Short-term Causal Relationship Research Articles

Dynamic linkages between the monetary policy variables and stock market in the presence of structural breaks: evidence from India

PurposeThe study aims to determine the long and short-term causal relationships between the variables associated with the adjustment of monetary policy and the stock market in India in the presence of structural breaks.Design/methodology/approachThe study employed the autoregressive distributed lag (ARDL) bounds test and the Error Correction Model to assess long- and short-term causal relationships. The study also used non-frequentist Bayesian inferences for the validity of estimation robustness. The Bai–Perron test is used to identify breakpoint dates for the Indian stock market index, and the Granger Causality test is employed to ascertain the direction of causality.FindingsThe F-bounds test reveals cointegration among the variables throughout the examined period. Specifically, the weighted average call money rate (WACR), inflation (WPI), currency exchange rate (EXE), and broad money supply (M3) exhibit statistical significance with precise signs. Furthermore, the study identifies the negative impact of the COVID-19 outbreak in March 2020 on the Indian stock market.Research limitations/implicationsAlthough the study provides significant insights, it is not exempt from constraints. A significant limitation is selecting a relatively limited time period, specifically from April 2008 to September 2023. The limited time frame of this study may restrict the applicability of the results to more comprehensive economic settings, as dynamics between the monetary policy and the stock market can be influenced by multiple factors over varying time periods. Furthermore, the utilisation of the Weighted Average Call Money Rate (WACR) rather than policy rates such as the Repo rate presents an additional constraint as it may not comprehensively account for the impacts of particular policy initiatives, thereby disregarding essential complexities in the connection between monetary policy variables and financial markets.Practical implicationsThe findings of the study suggest that investors and portfolio managers should consider economic issues while developing long-term investing plans. Reserve Bank of India should exercise prudence to prevent any discretionary measures that may lead to a rise in interest rates since this adversely affects the stock market. To mitigate risk, investors should closely monitor the adjustment of monetary policy variables.Social implicationsThe study has important social implications, especially regarding the lower levels of financial literacy among investors in India. Considering the complex nature of the study’s emphasis on monetary policy adjustments and their impact on the stock market. Investors face the risk of significant losses due to unexpected adjustments in monetary policy. Many individuals may need help understanding how policy changes impact their investments. Therefore, RBI must consider both price and financial stability when formulating monetary policies. Furthermore, market participants should consider the potential impact of fluctuating monetary policy variables when devising their long-term investment strategies. Given that adjustments in interest rates can markedly affect stock market dynamics, investors must carefully assess the implications of monetary policy decisions on their portfolios.Originality/valueThe study uses dummy variables in the ARDL model to represent structural breaks that emerged from the COVID-19 pandemic (as determined by the Bai–Perron multiple breakpoint test). The study also used the Perron unit root test to find out the stationary of the series in the presence of structural breaks. Additionally, the study also employed Bayesian inferences to affirm the robustness of the estimates.

Analyzing Nexus between Crude Oil, Gold, Dollar and Equity Markets with Structural Break: ARDL Evidence from India

This paper explores the price relations between the Crude oil, Gold, US dollar, and equities during several economic episodes. The long- and short-term causal relationships between asset markets are examined in this study. The research uses a ‘Vector Error Correction Model (VECM)’ and an ‘Autoregressive Distributed Lag (ARDL)’ bounds test to examine monthly data from April 1999 to March 2022. The results demonstrate the weak connections between asset markets throughout the different sample periods. Furthermore, the integration of most markets is uneven and changes over time. The currency and equity markets are adjusting to the long-run equilibrium only at a slow pace. We suggest that systematic risk factors must be taken into account while jointly modeling market linkages. This study improves on previous research in the subject by demonstrating the time-varying effects of asset price links on portfolio optimizations in different economic episodes.

