Search Results (31)

This study explores the relationship between renewable and non-renewable energy consumption, economic growth (EG), and ecological footprint (EF) in Poland’s top 18 energy-importing countries from 2000 to 2022. While the energy-growth-environment nexus is well-studied, limited attention has been paid to how a single major energy-exporting country influences sustainability in its trade partners, a gap this study aims to fill. A panel dataset was constructed using five key variables: real GDP per capita, Poland’s fuel exports, ecological footprint per capita, renewable energy consumption, and primary energy consumption per capita. Methodologically, the study employs panel cointegration techniques, including FMOL and DOLS estimators for long-run analysis, as well as the VECM and Granger causality tests for the short run. The study’s main contribution lies in its novel focus on Poland’s export influence and the application of advanced econometric models to examine long-run and short-run effects. Results indicate a stable long-run cointegration relationship. Specifically, a 1% increase in renewable energy use is associated with a 0.0219% rise in GDP per capita. Additionally, Poland’s fuel exports and ecological footprint positively impact growth, whereas primary energy use is statistically insignificant. These findings offer practical implications for policymakers in Poland and its trading partners aiming to align energy trade with sustainability goals. Full article

The principal objectives of this study were to determine how government spending on human capital, specifically on education and healthcare, impacts Saudi Arabia’s economic growth and its policy implications for sustained economic growth and development. Given the above objectives, this study examined the short-term dynamics and long-term relationships between government spending on human capital, measured by per capita education and healthcare expenditures, and its impact on Saudi Arabia’s economic growth, measured by per capita real GDP, from 1985 to 2021. The Non-linear Auto-regressive Distributed Lag (NARDL) models were used to estimate and examine the relationships. The study concluded that per capita GDP is negatively correlated with per capita government spending on healthcare and positively correlated with per capita spending on education in Saudi Arabia. Per capita GDP is also positively related to exports per capita. The results of the coefficient symmetry test show that per capita spending on healthcare and education causes long-term, asymmetric effects on Saudi Arabia’s per capita GDP, that is, the decline in per capita GDP resulting from a decrease in education spending per capita is larger than the increase in per capita GDP resulting from an increase in education spending per capita. However, the decline in per capita GDP resulting from an increase in healthcare spending per capita is larger than the increase in per capita GDP resulting from a decrease in healthcare spending per capita. The study also found unidirectional causality from per capita spending on healthcare, education, and exports to per capita GDP. Therefore, this study infers that increases in government healthcare spending reduce economic growth, whereas increases in spending on education contribute to it. Saudi Arabia’s economy also experiences export-led economic growth. The results of this study provide the government and policymakers with valuable insights with respect to the efficient allocation of scarce government resources to education and healthcare for sustained economic growth and development. Full article

The transition to a circular economy is imperative in order to confer considerable benefits upon the environment, the economy, and society. The present study aimed to analyse the interdependence and causal relationships between recyclable material consumption as the dependent variable and other independent variables, including the raw material footprint, the trade in recyclable materials, greenhouse gas emissions, investments in the circular economy sectors, the real GDP per capita, renewable energy sources, the circular material use rate, and the population within the 27 EU Member States from 2013 to 2021. In order to achieve the objective, a two-stage economic model was constructed using a panel approach. The research findings indicate a direct and positive correlation between the consumption of recyclable materials and all the aforementioned independent variables, with the exception of greenhouse gas emissions. This study confirms that innovation and investment significantly reduce environmental degradation, and, moreover, the efficiency of investment remains unaffected. A further relationship that emerged from this study is that developed countries have higher resource consumption, which is consistent with the cause of increased consumption being the rapid growth of the middle class around the world. The main conclusion is that Europe cannot achieve sustainable development without a circular economy. Full article

Digital innovation in agriculture has become a powerful force in the modern world as it revolutionizes the agricultural sector and improves the sustainability and efficacy of farming practices. In this context, the study examines the effects of digital technology, as reflected by the digital economy and society index (DESI), on key agricultural performance metrics, including agricultural output and real labor productivity per person. The paper develops a strong analytical method for quantifying these associations using predictive models, such as exponential smoothing, ARIMA, and artificial neural networks. The method fully illustrates how economic and technological components interact, including labor productivity, agricultural output, and GDP per capita. The results demonstrate that digital technologies significantly impact agricultural output and labor productivity. These findings illustrate the importance of digital transformation in modernizing and improving agriculture’s overall efficacy. The study’s conclusion highlights the necessity of integrating digital technology into agricultural policy to address productivity problems and nurture sustainable growth in the sector. Full article

