The influence of climate change on the sesame yield in North Gondar, North Ethiopia: Application Autoregressive Distributed Lag (ARDL) time series model.

Sesame is a major annual oil crop that is grown practically everywhere in tropical and subtropical Asia, as well as Africa, for its very nutritious and tasty seeds. Rising temperatures, droughts, floods, desertification, and weather all have a significant impact on agricultural production, particularly in developing countries like Ethiopia. Therefore, the main objective of this study is to examine the influence of climate change on the sesame yield in North Gondar, North Ethiopia, by using the autoregressive distributed Lag (ARDL) time series model. This study employed climate data from the Bahirdar Agrometeorological Center and secondary data on sesame production from the Ethiopian Statistical Service, spanning 36 years, from 1987 to 2023. Autoregressive Distributed LAG (ARDL) includes diagnostic tests for both short- and long-term autoregressive models. The results for the long-run and short-run elastic coefficients show a significant positive association between temperatures and sesame yield. Sesame yield and rainfall have a significant negative long-run and short-run relationship in North Gondar, North Ethiopia. ARDL results confirm that temperature and rainfall have significant effects on sesame productivity. Temperature had a considerable favorable effect on sesamen production, but rainfall had a negative effect in North Gondar, Ethiopia. Based on the evidence acquired from our study, we made several policy recommendations and suggestions to government officials, policymakers, new technologies, researchers, policy development planners, and other stakeholders in order to develop or implement new technology to halt its production and direct adaptation measures in light of the certainty of global warming and the characteristics of climate-dependent agricultural production.

Theoretical and Experimental Analysis of Single-Arm Bimodal Plasmo-Photonic Refractive Index Sensors.

In this paper, we study both theoretically and experimentally the sensitivity of bimodal interferometric sensors where interference occurs between two plasmonic modes with different properties propagating in the same physical waveguide. In contrast to the well-known Mach-Zehnder interferometric (MZI) sensor, we show for the first time that the sensitivity of the bimodal sensor is independent of the sensing area length. This is validated by applying the theory to an integrated plasmo-photonic bimodal sensor that comprises an aluminum (Al) plasmonic stripe waveguide co-integrated between two accessible SU-8 photonic waveguides. A series of such bimodal sensors utilizing plasmonic stripes of different lengths were numerically simulated, demonstrating bulk refractive index (RI) sensitivities around 5700 nm/RIU for all sensor variants, confirming the theoretical results. The theoretical and numerical results were also validated experimentally through chip-level RI sensing experiments on three fabricated SU-8/Al bimodal sensors with plasmonic sensing lengths of 50, 75, and 100 µm. The obtained experimental RI sensitivities were found to be very close and equal to 4464, 4386, and 4362 nm/RIU, respectively, confirming that the sensing length has no effect on the bimodal sensor sensitivity. The above outcome alleviates the design and optical loss constraints, paving the way for more compact and powerful sensors that can achieve high sensitivity values at ultra-short sensing lengths.

Air, land, and water pollutants and public health expenditures: Empirical data from selected EU countries in the transport sector.

The increase in economic activity, particularly in transport, leads to a significant increase in emissions of pollutants, such as ammonia, arsenic and cadmium, at the European Union (EU) level. This can seriously impact human health and, consequently, public health spending. Based on data from 15 European Union countries from 1992 to 2020, a panel co-integration approach is used to study these pollutants' short- and long-term co-movements and per capita health expenditure. The results show a long-term relationship between ammonia, arsenic and cadmium emissions and per capita health spending, as they are panel-cointegrated. Ammonia and cadmium emissions exert a statistically significant positive effect on health expenditure in the short run, and arsenic emissions have a statistically significant positive impact in the long run. The forecast assessment of reductions in health spending resulting from policies to reduce emissions of air, land and water pollutants, such as ammonia, arsenic and cadmium, from the transport sector supports investments in its policies that reduce pressure on health spending. The reduction in annual healthcare expenditure is greater when these reductions are made sooner and more severely. Indeed, varying the reduction in emissions for each pollutant by 10% and 100%, respectively, from the first year for all countries over a 3-year period results in an average annual reduction in health spending of 2.05% and 51.02%, respectively. However, if we wait until the third year, the annual reduction is only 0.77% and 17.63% respectively.