Air Pollution and Economic Development in KSA: Environmental Kuznets Curve and Implications for Sustainable Development

In a global context increasingly concerned about climate hazards, understanding the impact of economic growth on the environment is becoming crucial, especially for developing countries. This paper addresses this issue through the lens of carbon dioxide (CO2) emissions and examines how economic growth affects the environment in the case of KSA. The objective of this study is to test the validity of the environmental Kuznets curve over the period 1990-2020 using the Granger causality test. The results of the test indicate a long-term relationship between the variables, and the results of the long-term model confirm the environmental Kuznets curve hypothesis for the Saudi economy with a GDP per capita turning point of US$ 19977,794. The results suggest that there is a bi-directional, short-term causal relationship. The long-term causality is unidirectional from income to CO2 emissions. Our results suggest that the KSA should distinguish between economic policy and environmental policy to improve its environmental quality. Economic expansion leads to an increase in the level of pollution and the concentration of GHGs in the atmosphere if the government does not take the necessary precautions.

Multivariate time series analysis of COVID-19 Pandemic and gold price by using Error Corrections Model

Coronavirus (COVID-19) severe acute respiratory syndrome is an infectious disease that has a direct influence on the world’s population, as is obvious from the prevalence of COVID-19. However, as the COVID-19 virus continues to proliferate, its impact is becoming more and more significant and extensive. Accordingly, the study’s goal is to use the Error Corrections Model (ECM) to examine the relationships between the COVID-19 pandemic and gold prices. An Error Corrections Model was used by the researchers in this investigation. Error Correction Models and long-term equilibrium can be found for non-stationary time series that have been cointegrated together. Two-step estimation can be used to estimate the ECM since a proportion of the imbalance from one period is adjusted in the succeeding period, the system returns to equilibrium. The calculated cointegrating relations are used to create the error correction terms, and the estimation of VAR is done by a first differencing process, and then appears in the model as an explanatory variable, with the term of error correction. The COVID-19 pandemic and gold price data were made available to the general public in a sample data. For the year 2020, it includes data on daily observations for the relevant variables. R-language results show that the COVID-19 pandemic and gold prices have been demonstrated to have a long-term causal relationship. During the most recent period of variance decomposition, 99.76 percent of the variance in covid-19 explains only 0.24 percent of the variance in gold price. This explains that there is no short-term causal relationship between covid-19 and gold price, as indicated by the Wald test results, the granger causality results have shown that the gold price is influenced not only by itself but also by the Covid-19 pandemic. Finally, there is a positive relationship between the Covid-19 pandemic and the price of gold, and this has a negative impact on human life during 2020.

Investigating the Dynamic Relationship Between Greenhouse Gas Emissions and Gross Domestic Product in Türkiye

This study aims to investigate the causal relationship between Gross Domestic Product and greenhouse gas emissions in Türkiye from 1951 to 2018, using the Causal Decomposition Method that integrates Ensemble Empirical Mode Decomposition, Hilbert-Huang Transform, and Phase Coherence Methods. The primary focus is on identifying the key sectors contributing significantly to greenhouse gas emissions, particularly those connected to industrial production. The analysis reveals a one-way, short-term causal relationship from Gross Domestic Product to greenhouse gas emissions, spanning approximately 3 years. This finding suggests that changes in Gross Domestic Product have short-term effects on emissions, but not vice versa. Special emphasis is placed on the gases Cardon Dioxide, Methane and Nitrous Oxide, as they demonstrate a strong, consistent causal connection with Gross Domestic Product. The significance of this study lies in its utilization of the Ensemble Empirical Mode Decomposition approach to investigate this dynamic causality and address a notable gap in the existing literature. Empirical results indicate a complex yet observable association between Gross Domestic Product growth and greenhouse gas emissions in Türkiye, and that this relationship becomes more important, especially in the short and long term, with periodic fluctuations.

