Search

Quadratic Phase Coupling (QPC) serves as an essential statistical instrument for evaluating nonlinear synchronization within multivariate time series data, especially in signal processing and neuroscience fields. This study explores the precision of QPC detection using numerical estimates derived from cross-bicoherence and bivariate Granger causality within a straightforward, yet noisy, instantaneous multiplier model. It further assesses the impact of accidental statistically significant bifrequency interactions, introducing new metrics such as the ratio of bispectral quadratic phase coupling and the ratio of bivariate Granger causality quadratic phase coupling. Ratios nearing 1 signify a high degree of accuracy in detecting QPC. The coupling strength between interacting channels is identified as a key element that introduces nonlinearities, influencing the signal-to-noise ratio in the output channel. The model is tested across 59 experimental conditions of simulated recordings, with each condition evaluated against six coupling strength values, covering a wide range of carrier frequencies to examine a broad spectrum of scenarios. The findings demonstrate that the bispectral method outperforms bivariate Granger causality, particularly in identifying specific QPC under conditions of very weak couplings and in the presence of noise. The detection of specific QPC is crucial for neuroscience applications aimed at better understanding the temporal and spatial coordination between different brain regions. [ABSTRACT FROM AUTHOR]

South Korea is considered one of the leading countries in innovation and research and development (R&D), in particular in renewable energy R&D efforts, but it is also one of the more polluting countries. So, understanding the interplay between these variables is of great interest. The main objective of this article is to examine the relationship between carbon dioxide (CO2) emissions, economic growth (GDP), renewable energy consumption (RE), foreign direct investment (FDI), home patents (HP), and foreign patents (FP) in South Korea using data ranging from 1980 to 2018. For this purpose, the autoregressive distributed lag (ARDL) approach and Granger causality tests were employed. Estimates of long-run elasticity point to a positive relationship between GDP and CO2 emissions. FP and RE have a negative influence on CO2 emissions but they have a favorable effect on GDP. FDI and HP have positive impacts on CO2 emissions. Granger causality outcomes demonstrate that CO2 emissions, GDP, RE, and FP have long-term bidirectional causal relationships. In the short-run, there are unidirectional causalities running from FP and FDI to RE and from FDI and HP to GDP. Besides, there is a bidirectional causality between GDP and RE. South Korea should continue to promote renewable energies and facilitate the use of foreign patents, particularly those relating to renewable energies. This will lead to a reduction in its carbon emissions while benefiting its economic growth. [ABSTRACT FROM AUTHOR]

The current context of economic development requires paying close attention to the energy industry. Since 2022, European countries has been facing specific problems due to energy crises against the background of the geopolitical conflict and the measures provided by European forums in order to reduce dependence on energy imports from Russia. In this context, we aim to define a new model of energy consumption and the function of energy sustainability at the European level, aspects that will lead to highlighting the position of the 27 European member states in the period 2005--2022 in terms of their energy sustainability. The methodology used is based on the study of literature, the consolidation of databases, econometric modelling, and procedures for testing the validity of modelling results. The results of the study are useful to European energy policy decision-makers in view of the necessary adjustments to achieve the objectives of the 2030 and 2050 Agenda. [ABSTRACT FROM AUTHOR]

In line with Africa's commitment to keeping up with the United Nations Framework Convention on Climate Change, achieving a sustainable future requires balancing economic growth with environmental sustainability. This study investigates the long-term impacts of foreign direct investment, economic growth, agricultural production, and energy consumption on CO2 emissions across 43 African nations from 1990 to 2021. Despite significant research on the individual effects of these factors, the combined influence on CO2 emissions remains underexplored. Addressing this gap, this study employs cross-sectional augmented distributed lag estimators (CS-DL and AMG) and updated estimation packages to effectively examine the relationships between variables. Our findings are as follows: firstly, economic growth and energy use was shown to have a significant positive influence on CO2 in the long term. Also, foreign direct investment significantly promotes CO2 emissions. Secondly, the causality test shows a unidirectional causal relationship between CO2 emissions and foreign direct investment. The test also revealed a bidirectional relationship between GDP and CO2 emissions, as well as between energy consumption and CO2 emissions. Again, a bidirectional causation was observed between agricultural production and CO2 emissions. Thirdly, the impulse response analysis shows that GDP will contribute more to emissions over the 10-year forecast period. This study also proposes policy implications to lessen CO2 across the continent and advocates for the judicious adoption of existing policy frameworks like the 2030 Agenda for environmental Sustainability. [ABSTRACT FROM AUTHOR]

This study examines the relationship between renewable and non-renewable energy consumption, economic growth, and ecological footprint for the top 14 Russian-energy-importing countries in 2000–2018. Through PVAR analysis, this study calculates the long-term correlation between variables using a VEC model. The VAR approach addresses the endogeneity issue by including endogenous interactions between the variables in the model. Additionally, the impulse response functions and the influence of various variables on certain lags are evaluated. Cointegration between variables was assessed using DOLS and FMOLS panel analysis to investigate the long-term link further. Our findings demonstrate that there is a cointegration relationship between all series. According to the impulse response analysis results, an energy shock from Russia will negatively affect the countries' economic growth and decrease the ecological footprint. Increasing renewable and non-renewable energy consumption domestically increases ecological footprint, but the effect of renewable energy consumption on ecological footprint seems to be less. According to DOLS findings, a 10 percent increase in Russian fuel exports increases the elasticity of economic growth by 0.61%. In contrast, FMOLS findings indicate that a 10% increase in Russian fuel export increases the elasticity of economic growth by 1%. However, the direction of the relationship is positive when the ecological footprint is considered the dependent variable: While the non-renewable energy consumption elasticity coefficient was 0.030 in the DOLS model, it was found to be 0.023 in the FMOLS model. The elasticity value of the economic growth variable was found to be 0.69 in the FMOLS model. [ABSTRACT FROM AUTHOR]

