Search Results (172)

Today’s environmental issues are among the primary themes that researchers explore in their search for practical solutions to achieve the Sustainable Development Goals (SDGs), such as reducing carbon emissions and promoting sustainable practices. Renewable energy is crucial to overcoming future challenges, causing many countries to accelerate their adoption at various levels. In this context, the impact of renewable energy adoption on achieving the Sustainable Development Goals in the Ha’il region has been evaluated. Specifically, two techniques are employed. The first technique is an empirical model based on the Vector Error Correction Model (VECM), which identifies the SDGs related to renewable energy in achieving SDGH. Only three SDGs (SDG7, SDG12, and SDG13) were found to influence SDGH significantly. The second technique uses deep learning, specifically LSTM networks, to forecast SDGH behavior over a ten-year period about the three selected SDGs. The results indicate that these three SDGs play a crucial role in sustainable development in the Ha’il region. Therefore, this research produces strategic recommendations to optimize the adoption of renewable energy in the Ha’il region. These findings provide policymakers with a data-driven framework to enhance strategies, utilize resources more efficiently, and promote broader sustainability initiatives. Full article

Using time-varying Granger causality, Neural Networks Nonlinear VAR, and Wavelet Coherence analysis, we evidence the unstable effect of the money market rate on industrial production and consumer price index in Tunisia. The effect is asymmetric and depends on geopolitical risk (low versus high). We show that global geopolitical risk has both detriments and benefits sides—it is a threat and an opportunity for monetary policy transmission mechanisms. Interacted local projections (LPs) reveal short–medium-term volatility or dampening effects, suggesting that geopolitical uncertainty might weaken the immediate impact of monetary policy on output and prices. In uncertain environments (e.g., high geopolitical risk), economic agents—households and businesses—may adopt a wait-and-see approach. They delay consumption and investment decisions, which could initially mute the impact of monetary policy. Agents may delay their responses until they gain more information about geopolitical developments. Once clarity emerges, they may adjust their behavior, aligning with the long-run effects observed in the Vector Error Correction Model (VECM). Furthermore, we identify an exacerbating investor sentiment following tightening monetary policy, during global and local geopolitical episodes. The impact is even more pronounced under conditions of high domestic weakness. Evidence is extracted through a novel composite index that we construct using Principal Component Analysis (PCA). Our results have implications for the Central Bank’s monetary policy conduct and communication practices. Full article

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

Traditional price prediction of construction material concrete often adopts macroeconomic indicators as independent variables. However, since there is often a closer relationship between the raw materials of construction concrete and the production of construction materials, the price prediction of construction concrete based on raw material prices can more directly ensure the prediction accuracy. Therefore, this study proposes a Double-Branch Physics-Informed Neural Network (DB-PINN) model based on both macroeconomic indicators and raw material price factors for the construction concrete price prediction. In particular, this model utilizes an Artificial Neural Network (ANN) as the baseline algorithm and incorporates physical constraints, such as a Multiple Linear Regression (MLR) model and a Vector Error Correction Model (VECM) to modify the loss function. To improve the prediction accuracy of the DB-PINN model, a feature analysis of the effect of the raw material price factors on the construction concrete price is conducted. Results showed that the proposed DB-PINN model has high accuracy in concrete price prediction. Further, to explore the specific ways in which macroeconomic indicators affect the concrete price prediction, a Marginal Effect Analysis (MEA) is conducted. Moreover, a comparative analysis using a traditional ANN model is conducted to verify the efficiency of the DB-PINN model, and a parameter sensitivity analysis is performed to reveal the impact of each raw material price factor and macroeconomic indicator on the construction concrete price. This study incorporates the introduction of raw material prices as input parameters for construction concrete price prediction, which facilitates the development of urban construction concrete price management in the pre-project phase. Full article

