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]

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 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]

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]

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]

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]

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]

Inferring causal relationships from observational data is a key challenge in understanding the interpretability of Machine Learning models. Given the ever-increasing amount of observational data available in many areas, Machine Learning algorithms used for forecasting have become more complex, leading to a less understandable path of how a decision is made by the model. To address this issue, we propose leveraging ensemble models, e.g., Random Forest, to assess which input features the trained model prioritizes when making a forecast and, in this way, establish causal relationships between the variables. The advantage of these algorithms lies in their ability to provide feature importance, which allows us to build the causal network. We present our methodology to estimate causality in time series from oil field production. As it is difficult to extract causal relations from a real field, we also included a synthetic oil production dataset and a weather dataset, which is also synthetic, to provide the ground truth. We aim to perform causal discovery, i.e., establish the existing connections between the variables in each dataset. Through an iterative process of improving the forecasting of a target's value, we evaluate whether the forecasting improves by adding information from a new potential driver; if so, we state that the driver causally affects the target. On the oil field-related datasets, our causal analysis results agree with the interwell connections already confirmed by tracer information; whenever the tracer data are available, we used it as our ground truth. This consistency between both estimated and confirmed connections provides us the confidence about the effectiveness of our proposed methodology. To our knowledge, this is the first time causal analysis using solely production data is employed to discover interwell connections in an oil field dataset. [ABSTRACT FROM AUTHOR]

Malaysia's economy suffers from high public expenditures, especially current expenditures, which has led to a high percentage of the budget deficit. The research aims to identify the macroeconomic variables affecting the budget deficit in Malaysia and to identify the existence of a long-term relationship and a causality relationship between the budget deficit in Malaysia as a dependent variable, and the independent variables explaining it. The research relied on descriptive and quantitative analysis methods, as based on a variety of statistical and econometrics methods, including the use of the growth function, the unit root test (ADF), cointegration analysis and the Granger causality test to ensure the existence of causality between variables and determine the direction of that relationship. The research was based on annual time series data covering the period 1990-2019. The study model included: the budget deficit variable in Malaysia (% of GDP) as a dependent variable, while the independent variables are: government spending, fixed capital formation, public debt, foreign direct investment, exchange rate, average interest rate, and economic growth rate. The stability of the variables was tested using the ADF test in order to determine the degree of stability of the variables. The results showed that the time series under study are not stable at the level, but they are all stable at their first differences I(1). The results of the multiple regression model showed that the signs of the coefficients of the independent variables are consistent with economic logic, and that together they explain about 87.1 percent of the changes in the budget deficit in Malaysia during the study period. The regression coefficients for the variables of government expenditure, fixed capital formation, and foreign direct investment were positive to express the existence of a direct relationship between these independent variables and the budget deficit in Malaysia, as a dependent variable, while it is noted that the regression coefficient for the variables of public debt and the exchange rate The average interest rate and economic growth rate were negative, which is consistent with economic theory. It also turns out that the most important relative variable in influencing the dependent variable in the previous estimated model is the public debt variable, while the average interest rate variable comes in last. The results of cointegration indicated the presence of three vectors of cointegration between these variables, which confirms the existence of a long-term equilibrium relationship between the public budget deficit in Malaysia and the independent variables affecting it. The results of the Granger causality test also indicated the existence of a unidirectional causal relationship heading from government spending to the budget deficit, meaning that increasing government spending causes a budget deficit, and the existence of a unidirectional causal relationship that goes from foreign direct investment to the budget deficit, from the average interest rate to the budget deficit, and from the budget deficit to the exchange rate, and it also shows the existence of a causal relationship. There is a bidirectional relationship between the size of the public debt and the public budget deficit, and between the economic growth rate and the budget deficit in Malaysia. [ABSTRACT FROM AUTHOR]

Due to the incessant quest for economic growth, global energy demand has increased to a level that requires urgent rethinking. The Netherlands depends heavily on traditional energy sources, which are finite and equally emit a lot of greenhouse gasses, causing further environmental destruction. To sustain economic growth and protect the ecosystem, it is crucial for the Netherlands to consume energy efficiently. Given that policy directions are required, this paper investigates the effect of energy productivity on environmental degradation in the Netherlands between 1990Q1 and 2019Q4, using the Fourier ARDL (autoregressive distributed lag) and Fourier Toda–Yamamoto causality approaches. The Fourier ADL estimates indicate that all variables are cointegrated. Additionally, the long-run Fourier ARDL estimates indicate that investments in energy productivity could help reduce carbon dioxide emissions in the Netherlands. Furthermore, the Fourier Toda–Yamamoto causality outcomes suggest that energy productivity, economic growth, and renewable energy consumption have a unidirectional causal effect on CO2 emissions. These revealing outcomes provide significant policy insights for the Netherlands' energy productivity objectives in their new energy policy proposed in 2022. The government could, through the new energy policy, increase investment in smart meters and review existing fossil fuel subsidies and energy trade taxes. Additionally, the government could consider reviewing the Netherlands' economic structure by increasing the share of the primary and tertiary sectors to the rising economic growth to reduce energy consumption. [ABSTRACT FROM AUTHOR]

Using maize prices data from Tanzania's National Bureau of Statistics from 2002 through 2017, this paper analyses the impacts of export bans and seasonality on spatial domestic price transmission between deficit markets and the surplus Sumbawanga market; using a vector error correction (VEC) model with export ban and seasonality dummy variables. Results show that 45% of deficit markets converged in the long-run with the Sumbawanga market, with a significant negative sign at 10% level. Moreover, 64% of market pairs negatively impacted spatial domestic price transmission, while seasonality had significant impacts on the same between market pairs. A Granger causality suggests that 63%, 27%, and 10% of market pairs were bidirectional, unidirectional and no causality, respectively. Thus, government policies should incline towards increaseing maize production rather than imposing ad-hoc export bans, improving storage facilities, and mitigating climate changes to insulate seasonality: all of which will--through market mechanism--moderate consumer prices and ensure profitability among maize sellers. [ABSTRACT FROM AUTHOR]