Renewable energy consumption is a crucial solution to addressing pressing environmental issues, particularly climate change and air pollution. Investigating the factors that drive its adoption is highly significant, as it provides policymakers and stakeholders with valuable insights to accelerate the transition to renewable energy sources. Through this approach, we can minimise the negative consequences of our reliance on fossil fuels, thereby protecting the integrity of the environment. Therefore, the primary goal of this study is to thoroughly investigate the main factors that influence renewable energy consumption and environmental change in six specifically chosen ASEAN countries. The stationarity of the 1990–2019 data was tested using panel data techniques such as Levin, Lin, and Chu (LLC), Im Pesaran (IPS), and the Shin W-stat test. According to the stationarity tests, after the first order, all variables exhibit stationarity. Additionally, Pedroni's co-integration test result confirmed that there was a long-term relationship among the variables. Different methods, such as dynamic ordinary least squares (DOLS), fully modified ordinary least squares (FMOLS), and pooled ordinary least squares (POLS), are used for cointegration estimating. The results suggest that there is a positive co-integration between renewable energy use and GDP in six ASEAN countries, indicating a long-term relationship. The positive relationship between GDP and renewable energy use suggests that economic growth is the primary driving force behind ASEAN's renewable energy adoption. However, factors like carbon emissions, population density, and foreign direct investment (FDI) negatively impact the demand for renewable energy. The limited availability of renewable energy in certain ASEAN countries may discourage foreign direct investment (FDI) due to the inverse relationship between FDI and renewable energy use. The studies also revealed that carbon emissions, which contribute to environmental pollution, do not motivate industries to invest in renewable energy. This finding would challenge the Environmental Kuznets Curve (EKC) hypothesis. According to the EKC, there is a significant transition towards renewable energy as a response to environmental degradation. However, it is worth noting that several ASEAN countries have experienced economic growth while also experiencing higher levels of carbon emissions. Given that economic expansion might not be environmentally beneficial, this research has implications for ASEAN energy policies. The ASEAN region faces a challenge in investing in renewable energy due to the excessive dependence on fossil fuels. Therefore, an in-depth evaluation of the main factor behind ASEAN's environmental concerns, which promotes the adoption of renewable energy, can greatly influence policy decisions, particularly in attaining net zero emissions. Policymakers can utilise this comprehensive analysis to establish informed objectives for policies related to renewable energy and develop strategic plans, i.e. reforming fuel subsidies. The goal is to encourage the development of environmentally friendly and sustainable energy plans for the future in the ASEAN region. [ABSTRACT FROM AUTHOR]

In this article, we explore the relationships between economic growth and energy intensity in Bahrain, and we analyse how energy intensity, population, and other macroeconomic variables are associated with each in the short and long run. In the empirical section we use data span from 1980 to 2019 and we perform a vector error correction model and cointegration technique to detect causality between the variables used in the model. The main findings of this article show that economic growth, population and FDI increased energy intensity in Bahrain in the short run. Therefore, an urgent need to be embedded in the sustainable energy action plan to limit the harmful effects of economic activity on the environmental quality in Bahrain and to ensure environmental sustainability. [ABSTRACT FROM AUTHOR]

The current research examines the interlink within G-20 economies. Advanced econometrics methods were employed in the panel data collected from 1990 to 2019. Within an employed method, co-integration, panel unit tests, and vector error models were considered, unveiling long-term interactions and the casual interlink between proposed variables. This method is essential to highlight the complicated relationship between energy usage and economic factors. It also supports the global efforts to minimize environmental degradation and fossil fuel usage to achieve the Paris Agreement goals. The current study's findings show a significant and robust long-term equilibrium between primary energy consumption, financial growth, price of energy, and trade openness. The findings further suggest that fluctuations in primary energy consumption directly impact financial growth, with trade openness being essential to this relationship. Renewable energy sources also play a vital role in mitigating carbon emissions that precisely explain sustainable financial growth without comprising the protection of environmental integrity. For the member countries of the G-20, the findings emphasize improving energy efficiency, funding for renewable energy, and maintaining stable energy prices to promote sustainable financial growth. This paper contributes to the existing literature examining the co-integration between renewable energy consumption, financial growth, and trade openness for the long run, stresses the necessity of comprehensive energy policies, and employs superior econometric estimation techniques. [ABSTRACT FROM AUTHOR]

Coordinating the relationship between the digital economy, green innovation, and the urban-rural income gap is conducive to promoting common prosperity in high-quality development. This study aims to show that, in the short term, the development of the digital economy will promote the level of green innovation, which, in turn, will promote the development of the digital economy, but will also widen the urban-rural income gap. The study uses panel data of 273 prefecture-level cities from 2011 to 2019, and adopts the panel vector error correction model for quantitative analysis, combined with the theoretical analysis of the long- and short-term causal relationship among the digital economy, green innovation, and urban-rural income gap. The results reveal that, in the long term, the digital economy, green innovation, and urban-rural income gap demonstrate a double-circular causality that is positively promoting each other. The robustness test validates the conclusions. Therefore, while promoting the development of the digital economy and green innovation, the government should control the urban-rural income gap within a reasonable range, to provide theoretical and practical support for promoting the sustainable development of China's economy and realizing the goal of common prosperity. [ABSTRACT FROM AUTHOR]

EEG signals capture information through multi-channel electrodes and hold promising prospects for human emotion recognition. However, the presence of high levels of noise and the diverse nature of EEG signals pose significant challenges, leading to potential overfitting issues that further complicate the extraction of meaningful information. To address this issue, we propose a Granger causal-based spatial–temporal contrastive learning framework, which significantly enhances the ability to capture EEG signal information by modeling rich spatial–temporal relationships. Specifically, in the spatial dimension, we employ a sampling strategy to select positive sample pairs from individuals watching the same video. Subsequently, a Granger causality test is utilized to enhance graph data and construct potential causality for each channel. Finally, a residual graph convolutional neural network is employed to extract features from EEG signals and compute spatial contrast loss. In the temporal dimension, we first apply a frequency domain noise reduction module for data enhancement on each time series. Then, we introduce the Granger–Former model to capture time domain representation and calculate the time contrast loss. We conduct extensive experiments on two publicly available sentiment recognition datasets (DEAP and SEED), achieving 1.65% improvement of the DEAP dataset and 1.55% improvement of the SEED dataset compared to state-of-the-art unsupervised models. Our method outperforms benchmark methods in terms of prediction accuracy as well as interpretability. [ABSTRACT FROM AUTHOR]