The role of renewable energy and total factor productivity in reducing carbon emissions: A case of top-ranked nations in the renewable energy country attractiveness index.

On the one hand, economies, particularly developing ones, need to grow. On the other hand, climate change is the most pressing issue globally, and nations should take the necessary measures. Such a complex task requires new theoretical and empirical models to capture this complexity and provide new insights. Our study uses a newly developed theoretical framework that involves renewable energy consumption (REC) and total factor productivity (TFP) alongside traditional factors of CO2 emissions. It provides policymakers with border information compared to traditional models, such as the Environmental Kuznets Curve (EKC), being limited to income and population. Advanced panel time series methods are also employed, addressing panel data issues while producing not only pooled but also country-specific results. 20 Renewable Energy Country Attractiveness Index (RECAI) nations are considered in this study. The results show that REC, TFP, and exports reduce CO2 emissions with elasticities of 0.3, 0.4, and 0.3, respectively. Oppositely, income and imports increase emissions with elasticities of 0.8 and 0.3. Additionally, we show that RECAI countries are commonly affected by global and regional factors. Moreover, we find that shocks can create permanent changes in the levels of the factors but only temporary changes in their growth rates. The main policy implication of the findings is that authorities should implement measures boosting TFP and REC. These factors are driven mainly by technological progress, innovation, and efficiency gains. Thus, they can simultaneously reduce emissions while promoting long-run green economic growth, which addresses the complexity mentioned above to some extent.

The Drivers of Suicides in Turkey: An Econometric Approach.

This research examines the long-term drivers of suicides in Turkey, focusing on the effects of uncertainty and the Misery Index. In this regard, this study utilizes yearly data covering the 1980-2019 period. Additionally, it employs the augmented autoregressive distributed lag (AARDL) approach to confirm the cointegrating relationships in the proposed models and estimate the long-term effects of selected determinants on suicides. The empirical analysis produced the following long-run findings: First, cointegration exists between suicide and its determinants in Turkey. Second, uncertainty and the Misery Index significantly increase suicides. Third, tobacco consumption is positively associated with suicides. Fourth, economic growth and industrial output significantly inhibit suicides in Turkey. These findings underline that in developing open economies like Turkey, improving socio-economic factors can play a significant role in curbing suicidal behavior.

Health care expenditure and income in Canada: Evidence from panel data.

This paper investigates the long-run relationship between health care expenditures (HCE) and income using Canadian provincial data spanning a period of 40 years from 1981 to 2020. We study the non-stationary and cointegration properties of HCE and income and estimate the long-run income elasticities of HCE. Using heterogeneous panel models that incorporate cross-section dependence via unobserved common correlated factors to capture global shocks, we estimate long-run income elasticities that lie in the 0.11-0.16 range. Our results indicate that health care is a necessity good for Canada. These elasticity estimates are much smaller than those estimated in other studies for Canada. We find that HCE and income in Canada are cointegrated and that short-run changes in federal transfers significantly and positively affect HCE.

Environmental impact of globalization: The case of central and Eastern European emerging economies.

Against the backdrop of piling environmental concerns in the modern era of globalization, this study aims to check the validity of the Pollution Haven Hypothesis (PHH) in Eastern European emerging countries and the relevance of globalization. The study targets to reduce the lack of consensus on the globalization-economic complexity-environment in European countries. Besides, we also intend to explore the existence of an N-shaped economic complexity-related Environmental Kuznets Curve (EKC) controlling for the bearing of renewable energy on environmental degradation. For analytical purposes, both parametric and non-parametric quantile regression approaches are employed. Overall, we find a non-linear relationship between economic complexity and carbon emissions, and N-shaped EKC is verified. Globalization and renewable energy consumption boost and inhibit emissions, respectively. More importantly, the results confirm the moderating role of economic complexity in neutralizing the carbon emissions-boosting effect of globalization. On the other hand, the non-parametric findings show that the N-shaped EKC hypothesis does not hold for high emissions quantiles. Furthermore, for all emissions quantiles, it is found that globalization boosts emissions, economic complexity, and globalization jointly curbs emissions and renewable energy curbs emissions. Based on the overall findings, some vital environmental development policies are recommended. The conclusions support shaping policy options promoting economic complexity and renewable energy as key factors in mitigating carbon emissions.

