Relative responses of trade in goods and services to currency devaluation: Evidence from Ethiopia

Lack of hard currency is one of the key growth barriers in emerging countries, as imports vastly outstrip exports. In response, governments in these countries usually undertake a variety of policy packages, including the devaluation of the domestic currency. Despite these efforts, there have been no discernible adjustments to foreign balance. This study examines whether the response to devaluation differs significantly between the goods and services trade in Ethiopia. We estimate the long- and short-run elasticities of the disaggregated trade indicators using an autoregressive distributed lag (ARDL) and error correction mechanisms. The empirical results confirm a significant difference between the goods and service sectors in terms of their responses to the devaluation policy in the long and short run. The estimated long-run elasticities of devaluation are only statistically significant for service imports and trade balances with negative signs. The remaining sectors did not show any significant relationships. In addition, we obtain meaningful short-run elasticities for service imports, goods exports, and total exports, all of which have a negative sign. Domestic inflation accounted for a large portion of the short-run import dynamics, output growth, and FDI, which contributed significantly to long-term export performance. The current study reveals that the government should not rely exclusively on the devaluation policy to bridge its external imbalances, and should see alternative and more effective policy mixes to alter the demand and supply sides of foreign trade.

Technological innovation, militarization, and environmental change: evidence from BRICS economies

In the complex international society, the economic development and defense construction of BRICS countries have attracted increasing attention. This article incorporates technological innovation, militarization, and environmental change into a unified analytical framework to assess the potential impact of technological progress and defense military expenditure on the environment in BRICS countries. Based on CSD tests, unit root tests, and cointegration tests, this study constructs a CS-ARDL model to examine the long-term and short-term relationships among various variables from 1990 to 2021. The results show that technological innovation, military expenditure, and economic growth can significantly increase ecological footprint in the long run, while in the short term, technological innovation and economic growth significantly increase ecological footprint, and the impact of military expenditure is not significant. It is suggested that BRICS countries should focus on supporting low-carbon technology policies and research and development investment, while also considering the use of cutting-edge technology to improve military intelligence capabilities in order to reduce the negative impact of technological innovation and military activities on the environment.

Determinants of the personal savings rate in the Kingdom of Saudi Arabia using time savings deposits, 2012-2022

Personal saving is an important indicator of future economic prosperity. Despite the growth in gross domestic product per capita (GDPPC) from 2012 to 2022 in the Kingdom of Saudi Arabia (KSA), the personal savings rate remains low. Only a few studies have explored savings in KSA, but no study that used time savings deposits (TSD) to measure personal savings exists. Thus, this study aims to investigate the determinants of personal savings employing TSD. Using data from the Saudi Central Bank from 2012 to 2022, this study empirically examines the determinants of TSD. The autoregressive distributed lag cointegration technique determines the long-run relationship between the study's variables. The study finds that GDPPC, deposit interest rates (DIR), consumer loans (CLs), and real estate loans (RELs) significantly impact personal savings. Only GDPPC and RELs have a significant negative impact on personal savings. The study is among the few to examine savings in KSA but, unlike other studies, used TSD as a measure of personal savings. In conclusion, several implications and recommendations for policymakers and financial institutions were presented.

The consumer price index prediction using machine learning approaches: Evidence from the United States

The consumer price index (CPI) is one of the most important macroeconomic indicators for determining inflation, and accurate predictions of CPI changes are important for a country's economic development. This study uses multivariate linear regression (MLR), support vector regression (SVR), autoregressive distributed lag (ARDL), and multivariate adaptive regression splines (MARS) to predict the CPI of the United States. Data from January 2017 to February 2022 were randomly selected and divided into two stages: 80 % for training and 20% for testing. The US CPI was modeled for the observed period and relied on a mix of elements, including crude oil price, world gold price, and federal fund effective rate. Evaluation metrics-mean absolute percentage value, mean absolute error, root mean square error, R-squared, and correlation of determination-were employed to estimate forecasted values. The MLR, SVR, ARDL, and MARS models attained high accuracy parameters, while the MARS algorithm generated higher accuracy in US CPI forecasts than the others in the testing phase. These outputs could support the US government in overseeing economic policies, sectors, and social security, thereby boosting national economic development.

Testing the impact of renewable energy and oil price on carbon emission intensity in China's transportation sector

As the largest carbon emitter in the world, with its transportation sector contributing the largest shares of its emission, the need for a low-carbon transition economy has become a policy agenda for China because in order to reach carbon neutrality by 2050, lowering the intensity of carbon emissions in the transportation sector will be crucial. In this regard, we used the "bootstrap autoregressive distributed lag model" to explore the impact of clean energy and oil prices on the intensity of carbon emissions in China's transportation sector. The study found that an increase in oil prices decreases the intensity of carbon emissions in the short and long run. Similarly, an increase in the level of renewable energy and economic complexity declines the intensity of carbon emissions in the transportation sector. On the contrary, the research demonstrates that non-renewable energy contributes positively to carbon emission intensity. Therefore, the authorities must promote green technology to neutralize the transportation system's detrimental effects on China's environmental quality. The implications for successfully promoting carbon emission intensity mitigation in the transportation sector are examined in the conclusion.