The global economy has reported an unprecedented increase in growth rates over the last 2 decades, due to rapid evolution in transportation and communications. The rapid growth of international trade has increased the demand for fossil fuel, leading to exacerbated environmental risks. Air transportation is an essential operational practice in trade openness and has many economic benefits. However, its effect on CO2 emissions is not well understood. Studies on the causal relationships between air transportation, trade openness, economic growth, and CO2 emissions are lacking, especially across Middle Eastern countries. This study targets Saudi Arabia, one of the largest countries in the Middle East region in terms of economic capabilities and geographical area, to investigate the impact of air transportation, trade openness, and economic growth on CO2 emissions. To this end, data was derived from the World Development Indicators (WDI) established by the World Bank for the period 1991-2023. An autoregressive, distributed lag autoregressive distributed lag (ARDL) model was used to analyze associations among the study variables; the empirical findings confirm that air transportation, trade openness, and economic growth have positive and statistically significant effects on CO2 emissions in both long- and short-run scenarios. However, the results illustrate that economic growth alone is unable to sufficiently reduce CO2 emissions in Saudi Arabia, indicating a lack of connection between economic policies and environmental goals. Thus, these results indicate that the Environmental Kuznets Curve (EKC) hypothesis is not valid for Saudi Arabia. In addition, this study provides useful insights for policymakers to mitigate CO2 emissions. Suggestions include attracting foreign investment, modifying the structure of trade, mitigating the reliance on imports and enhancing exports, while focusing on green strategies for economic growth, replacing fossil fuels with clean and renewable sources, subsidizing environmentally friendly technologies, and enacting decarbonizing regulations. [ABSTRACT FROM AUTHOR]

In today's highly advanced industrialised and modernised world, China's economy is still growing, and its demand for energy is increasing daily. It is crucial to examine the connection between energy consumption, carbon emissions, and economic growth in order to promote economic growth based on energy conservation and emission reduction. Using Dezhou City in Shandong Province as an example, the study builds a VAR model of carbon emission, energy consumption, and economic growth in Dezhou City based on simplified macroeconomic sub-models, energy sub-models, and environmental sub-models. It then determines the correlation and influence mechanism between the three using tests like ADF unit root and Granger causality. The pertinent elements affecting Dezhou's carbon emissions were then investigated using grey correlation analysis. Finally, based on the study's findings, policy suggestions are made regarding energy use, carbon emissions, and economic expansion. It is necessary not only to restrain high-energy consumption industries and fundamentally optimize the energy consumption structure, but also to find new economic growth points and improve economic growth channels, so as to optimize the industrial structure. In this process, increasing the proportion of the tertiary industry is a key measure. In addition, the government needs to advocate the citizens to adopt a low-carbon lifestyle, and the concept of low-carbon environmental protection will be deeply rooted in the hearts of the people. This study will provide suggestions and theoretical guidance for China's energy consumption and carbon emissions, and help achieve high-quality growth of China and even the world economy. [ABSTRACT FROM AUTHOR]

This paper examines the asymmetric impact of financial development on economic growth in Mauritius during the period 1980–2021. The analyses were carried out using a nonlinear autoregressive distributed lag (NARDL) model in conjunction with additional complementary tests, including the NARDL bounds F-test for cointegration, the Brock-Dechert-Scheinkman (BDS) nonlinearity test, and the Wald test for asymmetries. The findings of the bounds F-test provide support for a nonlinear cointegration, whereas the BDS test shows the presence of nonlinearity in the data for all the variables. The Wald test results revealed an asymmetric relationship between financial development and economic growth in Mauritius, both in the short and long run. The NARDL findings show that, on average, positive changes in financial development lead to economic growth in the long and short run. Therefore, the paper encourages the government of Mauritius to continue pursuing policies aimed at the expansion of its financial sector since it has a significant positive impact on its economic growth. [ABSTRACT FROM AUTHOR]

The cement industry is among the top three polluters among all industries and the examination of the nonlinear and cointegration dynamics between cement production and CO2 emissions has not been explored. Focusing on this research gap, the study employs a novel Markov-switching autoregressive distributed lag (MS-ARDL) model and its generalization to vector error correction, the MS-VARDL model, for regime-dependent causality testing. The new method allows the determination of nonlinear long-run and short-run relations, regime duration, and cement-induced-CO2 emission cycles in the USA for a historically long dataset covering 1900–2021. Empirical findings point to nonlinearity in all series and nonlinear cointegration between cement production and cement-induced CO2 emissions. The phases of regimes coincide closely with NBER's official economic cycles for the USA. The second regime, characterized by expansions, lasts twice as long relative to the first, the contractionary regime, which contains severe economic recessions, as well as economic crises, the 1929 Great Depression, the 1973 Oil Crisis, the 2009 Great Recession, and the COVID-19 Shutdown and Wars, including WWI and II. In both regimes, the adverse effects of cement production on CO2 emissions cannot be rejected with varying degrees both in the long and the short run. Markov regime-switching vector autoregressive distributed lag (MS-VARDL) causality tests confirm unidirectional causality from cement production to CO2 emissions in both regimes. The traditional Granger causality test produces an over-acceptance of causality in a discussed set of cases. Industry-level policy recommendations include investments to help with the shift to green kiln technologies and energy efficiency. National-level policies on renewable energy and carbon capture are also vital considering the energy consumption of cement production. [ABSTRACT FROM AUTHOR]

Atmospheric motion vectors, which can be used to infer wind speed and direction based on the trajectory of cloud movement, are instrumental in enhancing atmospheric wind-field insights, contributing notably to wind-field optimization and forecasting. However, a widespread problem with vector data is their inaccuracy, which, when coupled with the mediocre effectiveness of existing correction methods, limits their practical utility in forecasting, often falling short of expectations. Deep-learning techniques are used to refine atmospheric motion vector data from the FY-4A satellite, notably enhancing data quality. Post-training data undergoes a thorough analysis using a quality evaluation function, followed by its integration into a numerical weather prediction system in order to conduct forecasting experiments. Results indicate a marked improvement in data quality post-error correction by the model, characterized by a significant reduction in root mean square error and a notable increase in correlation coefficients. Furthermore, refined data demonstrate a considerable enhancement in the accuracy of meteorological element forecasts, particularly for Asian and Western Pacific regions. [ABSTRACT FROM AUTHOR]

Energy, being one of the significant drivers of growth, has gained much focus in the last four decades. A connection between energy and growth exists in the economy: on the one hand, energy is a vital factor of production while growth improves living standards, which increases energy consumption on the other hand. Previous studies on the energy‐growth nexus paid less attention to sectorial differentials in the consumption of energy and growth. Also, the literature could not relate that to sustainable development. Thus, this study explores the sectorial differentials in energy consumption and how they affect the energy‐growth nexus within the context of sustainable development. The research employed secondary data for 46 sub‐Saharan Africa (SSA) countries for the period 2007 to 2020. Using the General Methods of Moments estimation technique, we found that all the measures of energy consumption positively affect economic growth. Also, CO2 intensity is positively related to growth in the economy while CO2 emissions from solid fuels negatively affect economic growth. Again, Foreign Direct Investment (FDI) is positively impacting economic growth and Gross Domestic Product (SSA) per energy use. Policy implications are discussed in the paper. [ABSTRACT FROM AUTHOR]

