Short-term Dynamics Research Articles

Modeling ICT adoption and electricity consumption in emerging digital economies: Insights from the West African Region

This study investigates the impact of Information and Communication Technologies (ICT) on electricity consumption in West Africa, employing a dynamic panel data model. The results show a significant long-term positive effect of ICT adoption on electricity consumption. Notably, internet connections increase the demand for electricity, with estimates ranging from 13.4 % to 19.3 %. While mobile phone subscriptions demonstrate modest positive effect of 6.85 %, personal computer ownership appears to have a negligible impact.The study contributes to the existing literature by offering a detailed examination of the distinct effects of different ICT components on electricity consumption, incorporating a novel estimation approach and sensitivity analyses that account for the COVID-19 pandemic and the Anglo-French linguistic divide. What's more, the analysis constitutes an initial effort in the examining both short-term and long-term dynamics of the ICT-electricity relationship in West African region.

Interconnected Stock Markets: Analysing Cointegration and Correlation among Global Stock Indices

This study examines the long-term and short-term relationships among five major global stock indices—NSE NIFTY (India), S&P 500 (USA), FTSE 100 (UK), Hang Seng (Hong Kong), and Nikkei 225 (Japan)—over the period from 2008 to 2023. Using advanced econometric techniques such as Johansen cointegration tests, Engle-Granger residual cointegration test and the Vector Error Correction Model (VECM), the research explores the degree of interconnectedness and cointegration among these indices. The results indicate strong positive correlations between NSE NIFTY, S&P 500, and Nikkei 225, suggesting a growing synchronization of these markets due to global economic integration. Johansen's test reveals the presence of multiple long-term equilibrium relationships, affirming that despite short-term deviations, these markets tend to move together in response to global macroeconomic factors. The analysis further highlights that the S&P 500 and FTSE 100 indices exhibit significant error correction mechanisms, underscoring their leadership role in global market dynamics. In contrast, NSE NIFTY shows a weaker adjustment toward long-term equilibrium, indicating its reactive nature to changes in other global indices, particularly the S&P 500. The study finds that while Hang Seng and Nikkei 225 are connected globally, their short-term linkages appear weaker, likely influenced by regional factors. Overall, the results emphasize the critical influence of the U.S. and Japanese markets on NSE NIFTY, with both long-term cointegration and short-term market dynamics shaping the Indian stock market’s movements. This research offers key insights for investors seeking to understand global stock market interdependencies and manage portfolio risk.

Oman’s Green Horizon: Steering Towards Sustainability Through Decarbonization and Energy Transition

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 (RGDPC) 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.

Evaluating the Effectiveness of Macroeconomic Determinants on the Performance of the Dhaka Stock Exchange: A Time Series Approach

The present study examines the causal relationships between selected macroeconomic factors and the Dhaka Stock Exchange Broad Index (DSEX) performance from January 2014 to December 2023. Time series econometric techniques include Dickey-Fuller (ADF) and Phillips-Perron (PP) tests to verify stationarity, Granger causality to investigate short-term dynamics, and Johansen's co-integration test to evaluate long-term relationships. The results show that while there is no long-term cointegration, there are significant short-term relationships between macroeconomic variables and the DSEX broad index. GDP growth and unemployment significantly impact stock performance, with two-way causality. While interest rates and the money supply have little impact on the index, inflation forecasts trends in inflation even if it doesn't directly influence it. Overall, the DSEX broad index shows how important it is to Bangladesh's macroeconomic dynamics by accurately predicting changes in GDP growth, unemployment, and inflation. The researcher provides unique insights into the mechanics of the Bangladeshi stock market, filling a vacuum in the literature on emerging countries. It analyzes how macroeconomic conditions affect the DSEX index over time, including the COVID-19 pandemic. The study investigates the influence of several macroeconomic variables on DSEX Index performance, providing significant information for Bangladeshi investors and policymakers looking to increase profits, inform policy choices, and promote economic stability. To have a better knowledge of stock market dynamics, future studies should include other macroeconomic aspects and regional markets.

