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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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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.)

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]

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]

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]

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]

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]

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]

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.)

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]

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]

Accurate inventories of grasslands are important for studies of greenhouse gas (GHG) dynamics, as grasslands store about one-third of the global terrestrial carbon stocks. This paper develops a framework for large-area grassland mapping based on the probability of grassland occurrence and the interactive pathways of fractional vegetation and soil-related endmember nexuses. In this study, grassland occurrence probability maps were produced based on data on bio-climate factors obtained from MODIS/Terra Land Surface Temperature (MOD11A2), MODIS/Terra Vegetation Indices (MOD13A3), and Tropical Rainfall Measuring Mission (TRMM 3B43) using the random forests (RF) method. Time series of 8-day fractional vegetation-related endmembers (green vegetation, non-photosynthetic vegetation, sand land, saline land, and dark surfaces) were generated using linear spectral mixture analysis (LSMA) based on MODIS/Terra Surface Reflectance data (MOD09A1). Time-series endmember fraction maps and grassland occurrence probabilities were employed to map grassland distribution using an RF model. This approach improved the accuracy by 5% compared to using endmember fractions alone. Additionally, based on the grassland occurrence probability maps, we identified extensive ecologically sensitive regions, encompassing 1.54 (104 km2) of desert-to-steppe (D-S) and 2.34 (104 km2) of steppe-to-meadow (S-M) transition regions. Among these, the D-S area is located near the threshold of 310 mm/yr in precipitation, an annual temperature of 10.16 °C, and a surface comprehensive drought index (TVPDI) of 0.59. The S-M area is situated close to the line of 437 mm/yr in precipitation, an annual temperature of 5.49 °C, and a TVPDI of 0.83. [ABSTRACT FROM AUTHOR]

In this paper we propose an new approach to multivariate collective models based on asymptotic distibutions, since the modeling problems posed have large samples. Collective risk models play an important part in Risk Theory and in Actuarial Mathematics. Inference based on these models is centered on claims totals. However, the new approach has in mind a different kind of risk, the risk of forest fires, where the variables of interest are the number fires and the total burnt areas. As a result, a special case bivariate risk model is derived with this intent. Besides single models, structured families whose models correspond to the treatments of a base linear model are considered. This leads to an ANOVA-like situation where we don't have to estimate the error, which enable to test the influence of several factors on the multivariate collective models mean vectors. Instead of F tests, we use χ 2 tests, availing us of asymptotic distributions that lightens the treatment. To illustrate the approach, an application to forest fires in Portugal with real data is presented. [ABSTRACT FROM AUTHOR]

The fundamental stages in designing a stock portfolio are each stock's selection and capital weighting. Selection and weighting must be conducted through diversification and price movement analysis to maximize profits and minimize losses. The problem is how the technical implementations of both are carried out. Based on this problem, this study aims to design these selection and weighting mechanisms. Stock selection is based on clusters and price movement trends. The optimal stock clusters are formed using the K-Means algorithm, and price movement analyses are carried out using the moving average indicator. The selected stocks are those whose prices have increasing trends with the most significant Sharpe ratio in each cluster. Then, the capital weighting for each preferred stock is carried out using the mean-variance model with transaction cost and income tax. After designing the mechanism, it is applied to Indonesia's 80 index stock data. In addition, a comparison is conducted between the estimated portfolio return and the actual one day ahead. Finally, the sensitivity of investors' courage in taking risks to their profits and losses is also analyzed. This research is expected to assist investors in diversification and price movement analysis of the stocks in the portfolios they form. [ABSTRACT FROM AUTHOR]