Analyzing the economic, social, and technological determinants of renewable and nonrenewable electricity production in China: Findings from time series models

China has advocated and developed renewable electricity to protect the environment and achieve sustainable development. However, non-renewable electricity continues to dominate. Here, we investigate the influencing factors of non-renewable and renewable electricity production in China and compare the influencing factors of non-renewable and renewable electricity production. Based on a time series analysis from 2006 to 2020, we discuss the long-term and short-term relationships between renewable electricity production, non-renewable electricity production, economic growth, aging, and electricity production capacity through the cointegration test and vector error correction model. The empirical results indicate that economic growth and electricity production capacity have a long-run relationship with renewable and non-renewable electricity production, whereas aging only has a long-run relationship with renewable electricity production. Furthermore, a short-term causal relationship exists between economic growth, aging, electricity production capacity, and renewable and non-renewable electricity production. These findings provide policy implications.

The Impact of Financial Technological Advancement (FinTech) on the Economic Growth: Evidence from Palestine

This study examined the impact of financial technology FinTech on economic growth in Palestine, moreover, the study used annual time series data from the Palestinian Ministry of National Economy (MoNE), the Palestinian Ministry of Finance and Planning (MoFP), as well as the Palestinian Central Bureau of Statistics (PCBS) and the Palestinian Monetary Authority (PMA), in addition to investigating the current study of the impact of FinTech on Palestinian economic development for the period (2008 to 2021). In addition, the co-integration was evaluated using a distributed autoregressive approach, and a vector error correction model was used to consider long- and short-term causation. However, the data shows that FinTech products (number of people using the Internet, number of broadband subscriptions, number of mobile cellular subscriptions, number of automated transfer machines, number of branches) contribute to the growth in financial inclusion, which in turn fuels the growth economy in Palestine. Moreover, these results indicate that the (PMA) in Palestine is focusing more on expanding access to financial services. Moreover, this study aims to explore opinions and attitudes regarding the emergence of financial technology in Palestine's future financial services industry. The study’s objectives were also achieved by speaking to a group of managers in the banking and financial services industries, who were administrative employees from departments with technical, software and information technology departments, in addition to departments specialized in technological fields electronic payment services. The study analysis was performed based on the objectivity of the data collected. The approach used in this study was used to produce and rank data-related results for these traits and patterns. According to the findings of the study, it is critical that existing banking and financial institutions, academic institutions, technology companies and business incubators work together to build a supportive environment for the Fintech system in Palestine. According to the ADFGLS co-integration test results, all variables are stable at the initial difference. The results of the ARDL technique show that the co-integration of the variables occurs and that the null hypothesis is rejected. The VECM results show that fintech products have a long-term and short-term causal relationship with both economic growth and financial development. Thus, the results indicate that financial technology in Palestine has many obstacles: rules and regulations, cybersecurity, customer distrust of digital services, etc. All of this is believed to have a direct and beneficial impact on the Palestinian economy.

Does the East and Southeast Asian Equity Market have Integration? A Study of Cointegration Analysis

Financial integration can improve the efficiency of capital allocation as well as help diversify risks. This study aims to find and analyze four cases. First, to find and analyze the long-term cointegration between East and Southeast Asian. Second, to find and analyze the short-term causal relationship between East and Southeast Asian equity market. Third, to find the most influential equity market from East Asian toward their Southeast Asian and the most influential equity market from Southeast Asian toward their East Asian. Last, to find the forecast structural analysis for five days horizon period of each country’s price, both East and Southeast Asia. This study uses Johansen’s cointegration method to test long-run relationships between East and Southeast Asian equity markets, Granger-causality, forecast variance decomposition method and forecast with VECM. This study uses daily indices prices collected from Refinitive covered from January 2002 to December 2019. Johansen’s test emphasize that there is a cointegration relationship between East Asian and Southeast Asian stock markets, but the integration process is incomplete. The cointegration vector also emphasize that ASEAN+3 members react differently to external shocks. This study found that the Japan Granger-cause will lead to all stock markets in Southeast Asia, while Singapore and Philippine Granger-cause will lead to all stock markets in East Asia. These results show that Japan is the market with the most connections in Southeast Asia, while Singapore and Philippine are the markets with the most connections in East Asia. Another point of this paper is to emphasize that Japan is the most influential stock market in East Asia, while Singapore is the most influential stock market in Southeast Asia. This study shows that policymakers in East and Southeast Asian countries should synchronize capital market standards, regulations and reduce barriers to capital flow to stimulate the integration of regional stock markets.