The asymmetric effect of agriculturalization toward climate neutrality targets.

Agricultural activities immensely contribute to greenhouse gas emissions (GHGs). This study investigates the heterogeneous effect of agricultural production (AGRIP) on three major GHGs emissions, i.e., carbon dioxide (CO2), nitrous oxide (N2O), and methane (CH4) under the Environmental Kuznets Curve (EKC) framework using a balanced panel data of 90 countries from the period 1991 to 2019. Second-generation panel unit root and cointegration tests are conducted to account for cross-sectional dependence. The findings suggest that there is a long run equilibrium among target variables. Evidence from panel quantile regression suggests that AGRIP significantly reduced CO2 emissions, and the effect is stronger in lower quantiles (least carbon emitters). On the other hand, AGRIP increases N2O and CH4 emissions in all quantiles. However, AGRIP is homogeneously distributed across N2O quantiles while the effect is stronger in higher quantiles (high methane emitters) in the case of CH4 model. Concerning agricultural trade, exports impede CO2 emissions but increase N2O and methane emissions. Agricultural imports are positively associated with all GHGs emissions. The effect of agricultural trade is largely heterogeneous and varies across different quantile levels of GHGs emissions. The EKC is fully valid for CO2 and N2O but not for the methane emissions model. Based on the results, it is suggested that high GHGs emitter should shift their agricultural activities from traditional to sustainable approaches along with green trade policies to achieve climate neutrality targets.

Identifying the nexus among environmental performance, digital finance, and green innovation: New evidence from prefecture-level cities in China.

Globally, nations are increasingly focusing on green innovation in their environmental protection efforts as part of sustainable development, and digital finance is playing a vital role in enhancing green innovation. Employing annual data from 220 prefecture-level cities between 2011 and 2019, we empirically analyze the connections among environmental performance, digital finance, and green innovation via the Karavias panel unit root test with structural breaks, the Gregory-Hansen structural break cointegration test and pooled mean group (PMG) estimation. The following four points are the key conclusions: (1) The results support cointegration links between these variables when structural breaks are considered. (2) The PMG estimation outcomes indicate that green innovation and digital finance may have a favorable long-term effect on environmental performance. (3) For better environmental performance and more green innovation, the level of digitalization of digital finance is crucial. (4) The potential of digital finance and green innovation to improve environmental performance has not been fully realized in the western region of China.

Do changes in land use, water bodies, and grazing pastures have a detrimental influence on environmental quality? Opportunities and threats to long-term growth.

Land use activities mainly for economic and agricultural purposes have converted one third to one half of our planet's land surface into urban expansion and agricultural practice, which has had significant impacts on natural ecosystems, food production, and environmental quality, attracting the attention of researchers and policymakers. Consequently, land use is emerging as a fundamental issue in global environmental change and sustainable development. This study represents an addition to the prevailing literature by investigating the asymmetric impacts of land-use and land-cover changes on environmental quality in Pakistan using time series data from 1961 to 2016. Carbon dioxide (CO2) emissions were deemed a dependent variable (a proxy for environmental quality), whereas built-up land, cropland, water bodies, and grazing land were considered independent. A nonlinear ARDL bound testing technique (NARDL) was used to investigate dynamic cointegration among the study variables. Moreover, this study used the BDS test and structural break unit root test to confirm nonlinearity and stationarity of the data set. The results confirm that the variables exhibit asymmetrical co-integration. There is a symmetric unidirectional causation, running from built-up land and grazing land towards CO2 emissions with coefficients of 10.570 and 17.045, respectively. Furthermore, asymmetric causality shows that any positive shocks to built-up land (6.134) and water bodies (20.335) significantly cause CO2 emissions. Similarly, a negative shock to grazing land (16.470) also causes CO2 emissions. By contrast, a neutral effect was found between cropland and CO2 emissions.

