and foster a supportive framework for green initiatives. Ultimately, this study contributes to efforts aimed at reducing the carbon footprint and promoting a more responsible economic model in Romania. The results not only provide concrete recommendations for relevant actors but also outline a strategic vision on how a more responsible economic model can be promoted in Romania, with the potential to influence sustainable development policies and inspire future initiatives.

The Role of the Federal Institutes of Education, Science and Technology in Forming a Workforce Aligned with Circular Economy Principles and Strategies

Objective: This study aims to assess the adherence of the themes and the current curricular matrices of the courses after high school at the Brazilian Federal Institutes of Education, Science, and Technology regarding the principles and strategies of the Circular Economy (CE). Theoretical Framework: In the current scenario of resource scarcity and climate emergency, CE emerges as a viable alternative. In Brazil, the Federal Institutes (FIs) can play a crucial role in training a qualified workforce for CE, offering professional education that meets market demands. Method: The methodology adopted is classified as qualitative and applied. Regarding its classification, it is descriptive. Regarding the design, it is classified as a documentary since the primary data source was the IFs' websites. Results and Discussion: The results reveal that 26.17% of the 554 courses evaluated have content related to CE. The analysis showed a predominance of disciplines, such as Environmental Management, but a gap in specific CE courses. Research Implications: From a practical perspective, this study provides insights into the need for changes in the current economic model regarding public education policies adopted in Brazil to prioritize areas of study. As for the theoretical contribution, the study discusses conceptual issues of CE based on findings from the Brazilian context in order to fill a research gap. Originality/Value The research highlights the importance of training professionals with critical and practical skills in CE, highlighting the need for curricular reform that meets new market demands and promotes a circular mindset.

Digitalisation in European regions: unravelling the impact of relatedness and complexity on digital technology adoption and productivity growth

ABSTRACT Digital technologies are the cornerstone of platform, knowledge, and network economies, driving the interconnectedness, innovation, and efficiency that define these economic models. Consequently, digitalisation has recently become a key policy objective for regional economies. This paper provides empirical evidence on how the adoption of new digital web technologies is shaped by previous regional digital capabilities. The analysis is based upon an economic complexity and relatedness framework using novel data on digital web technologies’ adoption for 278 European NUTS-2 regions between the years 2000 and 2022. Results show that regions tend to adopt new digital web technologies when they already master related digital capabilities. This paper also shows how digital complexity is associated with labour productivity gains at the regional level. Conclusions shed light on how regions adopt digital web technologies and serve as a tool for policymakers.

Optimal dispatch scheme considering system operational flexibility

To enhance the system's capacity to manage emergencies and foster the efficient utilization of newly generated energy, this paper introduces a redefined concept of system operation flexibility and proposes a two-stage optimized economic dispatch model. This model is tailored to ensure the system's operational flexibility. In this method, net load is pinpointed as the primary driver of flexibility demand, and a data-driven approach is employed to capture the uncertainties associated with net load across various risk levels. As flexibility supplier, conventional units work in tandem to optimize their operating points, fulfilling flexibility demand while adhering to the Do Not Exceed (DNE) baseline, thereby ensuring that system flexibility remains within safe limits. A case study on the modified SG-126 bus system demonstrates that the proposed methodology effectively highlights flexibility shortfalls and validates its efficiency in terms of cost and inter-regional coordination.

US land sector mitigation investments and emissions implications

The land sector is anticipated to play an important role in achieving U.S. GHG emissions targets by reducing emissions and increasing sequestration from the atmosphere. This study assesses how much different levels of investment could stimulate land-based mitigation activities in the U.S. By applying a dynamic economic model of the land use sectors, with representation of 26 forestry and agricultural mitigation strategies across 11 U.S. regions, the study shows that annual investments of $2.4 billion could deliver abatement of around 80 MtCO2e annually. Under an optimal allocation of investments, the forestry sector and the Corn Belt are projected to receive the largest share of funds. Restricting land-based activities eligible for funds significantly reduces overall potential mitigation. For instance, if $24 billion investments are allocated only to agricultural activities, mitigation declines by 48% to 54 MtCO2e/yr over the next ten years. Finally, the level of abatement from each policy depends on the timing of implementation as the lowest cost mitigation actions are generally taken by the policy implemented first.