In the 21st century, the increasing complexity and uncertainty of the global economy have heightened the need for accurate economic forecasting. Per capita GDP, a critical indicator of living standards, economic growth, and productivity, plays a key role in government policy-making, corporate strategy, and investor decisions. However, predicting per capita GDP poses significant challenges due to its sensitivity to various economic and social factors. Traditional methods such as statistical analysis, regression, and time-series models have shown limitations in capturing nonlinear interactions and volatility of economic data. To address these limitations, this study develops a per capita GDP forecasting model based on deep learning, incorporating key macroeconomic variables—the Consumer Price Index (CPI) and unemployment rate (UR)—to enhance predictive accuracy. This study employs five deep-learning regression models (RNN, LSTM, GRU, TCN, and Transformer) applied to real and placebo datasets, each incorporating combinations of CPI and UR. The results demonstrate that deep learning models can effectively capture complex, nonlinear relationships in economic data, significantly improving predictive accuracy compared to traditional models. Among the models, the Transformer consistently achieves the highest R-squared and lowest error values across various metrics (MSE, RMSE, and MSLE), indicating its superior ability to model intricate economic patterns. In addition, including CPI and UR as additional predictors enhances model robustness, with the TCN and Transformer models showing particularly strong performance in capturing short-term economic fluctuations. The findings suggest that the deep learning models, especially the Transformer, offer valuable tools for policymakers and business leaders, providing reliable GDP forecasts that support economic decision-making, resource allocation, and strategic planning. Academically, this study advances the understanding of deep learning applications in economic forecasting, particularly in integrating significant macroeconomic variables for enhanced predictive performance. The developed model is a foundation for informed economic policy and strategic decisions, offering a robust and actionable framework for managing economic uncertainties. This research contributes to theoretical and applied economics, providing insights that bridge academic innovation with practical utility in economic forecasting. Full article

Institutional quality (InQ) plays an important role in shaping economic growth (ECG), influencing how economies develop and perform. The literature addresses the nexus between InQ and ECG and the link between health and ECG; findings are often contradictory, creating knowledge gaps. Importantly, research on the interplay between InQ, health, and ECG in Sub-Saharan African (SSA) countries is particularly limited. This study aims to address this gap by evaluating how health impacts ECG, with an emphasis on the mediating role of InQ in the health–growth nexus in SSA. This study examines these interplays across 35 SSA countries from 2012 to 2022. The life expectancy at birth (LEX) and real gross domestic product per capita (GDP) are used as proxies for health outcomes and ECG, respectively. The system generalised method of moments estimator is employed to analyse data. Results show that the LEX has a strong positive effect on economic growth in SSA countries. Furthermore, the InQ indicators (such as control of corruption, government effectiveness, rule of law and political stability, and absence of violence) are positively correlated with ECG. When the LEX interacts with InQ indicators, InQ is identified as a key channel through which LEX influences ECG. The findings confirm that InQ plays a crucial role in the health–growth nexus, with the positive impact of LEX on ECG being more pronounced in countries with higher levels of InQ, while the effect is weaker in countries with lower levels of InQ. The findings of this study have crucial policy implications, highlighting the intricate link among institutional quality, health outcomes, and economic growth. This study’s findings provide essential insights for policymakers to design focused strategies that improve InQ and health outcomes to achieve sustained ECG in SSA. Full article

This paper examines the determinants of CO₂ emissions in Oman from 1990 to 2024, focusing on the impacts of energy consumption, economic growth, urbanization, financial development, and foreign direct investment. The analysis utilizes stepwise regression to systematically identify the most significant predictors, ensuring a parsimonious model. Robust least squares (ROLSs) are employed to account for potential outliers and heteroscedasticity in the data, providing more reliable estimates. Fully Modified Least Squares (FMOLSs) is applied to address issues of endogeneity and serial correlation, offering robust long-term coefficient estimates. Canonical cointegrating regression (CCR) further refines these estimates by handling non-stationary variables and ensuring consistency in the presence of cointegration. Cointegration tests, including the Johansen and Engle–Granger methods, confirm long-term equilibrium relationships among the variables; this study reveals several key findings. Energy use per capita (ENGY) and real GDP per capita (RGDP_C) are consistently significant positive predictors of CO₂ emissions. Urbanization (URB) also significantly contributes to higher emissions. Conversely, the Financial Development Index (FDX) and foreign direct investment (FDI) do not show significant effects on CO₂ levels. The high R-squared values across models indicate that these variables explain a substantial portion of the variation in emissions. Cointegration tests confirm long-term equilibrium relationships among the variables, with the Johansen test identifying two cointegrating equations and the Engle–Granger test showing significant tau-statistics for FDX, ENGY, and URB. The VEC model further highlights the short-term dynamics and adjustment mechanisms. These findings underscore the importance of energy policy, economic development, and urban planning in Oman’s efforts towards sustainable development and decarbonization. Full article