In recent years, with the improvement of data scale and computing power, pre- training models such as CNN and BERT have made rapid progress in the field of text classification. However, these models have poor ability to extract distribution features and poor generalization performance in small-sample scenarios. At present, to address this problem, the common practice is to improve the structure of the model or expand the training data set to improve the performance. However, these methods rely on a large number of data sets and a large amount of computing power to prune the network structure. A pre-training model optimization method based on granger causality test and optimal transmission distance is proposed. From the perspective of data distribution, a feature pathway structure that can stably extract distribution information in the pre- training model is generated. On this basis, the optimal combination of characteristic path structures is given based on the optimal transmission distance, and a multi-view structured representation with stability in statistical distribution is generated. Theoretical analysis and experimental results show that this method greatly reduces the data and computing power requirements in the process of model optimization. The results show that compared with the pre-training model based on the convolution structure such as CNN, there are 5, 7 and 2 percentage points improvement in the 20ng news, Ohsumed, R8 data sets respectively; compared with the pre-training model based on the Transformer structure such as BERT, there are 2, 3 and 2 percentage points improvement respectively. [ABSTRACT FROM AUTHOR]

Causal inference is a fundamental research topic for discovering the cause–effect relationships in many disciplines. Inferring causality means identifying asymmetric relations between two variables. In real-world systems, e.g., finance, healthcare, and industrial processes, time series data from sensors and other data sources offer an especially good basis to infer causal relationships. Therefore, many different time series causal inference algorithms have been proposed in recent years. However, not all algorithms are equally well-suited for a given dataset. For instance, some approaches may only be able to identify linear relationships, while others are applicable for non-linearities. Algorithms further vary in their sensitivity to noise and their ability to infer causal information from coupled vs. non-coupled time series. As a consequence, different algorithms often generate different causal relationships for the same input. In order to achieve a more robust causal inference result, this publication proposes a novel data-driven two-phase multi-split causal ensemble model to combine the strengths of different causality base algorithms. In comparison to existing approaches, the proposed ensemble method reduces the influence of noise through a data partitioning scheme in a first phase. To achieve this, the data are initially divided into several partitions and the base causal inference algorithms are applied to each partition. Subsequently, Gaussian mixture models are used to identify the causal relationships derived from the different partitions that are likely to be valid. In the second phase, the identified relationships from each base algorithm are then merged based on three combination rules. The proposed ensemble approach is evaluated using multiple metrics, among them a newly developed evaluation index for causal ensemble approaches. We perform experiments using three synthetic datasets with different volumes and complexity, which have been specifically designed to test causality detection methods under different circumstances while knowing the ground truth causal relationships. In these experiments, our causality ensemble outperforms each of its base algorithms. In practical applications, the use of the proposed method could hence lead to more robust and reliable causality results. [ABSTRACT FROM AUTHOR]

The elderly population and economic growth have been a contentious topic among researchers. Regardless of the economic growth rate, the population and its growth have a stimulating influence on economic development. This study aims to explore the relationship between the elderly population and economic growth in 15 Asian countries, based on secondary data gathered from the WDI (World Development Indicators) from 1961 to 2021. This research contributes to filling the empirical gap, capturing the Granger causality concerning the relationship between the elderly population and economic growth in the Asian context in a single study. The empirical findings highlighted a one-way Granger causality from economic growth to the elderly population for India, Japan, Malaysia, and Singapore while vice versa for Bangladesh, China, and Pakistan. Furthermore, for Nepal, there is a two-way Granger causality, while there is no Granger causality for remaining countries. To the best of the authors' knowledge, this study has been the first to investigate the relationship between the elderly population and economic growth for Asian nations, using a lengthy data series and a Granger causality test. The main findings will assist the governments, policymakers, and foreign investors in effective decision-making in this regard. [ABSTRACT FROM AUTHOR]

The economic cost of greenhouse gas (GHG) emissions to African economies have increased. Therefore, GHG emissions and their concomitant effect on the environment are fast becoming costly for emerging economies like Ghana. Hence, the justification for the growing literature on the subject. This study employed the Autoregressive Distributive Lag (ARDL) bounds test and Granger causality techniques with data from 1983 to 2014. The study examines the dynamic relationship between income growth, power consumption, and carbon dioxide (CO2) emissions in Ghana, capturing the role of domestic investment and foreign direct investment (FDI) in the nexus. All variables were found to be cointegrated in the long run based on the bounds test. The Granger causality test indicates a unidirectional causality from energy consumption to CO2 emissions and economic growth. Furthermore, a unidirectional causality from CO2 to economic growth was found in Ghana. Results from the error correction model and the bounds tests indicate that, while energy consumption increases carbon emissions by more than 44%, the lagged values of domestic investment were found to reduce CO2 emissions by more than 41% in both the short run and the long run. Due to the significant effect of domestic investments on the reduction of CO2 emissions, the study recommends policymakers to adopt policies that may increase domestic capital in place of FDI, which has been proven to exacerbate environmental degradation in host countries. [ABSTRACT FROM AUTHOR]

Since 2015, the United Kingdom (UK) has increased its investment in transportation infrastructure to compensate for years of underinvestment in comparison with peer countries. It is of critical importance to evaluate the effect of these recent investments, as well as historical ones, on economic development, both theoretically and empirically, to provide guidance on future infrastructure investment. However, few research looks into such policy adjustment on economic growth in the UK. To fill this gap, this paper investigates the relationship between transport infrastructure development and economic growth in the UK from different time spans. Principal component analysis (PCA) is used to construct a comprehensive measure of transport infrastructure development. This paper then applies Vector Error Correction Model (VECM) to investigate both long-run and short-run relationships between transport infrastructure development and economic growth from 1970 to 2017 in the UK. Empirical results suggest that transportation infrastructure has a long-run promotive effect on economic development. However, in the short run, this effect turns out to be significantly negative. The analysis of this paper indicates differentiated roles that the UK's transport infrastructure played in economic growth, which should be considered in future policy design of achieving economic sustainability in the UK. • This paper investigates the relationship between transport infrastructure development and economic growth in the UK. • Transportation infrastructure has a long-run promotive effect on economic development. • In the short run, transportation infrastructure fails to promote economic development. [ABSTRACT FROM AUTHOR]