Openness to international trade is often conducive to long-term economic development. Indeed, thanks to trade instruments based on an effective and selective policy of openness, and to human capital that is both qualified and specialised, trade liberalisation is a key determinant of healthy and progressive economic growth. The aim of this article is to study the nature and direction of causal relationships in a dynamic framework, between the variable to be explained (trade openness) measured by the volume of exports and imports as well as by human capital, and the variable to be explained (economic growth) measured by GDP at the level of Morocco. In this respect, we will attempt to examine this causal relationship in the Moroccan context. From this perspective, our question, which remains important, can be formulated as follows: To what extent does trade openness promote economic growth in Morocco? In order to find more relevant answers to our question, we carried out a mixed analysis (both theoretical and empirical) based on mortgagedeductive reasoning derived from a purely positivist posture, on the one hand, and, on the other, we applied econometric modeling to the case of Morocco, based on the ARDL approach. The data collected is annual and covers a period from 1985 to 2020. It was gathered from the HCP and World Bank databases. To do this, we used the ARDL method. The results received by the econometric modeling and the tests that verify the relationship and the direction of causality indicate that trade openness have a positive influence on Moroccan economic growth. [ABSTRACT FROM AUTHOR]

The globe is now in ecological turmoil as a result of the unrelenting increase in global warming. As a result, governments worldwide are committing to decarbonizing the environment, with the United Arab Emirates (UAE) and the Kingdom of Saudi Arabia (KSA) playing an important role in this effort. Hence, this paper evaluates the nonlinear (asymmetric) impact of natural gas consumption on renewable energy consumption and economic growth in the KSA and the UAE utilizing data stretching from 1990 to 2020. The study also considers other drivers of renewable energy consumption and economic growth, such as trade openness and CO2 emissions. The study utilizes nonlinear autoregressive distributed lag (ARDL) to evaluate these associations. The outcomes of bounds nonlinear ARDL (NARDL), affirm the long-run association between the variables in both countries. The nonlinear results show that positive and negative shocks in natural gas consumption have a negative impact on renewable energy in both UAE and KSA. In contrast, positive and negative shocks in natural gas consumption impact economic growth positively. The study proposed vital policy recommendations based on these results. [ABSTRACT FROM AUTHOR]

The competition in the world has shifted from natural resources and capital resources to human resources. The human resources have become the real power source of the economic growth. Firstly, the price of human resources in China is calculated. Secondly, in order to measure the human resources quality adjustment index, the indicators system is constructed. Third, the Hedonic method is used to calculate the human resources "pure price" of 31 provinces (autonomous regions and municipalities directly under the Central Government) in China. The "pure price" has no the factor of human resources quality. Lastly, comparing the price of human resources before and after quality adjustment. The results show that: first, the number of human resources in China increased continuously during 1995–2015 and decreased during 2016–2020. Second, the price of nominal human resources in China keeps rising. In 2020, the nominal price is 39,087 yuan per person which is 15.44 times as many as in 1995. Thirdly, after the quality adjustment, the price of human resources has decreased significantly. The multiple between the actual and nominal price of human resources is between 1.75 and 2.12. Fourthly, the province with high human resource quality adjustment index generally have high quality human resource level or quantity. Fifth, the top five provinces of actual human resource prices are Shanghai, Beijing, Guangdong, Tianjin, Zhejiang, the bottom five provinces are Guizhou, Yunnan, Henan, Xizang, Gansu. Finally, the paper puts forward some policy recommendations: Improving the data collection mechanism of human resources accounting to provide a basic guarantee for the accurate accounting of human resources. Improving the price of human resources in the central and western regions to attract the talents to transfer to the central and western regions. Enhancing the skills training of human resources to improve the quality of human resources in the western region. [ABSTRACT FROM AUTHOR]

Learning causality from large-scale text corpora is an important task with numerous applications—for example, in finance, biology, medicine, and scientific discovery. Prior studies have focused mainly on simple causality, which only includes one cause-effect pair. However, causality is notoriously difficult to understand and analyze because of multiple cause spans and their entangled interactions. To detect complex causality, we propose a self-paced contrastive learning model, namely N2NCause, to learn entangled interactions between multiple spans. Specifically, N2NCause introduces data enhancement operations to convert implicit expressions into explicit expressions with the most rational causal connectives for the synthesis of positive samples and to invert the directed connection between a cause-effect pair for the synthesis of negative samples. To learn the semantic dependency and causal direction of positive and negative samples, self-paced contrastive learning is proposed to learn the entangled interactions among spans, including the interaction direction and interaction field. We evaluated the performance of N2NCause in three cause-effect detection tasks. The experimental results show that, with the least data annotation efforts, N2NCause demonstrates competitive performance in detecting simple cause-effect relations, and it is superior to existing solutions for the detection of complex causality. [ABSTRACT FROM AUTHOR]

Research Highlights • An assessment of institutional quality, innovation, and economic growth in developing countries. • Long-run causality from innovation and institutional quality to economic growth is present. • Short-run directions of causality are varied among variables of interest. • Innovation and institutional quality generally positively impact economic growth. Continuous innovation is the lifeblood of a competitive economy. Furthermore, and arguably, the existence of institutions of governance of quality can be a catalyst for emerging economies to transition up the universal innovation value chain. In this context, we investigate temporal causal interactions among institutional quality, innovation, and economic growth for developing countries (DCs) spanning the period of 2005–2020. Employing a vector error-correction model (VECM), we find that for each case and specification (49 instances), there is evidence of the long-run causality from institutional quality and innovation to economic growth. Stated another way, in the long run, institutional quality and innovation Granger-cause economic growth. However, the short-run causality results differ depending on the specific measures of innovation and institutional quality. The strongest short-run conclusion is support for the feedback hypothesis for economic growth and innovation where there is a strong endogenous relationship between innovation, institutional governance, and economic growth. The empirical analysis shows over 70% of our observations support that economic growth and innovation jointly determine each other in the short run. The results suggest that DCs should develop and pursue long-term growth strategies that simultaneously develop innovation and improve institutional governance. [ABSTRACT FROM AUTHOR]

Purpose: This study investigates the causal connections and both short-term and long-term associations among corruption, investment, unemployment, and per capita economic growth across twenty-two Asia-Pacific nations spanning from 2012 to 2020. Design/Methodology/Approach: The research employs Granger causality and Vector Error Correction Model methodologies to tackle the research inquiries. Findings: The empirical results unveil bidirectional causality between corruption and per capita economic growth, whereas the unemployment rate and per capita economic growth share a unidirectional relationship. Conversely, no causal linkage is found among the remaining variables. In the short run, corruption has no significant impact on per capita economic growth and unemployment but does significantly and adversely affect the investment rate. On the other hand, in the long run, corruption significantly and negatively influences per capita economic growth. The investment rate and unemployment, in the long term, exhibit a substantial and positive influence on per capita economic growth. [ABSTRACT FROM AUTHOR]