Relationship between Industrialization, Urbanization and Industrial Ecology in Western China: A Panel Vector Auto-Regression Model Analysis

As the foundation of modern economic development, industry is the engine of industrialized and urbanized development. Industrial ecology is a high-level form of industry that is achieved after it has reached a certain stage, which guides the coordinated industrial development balancing mankind and nature. The implementation of industrial ecology is an important method and effective approach to realize the sustainable development of industrialization and urbanization. In this article, based on the inter-provincial panel data of western China during 2003-2018, the spatial development trends of industrialization, urbanization and industrial ecology are analyzed, and an empirical method is employed to conduct a robustness test based on the Panel Vector Auto-Regression (PVAR) model to determine the long-term interactions among these three aspects. The results show that it is difficult to manifest the short-term causal relationships among industrialization, urbanization and industrial ecology. After lagging for three periods, they present the Granger causality, the industrial ecology and industrialization have promoted urbanization, and the coefficient for the influence of industrial ecology on urbanization is 0.4612. However, industrialization and urbanization have negative impacts on industrial ecology, and with a 1% increase in industrialization or urbanization, the industrial ecology will decline by 0.2261% or 0.2850%, respectively. With the continuation of the lagging period, industrial ecology will have better interpretability than industrialization and urbanization, and industrialization and eco-friendly development have strong self-accumulation development mechanisms, while the self-accumulation mechanism of urbanization is not obvious, and it might even have a decline. By fulfilling the role of the regional leading industry, the state of unbalanced internal development can be improved, so as to realize mutual promotion between industrialization and urbanization. By improving the utilization rates of resources and energy, efforts should be made to implement green production, significantly promote industrial ecology, and boost high-quality development of both the regional economy and society.

Reality of short-term causality of Islamic and conventional banking term deposit rates in Pakistan

Purpose The purpose of this study is to find the empirical causal relationship between Islamic bank term deposit rates (IBTDR) and conventional bank term deposit rates (CBTDR) in the short-term. Design/methodology/approach This study analyzes the short-term causal relationship between the term deposit rates (TDRs) for the time period of three years 2015 to 2018 on monthly data of IBTDR and CBTDR. Granger causality test, variance decomposition and impulse response function are applied to examine if there is any short-term causal relationship between the IBTDR and CBTDR. Findings This empirical study establishes that the IBTDR are dependent on the CBTDR in the short-term. Practical implications This research provides an insight for the customers of TDRs of the Islamic banking system. This study is not only a significant insight for the end-users but also for the regulators and researchers as it provides important empirical evidence. This could lead to further research on the reasons for causality. Originality/value There has not been any study of this nature in Pakistan to identify the causality of the two-TDRs. This research expands the dynamics of research in the context of the banking sector.

Analysis of the relationship between exchange rate changes and profitability in Turkey: example of BIST manufacturing sector

Purpose- The aim of this study is to empirically investigate the relationship between the change in exchange rates and the profitability of firms in Turkey. The changes in real effective exchange rate as the change in exchange rates criteria and return on assets as well as return on equity are considered for measuring profitability. Methodology- The sample of the research consists of 37 companies that are listed in BIST 100, operating in the manufacturing sector and whose data can be accessed completely. The data of the companies within the scope of the research were obtained from the official websites of Borsa Istanbul, Public Disclosure Platform, and Finnet and made ready for analysis. For the purpose of the research, panel data analysis, panel unit root tests, panel regression analysis, causality analysis, and moderating effect analysis were carried out. Findings-. Changes in foreign exchange rates, foreign sales and asset size of the companies do not have any significant impacts on the return on assets and return on equity during the period between 1999-2019 in Turkey. It has also been found that asset size, foreign sales, and change in exchange rates are the reasons for the return on assets and similarly, asset size, foreign sales, and change in exchange rate are the reasons for the return on equity. Finally, changes in the foreign exchange rate and export sales have a short-term causal relationship with both return on assets and return on equity, and changes in the foreign exchange rate and total assets have a moderating effect on return on assets. Conclusion- As a result of the research, it has been revealed that firms are affected by changes in foreign exchange rates with a delay. It means firms were likely be affected by the changes in exchange rates with a lag, and similarly, the changes in exchange rates affected financial performance with a lag. It has also been revealed from the research that firms effectively use internal and external hedging methods that help reduce the adverse impacts of the changes in foreign exchange rates.