The standard linear Granger causality test, based on the vector autoregressive model (VAR), requires stationarity of the time series. A VAR model is fitted to the first-differences of the time series, when they exhibit trends and are not co-integrated. In the case of co-integration, the vector error-correction model (VECM) is used instead. Alternatively, a nonlinear information causality measure is suggested, called partial transfer entropy on rank vectors (PTERV), which uses locally ranked observations. It is model-free and of a more general purpose, as it can be directly applied to the original time series without pre-testing for stationarity or co-integration. The significance test of the PTERV detects effectively the connectivity structure of complex multivariate systems. In particular, the size and power of this test are comparable to that of the standard linear Granger causality approach (VAR or VECM) when applied to systems with only linear causal effects, while the PTERV test outperforms the linear causality test when nonlinear causal effects exist, as long as the sample size is large enough. The application of PTERV to stock market data and interest rates illustrates that it can be a useful tool in the causality analysis of financial time series. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between geothermal energy consumption, economic growth, and foreign direct investments in countries where geothermal energy production is possible. Panel Fourier Granger causality and panel Fourier Toda–Yamamoto causality tests (2020–2021) were applied, which can take into account smooth transitional structural breaks with trigonometric functions using quarterly data for the period 2016 Q1–2020 Q3. Data were obtained from the International Energy Agency (IEA), Federal Reserve Economic Data (FRED), and the OECD official website. According to the results obtained based on panels, there is one-way causality from economic growth to geothermal energy and one-way causality from geothermal energy consumption to foreign direct investments. The results obtained based on individual countries indicate that one-way causality from foreign direct investment to geothermal energy consumption was found for Mexico and Portugal, and one-way causality from geothermal energy consumption to economic growth was found for Italy and Mexico. On the other hand, causality from economic growth to geothermal energy consumption was observed for Germany, Japan, and USA. No significant results were found for Turkey and New Zealand, and it is understood that the macroeconomic structures of these countries are not affected by geothermal energy. The difference in the results reveals that the application recommendations on this subject should also be different. [ABSTRACT FROM AUTHOR]

This study examined the nexus between economic growth, energy consumption, and the environment with the moderating role of agricultural value addition and forest in Africa based on data sourced from 1980 to 2019. We employed both the time domain and frequency domain panel Granger causality estimation techniques to compare results across the different horizons. Extant literature suggests the inability of time domain estimation techniques to account for causality at different frequencies. The study also accounts for the nexus among our variables both at the single-country and multi-country levels. The results at the single-country level are at best mixed. The results of the panel Granger causality at the frequencies domain suggest that a bi-directional relationship exists between energy consumption and economic growth, and that energy consumption Granger causes carbon emissions in Africa. The results align with the feedback hypothesis on the one hand but contradict the conservation hypothesis on the other hand. The study has some policy implications. [ABSTRACT FROM AUTHOR]

The Global Fear Index (GFI) is a measure of fear/panic based on the number of people infected and deaths due to COVID-19. This paper aims to examine the interconnection or interdependencies between the GFI and a set of global indexes related to the financial and economic activities associated with natural resources, raw materials, agribusiness, energy, metals, and mining, such as: the S&P Global Resource Index, the S&P Global Agribusiness Equity Index, the S&P Global Metals and Mining Index, and the S&P Global 1200 Energy Index. To this end, we first apply several common tests: Wald exponential, Wald mean, Nyblom, and Quandt Likelihood Ratio. Subsequently, we apply Granger causality using a DCC-GARCH model. Data for the global indices are daily from 3 February 2020 to 29 October 2021. The empirical results obtained show that the volatility of the GFI Granger causes the volatility of the other global indices, except for the Global Resource Index. Moreover, by considering heteroskedasticity and idiosyncratic shocks, we show that the GFI can be used to predict the co-movement of the time series of all the global indices. Additionally, we quantify the causal interdependencies between the GFI and each of the S&P global indices using Shannon and Rényi transfer entropy flow, which is comparable to Granger causality, to confirm directionality more robustly The main conclusion of this research is that financial and economic activity related to natural resources, raw materials, agribusiness, energy, metals, and mining were affected by the fear/panic caused by COVID-19 cases and deaths. [ABSTRACT FROM AUTHOR]

This paper examines the asymmetric impacts of both renewable energy consumption and economic complexity on Saudi Arabia's economic growth in nonlinear autoregressive distributed lag (NARDL) frameworks. We firstly adopt the methodology of both standard NARDL and multiple threshold NARDL (MT-NARDL) models using quarterly data over the period 2000Q1–2015Q4. Then, we test the direction of the causal relationships between the underlying variables using both symmetric and asymmetric Granger causality tests. Our results from the standard NARDL model indicate that the long-term impact of a negative shock in renewable energy consumption on economic growth is significantly negative, while the short-term impact of either positive or negative shocks has a significant positive effect. Moreover, both positive and negative shocks in economic complexity have significant negative effects on economic growth in the long term, while economic growth is only affected by the negative change in economic complexity in the short term. Our estimates based on the best MT-NARDL model indicate that, in the long run, the effect of extremely large changes in renewable energy consumption is significantly different from the effect of small changes in renewable energy consumption on Saudi Arabia's economic growth. However, the effect of extremely large changes in economic complexity does not significantly differ from the effect of small changes in economic complexity on economic growth in the long run. Alternatively, in the short run, the effects of extremely large changes in both renewable energy consumption and economic complexity are not significantly different from the effects of small changes in those variables on economic growth. Finally, the symmetric/asymmetric causality nexus between positive/negative shocks to either renewable energy consumption or economic complexity and positive/negative shocks to economic growth is found to be neutral. The findings of this study provide deeper insights into the relationship between renewable energy consumption, economic complexity, and economic growth in Saudi Arabia and can be used for recommending different policies in the long run and short run. [ABSTRACT FROM AUTHOR]