Impacts of the population ageing on the effects of the nationwide emission trading scheme in China

Nowadays, population ageing is a common social phenomenon that occurs worldwide. Rapid ageing may have profound socioeconomic impacts, and thus it may influence effects of climate policy. Nevertheless, very few previous researchers have evaluated climate policy in an ageing society. In this paper, we attempt to narrow the research gap by incorporating ageing impact in climate policy evaluation. Specifically, we have modeled ageing impacts on labor supply, household electricity consumption, and health expenditure. The core of the research framework in this paper is a dynamic recursive Computable General Equilibrium (CGE) model. The model results show that population ageing tends to decrease private health expenditure but increase governmental health expenditure. In contrast, Emission Trading Scheme (ETS) decreases both private and governmental health expenditure. Both population ageing and ETS decrease labor employment, employment rate, GDP, and carbon emissions. The results imply that population ageing lays heavy burdens on social healthcare system, whilst climate policy reduces governmental health expenditure. In ageing societies, mitigation targets can be achieved less costly and more easily through implementing ETS.

An empirical study on correlation among poverty, inclusive finance, and CO2 emissions in China

This paper explores the nonlinear relationship between poverty and CO₂ emissions based on the panel data of 30 provinces in China from 2005 to 2019. In this study, the autoregressive distributed lag (ARDL) model is first used. Findings confirm that poverty has a negative impact on CO₂ emissions in the short run and a positive impact in the long run, while both effects of inclusive finance on CO₂ emissions are negative. In order to explore the reasons for the change in the coefficient of poverty, we introduce a moderating effect (ME) model and a dynamic panel threshold (DPT) model. The result shows that the negative effect of poverty on CO₂ emissions diminishes with the moderation of inclusive finance. When inclusive finance crosses the threshold value (IFI = 0.2696), the impact of poverty on CO₂ emissions will change from negative to positive gradually, which verifies the applicability of the "Poverty-CO₂ Paradox" in China and provides an empirical basis for breaking the "Poverty-CO₂ Paradox." Consequently, deepening poverty reduction and pushing the region's inclusive finance to the threshold level are proposed as effective ways to promote CO₂ emission reduction.

The impacts of fuel price policies on air pollution: case study of Tehran

This study aims to investigate the impacts of fuel price policies on the concentration of air pollutants in Tehran city. Autoregressive distributed lag (ARDL) estimation models were used to investigate the impacts of gasoline and diesel prices along with the weather and economic variables on the following traffic-related pollutants: carbon monoxide (CO), nitrogen dioxide (NO₂), and particular matter 10 micrometers or less (PM₁₀). In the short term, a 1% increase in gasoline prices leads to a 0.02 and 0.012% decrease in the concentration of CO and PM₁₀, respectively. In addition, in the short term, a 1% increase in diesel prices leads to a 0.008, 0.02, and 0.015 % decrease in the concentration of CO, PM₁₀, and NO₂, respectively. Results demonstrate that a 1% increase in gasoline prices leads to a 0.011 and 0.02 % increase in NO₂ concentration in the short term and long term, respectively. Fuel prices had a greater impact on air pollutant concentration in the long term than in the short term. In the long term, a 1% increase in diesel prices leads to a 0.011, 0.024, and 0.029 % decrease in the concentration of CO, NO₂, and PM₁₀, respectively. Although fuel price increases lead to a significant reduction in PM₁₀ and CO concentrations, other factors related to weather conditions (wind speed, temperature, and rainfall) as well as economic activities have a greater impact on air pollution. Therefore, other policies such as improving fuel quality and technology along with other economic policies can be more effective.

Inflation, inflation uncertainty and the economic growth nexus: An impact study of South Africa

Inflation and inflation uncertainty are instrumental in the determination of financial stability, and ultimately, economic growth. We investigated the impact of inflation and inflation uncertainty on growth in South Africa by applying the autoregressive distributed lag (ARDL) estimation techniques on quarterly data covering the period 1961Q1 to 2019Q4. Unlike previous studies on South Africa, we investigated the joint impact of inflation and inflation uncertainty in South Africa, and also, pioneered in comparing the impact of both variables on growth before, and after, inflation targeting. This provided an opportunity to assess the effectiveness of inflation targeting while also investigating any changes in the behavior of the variables. We found that inflation negatively harms growth in both the short and long run, while inflation uncertainty is a short-run phenomenon in South Africa with no bearing in the long run. To promote growth, policymakers should continue to pursue policies that ensure price stability.

• The paper investigated the impact of inflation and inflation uncertainty on economic growth in South Africa covering the period 1961Q1 to 2019Q4.