This study investigated the determinants of the Healthcare Quality Index (HQI) in Saudi Arabia over the period from 1990 to 2024. It specifically analyzed the impact of six key variables: Medical Insurance Penetration Rate (MIPR), Gross Domestic Product per Capita (GDP), Unemployment Rate (UR), Inflation Rate (IR), Government Healthcare Expenditure as a Percentage of GDP (GHE), and Foreign Direct Investment in the Healthcare Sector (FDI). Utilizing the Autoregressive Distributed Lag (ARDL) and Vector Error Correction Model (VECM) techniques, this research explored both the short-term dynamics and the long-term equilibrium relationships among these time-series variables, while also accounting for cointegration and potential endogeneity. This study contributes to the existing literature by applying the ARDL and VECM methodologies to comprehensively analyze the combined impact of these factors on HQI within the unique economic and social framework of Saudi Arabia, addressing a notable void in this specific context and exploring both transient fluctuations and sustained equilibrium relationships. The key findings revealed distinct influences across time horizons. In the short term, GDP and GHE significantly and positively affect HQI, whereas UR and IR demonstrate a significant negative impact. Short-term impacts of MIPR and FDI are found to be positive but not statistically significant. The long-term analysis indicates that MIPR, GHE, and FDI have a significant positive influence on HQI, while IR maintains a significant negative impact. GDP and UR effects are not statistically significant in the long term. Further analysis using Granger causality tests and VECM confirmed that MIPR, GDP, GHE, and FDI collectively Granger-cause HQI, with government healthcare expenditure playing a crucial role in correcting short-term deviations toward long-term equilibrium. This study concludes that long-term strategies focusing on expanding insurance coverage, increasing government healthcare investment, and attracting foreign direct investment are vital for significantly enhancing healthcare quality in Saudi Arabia. The sustained positive influence of these factors and the critical role of government spending in maintaining long-term stability underscore their importance for effective healthcare policy. While emphasizing these long-term drivers, policymakers should also remain cognizant of the significant negative short-term fluctuations caused by unemployment and inflation. Full article

This study investigates the impact of digital transactions on sustainable economic growth in Turkey, utilizing a vector autoregressive (VAR) model and quarterly data from 2006 to 2023. The results indicate a positive long-term association between digital payments and GDP. Granger causality tests and impulse response functions suggest a bidirectional relationship, highlighting mutual reinforcement between economic activity and digital financial adoption. The study also investigates three potential transmission channels linking digital payments to economic performance: household consumption, productivity, and financial intermediation. Evidence shows that digital payments are associated with increased consumption and financial sector activity, while the link to productivity is less conclusive. These findings imply that policymakers should prioritize digital financial infrastructure development and enhance regulatory frameworks to promote inclusive and sustainable economic growth. Full article

In the context of accelerated digitization and the transition to sustainability, this study explores the relationship between the use of cloud computing services and greenhouse gas (GHG) emissions in the IT and communications sectors at the European level using panel data provided by Eurostat for the period 2014–2021. The initial set included 14 countries, but due to incomplete data, the final analysis was performed on a consistent and complete dataset comprising 8 countries: Bulgaria, Cyprus, Denmark, Hungary, Latvia, Norway, Poland, and Romania. The applied methodology includes VAR and VECM econometric models, the Granger causality test, impulse response functions, and variance decomposition. The results show a long-term cointegrating relationship between the variables, highlighting the existence of mass and energy transfer to centralized infrastructures such as data centers. The IT subsectors (J62_J63) demonstrate superior efficiency in reducing GHG emissions compared to the general communications sector (J), highlighting the positive impact of a high level of digitization. Although the research provides valuable insights into the relationship between digitization and sustainability, a major limitation is that not all EU countries are represented. This study provides actionable policy recommendations to minimize the ecological impact of digital technologies and enhance resource efficiency in the green transition era. Full article

This study investigates the role of public health expenditure on human capital development in South Africa to promote economic development. Despite extensive public health investments and economic reforms, persistent socioeconomic challenges such as poverty, unemployment, and inequality impede sustainable economic growth. This study uses an autoregressive distributed lag model, a vector error correction model (VECM), quantile regression, and Granger causality analysis to assess the relationship between fiscal health policies and human development. The findings confirm that government health spending significantly enhances human development in the short and long run, while unemployment and population growth exert adverse effects. VECM variance decomposition results indicate that the influence of public health expenditure remains persistent, though diminishing over time, with growing contributions from unemployment. Quantile regression shows the heterogeneous impact of health spending across different levels of economic development, emphasising its greater effectiveness at higher development stages. Causality analysis reveals a unidirectional relationship from public health expenditure to human development; this shows the need for sustained healthcare investment. The study calls for policies combining health spending with economic strategies to boost productivity, reduce inequality, and promote inclusive growth. Strengthening institutional efficiency and ensuring macroeconomic stability are crucial for maximising long-term human capital to promote sustainable development. Full article