This paper analyses the effect of natural resource wealth on sustainable economic development in the top 8 resource-abundant sub-Saharan African countries according to the World Bank Wealth of Nations 2018 over the period 1981–2017. The study incorporates public debt as an explanatory variable in our analysis, which is an extension of previous natural resource-growth estimations, to throw more light on how natural resource abundance and debt overhang simultaneously affect economic growth in resource-abundant sub-Saharan African (SSA) countries. The NARDL bounds testing approach and asymmetric Granger causality tests are employed. Long-run asymmetric effect results show that an increase in natural resource rents significantly increases economic growth in Equatorial Guinea. However, an increase in natural resource rents in the Congo republic negatively affects economic growth, validating the resource curse hypothesis. No significant effect was found in other countries studied. In terms of asymmetric causality results, no bidirectional causality between natural resource rents and economic growth was noted. We, however, identified a weak unidirectional asymmetric causality relationship running from economic growth to resource rents in the Congo Republic and natural resource rents to economic growth in South Africa. The study, therefore, suggests the implementation of efficient public debt management policies and an improvement in the quality of institutions for effective management of public loans and resource revenues to stimulate economic development in these resource-rich countries. [ABSTRACT FROM AUTHOR]

Copyright of Uluslararasi Ekonomi ve Yenilik Dergisi is the property of Karadeniz Technical University, Depertmant of Economics 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.)

Anthropogenic and natural factors lead to substantial environmental degradation. This shift is aligned with the country's overall development, resulting in high demand for energy resources and a dramatic shift in human activities that contribute to haze pollution. Some of the countries in the South Asian region are ranked between one and twenty on the list of countries with the highest levels of PM2.5 pollution. The member countries have taken many steps to tackle global warming, but concern about haze pollution was found limited. Moreover, very little research was conducted on haze pollution, which led us to conduct this research in this region. This study used the panel data from 1998 to 2018 and a set of econometric models like long-term cointegrating relationship, fully modified ordinary least squares, and vector error-correction model Granger causality tests to examine the major drivers like anthropogenic and natural factors that might elevate haze pollution. Furthermore, our empirical results depict that (1) there is a long-term cointegrating relation between haze and the factors studied. (2) Energy consumption, urbanisation, and economic growth are the primary drivers of environmental degradation. (3) Rainfall has the most substantial influence on reducing haze pollution. The study concluded that (a) if the countries continue to develop at the same pace, all factors studied will continue to drive haze pollution to rise. (b) A decrease in PM2.5 pollution requires improvements in regional rainfall through vegetation, reducing reliance on fossil fuel-based energy sources, and increasing environmental education. (c) Slowing down the drive for urbanisation would not be cost-effective in reducing haze pollution in the region in the short run. Thus, reducing haze by adjusting the factors studied would not be easy in the short run and require the careful adoption of long-term policies. [ABSTRACT FROM AUTHOR]

Renewable energy is considered to be a useful tool to mitigate global carbon dioxide emissions and its benefits are linked to globalization. To investigate the heterogeneous effects of renewable energy and globalization at different levels of carbon dioxide (CO 2), the present study utilized the Quantile Autoregressive Lagged (QARDL) model. In addition, data for Mexico from 1990Q1-2018Q4 is used with the framework of the Environmental Kuznets Curve (EKC). The findings show that renewable energy mitigates CO 2 emissions regardless of the quantile level. However, globalization is found to raise the ratio of CO 2 emissions while interacting with only the higher quantiles of CO 2 emissions. Additionally, the Spectral Causal analysis reveals that the GDP and GDP squared granger cause the CO 2 emissions only in the long run. Also, renewable energy and globalization significantly granger cause CO 2 emissions in the short, medium, and long-run, suggesting that any policy shock in any independent series will lead to cause carbon emissions. Besides, the findings confirm the existence of the EKC hypothesis for Mexico. Some useful policy implications are suggested based on the short and long-run parameters. • Examines relationship between renewable energy, economic growth, and globalization with CO 2 emissions. • QARDL method is applied over the spanning from 1990Q1-2018Q4. • EKC is verified from middle to higher Quantile. • Globalization increase environmental pollution. • Renewable energy significantly reduces the level of environmental degradation. [ABSTRACT FROM AUTHOR]

Financial development and its relationships with economic growth and income inequality have recently received considerable attention. The present study investigates the relationships between financial development and (i) economic growth, using data from 44 countries; (ii) economic inequality, using data from 42 middle-income countries. Estimates are obtained through a panel Autoregressive Distributed Lag (ARDL) model for a period of 23 years (1995-2018). Results reveal that financial development contributes to economic growth in both groups of countries in the long run. However, the contribution financial development makes to economic growth is more noticeable in the case of upper-middle income countries. Additionally, Granger causality test based on Vector Error Correction (VEC) showed two-way Granger causality between financial development and economic growth. Findings disclosed an inverted U-shaped association between financial development and income inequality for both lower-middle income and upper-middle income countries. This study can aid policymakers in designing policies that can strengthen financial systems, thereby enhancing economic growth and reducing income inequality. [ABSTRACT FROM AUTHOR]

The question of how Brazil, Russia, India, China, and South Africa (BRICS countries) can substantially dissipate environmental pollution (EVP) remains unsolved. In this regard, this research explores the dynamic association between energy consumption (EGC), economic expansion (EXP), globalization (GLO), energy innovation (ENI), urbanization (URB), financial development (FID), and environmental pollution (EVP) using panel data from 1990 to 2020. This study integrated the augmented mean group (AMG), common correlated effect means group estimator (CC-MG), and fully modified ordinary least square (FMOLS) model approach to estimate the long-run interaction among the series. The findings of this study reveal a positive and significant association between economic expansion, energy consumption, urbanization, financial development, and environmental pollution. In contrast, globalization and energy innovation extensively abate EVP in the BRICS economies. Moreover, the outcome of the Granger causality test indicates that energy consumption and energy innovation have a bidirectional association with EVP. The Granger causality test further revealed a unidirectional causality between globalization, urbanization, financial development, and environmental pollution. Finally, this research has implications for policymakers in the BRICS countries. [ABSTRACT FROM AUTHOR]