From Heroes to Scoundrels: Exploring the effects of online campaigns celebrating frontline workers on COVID-19 outcomes.

This paper examines the effects of online campaigns celebrating frontline workers on COVID-19 outcomes regarding new cases, deaths, and vaccinations, using the United Kingdom as a case study. We implement text and sentiment analysis on Twitter data and feed the result into random regression forests and cointegration analysis. Our combined machine learning and econometric approach shows very weak effects of both the volume and the sentiment of Twitter discussions on new cases, deaths, and vaccinations. On the other hand, established relationships (such as between stringency measures and cases/deaths and between vaccinations and deaths) are confirmed. On the contrary, we find adverse lagged effects from negative sentiment to vaccinations and from new cases to negative sentiment posts. As we assess the knowledge acquired from the COVID-19 crisis, our findings can be used by policy makers, particularly in public health, and prepare for the next pandemic.

The Rise and Fall of Financial Flows in EU 15: New Evidence Using Dynamic Panels with Common Correlated Effects.

This paper assesses capital mobility for a panel of 15 European countries for the period 1970-2019 using dynamic common correlated effects modeling as proposed in Chudik and Pesaran (J Econ 188(2):393-420, 2015). In particular, we account for the existence of cross section dependence, slope heterogeneity, nonstationarity and endogeneity in a multifactor error correction model (ECM) that includes one homogeneous break. The analysis also identifies the heterogeneous structural breaks affecting the relationship for each of the individual countries. The ECM setting allows for a complete assessment of the domestic saving-investment relationship in the long-run as well as two other elements usually neglected: short-run capital mobility and the speed of adjustment. When we account for a single homogeneous break, this is found at the euro inception. We obtain that long-run capital mobility is high but not perfect yet. We also provide empirical evidence for the Ford and Horioka (Appl Econ Lett, 24(2), 95-97, 2017)'s hypothesis, who argue that goods market integration is a necessary condition to obtain zero correlation between domestic saving-investment. Our results stress the role played by the euro as a booster for both financial and real integration. However, a complete degree of economic integration has not been fully achieved. Short-run capital was highly mobile for the whole period, with some exceptions, coinciding with turmoil episodes. Additionally, from the application of the CS-DL threshold analysis proposed by Chudik et al. (Adv Econ, 36, 85-135, 2016), we find that economic risk and openness play a key role in capital mobility.

Deficit sustainability and fiscal theory of price level: the case of Italy, 1861-2020.

We test sustainability of the Italian government deficit over the period 1861-2020 using the fiscal theory of the price level (FTPL). This approach takes into account monetary and fiscal policy interactions and assumes that fiscal policy may determine the price level even if monetary authorities pursue an inflation-targeting strategy. We consider a cointegrated model with multiple structural changes to characterize the sustainability of public finances and the prevalence of monetary or fiscal dominance during subperiods. We also use recursive unit root tests for explosiveness to test fiscal sustainability and to detect episodes of potential explosive behaviour in Italian public debt. We find two structural changes for the public debt and one change in the primary budget surplus, the alternation of monetary and fiscal dominant regimes, as well as evidence of bubbles related to three episodes of the Italian fiscal performance. Our results reveal the sensitiveness of the primary balance and the debt paths to shocks hitting fiscal, macroeconomic, and financial variables.

The asymmetric nexus between air pollution and COVID-19: Evidence from a non-linear panel autoregressive distributed lag model.