In recent years, the socio-economic development in the Tibet region of China has experienced substantial growth. However, transportation increasingly strains the region’s fragile ecological environment. Most studies overlook the accurate measurement and analysis of factors influencing traffic carbon emissions in Tibet due to data scarcity. To address this, this paper applies an improved traffic carbon emissions model, using transportation turnover data to estimate emissions in Tibet from 2008 to 2020. Simultaneously, the estimated traffic carbon emissions in Tibet served as the predicted variable, and various machine learning algorithms, including Radial Basis Function Support Vector Machine (RBF-SVM), eXtreme Gradient Boosting (XGBoost), Random Forest, and Gradient Boosting Decision Tree (GBDT) are employed to conduct an initial comparison of the constructed prediction models using three-fold cross-validation and multiple evaluation metrics. The best-performing model undergoes further optimization using Grid Search (GS) and Real-coded Genetic Algorithm (RGA). Finally, the central difference method and Local Interpretable Model-Agnostic Explanation (LIME) algorithm are used for local sensitivity and interpretability analyses on twelve core variables. The results assess each variable’s contribution to the model’s output, enabling a comprehensive analysis of their impact on Tibet’s traffic carbon emissions. The findings demonstrate a significant upward trend in Tibet’s traffic carbon emissions, with road transportation and civil aviation being the main contributors. The RBF-SVM algorithm is most suitable for predicting traffic carbon emissions in this region. After GS optimization, the model’s R² value exceeded 0.99, indicating high predictive accuracy and stability. Key factors influencing traffic carbon emissions in Tibet include civilian vehicle numbers, transportation land-use area, transportation output value, urban green coverage areas, per capita GDP, and built-up area. This paper provides a systematic framework and empirical support for measuring, predicting, and analyzing factors influencing traffic carbon emissions in Tibet. It employs innovative measurement methods, optimized machine learning models, and detailed sensitivity and interpretability analyses. The results can guide regional carbon reduction targets and promote green sustainable development. Full article

Greenflation or inflation for green energy transition in Europe becomes a structural problem of new scarcity and poverty, under Austrian Economics analysis. The current European public agenda on the Green Deal and its fiscal and monetary policies are closer to coercive central planning, against the markets, economic calculus, and Mises’ theorem. In this paper, attention is paid to the green financial bubble and the European greenflation paradox: in order to achieve greater future social welfare, due to a looming climate risk, present wellbeing and wealth is being reduced, causing a real and ongoing risk of social impoverishment (to promote the SGD 13 on climate action, it is violated by SGD 1–3 on poverty and hunger and 7–12 on affordable energy, economic growth, sustainable communities, and production). According to the European Union data, the relations are explained between green transition and public policies (emissions, tax, debt, credit boom, etc.), GDP variations (real–nominal), and the increase of inflation and poverty. As many emissions are reduced, there is a decrease of GDP (once deflated) and GDP per capita, evidencing social deflation, which in turn means more widespread poverty and a reduction of the middle-class. Also, there is a risk of a green-bubble, as in the Great Recession of 2008 (but this time supported by the European Union) and possible stagflation (close to the 1970s). To analyze this problem generated by mainstream economics (econometric and normative interventionism), this research offers theoretical and methodological frameworks of mainline economics (positive explanations based on principles and empirical illustrations for complex social phenomena), especially the Austrian Economics and the New-Institutional Schools (Law and Economics, Public Choice, and Comparative Constitutional Economics). Full article