According to energy economists, the energy factor underlies economic growth and development. In this approach, energy is accepted as an indispensable production factor in the production of all goods and services. In addition, it is known that agricultural activities are at the forefront at the beginning of development, and a structural transformation has taken place from agriculture to industry and from the industrial sector to the service sector. However, due to the developments in the world economy in recent years and the fact that the agricultural sector plays a key role in the country, many countries attach special importance to the development of this sector. The development of the agricultural sector brings the need to increase the amount of energy used by this sector. The increase in energy costs day by day puts the agriculture sector in a difficult situation, and how the increasing energy consumption will affect the economic growth of the country emerges as a research topic. The literature investigating the relationship between energy consumption and economic growth rarely addresses this issue on a sectoral basis. In this context; It is important whether energy consumption in the agricultural sector can affect economic growth. This study aims to investigate the relationship between agricultural energy consumption and economic growth for the Turkish economy in the period of 1990-2018. The results of the ADF and PP unit root tests used for the stationarity analysis of the variables reveal that the variables become stationary at the first difference. The long-term relationship between the variables is investigated using the Hatemi J-Irandoust hidden cointegration test, which is one of the new generation tests. The coefficients of the variables and the causality relationship are examined with the FMOLS technique and the hidden error correction model, respectively. The findings show the existence of a cointegration between the + component of agricultural energy consumption and the + component of economic growth. The findings also indicate the existence of a positive relationship between the + component of agricultural energy consumption and the + component of economic growth. Finally, there is a unidirectional causality running from the + component of economic growth to + component of agricultural energy consumption. These results show that agricultural energy consumption has an impact on Turkey's economic growth. The study can offer some policy recommendations related to the agricultural sector for the growth of the Turkish economy. [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]

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 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]

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]

This study explores the potential of green digital finance and green agricultural growth to contribute to the green economic recovery of the BRICS economies. We examine the relationship between these variables using empirical data and various statistical techniques, including vector error correction, co-integration, unit root tests and long-run analysis. Our results suggest that well-functioning financial institutions play a crucial role in facilitating the structural transformation of green digital finance and promoting green agricultural growth to achieve green economic recovery in the BRICS region. Our findings underscore the need for pro-financial and green economic development policies and institutions to support and enhance economic recovery. Our results are robust and supported by our study. We also suggest future research directions for stakeholders interested in promoting sustainable economic growth in the BRICS countries. [ABSTRACT FROM AUTHOR]

Causal discovery is one of the most important research directions in the field of machine learning, aiming to discover the underlying causal relationships in the observed data. In practice, the time complexity of causal discovery will grow exponentially with increasing variables. To alleviate this problem, many methods based on divide-and-conquer strategies have been proposed. Existing methods usually partition the variables heuristically using scattered variables to achieve the dividing process, which makes it difficult to minimize vertex cut-set C and then leads to diminished causal discovery performance. In this work, we design an elaborated causal partition strategy called Causal Partition Base Graph (CPBG) to solve this problem. CPBG uses a set of low-order conditional independence (CI) tests to construct a rough skeleton S corresponding to the observed data and takes a heuristic method to search S for the optimal vertex cut-set C. Then the observed data can be partitioned into multiple variable subsets. We therefore can run a causal discovery method on each part and finally obtain the complete causal structure by merging the partial results. The proposed method is evaluated by various real-world causal datasets. Experimental results show that the CPBG method outperforms its existing counterparts, which proves that the method can support more effective and efficient causal discovery. The source code of the proposed method and all experimental results are available at https://github.com/DreamEdm/Causal. [ABSTRACT FROM AUTHOR]

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Cruise tourism has a great share in marine tourism and appears to be a fast-growing and income-generating sector in the travel industry. The cruise industry, which has a worldwide income of over 27 billion dollars, contributes to employment through the job opportunities it offers. The aim of this study is to analyze whether there is a short- and long-term co-integration relationship between the number of cruise tourists in the world and the world unemployment rate. In this context, data between 1991 and 2019 were analyzed using the Granger causality and vector error correction (VECM) methods. According to the results, there is an equilibrium relationship between the two series in the long term, a 1% change in the number of world cruise tourists reduces the world unemployment rate by 7.6%, and an imbalance between the two series in the short term will reach equilibrium after approximately three years. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for modelling the connectedness between asset returns. We adapt the measure of Diebold and Yilmaz, which is based on the forecast error variance decomposition of a VAR model. However, their connectedness measure hinges on critical assumptions with regard to the variance–covariance matrix of the error terms. We propose to use a more agnostic empirical approach, based on a machine learning algorithm, to identify the contemporaneous structure. In a Monte Carlo study, we compare the different connectedness measures and discuss their advantages and disadvantages. In an empirical application we analyse the connectedness between the G10 currencies. Our results suggest that the US dollar as well as the Norwegian krone are the most independent currencies in our sample. By contrast, the Swiss franc and New Zealand dollar have a negligible impact on other currencies. Moreover, a cluster analysis suggests that the currencies can be divided into three groups, which we classify as: commodity currencies, European currencies and safe haven/carry trade financing currencies. [ABSTRACT FROM AUTHOR]