Strength of viscous subduction interfaces: A global compilation

The long- and short-term dynamics of subduction zones are strongly affected by interface rheology. Current estimates of interface viscosity are based on endmember flow laws or simple mixing models, but the variation of possible materials on the subduction interface theoretically permits viscosity variations of up to five orders of magnitude. To better constrain the range of strength along deep/viscous subduction interfaces, we compiled a global database of rock types and block-and-matrix distributions in exhumed deep subduction interface mélange shear zones. We applied numerical shear experiments to each shear zone to quantify their bulk shear strengths/viscosities. Our results show that natural subduction shear zones have viscosities ranging from ∼1018 to 1020 Pa⋅s and shear strengths of ∼1−100 MPa. The variation among them is dominantly controlled by temperature, but even for shear zones deformed at the same temperature, variations of up to a factor of 50 are observed. These variations are attributed to differences in matrix composition, shear zone width, and block distributions. Our results are consistent with paleopiezometric and force-balance−derived estimates of interface strength, and they confirm the importance of variation in geological inputs and their spatial distribution along the interface for subduction dynamics.

Exploring the Moderating Effect of Institutional Quality on the Nexus between Remittances and Financial Development: A Comprehensive Analysis

Purpose –The purpose of this paper is to analyse the moderating effect of institutional quality on the relation between remittances and financial development in low-income economies. The long-term and short-term effects of remittances on financial development have been investigated.Design/methodology/approach –World Bank country classification is considered to categorize low-income countries. These nations are characterized as having Gross National Income (GNI) per capita falling under the threshold of $1,085. 17 countries are included in the dataset spanning the years 2002 to 2022. Panel Autoregressive Distributed Lag Pooled Mean Group (ARDL-PMG) approach is used in this study. Using the PMG approach allows for distinct analysis of long-term and short-term dynamics. Findings –According to the findings obtained from the PMG estimator, institutional quality serves as a moderating factor in the nexus between remittances and long-term financial development. These findings indicate that in nations with robust institutional frameworks, remittances are channelled more effectively and promptly towards bolstering financial development, in contrast to regions with weaker institutional structures. Furthermore, it is noted that institutional quality demonstrates a positive correlation with long-term financial development. However, these conclusions do not hold in the short term. Discussion –The results of this study align with the findings reported in the previous literature. By using the ARDL-PMG model and by considering low-income countries, an important contribution is made to the financial development literature.

Interplay between short and long time scales in adapting, periodically driven elastic flow networks

Existing theories of structural adaptation in biological flow networks are largely concerned with steady flows. However, biological networks are composed of elastic vessels, and many are driven by a pulsatile or periodic source, leading to spatiotemporal variations in the pressure and flow fields on short time-scales within each vessel. Here, we investigate the mathematical problem of how long-term adaptation in elastic networks is impacted by short-term pulsatile dynamics at the level of individual vessels. Using a a minimal one-loop network, we show that pulsatility gives rise to resonances that stabilize the loop for a much broader range of metabolic cost functions than predicted by existing theories. Our paper emphasizes the importance of correctly capturing the interplay of the short and long time-scales for a more realistic treatment of adaptation in periodically driven elastic flow networks. Published by the American Physical Society 2024

Building stock energy modeling to assess annual progress in stock energy efficiency and carbon emission reduction of commercial buildings

Short-term progress assessments of carbon dioxide emission reductions are essential for nations to meet their medium/long-term targets. Bottom-up building stock energy modeling (BSEM) methods can contribute to this issue but have not been applied because of the need to represent short-term building stock dynamics in building systems and energy conservation measures (ECMs). To fill this gap, this study developed a BSEM framework that integrates top-down building stock decomposition, including building systems and ECMs, and bottom-up physics-based energy demand quantification using reference building models representing building stock segments. The framework was verified through a case study of a Japanese commercial building stock. The results indicate that the developed model effectively captures the short-term dynamics and contributions of the technologies. Although there are significant errors in estimating some building subsectors and end uses, the model predicts the changes in the aggregated energy consumption with acceptable accuracy. The Japanese 2030 emission reduction target cannot be achieved with the current technology deployment trends; however, the shortfall can be addressed by applying additional measures. Owing to its applicability to diverse building stocks, the framework promotes the use of BSEM for policy assessment and evidence-based policymaking for climate change mitigation.