This article provides a macro-foundation for why the specific value of 2% is a valid inflation target. The approach postulates that innovations generate transactional cost savings by comparison to barter. The optimal velocity of money is derived as a function of productivity growth and of long-term and short-term interest rates, with coefficients reflecting the leverage ratio of depository institutions and the degree of bias in technical progress in the transaction technology. The model is tested for the U.S. (for aggregates M1, M1RS, and M1S) over the period 1959–2007. Setting the inflation target rate equal to the growth rate of velocity leads to an inflation rate near 2% and is akin to pursuing the Friedman k-% rule. This rule provides flexibility to prevent deflation. A long-term Taylor-type rule is derived. A robustness test is also conducted by extending the sample period up to 2023, covering sustained episodes of unconventional U.S. monetary policy. Full article

Nowadays, the agri-food sector is facing fundamental challenges. According to the FAO study, the amount of arable land per capita in the world will decrease from 0.6 hectares per person in 2000 to 0.2 hectares by 2050, while the demand for food will increase by 70%. With today’s yield growth of 1.5% per year, such changes could result in global food shortages. Therefore, the governments of developed and developing countries should support initiatives for the digitization of agri-food businesses and the introduction of new technologies to increase the volume of food production. Russia’s war against Ukraine is the main cause of the global food crisis, which could bring serious political and economic consequences. The agricultural and food sector of Ukraine is about 10% of GDP. For many years, the Ukrainian agro-industrial complex, before the full-scale invasion of Russia, occupied a leading position among the global exporters. Ukraine supplied 10% of world wheat exports, more than 14% of corn and more than 47% of sunflower oil. A full-scale war has become a real test for the Ukrainian agri-food industry. The invasion entailed the destruction of food production processes and logistics chains. Many sowing areas were mined, equipment and warehouses were destroyed. At the end of 2022, Ukraine exported agricultural products worth USD 23.6 billion. Although the figure for 2022 is 15% less than the record of 2021 (USD 27.9 billion), last year’s value of exports became the second since the independence of Ukraine. Disruptions to Ukrainian exports exacerbated the rise in food prices, which, according to the FAO index, increased by 54% in February 2022. In March 2023, prices fell, but they were still 6.4% higher than in 2022. The purpose of the study is to assess the level of digital transformation of the Ukrainian agri-food industry in order to ensure food security at the national and international levels. Digitalization of the agri-food industry in Ukraine should be considered a source of deep systemic transformations, which involves the use of digital technologies at the business level to optimize business operations, increase company productivity, and improve interaction with suppliers and customers. For agri-food companies, the issue of digitalization concerns not only technological modernization, but also a complete change of business processes: farm management systems, data processing and harvest forecasting, agricultural processing, food quality management, systems for creating added value for products, warehouse management systems, and human resources management. Nowadays, digitalization can accelerate the transformation of the agri-food industry across the entire supply chain, from manufacturing and purchasing processes to distribution, logistics and finance. Innovative technologies that can become breakthrough in the agri-food industry are as follows: bioinformatics; synthetic biology; food design; smart farming; vertical farms; aquaculture; bioinformatics; genetics; alternative sources of protein; technology of conservation and extension of the shelf life of food products. In Ukraine, a number of agri-food enterprises are moving to Industry 4.0. The most innovative companies in Ukraine are the largest exporters “Kernel”, “MHP”, “ASTARTA-KYIV”. “Kernel”, a large producer and exporter of sunflower oil, has been successfully implementing innovations for the agro-industrial complex of Ukraine for many years. The company uses digital technologies at all stages—from growing products to sales. The company’s IT team digitized logistics, trading, and document management. All information about the processes taking place in agri-food production is collected in the “Kernel DigitalAgriBusiness” innovative ecosystem. “MHP”, the largest producer and exporter of chicken in Ukraine, continues to use biogas to produce electricity, industrial steam, and heating. “MHP” biogas projects are a significant contribution to the company’s energy independence and environmental responsibility. “ASTARTA-KYIV”, a vertically integrated agricultural holding, developed a complex system of IT solutions for agribusiness management “AgriChain”, which includes management of the land bank of the agricultural company (AgriChain Land), agricultural production (AgriChain Farm), monitoring of crops (AgriChain Scout), logistics of goods (AgriChain Logistics), warehouse management (AgriChain Barn), business processes (AgriChain Kit). Digital transformations are also being followed in the dairy industry. “Bel Shostka Ukraine” company is engaged in the digital transformation of the milk harvesting process. According to our research, breakthrough innovations are predominantly implemented by large Ukrainian agri-food companies, since they have significant financial resources for R&D, while SMEs are concentrating their efforts on the digitalization of business operations and implementation of energy efficient technologies. Full article