A comprehensive cost mapping of digital technologies in greenhouses

Conventional greenhouse producers face significant challenges in integrating advanced Industry 4.0 technologies into their production processes. One of the main obstacles is the lack of clarity regarding the components of technological costs. This article develops a cost mapping for the implementation of such technologies in the context of greenhouses. The mapping distinguishes between capital expenditures (CAPEX) and operational expenditures (OPEX), categorizing the key technological components and their financial implications. Based on the general findings from the literature review, several cost areas can be identified and classified as follows: hardware acquisition, installation and retrofitting, integration and customization, software and services, operational and maintenance costs. This cost structure will serve as a basis for future economic models and cost-benefit analysis (CBA), promoting strategic decision-making and a more informed and precise selection of digital technologies.

Are random price generators useful for health policy processes and analysis? effectiveness of random price generators in health policy processes and analysis

This paper discusses why using random price generators in an economic model for medical markets may be useful. It summarizes the different steps for the development of the model and describes an original contribution of the physicians’ choice model with random drug prices applied to Type 2 diabetes in the US. Pharmaceutical markets have been supply-driven to boost life science and medical technologies; however, with the widening inequalities inside national health systems and the global agenda for universal health coverage, more economic research is being done to strengthen the analysis of demand for healthcare services. Research, especially, examines more disaggregated levels of demand systems to understand the heterogeneity of physicians’ choices and better capture patient needs. It can also be an approach to calibrate supply and demand adjustments in medical markets. This paper argues that choice modeling is particularly relevant, using random price generators in a structural model where a demand approach is useful. If validated by additional experimental studies, this first study by Professors Huttin & Hausman (2021) could be used in advanced value assessment frameworks. Random price generators on medical markets could also be tested with additional models that fit oligopolistic market structures (e.g., models for differentiated product markets such as the Berry–Levinsohn–Pakes model). It may also help with a policy analysis process that addresses major disruptive transformations of market dynamics, evolving boundaries in science, fast digitalization, and artificial intelligence-based information systems.

Addressing Internal Migration Challenges in Kazakhstan: A Systematic Approach

This research article provides a comprehensive analysis of migration processes within Kazakhstan, focusing on the challenges in internal migration. The current trends in internal migration are leading to substantial differences in the distribution of the workforce, negatively affecting both the economy of the country and regional inequalities. The authors present an economic and mathematical model that demonstrates a connection between a decrease in the appeal of migration to certain locations and a fall in the gross regional product. Furthermore, a thorough examination of the legal framework, program execution, and policy documents reveals problems in the effective use of regulatory mechanisms, particularly in relation to rural migration issues. The study presents a systematic approach and an organizational regulatory scheme to improve the scientific methodology. This scheme takes into account numerous aspects and conditions that affect internal population migration dynamics. Received: 11 July 2024 / Accepted: 31 October 2024 / Published: 05 November 2024

Economic system of developed socialism: Experience and lessons

The paper deals with the analysis of economic performance and economic policy of the USSR at the stage of “developed socialism”, fundamentals and characteristics of the Soviet economic model and mechanisms of its emerging crisis. Special attention is paid to the developed socialism phenomenon of Dutch disease and its influence on economic and political trends. Analysis has been done through the prism of the experience of the last 50 years of Russian economic development. Conclusions are drawn about the lessons learned by modern Russia from the experience of the 1970s.

Application of Big Data in Digital Currency and Blockchain Finance

With the rise of emerging economic models, the cryptocurrency system, centered on the internet and electronization, is gradually returning to its core functions — storage, data processing, and related services. The deep value of massive data lies in rapid mining to uncover its inherent patterns, which is not only crucial for the operation, regulation, and monetary policy implementation of cryptocurrencies but also serves as an essential support for the transition from paper money to digital currencies. Given this, this research plans to conduct a detailed exploration of the application of big data in the field of cryptocurrency and blockchain finance, aiming to contribute to the continuous progress of China’s financial industry.