The Federal Reserve has played an arguably important role in financial crises in the United States since its creation in 1913 through monetary policy tools. Thus, this paper aims to analyze the impact of monetary policy on the United States' economic growth in the short and long run, measured by Gross Domestic Product (GDP). The Vector Autoregressive (VAR) method explores the relationship among the variables, and the Granger causality test assesses the predictability of the variables. Moreover, the Impulse Response Function (IRF) examines the behavior of one variable after a change in another, utilizing the time-series dataset from the first quarter of 1959 to the second quarter of 2022. This work demonstrates that expansionary monetary policy does have a positive impact on economic growth in the short term though it does not last long. However, in the long term, inflation, measured by the Consumer Price Index (CPI), is affected by expansionary monetary policy. Therefore, if the Federal Reserve wants to cease the expansionary monetary policy in the short run, this should be done appropriately, with the fiscal surplus, to preserve its credibility and trust in the US dollar as a global store of value asset. Also, the paper's findings suggest that continuous expansion of the Money Supply will lead to a long-term inflationary problem. The purpose of this research is to bring the spotlight to the side effects of expansionary monetary policy on the US economy, but also allow other researchers to test this model in different economies with different dynamics. [ABSTRACT FROM AUTHOR]

This paper is aimed at examining the short- and long-run relationships among consumption of renewable and non-renewable energies, economic growth, and CO2 emissions in Mexico during the period 1973–2018. Data were obtained from the World Bank and the British Oil Company BP. A cointegration model together with a fully modified ordinary least squares (FMOLS) is used to estimate possible associations among all variables. Subsequently, the Granger causality test is applied to empirically verify the environmental Kuznets curve (EKC) hypothesis. The main empirical results are that the variables under study explain CO2 emissions in the short and long run, with economic growth having the greatest influence on CO2 emissions, which provides empirical evidence of an EKC for Mexico. Moreover, consumption of renewable and non-renewable energies and economic growth causes in the Granger sense the current levels of CO2 emissions. Moreover, lagged GDP and lagged non-renewable energy consumption do influence (Granger cause) current CO2 emissions. Long-term policies should encourage the use of renewable energies in the electricity, transport, manufacture, and construction sectors. [ABSTRACT FROM AUTHOR]

This study explores the relationship between economic growth and carbon dioxide and the moderating effect of institutional quality in Nigeria from 1990 to 2020, by employing long-run and short-run dynamic ARDL regression, quartile regression and Granger causality test for the estimation. Utilizing CO2 per capita emissions; GDP per capita, a proxy for economic growth; capital stock (CAPSTK), proxy for capital investment in Nigeria and control of corruption and regulatory quality (COC and RGQ) which represent the effective environmental regulations and laws put in place for the control and prevention of environmental degradation, the study found a significant cointegration between CO2 emissions and economic growth (lnGDP) in Nigeria. Furthermore, an N-shaped nexus exists between CO2 emissions and economic growth in the long-run and short-run instead of the inverted U-shape curve postulated by the EKC hypothesis. This was confirmed by both ARDL and quartile regression results. Similarly, InCAPSTK contributed significantly to the growth of CO2 emissions in Nigeria both in the long run and short run; although, the short run did so at 10% significant level. Contrary to expectations, control of corruption (COC) contributes significantly to CO2 emissions in the long run, but when it interacts with income (InGDP × COC), it significantly contributes to the reduction of CO2 emissions. More so, regulatory quality (RGQ) had no significant impact on CO2 emissions in Nigeria either in the long run or short run, even when it interacts with InGDP. This finding is further supported by the quartile regression outcomes and Granger causality. The study therefore concludes that CO2 emissions–economic growth nexus in Nigeria assumes an N-shape both in the long run and short run. Based on the results, the study recommends that Government should pursue industrialisation policy with sophisticated method of production that will bring about rapid economic progress and at the same time support environmental sustainability. [ABSTRACT FROM AUTHOR]

This study examines the causal relationship between road transportation and economic growth in 31 provinces and municipalities in China by using province-level panel data covering the period from 1980 to 2015. This study adopts the Granger causality analysis based on meta-analysis in heterogeneous mixed panels. The empirical model also considers the heterogeneity of the coefficients and the cross-sectional correlations of the panel data. The empirical results confirm that the bi-directional causal relation between road transportation and economic development is the dominant type in various provinces and municipalities in China. Moreover, the results reveal a unidirectional causality running from road transportation to economic growth in Beijing, Shanghai, Heilongjiang; a unidirectional causality running from economic growth to road transportation in Tianjin, Fujian and Hunan; and no causal relation in Tibet. The results imply that road transportation and economic growth in most parts of China are mutually coordinated. [ABSTRACT FROM AUTHOR]

We model a mixture of asymmetric and nonlinear bidirectional and unidirectional causality between four macroeconomic variables (exchange rate, GDP, global economic policy uncertainty, and relative CPI) and stock returns of BRICS economies in the frequency-domain using the information flow theory. The Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-based Rényi effective transfer entropy approach is used to establish dynamic flow of information between macroeconomic variables and stock returns of BRICS. The original return series suggested insignificant information flow between most macroeconomic variables and stock returns. However, we reveal both asymmetric and tail dependent analyses at diverse scales between macroeconomic variables and stock returns of BRICS economies. Moreover, we find negative significant flow of information between the variables, in that knowing the history of one variable (either stock or macroeconomic variable), in this case, indicates considerably more uncertainty than knowing the history of only the other variable (either stock or macroeconomic variable). We also observe that global economic policy uncertainty has the most significant adverse causal relationship with stock returns of BRICS, especially in the long term. These results have important implications that investors and policymakers should take into account. Regulators should consider instituting sound policy actions geared towards minimising long-term effects of external shocks and uncertainties. [ABSTRACT FROM AUTHOR]