Energy is the key driver of economic growth; however, the economic leadership position of G7 countries and the rising global manufacturing hub status of the ASEAN-5 countries have yet to achieve the Sustainable Development Goals. Thus, this paper aims to examine the effects of real GDP per capita, urban population, the number of individuals using the internet, carbon dioxide emissions, total trade and net foreign direct investment (FDI) inflows on the renewable energy consumption (REC) of G7 and ASEAN-5 countries from 1990 to 2021 yearly data. Using Studenmund’s and Gujarati and Porter’s procedures of the panel data model, the panel fixed-effect econometric modelling held the best outcome for both G7’s and ASEAN-5 countries’ REC models. Based on the findings, urban population highly and positively affects REC in G7 countries. However, there is also a positive and strong relationship between net FDI inflows and REC in ASEAN-5 countries. The empirical findings prove the importance of macroeconomic, socioeconomic and environmental variables for the outcomes of REC policies across both developed and developing countries. Full article

Democracy has both a direct and an indirect relationship with sustainable development. Democracy is related to the movement toward long-term economic development directly, and indirectly, democracy can provide the means to create the institutional structures needed to create links between the political systems, the culture of participation, and the social values of a society. Since economic development is a multidimensional concept and one of its primary requirements is to achieve a high level of income and appropriate economic growth, knowing the relationship between democracy and economic growth is especially important for policymakers. Many important questions are raised about the relationship between democracy and economic performance. What is the relationship between democracy and economic growth? Is this relationship different in developed countries and developing countries? Considering the effects of democracy and economic growth on the welfare of communities, the main purpose of this study was to investigate the causal relationship between democracy and economic growth from 1990–2020 for the OECD and selected developing countries. The results showed that the conflict and skeptical hypotheses had been established in OECD and developing countries, respectively. It was concluded that the pattern of economic growth and development in OECD countries differed from that in developing countries. For OECD countries, real per capita GDP growth was mainly affected by previous per capita GDP growth, and the effect of democracy on per capita economic growth was negative. Moreover, the results indicated that in developing countries, democracy alone had not triggered economic growth and that real per capita GDP growth depended on other important structural variables such as social and physical infrastructure. Full article

This research aims to identify the influence of fiscal policy, financial development and economic growth on the increase of renewable consumption in Romania. To achieve our objective, we employ bivariate regressions through the Autoregressive Distributed Lag method, over the 2000–2020 period, to examine these influences. We find clear evidence that the variables observed (implicit tax rate on energy, external debt stocks, real GDP per capita, environmental tax revenues from energy taxes, and market capitalisation of listed domestic companies) have significant effects on the use of renewable energy. Four unidirectional causal relationships were identified in the long run: two from independent variables towards the dependent variable and two from the dependent variables towards two other independent variables. The importance of this study is that its results can contribute to the finding of the most suitable solutions to improve renewable energy consumption in Romania and mitigate the impact of climate change. Consequently, the results of this study reveal significant conclusions and policy recommendations for Romania moving towards sustainable and green economic growth, through a balanced set of policies and measures smartly applied, accompanied by a solid rate of absorption of green funds. Full article

Digital and technological transformation has gained significant attention not only due to the exposure of the latest technologies but also due to its considerable impact on sustainable economic growth. This research determines the influence of digital and technological advancement on sustainable economic growth. Digital and technological advancement is composed of three variables; E-government Development Index (EGDI), Internet Users’ (IU) growth, and information and communications technology (ICT) exports. Besides that, the urbanization and unemployment rate have been considered as control variables. The dataset consists of the year 2004–2020 for 21 Asian region partner countries along Belt and Road (BRI) region. The conclusions of the two-step system GMM were validated through the D-K fixed effect regression technique. Findings indicate that increase in EGDI, ICT exports, and internet users’ growth has a significant and positive influence on sustainable economic growth which leads that digital and technological advancement having a positive influence on sustainable economic growth. Moreover, urbanization has a partial positive impact, while unemployment has a negative influence on sustainable economic growth as Asian regions are emerging economies and the rate of unemployment is very high, which is affecting the real GDP per capita. It is evident and suggested that improvement in the EGDI index, internet users’ growth, ICT exports, and reduction in the unemployment rate would enhance the balanced sustainable economic growth for all Asian countries of the BRI region. Full article