ЛЮДСЬКИЙ КАПІТАЛ В КОНЦЕПЦІЇ ВІДКРИТОЇ НАУКИ

Introduction. Any economic system's development is only possible with capital participation. Over several centuries, a stable concept has emerged that considers human capital as an essential source of investment for innovation, scientific and technological progress, and the stable development of the state. At the same time, the practical realization of this concept is associated with such categories as education, science, healthcare, competencies, skills, professional abilities of employees, intellectual output, etc. The purpose of the article: The aim is to comprehensively investigate the innovative potential of human capital from the perspective of a driving force for progress in the knowledge age. Methods: Modern science is based on openness and accessibility as tools of scientific search and research. Human capital is an essential element of this search, expressed in intellectual abilities, self-development, and theoretical and practical generalizations. The basis for more profound research and generalizations is the study of internal and external influences on the formation of human capital and the definition of its role in the scientific sense. Results: Analyzing human capital through the prism of its structural formation, it is worth noting that its meaning has changed in different periods. In the twentieth century, when the economy was formed in the context of industrial development, human capital was focused on technological excellence and ingenuity. In the 21st century, economic development is dominated by the intellectual and scientific component, which is the basis for the functioning of the national system. Therefore, human capital in this period is gaining new meaning as a knowledge economy. In forming the country's national economy, the intangible form of capital is becoming increasingly important, where human resource is the basis of socio-economic development. Conclusions. As a result of the theoretical analysis of existing approaches and new proposals, the innovative economic development model is developing dynamically. Human (intellectual) capital is prioritized in this model, stimulating informatization and introducing modern scientific and technological innovations. Human capital is a complex phenomenon that adapts to modern innovative and technological changes and acts as an organizational motivator for achieving a high level of industrialization and a civilized environment.

Optimizing public investments: A Sustainable Economic, Environmental, and Social Investment Multi-criteria Decision Model (SEESIM)

One of the most important aspects for the economic and social growth of a country is high efficiency of public investments. This imperative has never been more critical, especially in the context of Europe and Italy, where the integration of environmental, economic, and social goals is essential to address the multifaceted challenges of the 21st century. This research introduces the Sustainable Economic, Environmental, and Social Investment Model (SEESIM), a novel multi-criteria decision-making framework designed to ensure the sustainability of projects as their validity over time, especially from a National Recovery and Resilience Plan perspective. Utilizing the Analytic Hierarchy Process (AHP), SEESIM integrates environmental, economic, and social criteria as useful tool to evaluate a project in the post-funding phase, ensuring a balanced approach to efficiency and sustainability. The model is adaptable to various regional contexts, providing a transparent and replicable methodology for assessing the impacts and costs of public investments. An experimental scenario demonstrates SEESIM’s utility in guiding investment decisions, highlighting its potential to significantly enhance the effectiveness of public resources in achieving long-term sustainability goals. SEESIM represents a pivotal advancement in sustainable development strategies, offering a comprehensive tool for integrating multi-dimensional sustainability criteria into public investment decisions.

Optimal dimensioning of renewable energy generation and storage systems

With high energy demand and large available area, agricultural farms offer significant potential for renewable energy investments, like photovoltaic systems and electrical energy storages. However, the profitability of such investments depends strongly on self-consumption, so accurate planning requires computation-intensive simulation and optimization considering local consumption. This study presents a novel methodology for the optimal dimensioning and configuration of photovoltaic systems and electrical energy storages using efficient techniques from continuous non-linear optimization. Combining physical and economic models with measured consumption data, an investment’s net present value over 20 years is maximized. Using gradient-based solver WORHP, the simulation, optimal control, and optimal dimensioning of the local energy system are calculated simultaneously, allowing for efficient computation over an entire year of hourly data to capture both daily and seasonal variations.The approach is demonstrated with simple use cases, including an exemplary day of a dairy farm’s consumption, for which optimal systems with and without storage achieve 77% and 43% of autarky, respectively. Saturation effects of optimal plant size can be observed when sizes are large enough for optimal self-consumption but not expanded further for grid export. With energy storage, this saturation is reached at higher values. Optimizing photovoltaic plants with different orientations to match the specific consumption patterns characteristic of the dairy farm achieves similar autarky as a single plant while reducing investment costs by more than 20%. While thorough validation and comparison against heuristic methods predominantly used in the field is part of ongoing research, the presented use cases demonstrate the flexibility and efficiency of the proposed method and highlight its promise as a planning tool in the agricultural domain and beyond.