An econometric analysis for food security in Tunisia

Food security issue is getting more attention in middle-income countries such as Tunisia after the revolution 2011, where many factors affecting its food security are multiplied. An econometric analysis of food security was done through the Vector Error Correction Model approach (VECM). The result of this approach shows that there is a significant long-term causality between the dependent variables and the explanatory variables. Some signs of variables like land under cereals assert the hypothesis of Ricardo’s land rent theory and also attract attention for the preservation of land fertility in climate change context. However, there is a short-term causal relationship between food security and independents variables like: land under cereals, inflation and food imports. These results confirm that the issue of food security in Tunisia is a question of threat in the short and long-term instability. So, it is important today to readjust some factors to ensure food security in Tunisia like controlling inflation and lowering the food importation as short-term measures and preserving and improving the fertility of land under cereals and adopting climate change as long-term measures.

A VECM Analysis of the Relations the CPI, PPI, GDP Per Capita, Exchange Rate in the Republic of Azerbaijan

Aims: The study focuses on an empirical analysis of a macroeconomic indicators system, that reflect the level and pace of a country's socio-economic development, such as CPI, PPI, GDP per capita, exchange rate, taking into account the consequences of the COVID19 pandemic and oil prices on the example Republic of Azerbaijan.
 Study Design: The study consists of four sections. It includes Introduction, Literature Review, Methodology, Results and Discussion and Conclusion.
 Place and Duration of Study: The study was conducted for 4 months of 2020 in the department of "Mathematical support of economic research" of the Institute of Economics of Azerbaijan National Academy of Sciences.
 Methodology: Within the dynamic VEC model, taking into account the COVID19 pandemic and oil prices, the long-run and short-run effects of macro indicators system on each other were studied by means of causality, impulse responses and variance decomposition on the monthly statistics covering the period 2015M01-2020M07 for the Republic of Azerbaijan.
 Results: Calculations based on the established stable VEC (5) model revealed that there is a long-term causal relationship from the triad (CPI, PPI, Ex_Rate) to all endogenous variables. There are a short-term bi-directional causal relationship between CPI and GDP_Per_Capita and between PPI and Ex_Rate. From PPI and Ex_Rate to GDP_Per_Capita; from Ex_Rate to CPI, there are a unidirectional short-term causal relationship.
 Conclusion: Summarizing the results, we can write the following long-term expressions: the change a) in the GDP_per_Cap is influenced by the PPI and CPI variables negatively, and Ex_Rate – positively; b) in the CPI is influenced by the GDP_per_Cap and PPI variables negatively, and Ex_Rate – positively; c) in the PPI is influenced by the Ex_Rate and CPI variables negatively, and GDP_per_Cap – positively, so that the negative influence of the CPI is greater; d) in the Ex_Rate is influenced by the PPI and CPI variables negatively, and GDP_per_Cap – positively. Has been also identified that the indicator PPI has a more negative effect on changes in GDP_per_Cap, CPI and Ex_Rate.