Studies on the determinants of environmental quality in the European Union (EU) are plentiful. However, to the best of my knowledge, there has been no research on the linkage between stock market development and environmental quality in the EU, while controlling for natural resource rents, trade openness and energy consumption at the same time. This study aided in filling the above gap using a panel data of 17 EU member countries from 1995 to 2014. In attaining the above goal, second and third generation econometric techniques that control for slope heterogeneity, cross-sectional dependence and structural breaks among others were employed for the analysis. From the cross-sectional dependence test, there were correlations in the residual terms. Also, the heterogeneity test affirmed the slope parameters to be heterogeneous in nature. Moreover, the unit root tests confirmed the series to be stationary, while the cointegration tests found the variables to be significantly related in the long run. The cross-sectional autoregressive distributed lag, Cross-sectionally augmented distributed lag and the cross-sectional augmented error correction estimators were used to explore the elasticities of the predictors, and from the results, stock market development improved environmental quality by mitigating ecological footprint. Besides, natural resource rents, trade openness and energy utilization deteriorated the ecosystem of the studied nations. On the causalities between the variables, there was no causation between stock market development and ecological pollution. However, bilateral causalities between natural resource rents and environmental degradation and between energy utilization and ecological pollution were unfolded. Finally, a causation from trade openness to ecological pollution was disclosed. Based on the findings, it was recommended among others that policymakers should force all listed entities to adopt greener practices in their operations. This could help to reduce the escalating rate of pollution in the region. [ABSTRACT FROM AUTHOR]

Aviation gasoline is a fuel for spark-ignition piston internal combustion engines, which are usually used in light aircraft (small aviation and general aviation). This technique is widely used for regional and interregional transportation, for the initial training and retraining of aviation staff, for private use, for agricultural purposes, for the development of aviation sports and tourism, and for combat and rescue operations. This article gives some estimates of the production and consumption of aviation gasoline in the EU, North and South America, Asia–Pacific, Africa, and CIS countries. Export possibilities and the reliance on import within different regions are analyzed. Economic indicators for aviation gasoline are calculated by assessing the share of its production in the GDP and per capita consumption. In the context of the transition to unleaded aviation gasoline, the structure of the piston aviation fleet and its readiness for the transition are considered. The paper also analyzes the following existing components of unleaded aviation gasoline: technical capabilities and promising components. [ABSTRACT FROM AUTHOR]

The capital market as one of the important instruments in the economy requires indicators to determine growth in it. The Composite Stock Price Index (IHSG) is used as one of the capital market indicators with various influencing factors. This research was conducted to test and analyze the effect of inflation, exchange rate, the Bank Indonesia (BI) rate, and money supply (M2) on the Jakarta Composite Index (JCI) in the period from January 2017 to March 2022. In this study, quantitative descriptive research was carried out with JCI as the dependent variable and inflation, exchange rate, BI-rate, and money supply as independent variables. The research used the vector autoregression (VAR) method using the EViews 12 analysis tool. From the tests conducted it showed that between variables only had a one-way relationship, with the R-squared value indicating that the independent variables in the model were able to explain changes in the dependent variable that occurred. The variables in the model are also indicated to have a simultaneous effect on the dependent variable; this is based on a high F-statistic value. [ABSTRACT FROM AUTHOR]