Health impact and cost-effectiveness of vaccination using potential next-generation influenza vaccines in Thailand: a modelling study

IntroductionThailand was one of the first low- and middle-income countries to publicly fund seasonal influenza vaccines, but the lack of predictability in the timing of epidemics and difficulty in predicting the dominant influenza subtypes present a challenge for existing vaccines. Next-generation influenza vaccines (NGIVs) are being developed with the dual aims of broadening the strain coverage and conferring longer-lasting immunity. However, there are no economic evaluations of NGIVs in Thailand.MethodsWe estimated the health impact and cost-effectiveness of NGIVs in Thailand between 2005 and 2009 using a combined epidemiological and economic model. We fitted the model to data on laboratory-confirmed influenza cases and then simulated the number of influenza infections, symptomatic cases, hospitalisations and deaths under different vaccination scenarios based on WHO-preferred product characteristics for NGIVs. We used previous estimates of costs and disability adjusted life years (DALYs) for influenza health outcomes to estimate incremental net monetary benefit, vaccine threshold prices and budget impact.ResultsWith the current vaccine programme, there were an estimated 61 million influenza infections. Increasing coverage to 50% using improved vaccines reduced infections to between 23 and 57 million, and with universal vaccines to between 21 and 49 million, depending on the age groups targeted. Depending on the comparator, threshold prices for NGIVs ranged from US$2.80 to US$12.90 per dose for minimally improved vaccines and US$24.60 to US$69.90 for universal vaccines.ConclusionInfluenza immunisation programmes using NGIVs are anticipated to provide considerable health benefits and be cost-effective in Thailand. However, although NGIVs might even be cost-saving in the long run, there could be significant budget implications for the Thai government even if the vaccines can be procured at a substantial discount to the maximum threshold price.

Disinformation and Mutual Trust: An Economic Model

Information or disinformation is more likely to be believed when it originates from a trusted person or source, indicating that the impact of disinformation varies significantly based on the level of trust involved. Additionally, one individual's judgment can be influenced by the judgments of others, and conversely, an individual's judgment can influence those around them. To explore this dynamic, the author constructs a model for uncovering disinformation and integrates it with a model of disinformation dissemination. The findings show that as the level of mutual trust within an economy or society rises, the likelihood of uncovering disinformation increases. Furthermore, a high level of mutual trust significantly reduces the acceptance and belief in disinformation, which in turn enhances the efficiency of various economic activities. Thus, mutual trust emerges as a key factor in achieving high levels of economic, social, and potentially political performance.Copyright© 2024 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.

Thermodynamic and economic analysis of ocean thermal energy conversion system using zeotropic mixtures

Zeotropic mixtures offer a promising strategy for enhancing the thermodynamic efficiency and economic feasibility of ocean thermal energy conversion (OTEC) systems. This study investigates two binary mixtures containing R32: R32/R125 and R32/R134a. Through the development of comprehensive thermodynamic and economic models, the research examines the impact of mass fraction and evaporation temperature on the efficiency and cost-effectiveness of the OTEC system. The results indicate that, especially at high evaporation temperatures, the R32/R134a mixture—characterized by significant temperature glide—substantially increases the total energy production capacity of the OTEC system. Compared to pure R32, the OTEC with R32/R134a (mass fraction of R32 is 0.55) has a net output power increase of 9.87kW and a reduction in LCOE of about 61.4 %. In addition, the advantages of R32/R125 mixtures over pure working fluids are not significant due to the small glide temperature. Ultimately, this investigation enhances the overall performance of OTEC systems, thereby supporting sustainable energy solutions for island communities.

Forecasting the daily exchange rate of the UK pound sterling against the US dollar

This paper is the first to use an economic theory-based model—the monetary model of exchange rates within a rational expectations present value framework—to forecast the daily exchange rate of a major currency. Our out-of-sample forecast evaluation period, spanning from 1990 to 2024, is longer than that of any other exchange rate forecasting study. We find that our model's forecasts outperform the random walk across all forecasting horizons, ranging from one day to five years. Moreover, a trading strategy based on our model's forecasts yields economically and statistically significant excess returns, surpassing those of the carry trade strategy.