In this study, we aim at investigating dynamic relations among steel price indices of all seven regional markets in China, spanning the period of 2010M1–2021M4. With daily data, we apply techniques of vector error correction modeling and the directed acyclic graph to analyze the issue of contemporaneous causality among the seven indices, the latter of which is facilitated through the LiNGAM algorithm. We find via vector error correction modeling that each of the seven indices is part of cointegration relations and responses to long-run equilibrium disturbances. We arrive at causal paths via the LiNGAM algorithm for enabling innovation accounting analysis. With this analysis, we find sophisticated dynamics in price adjustment processes following shocks, for which east and northeast regions have the most significant impacts on other regions, followed by south and north regions, followed by the southwest region. Central south and northwest regions are not found to have significant impacts on others. These results suggest that policies on regional steel prices in the long run might be designed with most attention paid to five of the seven regions, particularly east and northeast regions. [ABSTRACT FROM AUTHOR]

This article presents a novel approach for reconstructing an equivalent underlying model and deriving a precise equivalent expression through the use of direct causality topology. Central to this methodology is the transfer entropy method, which is instrumental in revealing the causality topology. The polynomial fitting method is then applied to determine the coefficients and intrinsic order of the causality structure, leveraging the foundational elements extracted from the direct causality topology. Notably, this approach efficiently discovers the core topology from the data, reducing redundancy without requiring prior domain-specific knowledge. Furthermore, it yields a precise equivalent model expression, offering a robust foundation for further analysis and exploration in various fields. Additionally, the proposed model for reconstructing an equivalent underlying framework demonstrates strong forecasting capabilities in multivariate time series scenarios. [ABSTRACT FROM AUTHOR]

Japan's recent enunciation of a "Free and Open Indo-Pacific" policy aims to promote principles such as free trade, freedom of navigation, and encouraging economic prosperity with building commitment to stability and peace connecting the economic hub of Asia to Africa (MFA, 2019). Natural gas use continues to dominate Japan's energy mix despite efforts to improve environmental protection and attain the Sustainable Development Goals (SDGs). Also, increasing production and investment typically leads to a better quality of life and more disposable income for the populace, which promotes economic growth. To this end, the study investigates the relationship between economic growth, trade openness, and natural gas consumption, as well as gross fixed capital formation and carbon dioxide emissions to overcome omitted variable problems to explore the natural gas-economic growth hypothesis in Japan over the period 1980–2020. Empirical results reveal a long-run relationship among the variables under consideration. By applying the Toda and Yamamoto approach to Granger causality testing, a two-sided causality running from CO2 and economic growth was revealed, while a one-sided causality from economic growth to natural gas consumption. This outcome suggests that the natural gas-economic growth hypothesis is not valid for Japan. According to these outcomes, policymakers in the energy sector should consciously diversify their energy portfolio in line with the Sustainable Development Goals (SDGs) for Japan. Also, it is recommended that there should be a provision of access to skilled labor to increase productivity and export finished products to boost a nation's economy. [ABSTRACT FROM AUTHOR]

Maize stands as a cornerstone of Indonesia's agricultural landscape, serving both as a vital food source and an essential fortifier. However, the marketing process of smallholder maize in Indonesia has yet to reach an optimal level of efficiency. This research endeavors to delve into the vertical integration of smallholder maize in the Indonesian agricultural sector. To conduct this analysis, we employ a forward-looking predictive model, applying the Vector Error Correction Model (VECM) to analyze time series data related to smallholder maize in Indonesia. Our findings yield critical insights that shed light on the intricate dynamics of smallholder maize markets in the archipelago. Notably, our research underscores the long-term integration between producer-level smallholder maize markets and consumer-level smallholder maize markets in Indonesia. This integration implies that changes in producer-level smallholder maize prices are intrinsically linked to shifts in consumer-level smallholder maize prices in the country. These findings provide a valuable foundation for collaborative efforts within the agricultural sector, guiding stakeholders toward more effective strategies for optimizing smallholder maize markets in Indonesia. [ABSTRACT FROM AUTHOR]

Introduction: Tobacco, an important commercial crop, plays a crucial role in farmers' incomes and livelihoods to a sizable population and contributes significant exchange earnings to the Indian economy. Currently, India is the second-largest tobacco producer after China, with a production of 758 million kg (13% of global production) and exports of 190 million kg of tobacco (9% of global tobacco export volume). However, there are uncertainties surrounding the tobacco sector, such as growing public health and environmental issues associated with tobacco production and consumption and changing national and international tobacco-related policy regimes. In this context, the current study investigates the determinants of tobacco exports and geographical shifts in export destinations over the years. Methods: The statistical models employed are co-integration, and vector error-correlation models to test the short-run and long-run dynamics relationship between tobacco exports and the explanatory variables, and the Markov chain approach to find out geographical shifts in export destinations. Results and discussion: The econometric model estimated the relationship between the tobacco export volume with domestic production, export price, and global demand for Indian tobacco, and investigated the geographical shift in export destinations of tobacco in the context of changing global and national policy regimes on the sector. The econometric modeling framework confirms that there exists a statistically significant relationship between Indian tobacco export demand, domestic production, export price, and world demand for Indian tobacco. The geographical shift was evident in major export destinations during the post-WHO-FCTC (Framework Convention on Tobacco Control) regime. The model findings direct that India should take advantage of the export price, and global demand for tobacco as India ratified WHO-FCTC; there is no scope for horizontal expansion of the area under tobacco. This modeling framework aids as a tool to direct and explore the possible options with a greater emphasis on export-centric farming system in tobacco production by augmenting crop compliance and quality to meet the standards of international markets. [ABSTRACT FROM AUTHOR]

This study aimed to identify the determinants of economic growth (Y) in Kazakhstan over the period 1990-2022. In other words, the effects of urbanization (URB), natural resources (NR), trade openness (TO), foreign direct investment (FDI), and ICT variables on Y were to be determined. In the study, ARDL method and Vector Error Correction Model (VECM) were employed to determine the short and long-term effects. As a result of the analysis, it was found that URB, TO, FDI, and ICT increased Y in the short and long run. On the other hand, NR did not affect Y. The results of the VECM revealed a bilateral causality between URB, TO, FDI, ICT, and Y in the short and long run. Finally, no causality was found between NR and Y. These findings may help policymakers in realizing Kazakhstan's economic development goals. [ABSTRACT FROM AUTHOR]