This study aims to analyze the asymmetric long-run relationship between economic growth (EG), foreign direct investment (FDI), and carbon emissions (CO2) within the context of the environmental Kuznets curve (EKC) and the pollution haven hypothesis (PHH) in China. Employing the quarterly data from 1982Q1 to 2018Q4, we have used novel techniques to meet the stated objectives of our study, named quantile ARDL and quantile Granger causality. The study provides novel outcomes using the advanced quantile ARDL and quantile Granger causality tests. The significant implication of this method is that it provides locational asymmetry. We find strong evidence of the EKC and PHH for China based on the empirical results of linear and nonlinear ARDL models. Similarly, findings of quantile Granger causality validate the bidirectional relationship among all variables in upper and lower quantiles. Moreover, the results of the Wald test confirm the asymmetric long-run relationship between FDI and carbon emissions (CO2). Thus, legal measures must be enhanced, accepted, rigorously imposed, and monitored in all provinces to assure a further reduction in carbon emissions. This study will be conducive for the policymakers to combat environmental contamination concerning economic growth and FDI inflow in China. [ABSTRACT FROM AUTHOR]

Most developing economies have recently experienced significant economic growth without corresponding substantial poverty reduction and improved population wellbeing. This paper uses a nonlinear autoregressive distributed lag model to explore the growth-poverty relationship in Tanzania using annual time series data on per capita consumption expenditure, real GDP, GINI index, and unemployment from 1991–2020. To explore the causality among the variables and long-run asymmetry between per capita consumption expenditure and economic growth, the study employs Granger causality and Wild test respectively. The results confirm the presence of long and short-run asymmetric behavior of economic growth. Besides, in the short-run, the Granger causality test supported the feedback hypothesis between economic growth and consumption expenditure, and the unidirectional hypothesis from income inequality and unemployment to consumption expenditure. In the long-run, unidirectional causality was observed from consumption expenditure to both economic growth and unemployment. The study submits that while economic growth exhibits poverty reduction features, growth alone is not sufficient to alleviate poverty because the interaction of income inequality with economic growth dampens the poverty-reducing effects of economic growth. Therefore, economic growth has a significant explanation for poverty but not all about the evolution of poverty. The study opens policy perspectives with wide international relevancy as outlined in the policy implication section. [ABSTRACT FROM AUTHOR]

Sustainable development goal 17 emphasizes the importance of international trade in achieving inclusive growth. Likewise, countries with high level of corruption will experience limited growth. In order to examine the causality, this study considers the impact of international trade and corruption on the economic growth of selected sub-Saharan African countries (Angola, Ghana, Kenya, Nigeria, and South Africa) from the period 2000-2019. The granger causality test and fixed effect method of estimation were adopted. The result of the granger causality tests shows that there is no causality between import and economic growth, there is unidirectional causality between export and economic growth and there is no causality between control of corruption and economic growth. Furthermore, it was discovered that export, import, and control of corruption have a positive impact, 25.4%, 32%, 45.5% respectively, on the economic growth of selected sub-Saharan African countries. Hence from these findings, policies towards export promotion should be encouraged as export is crucial for the economic growth of these countries and policies to curb corruption should be implemented in order to promote economic growth in the selected sub-Saharan African countries. [ABSTRACT FROM AUTHOR]

The information and communication technology (ICT) revolution has brought positive spill-over effects on institutions and economies across the globe, but it has also increased the information gaps between countries. A key characteristic that may explain these widening gaps is the deepening endogenous relationships between ICT infrastructure, institutions of governance, and economic growth in many developing countries. Thus far, the links between these variables have not been discernible in developing economies, so few studies have explored them. In this paper, we investigate the possible Granger causal relationships among institutional quality, economic growth, and ICT infrastructure development for a sample of developing countries for the period from 2005 to 2019. The application of a vector error-correction model reveals strong inter-relationships between all the variables in the short run. In the long run, institutional quality and ICT infrastructure development stimulate economic growth. These complex relationships are explored and lessons are drawn for policymakers. We assess interactions between institutional quality and ICT infrastructure as well as economic growth. We deploy a panel Granger causality test for low- and lower middle-income countries from 2005 to 2019. We show that there is Granger causality between the variables in the short and the long term. For each case and specification, there is support for the hypothesis that ICT infrastructure and institutional quality both Granger-cause growth in the economy. In the short run, we note a feedback relationship between institutional quality and economic growth. Other short-run results are more varied, based on the particulars proxies for institutional quality and ICT infrastructure. [ABSTRACT FROM AUTHOR]

Driven by the increasing level of environmental degradation in developing countries, especially Nigeria, and the conflicting empirical results, this study investigates the impact of economic growth, natural resources, urbanization, human capital, financial development, foreign direct investment, and trade openness on the ecological footprint (EFP) in Nigeria between 1970 and 2017. The study accounts for structural breaks in the data using the Zivot–Andrews unit root method along with other conventional unit root tests. Autoregressive Distributive Lag (ARDL) and Granger causality estimation techniques are used to determine the short- and long-run effect as well as the causal direction between the variables. The outcomes of the study suggest that economic growth, foreign direct investment, and trade openness contribute positively to EFP, hence worsening environmental quality. However, urbanization, financial development, natural resources, and human capital abate the EFP in Nigeria. The findings from the granger causality test reveal a unidirectional causality from economic growth, foreign direct investment, and trade openness to EFP; while bidirectional causality exists between natural resources and EFP; and urbanization and EFP. The results are robust to different estimation techniques. Lastly, measures to ensure environmental sustainability in the context of Nigeria are suggested. [ABSTRACT FROM AUTHOR]