This study examines the crowding-out effect in Albania from 2000 to 2022, specifically investigating the relationship between government investment (GI) and private investment (PI). Using time series data for gross domestic product (GDP), GI, PI, and real interest rates (RI), we applied the Johansen cointegration test and vector error correction model (VECM) to analyse the long-run and short-run relationships among these variables. Our results indicate a significant long-run correlation between GI and PI, suggesting that increased government investment in Albania leads to reduced private investment, demonstrating the crowding-out effect. Moreover, we observe a positive connection emerges between real interest rates and private investment. Consistent with prior research, Funashima and Ohtsuka (2019) identified both crowding-out and crowding-in effects in Japan, echoing our findings. Similarly, Bedhiye and Singh (2022) noted a negative correlation between government and private investment in developing economies. These findings have critical implications for policymakers, underscoring the potential negative consequences of government investment on private investment and economic growth. Effective policy implementation necessitates a delicate equilibrium between government investment and its potential adverse effects. In summary, this study offers valuable insights into government-private investment interactions in Albania, highlighting the crowding-out effect and the influence of real interest rates. These insights contribute to informed policymaking for sustainable economic growth. [ABSTRACT FROM AUTHOR]

This paper studies whether the carbon intensity of human well-being has an Environmental Kuznets Curve (EKC) pattern with respect to economic growth in Bhutan using annual data from 1971 to 2018. Furthermore, it examines the short-term and long-term dynamics of the causal relation between emissions and growth in wealth and also whether recent trends of economic growth are carbon neutral. Econometric estimations, such as ordinary least squares, vector error correction model and variance decomposition, are applied alongside other appropriate statistical tests. The findings show that, in Bhutan, the carbon intensity of human well-being increases with economic growth and fails to support the EKC hypothesis. Long-run causality was found running from the carbon intensity of human well-being to GDP per capita and also from population growth to GDP per capita. The econometric analysis also revealed that the increasing emission of carbon dioxide is being absorbed and its negative effects are negated through rising forest cover hence, Bhutan appears to be a carbon-neutral nation. This paper is a valuable contribution to the literature and has significant policy implications. Furthermore, it provides an integrated and sustainable growth model discourse to the rest of the world which is ailing with severe emissions and climate change. [ABSTRACT FROM AUTHOR]

The majority of studies analyzed show a positive and statistically significant impact of renewable energy consumption on economic growth. Nevertheless, some studies suggest a limited effect, while others find no statistically significant effect. Faced with this problem, we conducted a study aimed at analyzing the impact of renewable energy consumption on economic growth in the Kingdom of Saudi Arabia for the period 1990–2020. To determine the integration properties of the variables, we utilized the sharp and smooth structural breaks unit root test developed by Shahbaz, Omay and Roubaud (SOR). We also used the bootstrap approach of testing ARDL limits to examine the cointegration between variables. Using the VECM model, we studied the causal relationship between economic growth and its determinants. The results show, in the short and long run, the existence of a bidirectional causality between renewable energy consumption and economic growth (Feedback Hypothesis). Thus, there is a bidirectional relationship between GDP and capital and also between GDP and labor, in the long run. Therefore, an important policy implication resulting from this analysis is that renewable energy can be considered as an important factor for sustainable economic development in Saudi Arabia. The findings for Saudi Arabia may also be relevant for oil exporting countries to achieve efficiency and promote the renewable energy sector beyond oil. [ABSTRACT FROM AUTHOR]

In recent years, political rhetoric implying international trade hinder employment has gained momentum. We argue that the dynamics between the unemployment rate and the current account balance have both economic and political relevance. The presence of cointegration between the unemployment rate and the current account balance to GDP ratio and the nature of their short-run fluctuations will help us analyze the dynamics between the two objectively to evaluate the political rhetoric. We use quarterly data from 1948: Q1 to 2020: Q1 on the unemployment rate and current account balance to GDP ratio in the United States. Traditional cointegration tests fail to detect any cointegration. However, threshold cointegration tests confirm statistical evidence of threshold cointegration between the two. The estimated threshold vector error-correction model shows statistically significant evidence of falling unemployment rate coupled with deteriorating current account balance. This finding indicates that as unemployment rates go down, the current account balance deteriorates to maintain the long-run co-movement. Arguably, as the unemployment rate decline, concurrently imports rise faster than exports causing the current account balance to deteriorate. This finding thereby refutes the political rhetoric. [ABSTRACT FROM AUTHOR]