Copyright of Jurnal Ekonomi Syariah Teori dan Terapan is the property of Universitas Airlangga and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Understanding the complex interactions between the economy and the environment is crucial for promoting sustainable development and mitigating the negative impact of human activities on the Planet. The importance of this issue for Serbia is evident as the country strives to balance economic growth and environmental protection to ensure a sustainable and resilient future. Therefore, the main objective of this study is to investigate and model the relationship between CO2 emissions, economic growth, electricity consumption, and trade openness in Serbia. Initially, an Autoregressive Distributed Lag (ARDL) model is used to characterize the Environmental Kuznets Curve (EKC) using data from the period from 1995 to 2019, followed by the construction of a bootstrap logistic regression model to predict environmental quality in Serbia. Long-term estimates of the model confirm an inverted U-shaped relationship, where all three variables exert a statistically significant influence on CO2 emissions. In the short run, however, a causal relationship is only observed between electricity consumption and CO2 emissions. The logistic regression results show that all three factors significantly influence environmental quality. The study proposes policy recommendations for Serbia, such as promoting sustainable economic growth, implementing long-term programs to reduce CO2 emissions, reviewing trade policies to prioritize sustainable practices, and investing in renewable energy sources to reduce emissions. [ABSTRACT FROM AUTHOR]

The power laws of network geometric properties are widely adopted to characterize the critical phenomena of complex systems, which may not describe the diverse interactions in systems effectively. By comparison, the causality represents the complicated interactions. Therefore, in this work, the power law of the causal effects in systems is studied to reflect the critical states from the perspective of causality. The causal Bayesian networks and effective information are adopted to construct the causal relationships and quantify the causal effects. The study on 20 systems from different fields shows that for a considerable proportion of systems, the causal effects of the factors in systems follow the power laws. For such causal power laws, an explanation based on the principle of maximum entropy is proposed and verified. The causal power laws may imply some critical states of systems, and can provide the basis for the quantification of systems' states and functions. [ABSTRACT FROM AUTHOR]

This paper extends the multivariate index autoregressive model to the case of cointegrated time series of order (1,1). In this new modelling, namely the vector error-correction index model (VECIM), the first differences of series are driven by some linear combinations of the variables, namely the indexes. When the indexes are significantly fewer than the variables, the VECIM achieves a substantial dimension reduction with reference to the vector error correction model. We show that the VECIM allows one to decompose the reduced-form errors into sets of common and uncommon shocks, and that the former can be further decomposed into permanent and transitory shocks. Moreover, we offer a switching algorithm for optimal estimation of the VECIM. Finally, we document the practical value of the proposed approach by both simulations and an empirical application, where we search for the shocks that drive the aggregate fluctuations at different frequency bands in the US. [ABSTRACT FROM AUTHOR]

Over the past three decades, industrial innovations and technological advancements have changed business dynamics, adversely devastating the overall environment. As a result, our oceans have been severely affected due to climate change and global warming. To address this issue, this study investigates the factors that cause ocean CO2 using a sample of 44 countries over 2012–2021 and explores a dynamic and causal relationship between economic growth, ocean carbon dioxide emissions, energy consumption, and control variables relating to the ocean industry. This study finds that increasing economic activity tends to increase ocean carbon emissions. The results support the evidence of the environmental Kuznets curve (EKC) hypothesis suggesting an inverted U-shaped association between ocean emissions and real income for the sample countries. Moreover, this study reports that ocean health index, maritime container transport, trade of fishery and ocean species, aquaculture production and marine species, and employment rate in the fishery processing sector are the significant factors of ocean CO2. Region-wise analyses suggest that real income positively influences ocean emissions and confirm the evidence of the EKC hypothesis in European sample countries but these relationships have an insignificant effect in Asia and the Pacific and the American regions. Furthermore, a short-run unidirectional panel causality flows from the production of aquaculture and other species to RD&D, from OHI and GDP to trade of fishery and other species, and from OHI to employment rate in the fishery sector. Likewise, bidirectional causality runs from energy consumption and maritime transport to ocean CO2 in the long term. Regarding the long-run causal association, the results determine that all of the estimated coefficients of the lagged error correction terms are statistically significant which explains that they are crucial in the adjustment process as they deviate from the long-run equilibrium. [ABSTRACT FROM AUTHOR]

High level of greenhouse gases emission in fossil fuels has induced a significant transition from conventional energy sources to renewable energy. However, using renewable energy in electricity grids has limitations, such as intermittency and lack of efficient energy storage. This paper focuses on constructing the quanto options contract to help renewable energy producers hedge the risk against low photovoltaic (PV) production and electricity prices. To achieve our objective, we first model the interday PV power production with a combination of a deterministic and stochastic model. We analyse empirical data for daily solar production in three operators in Germany ranging from 1 January 2016 to 31 December 2020. We discovered that all estimated parameters of the deterministic process are highly significant. Meanwhile,we use an autoregressive (AR) process to describe the random behavior in the production. The results demonstrated that AR(2) is suitable enough to explain its stochastic factor. For the error terms analysis, we observed a clear sign of seasonal heteroskedasticity supported by the left skewed density. In addition, we observed a clear seasonal pattern in the squared error terms, where we suggest using seasonal variance and skewed normal distribution to describe its dynamic. As for an application, we embed the PV model in the power price modeling. We found that AR(3) process is sufficient to explain the price behavior and that normal distribution best fits the error terms. Using the suggested PV and power price model, we construct the quanto options contract for renewable energy producers using Monte Carlo simulations. We discovered that the electricity price payoffs are consistent throughout the year, whereas PV and quanto options payoffs vary greatly depending on the season. The quanto options price result shows that the prices vary during four seasons, with the highest variation in July. [ABSTRACT FROM AUTHOR]

In this paper we further develop a notion of causal predictability defined in [A. Merkle, Predictability and Uniqueness of Weak Solutions of Stochastic Differential Equations, Analele Stiintifice ale Universitatii Ovidius Constanta, 2022] as a concept of dependence which is based on Granger's definition of causality. More precisely, in [A. Merkle, Predictability and Uniqueness of Weak Solutions of Stochastic Differential Equations, Analele Stiintifice ale Universitatii Ovidius Constanta, 2022] causal predictability is defined between filtrations, but now we introduce causal predictability between stochastic processes and filtrations. Also, we provide some properties of this new concept. Then we apply the given causality concept to the uniqueness of weak solutions of the stochastic differential equations and in financial mathematics. Granger [Investigating causal relations by econometric models and cross spectral methods, Econometrica. 37 (1969), pp. 424–438] has considered causality concept between time series. In this paper we consider continuous time processes, since continuous time models represent the first step in various applications, such as in finance, econometric practice, neuroscience, epidemiology, climatology, demographic, etc. [ABSTRACT FROM AUTHOR]