The issue of increased renewable energy consumption has been widely debated, and this has become a central energy policy concern for developing and developed countries. The existing literature provides evidence that there is a positive relationship between energy consumption and economic growth in developed economies. However, findings in respect of developing/emerging economies remain inconclusive. Thus, this paper aims to investigate the impact on renewable energy consumption on economic growth by controlling other macroeconomic variables for regions of Sub-Saharan Africa (East, Central and West) covering the 1990–2018 sample period. For this purpose, common correlated effects mean group estimator (CCEMG) and Dumitrescu-Hurlin Granger causality test approach are used to consider both cross-sectional dependency and cross-country heterogeneity across countries. The CCEMG result indicates that an increase in renewable energy consumption led to reduction in economic growth even when the sample is analyzed based on geographical locations as East, West, and Central Africa. Granger causality results validate the feedback hypothesis for only Central Africa; the growth hypothesis is supported for East and West Africa. The empirical results suggest that energy planners, governments, and policy makers must act together to increase the renewable energy consumption share in her energy mix to promote economic growth for regions of Sub-Saharan Africa. [ABSTRACT FROM AUTHOR]

In recent years, the level of local economic, social, and financial development had been greatly improved. However, due to the large gap between regions, the economic and financial industries in underdeveloped regions restricted the further development of the whole national economy to a great extent. It was known from the existing research that economic development was inseparable from the support of the financial industry, but the role of the financial industry in the process of regional economic development had not attracted attention. Therefore, from the perspective of economic development in underdeveloped areas, this paper put forward relevant assumptions by analyzing the relationship between economic growth and financial factors. In view of the development of financial industry in underdeveloped areas, this paper mainly analyzed from three aspects: banking, securities, and insurance, and selected corresponding indicators to observe the changes of banking, security, market and insurance market. Vector autoregressive model and vector error correction model were used to carry out cointegration test and Granger causality test for financial and economic-related indicators in underdeveloped areas. According to the impulse response function and variance decomposition results, this paper analyzed the dynamic relationship between financial development and economic growth, as well as the disturbance and duration of financial factors on economic development. The empirical results showed that the VAR model can better analyze the relationship between financial growth and economic growth, as well as the role of financial factors in the process of economic development. This study can provide reference for formulating financial development policies suitable for economic development. [ABSTRACT FROM AUTHOR]

It is evident that there is nexus among electricity consumption, foreign direct investment and aggregate economic activity. Unfortunately, the causal relationship among the three variables in Nigeria based on modern econometric methods, recent time-series data and ways that sufficiently cater for inflation and population growth has not been adequately investigated. This study, among other things, used a trivariate vector error correction model, autoregressive distributed lag bounds test for cointegration and Granger causality test to analyse the causal relationship among electricity consumption, foreign direct investment and aggregate economic activity based on time-series data from 1970 to 2018. The study found the presence of neutral causality between electricity consumption and aggregate economic activity in the short run as well as unidirectional causality from aggregate economic activity to electricity consumption in the long run. The study also found the presence of unidirectional causality from foreign direct investment to electricity consumption as well as neutral causality between foreign direct investment and aggregate economic activity in both the short run and the long run. It is therefore recommended that steps should be taken to adequately increase foreign direct investment and aggregate economic activity in ways that will guarantee an optimal increase in electricity consumption in Nigeria. [ABSTRACT FROM AUTHOR]

This study aims to analyze the impact of ICT, renewable energy consumption, and financial development on CO2 emissions in selected developing countries of East and South Asia. Using panel data spanning 1985–2020, Pooled Mean Group (PMG) estimator is used to analyze the short-run and long-run effects. Results suggest that ICT and financial development positively contribute to the degradation of the environment in the long run, while their impact on CO2 emissions is insignificant in the short run. On the other hand, renewable energy consumption affects environmental quality positively in both the long run and short run. It is also examined that economic growth affects CO2 emissions positively but the squared economic growth reduces CO2 emissions which validates inverted U-shaped EKC hypothesis. The empirical findings of the Granger Causality test suggest unidirectional causality from ICT and financial development to CO2 emissions, while a bi-directional relationship is found among renewable energy and CO2 emissions. Results imply that governments in these countries need to invest in renewable energy to control environmental degradation. [ABSTRACT FROM AUTHOR]

Bangladesh observed an unprecedented growth in gross domestic product (GDP) and foreign direct investments (FDI) over the last decade. However, the country also observed an annual average increase in greenhouse gas emissions (GHGs) by 3.3% between 2000 and 2016. Therefore, this study aims to understand the dynamic relationship among GDP, FDI and GHGs, and investigate the existence of the Environmental Kuznets Curve (EKC) so that appropriate policy options could be taken to reduce emissions while sustaining economic growth. This study adopted the Vector Error Correction Model (VECM) and Granger Causality tests to achieve the objectives. Results suggest the EKC hypothesis to be positive for the nation. The causality test negates short- and long-run causality from energy consumption per capita (ECPC), GDP per capita (GDPPC), and FDI to GHGs at a significance level of 0.05. However, a bi-directional causality is running from ECPC to FDI and GDP per capita in the short-run. By scholarly contributing to the debate between development and GHGs, this study attempts to bridge-up the current gap in the literature, i.e., scientific investigation of the EKC hypothesis and the effects of FDI on energy consumption and GHGs in Bangladesh. [ABSTRACT FROM AUTHOR]

This paper investigates the causal relationship between bank development and economic growth in the US before and after the financial crisis in 2008. Based on the quarterly panel data of US states for 2002Q1-2012Q4, this paper uses two-step system GMM method to test the long-term and short-term impact of bank on growth. In addition, panel Granger causality test is used to verify the causality between bank and growth in different periods. The study finds that banks have played a significant role in the long-run economic growth process and there exists a bidirectional causality between bank and growth among US states. In the short term, the financial crisis has corrected the short-run relationship between bank and growth. The relationship between bank and growth has changed from one-way causality before the crisis to two-way causality after the crisis. Before the crisis, economic growth promotes bank credit, but bank development does not promote growth. After the crisis, banks play the role of economic driver through credit, savings and services, and economic growth drives bank savings and scale expansion. The financial intermediary service functions of banks, especially credit to private sector and absorbing savings to transfer loans, determine the role of bank development in economic growth. Policy makers should support bank development in the long run to establish a mature financial intermediary system and promote regional economic development. [ABSTRACT FROM AUTHOR]