This study investigated the dynamic factors influencing food security in Saudi Arabia, a critical concern for the nation’s stability and development. The purpose of this research was to analyze the impact of several key determinants on the Food Security Index and to distinguish between their short-term and long-term effects, thereby providing evidence-based policy recommendations. Using annual time-series data spanning 1990 to 2023, the research employs the Autoregressive Distributed Lag (ARDL) and Vector Error Correction Model (VECM) methods. We specifically examined the roles of agricultural GDP contribution, agricultural insurance coverage, food price stability, government policies related to agriculture, climate change impacts, agricultural productivity, and technology adoption. Short-run estimates reveal that agricultural GDP contribution, government policies, and agricultural productivity express a significant positive influence on food security. Importantly, climate change showed a counterintuitive positive association in the short term, potentially indicating immediate adaptive responses. Conversely, food price stability exhibited an unexpected negative association, which may indicate that the index captures high price levels rather than just volatility. The long-run analysis highlights the crucial importance of sustained factors for food security. Agricultural GDP contribution, agricultural insurance coverage, and agricultural productivity are identified as having significant positive impacts over the long term. In contrast, climate change demonstrates a significant negative long-run impact, underscoring its detrimental effect over time. Government policies, while impactful in the short term, become statistically insignificant in the long run, suggesting that sustained structural factors become dominant. Granger causality tests indicate short-term causal relationships flowing from climate change (positively), agricultural GDP contribution, government policies, and agricultural productivity towards food security. The significant error correction term confirms the existence of a stable long-run equilibrium relationship among the variables. On the basis of these findings, the study concludes that strengthening food security in Saudi Arabia requires a multifaceted approach. Short-term efforts should focus on enhancing agricultural productivity and implementing targeted measures to mitigate immediate climate impacts and refine food price stabilization strategies. For long-term resilience, priorities must include expanding agricultural insurance coverage, investing in sustainable agricultural practices, and continuing to boost agricultural productivity. The study contributes to the literature by providing a comprehensive dynamic analysis of food security determinants in Saudi Arabia using robust time-series methods, offering specific insights into the varying influences of economic, policy, environmental, and agricultural factors across different time horizons. Further research is recommended to explore the specific mechanisms behind the observed short-term relationship with climate change and optimize food price policies. Full article

In the aftermath of global financial crises and amid increasing complexity in banking operations, understanding and managing various types of risk—especially liquidity, credit, and solvency risks—has become a global concern for financial stability. This study addresses a critical gap in the literature by examining the dynamic interrelationships among these three types of risk in the context of emerging markets. Using data from 21 banks listed on the Iranian capital market from 2011 to 2023, we employ a Panel Vector Error Correction Model (VECM) alongside panel impulse response analysis to assess both short- and long-term dynamics. Our results reveal that an increase in liquidity positively impacts bank solvency, while credit risk negatively affects solvency but does not significantly influence liquidity risk. These findings contribute to the theoretical understanding of systemic risk interactions in banking and provide practical insights for policymakers and financial institutions seeking to enhance risk management strategies in volatile market environments. Full article

This study aimed to assess the impact of climatic alteration on food security in Saudi Arabia. Date productivity, temperature, and precipitation represent the data which were collected from various sources linked to the study subject and cover the period from 1980 to 2023. The Engle–Granger two-step procedure, the VECM, and forecast analysis were applied to test the long-term relationship, short-term integration, and forecasting, respectively. Moreover, qualitative analysis was used to reveal the influence of climatic change on food security. The results discovered long-term co-integration between date productivity and temperature. Additionally, the results revealed that there has been long-running co-integration between date productivity and the precipitation series. Temperature and precipitation negatively and significantly impacted date productivity during the study period. With reference to forecast results, the graph was validated using various forecast indicators: the Alpha, Gamma, Beta, and Mean Square Error equivalents were 1.0, 0.0, 0.0, and 5.47, respectively. Moreover, the growth rates of date productivity were equal to 0.82 and 0.08 for the periods from 1980 to 2022 and 2023 to 2034 (forecast), respectively, indicating that there is a decrease in the growth rate of date productivity (0.08) during the forecast period. From these results, the conclusion is that climatic change (temperature and precipitation) negatively impacts date productivity. In addition, the growth rate during the forecast period decreased, indicating that climatic change is affecting food security currently and will continue to do so in the future. This study recommended specific policy interventions and innovations in agricultural practices, including developing and implementing a national framework focused on climate-smart agriculture, balancing productivity, adaptation, and mitigation. This could be aligned with Vision 2030 and the Saudi Green Initiative. Additionally, this could include investing in research and development by increasing public–private partnerships to support agricultural R&D in arid regions, with a focus on heat- and drought-resistant crop varieties and water-efficient farming systems. Regarding agricultural innovations, these could include the use of renewable energy, particularly solar energy, the expansion of rainwater harvesting infrastructure, recycling treated wastewater for agriculture, and reducing reliance on groundwater sources. Full article