Measurements of systems taken along a continuous functional dimension, such as time or space, are ubiquitous in many fields, from the physical and biological sciences to economics and engineering. Such measurements can be viewed as realisations of an underlying smooth process sampled over the continuum. However, traditional methods for independence testing and causal learning are not directly applicable to such data, as they do not take into account the dependence along the functional dimension. By using specifically designed kernels, we introduce statistical tests for bivariate, joint, and conditional independence for functional variables. Our method not only extends the applicability to functional data of the Hilbert–Schmidt independence criterion (hsic) and its d-variate version (d-hsic), but also allows us to introduce a test for conditional independence by defining a novel statistic for the conditional permutation test (cpt) based on the Hilbert–Schmidt conditional independence criterion (hscic), with optimised regularisation strength estimated through an evaluation rejection rate. Our empirical results of the size and power of these tests on synthetic functional data show good performance, and we then exemplify their application to several constraint- and regression-based causal structure learning problems, including both synthetic examples and real socioeconomic data. [ABSTRACT FROM AUTHOR]

Copyright of Jurnal Ilmu Ekonomi Terapan is the property of Universitas Airlangga and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Exchange rate (EXR) stability is crucial for especially the developing economies. This study examines the macroeconomic variables which affect EXR in member countries of South Asian Association for Regional Cooperation (SAARC). To do the analyses, data from 1981 to 2019 was relied upon. To estimate the long-run coefficients, Fully Modified Ordinary Least Square (FMOLS) is incorporated. The outcomes of FMOLS exhibit that the variables such as trade, inflation, GDP, total debt services, trade openness, and tariff rate are inversely related to EXR while broad money is found to affect the EXR in positive. As a policy prospect, The Central Bank (CB) is needed to follow tight monetary policy to curtail liquidity of money in economy. It is recommended that CB to discourage government project financing that is to cause increase in money supply but to initiate trendline to facilitate GDP growth, trade, and imposition of tariff to curtail rising EXR. [ABSTRACT FROM AUTHOR]

In this study, the nexus between carbon emissions, financial development and energy consumption is investigated for South Africa using annual time series data from 1990 to 2021. The mounting pressure on nations to reduce their carbon footprint has motivated this study. The findings of this study will contribute to establishing economic variables that South Africa can take advantage of in the journey to reducing carbon emissions. Two energy consumption proxies were used, namely, total energy consumption and electricity. Employing autoregressive distributed lag approach to cointegration and error correction (ECM)-based Granger causality test, the study found a unidirectional causal flow from carbon emissions to total energy consumption in the short run and the long run, and no causality was found when energy consumption was measured by electricity. The study also found a unidirectional causal flow from carbon emissions to energy consumption and from energy consumption to financial development in the long run and in the short run. The findings of the study reveal conscious decisions South Africans are making to reduce the carbon footprint coupled with the financial sector ability to design instruments that suit the customer requirements. Policy implications are also discussed. [ABSTRACT FROM AUTHOR]

Most nonlinear vector autoregressive methods in the econometric literature are based on specific functional forms, such as the smooth transition autoregressive model. This study proposes a general form of the nonlinear vector autoregressive model based on global approximators, such as neural networks, Volterra, and Weiner series. The simulation results of 20 linear and nonlinear multivariate time series processes indicate that nonlinear vector autoregressive methods, especially multi‐output neural networks, are more accurate based on the root mean square error and model confidence set criteria. Applying the global approximator approach to a small‐scale macroeconometric model reveals that the new approach can improve forecast accuracy compared to linear and other nonlinear vector error correction models. In addition, forecasting the relevant variables in a typical exchange rate and monetary policy models based on nonlinear specifications gives more successful results than in the linear case. [ABSTRACT FROM AUTHOR]

The social sciences literature is replete with microeconomic models on racial discrimination of minority employment. This paper contributes to the empirics by testing the hypothesis of short run and long run employment relationships among racial groups in the U.S. labor market. The author employs a VECM model using national quarterly BLS data for years 2000 to 2023 to test for short-run and long-run Granger causality between employment among racial groups in 5 broad occupational categories: management and professional; services; sales and office; natural resources, construction, and maintenance; and production, transportation, and material moving. The results overwhelmingly reject the hypothesis of independence in employment among the racial groups. Among other findings, there is evidence to suggest that employment of African Americans in the natural resources, construction, and maintenance occupations Granger cause employment in the White and Latino communities in the short run. This short run burst of African American employment in these occupations may be a sign of forthcoming economic downturn. In this same group of occupations, Latin employment Granger causes Asian and White employment, while Asian employment Granger causes White and Latin employment. GDP and these racial groups Granger cause White and Latin employment in the long run. [ABSTRACT FROM AUTHOR]

In this paper, we aim to create an intelligent system to predict the wholesale market prices and provide the input variables for producers to optimally approach the electricity Day-Ahead Market (DAM) and Balancing Market (BM). We create a data set that includes open-source fundamental features collected from ENTSO-E, OPCOM, etc. and predict the DAM price using a univariate Long Short-Time Memory (LSTM) model and the sign (the deficit or surplus) of the system using ten Machine Learning (ML) classifiers. These new features are inserted as input to predict the imbalance prices with a stacked multivariate LSTM model. The predicted prices on DAM, system sign, and prices on BM are required to estimate the income from trading on DAM/BM and create optimal bidding strategies. This approach that combines classification to obtain a new significant feature (namely the imbalance sign) that is included into the input data set to further predict the imbalance prices using recurrent neural networks is an original method that can bring competitive advantages by creating optimal bids. Compared with the baseline, the proposed forecasting method for BM price improves Mean Absolute Error (MAE) by 14.63%, RMSE by 20.45%, MAPE by 9.5% and R2 by 1.76%. Probability of the sign is used as probability to trade on BM for increasing or decreasing the output, whereas MAE from the predicted BM prices is used to enhance the optimal bidding pairs of prices and quantities on DAM. Therefore, in this paper, we propose a complex method that integrates variables and results from both DAM and BM markets and combines prediction and optimization models to attain competitiveness in the electricity markets. [ABSTRACT FROM AUTHOR]