• Using the autoregressive distributed lag estimation techniques, the paper found that inflation harms economic growth in both the short- and long-run in South Africa while inflation uncertainty is a short-run phenomenon as it affects economic growth only in the short run,

• Interestingly, after adoption of inflation targeting, inflation uncertainty lost it relevance as a factor determining economic growth in South Africa.

The impact of economic growth, energy consumption, trade openness, and financial development on carbon emissions: empirical evidence from Malaysia

Globally, the rising concentration of anthropogenic greenhouse gases emission in the atmosphere is extremely detrimental to the environment. The high concentration among all greenhouse gases is carbon dioxide emission. Therefore, this study explores the linkages between energy consumption, trade openness, globalization, urbanization, and carbon dioxide emission for Malaysia over the spans from 1978 to 2018. ARDL bound testing model was employed to investigate involvement in the elevation of carbon dioxide emissions in the economy. The study illustrates that a 1% growth in energy consumption, trade openness, and urbanization will deteriorate the environment by 0.18%, 0.03%, and 2.51% respectively. Further, variance decomposition analysis predicts that all the determinants in the study have significantly caused carbon dioxide emission in Malaysia. The paper presents scientific support for further studies and argues for the use of innovation shocks as a policy instrument for a prosperous future by formulating more successful environmental policies.

Exchange rate changes, price level and the income effects on trade balance in South Africa

This paper examines the long-run and short-run effects of the exchange rate changes on the net trade balance in South Africa and compares the effects to those induced by the foreign and domestic price and income effects. Using annual data from 1970 to 2019 and the autoregressive distributed lags bounds testing approach, evidence shows that the long-run elasticities of the exchange rate on the net trade balance are bigger than the short-run impacts. In both the short run and long run, the impact of domestic income on the net trade balance is the biggest followed by consumer prices and then last is the nominal effective exchange rate. The long-run impact of the domestic income is nearly double that of the exchange rate. This shows that domestic income has a bigger effect on the net trade balance than both the price levels and the exchange rate effects. These results imply that policymakers designing the exchange rate policy should complement this with an appropriate industrial policy which emphasises the value of import-substituting industries to eliminate the net trade deterioration. At the same time, the design of the inflation targeting framework matters. Hence, there is a need to pursue import-substitution strategies to strengthen the domestic industrial base with all the associated multiplier effects throughout the economy and this contributes to the reduction of the worsening trade deficits.

Use of meteorological parameters for forecasting scarlet fever morbidity in Tianjin, Northern China

The scarlet fever incidence has increased drastically in recent years in China. However, the long-term relationship between climate variation and scarlet fever remains contradictory, and an early detection system is lacking. In this study, we aim to explore the potential long-term effects of variations in monthly climatic parameters on scarlet fever and to develop an early scarlet-fever detection tool. Data comprising monthly scarlet fever cases and monthly average climatic variables from 2004 to 2017 were retrieved from the Notifiable Infectious Disease Surveillance System and National Meteorological Science Center, respectively. We used a negative binomial multivariable regression to assess the long-term impacts of weather parameters on scarlet fever and then built a novel forecasting technique by integrating an autoregressive distributed lag (ARDL) method with a nonlinear autoregressive neural network (NARNN) based on the significant meteorological drivers. Scarlet fever was a seasonal disease that predominantly peaked in spring and winter. The regression results indicated that a 1 °C increment in the monthly average temperature and a 1-h increment in the monthly aggregate sunshine hours were associated with 17.578% (95% CI 7.674 to 28.393%) and 0.529% (95% CI 0.035 to 1.025%) increases in scarlet fever cases, respectively; a 1-hPa increase in the average atmospheric pressure at a 1-month lag was associated with 12.996% (95% CI 9.972 to 15.919%) decrements in scarlet fever cases. Based on the model evaluation criteria, the best-performing basic and combined approaches were ARDL(1,0,0,1) and ARDL(1,0,0,1)-NARNN(5, 22), respectively, and this hybrid approach comprised smaller performance measures in both the training and testing stages than those of the basic model. Climate variability has a significant long-term influence on scarlet fever. The ARDL-NARNN technique with the incorporation of meteorological drivers can be used to forecast the future epidemic trends of scarlet fever. These findings may be of great help for the prevention and control of scarlet fever.

Industrialization, Freight Transport and Environmental Quality: Evidence from Belt and Road Initiative Economies

Belt and Road initiative has been proposed by China to initiate the cooperation among relevant countries in sector of energy and Trade. The study investigate highlighting the relationship between industrial value added per capita, transport freight and CO₂ emission among the partner countries of Belt and Road initiatives by using panel of 33 economies from 1986-2017. Study includes panel autoregressive distributed lag model (ARDL) to estimate the long-run relationship among variables. Estimated results of pool mean group (PMG) indicates that increase in industrial value added per capita and transport freight deteriorates the quality of environment in long-run. However, short-run results of granger causality reveals positive and unidirectional causality running from industrial value added per capita to emission of CO₂ while transport freight and CO₂ emission shows bidirectional causality. The study emphasized to formulate environment friendly policies in industrial and transport sector.