Sociodemographic, economic, and academic factors linked with resilience in university students during covid-19 pandemic: a Brazilian cross-sectional study

BackgroundResilience is a crucial factor in students’ mental health, playing an important role in their successful adaptation to the academic environment. However, there is a lack of understanding about resilience and its associated factors in students from different undergraduate courses. This study aimed to describe the resilience profile of undergraduate students from various courses in Brazil and identify sociodemographic, economic, and academic factors associated with resilience.MethodsThis study has data from a cross-sectional multicenter study involving undergraduate students from eight Federal Institutions of Higher Education in Minas Gerais, Brazil. The dependent variable was resilience, measured using the Connor-Davidson Resilience Scale (CD-RISC) 10-item version, with sociodemographic, economic, and academic factors considered independent variables. Data was collected virtually via a self-administered questionnaire between October 2021 and February 2022 (during the covid-19 pandemic). Independent samples t-tests and ANOVAs were conducted to compare resilience scores between independent variables, and Tukey’s post-hoc test was performed when necessary. Multiple linear regression was performed to create three models.Results8,650 undergraduate students were included in this study. The average score on the resilience scale was 19.86 ± 8.15, with a normal distribution. The respondents ranged from 18 to 71 years old, averaging 23.9 ± 6.33. Being female, not having a religious belief, having low per capita family income, having had a decrease in the family income, not being heterosexual, or having the head of the family with a low education level were the main factors associated, individually, with low resilience scores in the sociodemographic and economic multiple linear regression model constructed. Being from linguistics, letters and arts courses, being enrolled in fewer subjects, or being from UFMG were the main factors associated, individually, with low resilience scores in the academic multiple linear regression model constructed.ConclusionsThe study’s findings revealed that sociodemographic, economic, and academic variables were significantly associated with resilience scores in undergraduates from Minas Gerais during the covid-19 pandemic. These findings can help universities develop target strategies to promote students’ resilience and reduce the risk of poor mental health among this population.

Special section introduction: epistemic politics in international and comparative political economy

ABSTRACT Economic orthodoxy has faced multiple challenges over recent years, ranging from the global financial crisis and economic populism to the Covid-19 pandemic and the spectre of stagflation. This has generated significant policy innovations, like quantitative easing and the furlough schemes, while heterodox economic ideas (briefly) gained notoriety. Yet these shifts have frequently been grafted onto pre-existing institutional and discursive templates, anchored by the assumption that they represent a temporary deviation from ‘normal’ equilibrium conditions. To make sense of these dynamics of crisis, change and continuity, this special section showcases an ‘epistemic politics’ approach that foregrounds the political life of economic knowledge. It is framed around three questions. What has been the role of economic knowledge and expertise in shaping policy and regulation over the past decade? To what extent have dominant economic ideas, theories and models been impacted by political conflicts and emergent issues? How have policy makers navigated economic crises and uncertainty through cognitive and discursive practices? This introductory paper sets out the ambition for the special section by defining our understanding of epistemic politics, before detailing the threefold contribution it makes to wider scholarship in IPE and CPE. It concludes with an overview of the five papers.

Green hydrogen, power, and heat generation by polymer electrolyte membrane electrolyzer and fuel cell powered by a hydrokinetic turbine in low-velocity water canals, a 4E assessment

Thousands of kilometers of man-made low-velocity water transfer canals around the world can be used as a source of renewable energy for electricity and green hydrogen production. These canals have not been well investigated as an energy source, according to the literature. In the present paper, three technologies of Hydrokinetic Turbine (HKT), Polymer Electrolyte Membrane Fuel Cell, and Electrolyzer (PEM-FC/EL) are utilized to produce electricity, green hydrogen, and heat using these canals. Thermodynamic, economic, and environmental models of the cycle are presented, coded in the Engineering Equation Solver software, and finally validated with published research and manufacturers’ data. Two scenarios were examined, first HKT, PEMEL, and PEMFC were used for electricity generation (power-to-hydrogen-to-power, P2X2P) and second only HKT and PEMEL were used for green hydrogen production (power-to-hydrogen, P2X). While both scenarios are economical, the P2X scenario has a smaller payback period (less than 2 years) and a higher net present value. Practical correlations are derived to determine the rate of hydrogen production, power generation, and emission reduction as a function of water velocity. The round-trip energy and exergy efficiency of the system is 46.17% and 20.78% and it reduces carbon dioxide by 0.874 tons/year when water velocity is 1.5 m/s.