Global evidence of time-frequency dependency of temperature and environmental quality from a wavelet coherence approach

The concern that the global emissions or carbon mitigation plans have not yielded the much desired significant improvement in health, air and environmental quality especially since the Conference of Paris has further created some ambiguities. This has further made environmentalists and policymakers wonder if the December 2015 Paris Climate Agreement is “better than no agreement”. In advancing the studies of global temperature and carbon emission nexus, the current study rather applied the time-frequency dependency of average global mean temperature anomalies and global carbon dioxide (CO2) emissions from fossil fuels for the annual data from 1851 to 2017. The present study uses the wavelet coherence technique and the Toda and Yamamoto causality approach that allows the investigation of both the long- and short-term causal relationship between the global average temperature and global CO2 emissions. The findings of this study indicate that (i) significant vulnerabilities in global average temperature and global CO2 emissions are observed at different time periods and different frequency levels; (ii) global CO2 emissions have a strong power for explaining global average temperature at different time periods; (iii) between 1880 and 1910, global average temperature and global CO2 emissions are positively correlated at medium term; and (iv) the outcome of Toda and Yamamoto causality reveals that global CO2 emissions cause global average temperature and this outcome is in line with the outcome of wavelet coherence approach.

Short-Term Causal Relationships between the Oil Sector and Economic Growth in the Mexican Economy: FG-ARDL Approach

La presente investigación tiene como objetivo analizar las relaciones causales de corto plazo entre el sector petrolero y el crecimiento económico; utilizando dos metodologías, el modelo ARDL y la propuesta basada en lógica difusa, el Autorregresivo de Rezagos Distribuidos Gaussiano Difuso FG-ARDL. Para ello se analizaron 59 variables del sector petrolero y su relación con el Indicador Global de la Actividad Económica, y el correspondiente indicador de actividades primarias, secundarias y terciarias, en formato mensual entre enero 1997 y diciembre 2019. El modelo FG-ARDL logró mejores estimaciones, permitiendo identificar con mayor precisión la influencia de las variables derivadas de la industria petrolera en el crecimiento económico. La principal recomendación es evaluar otras relaciones económicas para verificar la eficiencia de la nueva metodología, en la que la primordial limitación es su dependencia al método ARDL, por lo que no proporciona nuevas relaciones causales. La conclusión más relevante es que el consumo interno de gasolina y PEMEX diésel son las principales variables que impulsan el crecimiento económico de corto plazo, resultado que se observa de mejor forma en el modelo propuesto.

A wavelet coherence analysis: nexus between urbanization and environmental sustainability.

The present study aims to explore the co-movement between urbanization and environmental sustainability for the period 1950 to 2014 using the wavelet coherence technique within the global framework. The wavelet coherence technique allows us to investigate both the long-run and short-term causal relationships between urbanization and environmental sustainability within the global framework. The findings reveal that (i) wavelet correlation from the global perspectives indicates a significant wavelet relationship between urbanization and environmental sustainability for medium- and long-term horizons; however, the correlations are not significant for the short-term horizons; (ii) significant vulnerabilities in urbanization and environmental sustainability are observed at different periods and different frequency levels; (iii) urbanization has reliable power for explaining environmental sustainability at different periods between 1970 and 2000. Based on our study, we suggest that global urban planners and policymakers should support modern environment-friendly technologies and renewable energies to control global CO2 emissions and pollutions created by industrialization; policy action to fight climate change is as well recommended as evidence supports to compact city theory.

The Nexus Between Infrastructure and Export: An Empirical Evidence from Pakistan

This research, by using the autoregressive distributive lag method, examines the long- and short-term causal relationship between infrastructure and exports in Pakistan over the period 1990–2017. The empirical results revealed the existence of short- and long-term bi-directional causality concerning infrastructure and export in Pakistan. The results demonstrated that infrastructure strongly improves export in the short and long run. Conversely, export encourages the quality and availability of infrastructure in Pakistan in the long run. Furthermore, this study also uses sub-indices of infrastructure individually as dependent and independent variables. The study result demonstrated that the long- and short-term effects of infrastructure and its sub-indices (transport, electricity, communication, finance) on export is positive and significant. Also, when infrastructure sub-indices are used as dependent variables, the results indicate that the effect of export on sub-indices is positive and significant in the long run; however, in the short run, it is insignificant. The bi-directional linkage between infrastructure and export suggests that improving the quality and increasing the availability of infrastructure would enable Pakistan’s economy to catch up with the advanced economies, specifically in export. Furthermore, control variables of per-capita GDP, exchange rates, human capital, and domestic spending also expand the bi-directional causal relationship between Pakistan’s infrastructure and exports.