The Middle East and North African (MENA) economies experienced substantial economic fluctuations due to variations in carbon emissions and energy use. For this purpose, the present study examines the factors influencing carbon emissions in MENA economies, particularly economic growth and energy use. To this end, this study disaggregates economic growth into three sectors (agriculture, industry, and services) and energy use into renewable and non-renewable, and examines their environmental impacts by including the roles of urbanization and trade openness in the environment Kuznets curve (EKC) framework. This study uses panel data from 16 MENA countries over the period 1990–2018 to estimate the short-run and long-run coefficients and the Granger causality between the variables. The empirical results using the Mean Augmented Group (AMG) and the Common Correlated Effects Mean Group (CCE-MG) revealed that (i) the signs of GDP per capita and its squared scores validate the EKC hypothesis only at the aggregate level; (ii) the coefficients of sectoral GDP show that the industry and services sectors have the highest contributions to carbon emissions in the MENA region; (iii) non-renewable energy increases emissions, whereas renewable energy lessens them. The outcomes of the Granger causality confirm (i) a bidirectional relationship between emissions and per capita GDP as well as sectoral GDP, between CO2 emissions and renewable energy, and between per capita GDP and renewable and non-renewable energy however; (ii) a unidirectional causal impact running from non-renewable energy to CO2 emissions is found in the short term. The study calls for effective policies to focus on curbing emissions in secondary and tertiary economic sectors by growing the part of renewable energy in the total energy mix. [ABSTRACT FROM AUTHOR]

The problem of climate change, which causes various negativities in the global sense, is one of the important research topics. It is necessary to determine the factors affecting carbon dioxide (CO2) emissions, which are the main determinants of climate change, and to take measures for this. In this study, based on the hypothesis that economic growth, energy usage, trade openness, and foreign direct investment affect CO2 emissions, it was aimed to examine the effects of economic growth, energy usage, trade openness, and foreign direct investment on CO2 emissions for G8 countries using annual data for the period 1990–2018. For this purpose, first, a literature review was done in the study. Then, cross-section dependency and heterogeneity tests were performed as empirical analyses. Afterward, unit root tests, cointegration analyses, and causality analyses were performed in the study. Finally, in the study, short-term parameters and long-term parameters were estimated to capture possible dynamic relationships between variables. The Westerlund Error Correction Model (ECM) panel test for cointegration showed that there is a cointegration relationship between these variables for both the entire panel and the cross-section units. The results of the Augmented Mean Group (AMG) estimator method showed that (i) economic growth has no effect on CO2 emissions in 7 of 8 countries, (ii) energy usage increases CO2 emissions in 4 of the countries studied but decreases it in one of them, and (iii) foreign direct investments and trade openness do not affect CO2 emissions in 4 countries but positively affects in 2 countries and negatively in 2 countries. According to the results obtained from the Pooled Mean Group (PMG) analysis, while economic growth, energy usage, and trade openness affect CO2 emissions in the long run, economic growth, energy use, and trade openness affect CO2 emissions in the short run too. According to Dumitrescu-Hurlin panel causality results, it was seen that there is no causal relationship between CO2 emissions, economic growth, and energy use. While there is a unidirectional causality from CO2 emissions to foreign direct investments, it was determined that there is a bidirectional causality between trade openness and CO2 emissions. When the results were examined in general, it was understood that the variables of economic growth, trade openness, foreign direct investment, and energy usage are effective on CO2 emissions in the G8 countries. It would be beneficial for countries to include the objectives of making production with clean production technologies, ensuring efficient use of energy, and expanding the use of renewable energies among their main targets. [ABSTRACT FROM AUTHOR]

The development of non-cash payments is predicted to have an effect on real money demand, not only in Indonesia. The use of electronic money in Indonesia itself continues to increase especially in the covid-19 pandemic and it supported by technological developments. The purpose of this study was to analyse the effect of non-cash payments on real money demand in Indonesia. The method used is Vector Error Correction Model (VECM) using secondary data from Januari 2019 to December 2022. The independent variable is the amount of money circulated by Bank Indonesia, and the independent variables used are the value of debit / ATM card transactions, credit cards, and e -money. The results indicate that in the long run debit cards and emoney have a significant negative effect on money supply, while credit cards have positive effect on money supply but it’s not significan. While in the short term, all variables debit cards, credit cards and e-money are not significant on money supply. [ABSTRACT FROM AUTHOR]

In many developing economies, high and increasing public debt profile constitutes an essential means of financial risk. An appropriate debt management is germane for survival of business and good international reputation though its effect on private sector credit mobilization had been seldomly investigated. This study seeks to know whether strategic debt management approach exacts a significant effect on the Nigerian private sector and Africa at large resulting to higher credit availability for sustainable enterprise establishment. The study used a time-series observation spanning from 1981–2021. The method of data analysis employed the unit root test for stationarity. Johansen cointegration and vector error correction approach. The result of the unit root test indicates the series were all stationary after first difference and thus were integrated of order1. The Johansen cointegration test support the existence of a cointegrating series between the private credit and its determinants. More empirical evidence from the study shows that proper debt management and increase revenue generation through net taxes on products accounted for 0.93 and 1.32% increase in private sector credit mobilization, while total external debt stock was responsible for a significant negative influence of 0.60% on private sector credit mobilization. The study recommends that the government should always be proactive in their strategic and innovative approach to debt management, revenue generation and sources of funds. This will help not only to avoid crowding out of the private sector but will enhance adequate credit mobilization for effective operations of the private sector. [ABSTRACT FROM AUTHOR]

Since the 1980s, Turkey has experienced a significant increase in both foreign direct investment (FDI) and its ecological footprint (EFP). While FDI is widely acknowledged as a pivotal driver of economic growth, its impact on environmental degradation is multifaceted and debated. Moreover, a country's institutional framework plays a key role in shaping this relationship. Yet, the influence of institutional structures on the FDI-environment nexus is often neglected in current literature. In this study, we investigate the environmental implications of FDI in Turkey from 1984 to 2018, employing time series analysis with a Fourier extension and accounting for institutional quality. Fourier function models give more effective results in modeling structural breaks. We first use Fourier techniques to assess the unit root and cointegration relationship. Upon establishing cointegration, we employ the DOLS estimator, extended with Fourier terms, to determine the long-term coefficients. We then assess the causal relationship between the variables using the Fourier causality test. Our findings indicate that while FDI exacerbates environmental degradation (supporting the pollution haven hypothesis), the interaction term of FDI-institutional quality mitigates this degradation (supporting the pollution halo hypothesis). Given these empirical findings, this study suggests that strengthening Turkey's institutional quality has the potential to amplify the environmental advantages of FDI, alongside its economic benefits. [ABSTRACT FROM AUTHOR]