Unlocking sustainable development: Evaluating the impact of monetary and fiscal policies on ecological footprint in India

India's rapid economic growth underscores the increased necessity of pursuing the paradigms of sustainable development. However, concerns over environmental sustainability have risen in tandem with the country's aspirational goals for economic progress. Therefore, a crucial aspect of managing this is to create a balance between economic prosperity and environmental quality, wherein comprehending how fiscal and monetary policies affect India's ecological footprint becomes an utmost priority. In this vein, this study evaluates the impact of fiscal and monetary policies on India's ecological footprint while controlling for economic growth, squared economic growth, and renewable energy consumption between 1990 and 2022. Given the complexity of these links and well-suitability for examining both short-term dynamics and long-term relationships, this study employs the Autoregressive Distributed Lag (ARDL) model as its methodology for the empirical analysis. Thus, the findings indicate a significant long-term relationship between India's ecological footprint, fiscal policy, monetary policy, economic growth, squared economic growth, and renewable energy consumption while considering the ecological footprint as the dependent variable. Surprisingly, fiscal policy demonstrates insignificat negligible impacts on the ecological footprint in both the short and long run. Conversely, the result underscores that monetary policy effectively curbs the ecological footprint in both the short and long run. Furthermore, the study validates the Environmental Kuznets Curve (EKC) hypothesis in the long run. However, no evidence supports the EKC hypothesis in the short run. Besides, the outcome unveils that renewable energy consumption reduces the ecological footprint with no temporal differences. Hence, the policy implications derived from these findings emphasize the crucial role of monetary policy and integrating renewable energy strategies into policy frameworks for achieving sustainable development outcomes, in addition to the other policy interventions that prioritize ecological conservation alongside economic development, ensuring a balanced approach to solve environmental and economic challenges in India.

Season-specific genetic variation underlies early-life migration in a partially migratory bird.

Eco-evolutionary responses to environmentally induced selection fundamentally depend on magnitudes of genetic variation underlying traits that facilitate population persistence. Additive genetic variances and associated heritabilities can vary across environmental conditions, especially for labile phenotypic traits expressed through early life. However, short-term seasonal dynamics of genetic variances are rarely quantified in wild populations, precluding inference on eco-evolutionary outcomes in seasonally dynamic systems. This limitation applies to seasonal migration versus residence, constituting one key trait where rapid microevolution could rescue partially migratory populations from changing seasonal environments. We fitted novel quantitative genetic 'capture-recapture animal models' to multi-generational pedigree and year-round resighting data from 11 cohorts of European shags (Gulosus aristotelis), to estimate season-specific additive genetic variances in liabilities to migrate, and in resulting expression of migration, in juveniles' first autumn and winter. We demonstrate non-negligible genetic variation underlying early-life migration, with twice as large additive genetic variances and heritabilities in autumn than winter. Since early-life survival selection on migration typically occurs in winter, highest genetic variation and strongest selection are seasonally desynchronized. Our results reveal complex within- and among-year dynamics of early-life genetic and phenotypic variation, demonstrating that adequate inference of eco-evolutionary outcomes requires quantifying microevolutionary potential on appropriate scales and seasonal timeframes.

Modeling human activity comprehension at human scale: prediction, segmentation, and categorization.

Humans form sequences of event models-representations of the current situation-to predict how activity will unfold. Multiple mechanisms have been proposed for how the cognitive system determines when to segment the stream of behavior and switch from one active event model to another. Here, we constructed a computational model that learns knowledge about event classes (event schemas), by combining recurrent neural networks for short-term dynamics with Bayesian inference over event classes for event-to-event transitions. This architecture represents event schemas and uses them to construct a series of event models. This architecture was trained on one pass through 18 h of naturalistic human activities. Another 3.5 h of activities were used to test each variant for agreement with human segmentation and categorization. The architecture was able to learn to predict human activity, and it developed segmentation and categorization approaching human-like performance. We then compared two variants of this architecture designed to better emulate human event segmentation: one transitioned when the active event model produced high uncertainty in its prediction and the other transitioned when the active event model produced a large prediction error. The two variants learned to segment and categorize events, and the prediction uncertainty variant provided a somewhat closer match to human segmentation and categorization-despite being given no feedback about segmentation or categorization. These results suggest that event model transitioning based on prediction uncertainty or prediction error can reproduce two important features of human event comprehension.