This research examines the critical issue of greenhouse gas emissions, focusing on carbon dioxide (CO₂) as a significant contributor to climate change and its threats to environmental sustainability. The primary objective of this paper is to highlight the environmental impacts resulting from economic growth, energy consumption, and agricultural development in Saudi Arabia. The purpose of the empirical research is to investigate the dynamic causal relationships between CO₂ emissions, agricultural development, economic growth, energy consumption, and additional control variables in Saudi Arabia from 1990 to 2022. It is hypothesised that increases in agricultural land, economic activity, and energy use contribute to rising CO₂ emissions. This study examines these relationships using the Autoregressive Distributed Lag (ARDL) and Fully Modified Ordinary Least Squares (FMOLS) methodologies, along with unit root tests, the ARDL bounds test, and Vector Error Correction Model (VECM) causality analysis, to assess both short-term and long-term interactions among the variables. The findings reveal that agricultural land expansion, economic growth, and energy consumption significantly contribute to increased CO₂ emissions. Specifically, a 1% increase in agricultural land correlated with a 0.16% rise in CO₂ emissions, while a 1% increase in economic growth and energy use led to 0.28% and 0.85% rises, respectively. These results underscore the environmental challenges posed by economic expansion and energy dependence. This paper emphasises the need for policies that balance economic growth with emissions reduction, in line with Saudi Vision 2030. Transitioning to a low-carbon, circular economy supported by renewable energy and innovation is essential for sustainable development and climate change mitigation. Full article

This study examines the short- and long-term effects of various economic, environmental, and policy factors on renewable energy production (REP) in Saudi Arabia from 1990 to 2024, using the Autoregressive Distributed Lag (ARDL) approach and Vector Error Correction Model (VECM) techniques. The analysis focuses on fossil fuel consumption (FFC), renewable energy investment (REI), carbon emissions (CEs), energy prices (EPs), government policies (GPs), technological advancements (TAs), socioeconomic factors (SEFs), and economic growth (EG) as determinants of REP, measured as electricity generated from solar power sources in kilowatt-hours (kWh). Short-term findings reveal a positive momentum effect, where prior REP levels significantly influence current production, driven by factors such as learning by doing, economies of scale, and consistent policy support. However, FFC negatively impacts REP, highlighting resource competition and market dynamics favoring fossil fuels. Positive short-term influences include REI, CEs, EPs, GPs, TAs, SEFs, and EG, which collectively enhance renewable energy adoption through investments, technological innovation, policy incentives, and economic development. Long-term analysis underscores a strong negative relationship between FFC and REP, with a 7503-unit decline in REP associated with increased fossil fuel dependency. Conversely, REP benefits from REI, CEs, EPs, GPs, TAs, and EG, with significant contributions from technological advancements (3769-unit increase) and economic growth (9191-unit increase). However, SEFs exhibit a slight negative impact, suggesting that rapid urbanization and population growth may outpace renewable infrastructure development. Overall, the study highlights the complex interplay of factors shaping renewable energy production, emphasizing the importance of sustained investments, supportive policies, and technological innovation, while addressing challenges posed by fossil fuel reliance and socioeconomic pressures. These insights provide valuable implications for policymakers and stakeholders aiming to accelerate the transition to renewable energy in Saudi Arabia. Full article

Environmental sustainability remains a critical challenge in the face of global economic development. This study explored the complex interactions among renewable energy consumption, urbanization, trade openness, and economic development, focusing on their effects on environmental quality in 34 high-income European and Asian economies from 1970 to 2022. Using linear Bayesian regression and the Vector Error Correction Model (VECM), the analysis examined short- and long-term impacts to uncover nuanced relationships. Results demonstrated that economic development contributed to environmental degradation over the long term while mitigating it in the short term. Renewable energy consumption supported economic growth but showed limited efficacy in reducing ecological footprints across different time frames. Urbanization and trade openness emerged as significant drivers of long-term environmental degradation, emphasizing the need for targeted policy interventions. This study examined the link among economic progress and environmental sustainability, and identified key areas for improvement in urban planning, renewable energy, and trade policies. The findings provide a framework for policymakers to balance development with environmental preservation. Full article

The energy stock price index maps the development trends in China’s energy market to a certain extent, and accurate forecasting of China’s energy market index can effectively guide the government to regulate energy policies to cope with external risks. The vector error correction model (VECM) analyzes the relationship between each indicator and the output, provides an external explanation for the way the indicator influences the output indicator, and uses this to filter the input indicators. The forecast results of the China energy stock price index for 2022–2024 showed an upward trend, and the model evaluation parameters MAE, MAPE, and RMSE were 0.2422, 3.5704% and 0.3529, respectively, with higher forecasting efficiency than other comparative models. Finally, the impact of different indicators on the Chinese energy market was analyzed through scenario setting. The results show that oscillations in the real commodity price factor (RCPF) and the global economic conditions index (GECON) cause fluctuations in the price indices of the Chinese energy market and that the Chinese energy market evolves in the same manner as the changes in two international stock indices: the MSCI World Index and FTSE 100 Index. Full article