With 2.5 global hectares (gha) per capita against 2.7 gha per capita, China's ecological footprint is desirably below the world's average ecological footprint per capita. Undesirably, the country's per person ecological footprint outweighs the world's average biocapacity per person of 1.7 gha, thus signifying an enormous pressure on the country's ecological capacity. This reason accounts for the motivation to explore the dynamics of ecological footprint for China over the period 1971–2016 by employing a series of empirical techniques that include quantile-on-quantile regression (QQR), spectral Granger causality (SGC), and quantile regression. Indicatively, the empirical findings are in folds. First, from the QQR, economic growth exerts a positive effect on (i) ecological footprint especially in the middle quantile (0.4–0.7) and (ii) all quantiles (0.01–0.95) of economic growth. Second, both fossil fuel and primary energy utilization exert a positive impact on (i) all quantiles (0.01–0.95) of ecological footprint and (ii) all quantiles (0.01–0.95) of the two energy profiles. Third, it is surprising to see renewable energy utilization exerting a positive effect on ecological footprint at the lower tail (0.1–0.40) and on renewable energy use at the higher tail (0.70–0.95). Additionally, the SGC result revealed Granger causality from primary energy use and economic growth to the ecological footprint in the long-run without reverse. Additionally, without reverse, there is a Granger causality from renewable energy use to the ecological footprint in the short-, medium-, and long-term. Importantly, the overall policy implication suggests a more drastic decoupling of the country's growth from the supply side (ecological pressure and environmental deprivation). [ABSTRACT FROM AUTHOR]

The current study employs a Granger causality test within a Quantile approach investigating CO2 emission determinants in China. Results show urbanization, financial development and openness to trade are leading determinants of CO2 emissions in China. These results highlight climate change issues while taking advantage of a new methodology to fill a gap in the current literature. Our findings show key implications for PRC government policy related to pollutant reduction policy. [ABSTRACT FROM AUTHOR]

This study has investigated the Environmental Kuznets Curve (EKC) hypothesis and focuses on the resource stock of the country in relation to the selection of environmental degradation indicators. Acknowledging the critical role of the EKC for policy formulation and development strategies, this study has examined the validity of the EKC hypothesis by exploring the relationship between economic growth, urbanization, energy consumption, trade openness, human capital and ecological footprints for the period 1972–2018 in Bangladesh. The autoregressive distributive lag bounds testing approach is applied for this purpose, taking into account the structural break in the time series. A fully modified OLS estimation has also been applied as the robustness check of the results. Results from the empirical analysis confirm the existence of the EKC in Bangladesh in both the long run and short run. The causal nexus among the variables is examined by applying the Vector Error Correction Granger causality test. The causality test result shows that economic growth and urbanization cause ecological footprints in both the short run and the long run. Based on this result, it can be inferred that economic growth activities in Bangladesh can be continued and extended with minimal ecological cost through structural economic change and proper environmental management. [ABSTRACT FROM AUTHOR]

The study examined the short- and long-run nexus between industrial growth, economic growth, oil production, inflation, and trade deficit of China, using quarterly data from 1995Q1 to 2020Q4. We used autoregressive distributed lag model (ARDL), after affirmation of long-run relationship and uni-directional causality. To examine short-run dynamics, an error correction model (ECM) was specified, and Granger causality test was conducted to find the direction of causality between variables. Empirical findings confirmed cointegration between trade deficit and industrial growth, economic growth, inflation, and oil production. Further, uni-directional causality between trade deficit and all independent variables was found, running from independent variables towards trade deficit. Results revealed that growth of industrial production index play a substantial role in determining trade balance of China. The study further confirms the effectiveness of economic growth and inflation for trade deficit, in both short run and long run. The findings are consistent with extant literature. On the policy front, a couple of suggestions including trade policies with special focus on industrial reforms, and loose monetary policies are made on the basis of derived conclusions. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this research is to examine the validity of the agriculture-induced environmental Kuznets curve (EKC) hypothesis with evidence from an autoregressive distributed lag (ARDL) approach with a structural break including real income and energy consumption in the model for Ghana over the period 1980–2014. Design/methodology/approach: The ARDL approach with a structural break was used to analyze the agriculture-induced EKC model which has not been studied in Ghana. The dynamic ordinary least squares (DOLS), canonical cointegration regression (CCR) and fully modified ordinary least squares (FMOLS) econometric methods were further used to validate the robustness of the estimates, and the direction of the relationship between the study variables was also clarified using the Toda–Yamamoto Granger causality test. Findings: The ARDL results revealed that GDP, energy consumption and agricultural value added have significant positive effects on CO2 emissions, while GDP2 reduces CO2 emissions. The Toda-Yamamoto causality test results show a bidirectional causality running from GDP and energy consumption to CO2 emissions whereas a unidirectional long-term causality runs from GDP2 and agriculture value-added to CO2 emissions. Practical implications: This finding validated the presence of the agriculture-induced EKC hypothesis in Ghana in both the short run and long run, and the important role of agriculture and energy consumption in economic growth was confirmed by the respective bidirectional and unidirectional causal relationships between the two variables and GDP. Thus, a reduction in unsustainable agricultural practices is recommended through specific policies to strengthen institutional quality in Ghana for a paradigm shift from rudimentary technology to modern sustainable agrarian technologies. Originality/value: This study is novel in the EKC literature in Ghana, as no study has yet been done on agriculture-induced EKC in Ghana, and the other EKC studies also failed to account for structural breaks which have been done by this study. This study further includes a causality analysis to examine the direction of the relationship which the few EKC studies in Ghana failed to address. Finally, dynamic ordinary least squares (DOLS), canonical cointegration regression (CCR) and fully modified ordinary least squares (FMOLS) methods are used for robustness check, unlike other studies with single methodologies. [ABSTRACT FROM AUTHOR]