Short-Term Growth Dynamics of Spontaneous and Planted Vegetation on Subtropical Extensive Green Roof as Renaturalized Biotope

Spontaneous vegetation within a managed green space is often regarded as unwelcoming and insignificant weeds. This perception is still deep-rooted among green-space managers and the general public worldwide; they are generally uncertain about the management needs after allowing these groups of flora to take root. The short-term growth dynamics of both spontaneous and planted vegetation should be analyzed, and a widely acceptable, feasible management plan to balance aesthetic and ecological functions should be formulated with the backing of data and analysis for such fast-growing flora in tropical and subtropical regions. A manicured, extensive green roof with only seven (two native, five exotic) plant species was transformed into a renaturalized biotope by replacing 15 native ferns and forb species over 15 months. After planting, a baseline plant survey was conducted, with 54 plant species representing spontaneous growth and 14 planted species alive (7 planted native species survived, plus 7 species planted prior to renaturalization revived). Three quarterly plant surveys recorded the cover-abundance of each species, and the growth dynamics of the planted and spontaneous plant species were evaluated over the first year of study. During each quarterly survey, the number of planted and spontaneous plant species remained stable (ranging from 14 to 16 species and 51 to 54 species, respectively), with a constant turnover of 11 to 12 die-out species and 11 to 12 newly colonized or revived species. Plant coverage of different plant forms fluctuated slightly (within 7%) in the quarterly surveys according to seasonal changes, except for ferns, which outperformed (12% increase in coverage in a year) all the other plant forms. The height of the planted vegetation fluctuated in a year, being shorter during the summer, while the height of spontaneous vegetation remained stable throughout the year, exhibiting resilience to scouring heat. The seasonal growth tendencies of both planted and spontaneous plants were illustrated in relation to their species ranks, and further hierarchical cluster analysis was conducted for the clustering of spontaneous species. Their differential growth patterns provided comprehensive information or supported decisions regarding plant selection and maintenance, which is a scientific novelty within this unexplored topic. Management recommendations based on the findings were suggested to fulfill both aesthetic and ecological needs. Species with stable and less stable growth patterns could be useful to meet maintenance efficiency and biodiversity enhancement needs, respectively. These findings provide insights to form guiding principles for choosing plant species for renaturalization projects.

Singular spectrum analysis to estimate core inflation in Brazil

This paper presents a set of new core inflation measures for Brazil based on the Singular Spectrum Analysis (SSA) method. The measures are based on the official target used in the Brazilian inflation targeting regime. The period of analysis ranges from the beginning of the inflation targeting regime in 1999 and 2021. The SSA measures were compared to the inflation core ones used by the Central Bank of Brazil, through the evaluation of unbiasedness, short-term adjustment dynamics and predictive ability. The measures estimated by SSA meet the desired properties and have a greater predictive capacity than the other inflation cores.

Краткосрочная динамика интересов студентов гуманитарного направления на основе анализа семантического поля понятия «интересы» (по данным психолингвистического эксперимента)

The aim of the research is to identify the short-term dynamics of youth interests based on psycholinguistic experiment data. The article examines the semantic field of the notion “interests”, obtained through a series of psycholinguistic experiments conducted from 2019 to 2023. The scientific novelty of the research lies in considering interests as an independent system, whose structure provides insight into its social context (values, identity characteristics, socialization features, and social activity of youth) and the factors influencing its changes. As a result, the core and periphery of the “Interests” field have been identified through graphosemantic analysis. The core and near-periphery zones include the fields “Creativity”, “Commercial Activity”, “Internet”, and “Information”. The analysis reveals connections between individual groups of hobbies, gender-based differences in the field structures of the informants, and describes the dynamic changes in interest towards specific activities. Factors such as professional activities and digitalization are identified as determining the stability and dynamic changes in interests.