Does political risk matter for economic and financial risks in Venezuela?

In this paper, the time–frequency dependency of political risk as well as economic and financial risks is explored in Venezuela using quarterly data from 1984Q1 to 2018Q4. The present study uses the wavelet coherence technique, which allows the investigation of both the long and short-term causal relationships between political risk and economic and financial risks in Venezuela. The findings of this study indicate that: (i) significant vulnerabilities in political risk, economic risk, and financial risk are observed at different time periods and different frequency levels; (ii) political risk has a strong power for explaining economic risk from 1995 to 2005 in the long run, while between 1984 and 2010, economic risk and political risk are positively correlated at different frequency levels; (iii) in the long run, changes in political risk significantly lead to changes in financial risk in Venezuela.

The nexus of environmental quality with renewable consumption, immigration, and healthcare in the US: wavelet and gradual-shift causality approaches.

Given that the volume of carbon emissions in the US is a significant share of the global greenhouse gas emissions, some salient factors are being currently examined so as to reverse the threat to global environmental sustainability. To this regard, the current study investigates the co-movement and long-term and short-term causal relationship between CO2 emission (a proxy for environmental quality) and renewable consumption, immigration, and healthcare by using the wavelet coherence approach which primarily provides information on dynamic correlations over time and for different time scales. The coherence approach allows the one-dimensional time data into the bi-dimensional time-frequency sphere between the variables. In addition to investigating the causal relationship between CO2 and renewable consumption, immigration, and healthcare, this study also employs gradual-shift causality and Toda-Yamamoto causality tests. With this, the study found a high variation for CO2 emission in the US at 8 scales (8 quarters) from 1999 to 2008. Additionally, there is significant feedback causality between CO2 emission and renewable consumption at different scales while a positive correlation between the variables is observed in the short run. Similarly, the result reveals that immigration significantly causes CO2 emission in the US from 2008 to 2010 and a two-way causality is detected between CO2 emission and healthcare at different frequencies and time period. Moreover, the Toda-Yamamoto causality and gradual-shift causality tests provide supportive evidence to the outcomes of the wavelet coherence-based causality test in this study. Overall, the investigation offers significant policy directive especially toward addressing the potential adverse effects from the country's immigration and healthcare amendments.

Dynamic linkages among CO2 emissions, health expenditures, and economic growth: empirical evidence from Pakistan.

The linkage between high concentration of greenhouse gases (GHGs) and climate change is well recognized as there is severe influence of climate change on public health. Carbon dioxide is most prominent GHG which deteriorates the environment and impacts human health. On the parallel, economic growth also affects health conditions sometimes positively or vice versa. The objective of this research work is to examine the dynamic linkages among CO2 emissions, health expenditures, and economic growth in the presence of gross fixed capital formation and per capita trade by using auto regressive distributive lag (ARDL) model for Pakistan covering annual data from the year 1995-2017. Our empirical results show that there is significant long run as well as short-term causal relationship between health expenditure, CO2 emissions, and economic growth in Pakistan. Bidirectional relationship of Granger causality is found between health expenditures and CO2 emissions, and further between health expenditures and economic growth. Short-run unidirectional causality is running from carbon emissions to health-related expenditures. The bidirectional causal relationship is also investigated between carbon emissions and growth as well as gross fixed capital formation and growth. Then, policy recommendations towards controlling pollution, particularly CO2 emissions and health expenditures without compromising economic growth are suggested. Graphical abstract .