Spatio-temporal mechanisms of consolidation, recall and reconsolidation in reward-related memory trace.

The formation of memories is a complex, multi-scale phenomenon, especially when it involves integration of information from various brain systems. We have investigated the differences between a novel and consolidated association of spatial cues and amphetamine administration, using an in situ hybridisation method to track the short-term dynamics during the recall testing. We have found that remote recall group involves smaller, but more consolidated groups of neurons, which is consistent with their specialisation. By employing machine learning analysis, we have shown this pattern is especially pronounced in the VTA; furthermore, we also uncovered significant activity patterns in retrosplenial and prefrontal cortices, as well as in the DG and CA3 subfields of the hippocampus. The behavioural propensity towards the associated localisation appears to be driven by the nucleus accumbens, however, further modulated by a trio of the amygdala, VTA and hippocampus, as the trained association is confronted with test experience. Moreover, chemogenetic analysis revealed central amygdala as critical for linking appetitive emotional states with spatial contexts. These results show that memory mechanisms must be modelled considering individual differences in motivation, as well as covering dynamics of the process.

Impacts of Natural Gas Pipeline Congestion on the Integrated Gas–Electricity Market in Peru

This paper investigates the impact of natural gas pipeline congestion on the integrated gas–electricity market in Peru, focusing on short-term market dynamics. By simulating congestion by reducing the primary natural gas pipeline’s capacity, the study reveals significant patterns in production costs and load flows within the electrical network. The research highlights the critical interdependencies between natural gas and electricity systems, emphasizing how constraints in one network can directly affect the other. The findings underscore the importance of coordinated management of these interconnected systems to optimize economic dispatch and ensure the reliability of both gas and electricity grids. The study also proposes strategic public policy interventions to mitigate the financial and physical impacts of pipeline congestion, contributing to more efficient and resilient energy market operations.

COP 28 Policy Perspectives: Achieving Environmental Sustainability through FDI, Technological Innovation Index, Trade Openness, Energy Consumption, and Economic Development in N-11 Emerging Economies

Despite remarkable success in attracting foreign direct investment (FDI) to achieve maximum economic growth, the Next-11 emerging economies grappling with an undesirable situation of environmental degradation have become a hot topic at COP28. Researchers have long focused on this connection, emphasizing the urgent need for international and national environmentalists to promote sustainable development (SD) in these rapidly growing economies under the United Nations (UN) Framework Convention on Climate Change action plans. As a result, this study examines the role of FDI in the N-11 emerging economies, focusing on energy usage and technological innovation within the theoretical framework of the Halo-Haven hypothesis, covering the period from 1990 to 2022. We utilize ARDL, FMOLS, and DOLS techniques to analyze both short-term dynamics and long-term equilibrium relationships, effectively managing heterogeneity, time dynamics, and cross-sectional dependence issues to produce comprehensive results. The long-term analysis supports the haven hypothesis, demonstrating an affirmative relationship between FDI, economic growth, and carbon emissions, whereas energy usage is negatively associated with carbon emissions. Furthermore, the D-H test established a reciprocal causal relationship between variables such as FDI, economic growth, trade openness, and environmental pollution. However, we found a one-way causal correspondence in the usage of green energy, the technological innovation index, and carbon emissions. Given the mixed findings, policymakers should focus on attracting FDI to the green energy sector while reinforcing regulations and implementing stringent oversight for FDI in energy-intensive industries. This approach will ensure that such investments adhere to high environmental standards, thereby benefiting future generations.

Features of Bioluminescence Dynamics of Photobacterium phosphoreum IMV B-7071

The problem of the prolonged and stable intensity of bioluminescent signals is relevant in the development of any test systems that use biological objects. The aim of this work was to study the features of bioluminescence dynamics of Photobacterium phosphoreum IMV B-7071 in a liquid and on different stationary media. Methods. Bioluminescence studies were performed in liquid, agarose, and cellulose-cotton media. Bacterial suspensions were cultivated at 21°C in the mediums with standard composition. We studied both the background glow and its dynamics under conditions of mixing a liquid medium. Bioluminescence was recorded using digital photography with subsequent image processing of the samples. The measurements of luminescence were made by digital photo or video recording using Olympus digital camera SP560UZ, CANON 700D, and mobile device camera Samsung Galaxy 9 Note with specialized applications for mobile devices "Colorimeter (Lab Tools Apps)" and Camera Color Counter (Keuwsoft) at maximum light sensitivity in the automatic white balance mode at a fixed distance from the sample. Image processing was carried out using ImageJ and Origin Pro. Spectra of bacterial luminescence and its dynamics over time were measured using an LOMO MDR-23 spectrometer in the range of 200–750 nm. Results. The results of the study prove that in aqueous or solid agar and also on cellulose cotton medium, the intensity of bioluminescence of P. phosphoreum gradually increases, reaching a maximum within approximately 2 days, after which it slowly fades. It was established that the bioluminescence of photobacterium P. phosphoreum is a non-stationary process and has characteristic features of temporal dynamics associated with both the dynamics of the oxygen concentration in the environment of bacterial suspensions and the dynamics of the bacterial population density. Analysis of the luminescence spectra of bacteria shows that luminescence occurs mainly in the blue and green regions of the spectrum with luminescence maxima in the range of 460–520 nm, but the ultra-weak glow is also registered in the UV and red spectral ranges. The variability of photobacterial luminescence spectra over time in the spectral ranges of the main luminophores causes color fluctuations between the blue and green ranges. Conclusions. The key parameters of multi-day background and short-term induced bioluminescence dynamics of photobacteria in different environments were clarified, and the certain variability of the spectral characteristics of luminescent radiation over time was shown. The revealed features of the dynamics of the bioluminescence of P. phosphoreum must be taken into account in practical application to assess the toxicity of substances of various nature, as well as in environmental monitoring.

Short-term soil fungal community dynamics following fire in mediterranean climate-type banksia woodlands

Fire is a dominant ecosystem process in many Mediterranean climate type ecosystems, and is predicted to increase in severity and frequency, shifting away from previous regimes in many regions. Responses of flora and fauna to fire are relatively well studied, but less is known about the responses of belowground microbiota. We quantified soil fungal dynamics over the first 12–15 months after fire, focusing on attributes of the fire regime (season, interval, severity). Soil samples were collected from three sites in a threatened woodland ecosystem in southwestern Australia, a Mediterranean-type climate region. Fungal taxa were identified via high throughput sequencing of the ITS subregion and taxonomy assigned using reference databases. Richness, diversity, abundance, community composition, and functional groups were quantified. Over the post-fire sampling period, richness and diversity declined and soil fungal community composition changed significantly throughout the sampling period, with family level taxa and functional groupings experiencing the most change. Through the sampling period, an increase in saprotrophic and endophytic fungi was observed, along with a decrease in all pathogenic fungi. We found that the post-fire fungal community is quite dynamic in the first 12–15 months after fire. We found little effect of fire interval or fire season, though our inference was limited. Our work contributes to putting belowground biota into the same conceptual frameworks as aboveground taxa and serves to inform fire managers in fire-prone Mediterranean climate type regions.

Hubungan Jangka Pendek dan Jangka Panjang antara Pembiayaan Perbankan Syariah dan GDP di Indonesia dengan menggunakan metode Vector Error Correction Model

This research analyzes the relationship between Islamic bank financing and economic growth as measured by Gross Domestic Product (GDP) using the Vector Error Correction Model (VECM) to explain the short-term and long-term relationship dynamics between these two variables. The results of this research found that there is a long-term negative relationship between Islamic bank financing and GDP. These findings indicate that, although Sharia financing is expected to support economic growth through Sharia-based financial principles, the reality shows that the direct effect on overall economic growth is more complex and not always unidirectional. Apart from that, this research also finds that in the short term, both sharia financing and GDP adjust back to long-term balance after experiencing deviations, which means a strong economic correction mechanism exists. The stable VAR stability test results validate the model's reliability in describing the accurate relationship between sharia financing and economic growth in Indonesia. These findings provide important insights for policymakers and Islamic banking practitioners in developing more effective strategies to encourage inclusive economic growth.