The emergence of a new coronavirus (COVID-19) has become a major global concern that has damaged human health and disturbing environmental quality. Some researchers have identified a positive relationship between air pollution (fine particulate matter PM2.5) and COVID-19. Nonetheless, no inclusive investigation has comprehensively examined this relationship for a tropical climate such as India. This study aims to address this knowledge gap by investigating the nexus between air pollution and COVID-19 in the ten most affected Indian states using daily observations from 9th March to September 20, 2020. The study has used the newly developed Hidden Panel Cointegration test and Nonlinear Panel Autoregressive Distributed Lag (NPARDL) model for asymmetric analysis. Empirical results illustrate an asymmetric relationship between PM2.5 and COVID-19 cases. More precisely, a 1% change in the positive shocks of PM2.5 increases the COVID-19 cases by 0.439%. Besides, the estimates of individual states expose the heterogeneous effects of PM2.5 on COVID-19. The asymmetric causality test of Hatemi-J's (2011) also suggests that the positive shocks on PM2.5 Granger-cause positive shocks on COVID19 cases. Research findings indicate that air pollution is the root cause of this outbreak; thus, the government should recognize this channel and implement robust policy guidelines to control the spread of environmental pollution.

Combined Use of Cointegration Analysis and Robust Outlier Statistics to Improve Damage Detection in Real-World Structures.

Due to the need for controlling many ageing and complex structures, structural health monitoring (SHM) has become increasingly common over the past few decades. However, one of the main limitations for the implementation of continuous monitoring systems in real-world structures is the effect that benign influences, such as environmental and operational variations (EOVs), have on damage sensitive features. These fluctuations may mask malign changes caused by structural damages, resulting in false structural condition assessment. When damage identification is implemented as novelty detection due to the lack of known damage states, outliers may be part of the data set as the result of the benign and malign factors mentioned above. Thanks to the developments in the field of robust outlier detection, the current paper presents a new data fusion method based on the use of cointegration and minimum covariance determinant estimator (MCD), which allows us to visualize and to classify outliers in SHM data, depending on their origin. To validate the effectiveness of this technique, the recent case study of the KW51 bridge has been considered, whose natural frequencies are subjected to variations due to both EOVs and a real structural change.

Investigating the role of export product diversification for renewable, and non-renewable energy consumption in GCC (gulf cooperation council) countries: does the Kuznets hypothesis exist?

This study explores the effects of renewable and nonrenewable energy demand on export product diversification, economic growth, natural resources, human capital, and trade in GCC (Gulf Cooperation Council) countries using data of six countries from 1990 to 2019. The empirical analysis integrates the panel unit root tests (IPS and CIPS), panel quantile regression, and fully modified OLS models. The empirical results confirm that there exists a significant negative relationship between renewable energy and export diversification; signifying that diversification of products will reduce renewable energy. Similarly, when compared to the square of export product diversification, it shows a positive and significant correlation. The empirical findings highlighted the presence of Kuznets's hypothesis between export product diversification, renewable, and non-renewable energy consumption. Furthermore, the findings suggest that natural resources and economic growth may increase overall energy consumption in GCC countries. It implies an important policy suggestion that encouraging export diversification will reduce GCC countries' reliance on oil to meet energy demand.

The Effect of Paid Maternity Leave on Child Health in the Middle East and North Africa.

Background: The paid maternity leave is one of the factors affecting the health status of children, but this maternity leave in the Middle East and North Africa is not only less than in developed countries but also the mortality rate of children under 5 years is higher in these countries. Therefore, this study was conducted to investigate the paid maternity leave on children's health in the Middle East and North Africa. Methods: This descriptive-analytical and applied study was conducted by Panel data regression method with cross-sectional dependence and Common Correlated Effect Mean Group (CCEMG) and Augmented Mean Group (AMG) estimators for 2000 and 2019. The statistical population was 12 countries in the Middle East and North Africa, and annual time series data were extracted from World Bank databases. The study models, cross-sectional dependency tests, Pesaran unit root, Westerlund cointegration, and other required tests were estimated in Stata 16 software. Results: The average paid maternity leave for 12 countries in the Middle East and North African countries between 2000 and 2019 was 68.8 days, and in 2019, it was 78 days. The effect of maternity leave on infant mortality rate in the Augmented Mean Group and Common Correlated Effect Mean Group were -0.0018 and -0.0006, respectively, and, the effect on the under-5 mortality rate in the mentioned methods was -0.0023 and -0.0007, respectively. The coefficient of female labor force participation rate on infant mortality rate was -0.056 and the under-5 mortality rate was -0.049. Conclusion: Increasing maternity leave had a negative effect on infant and child mortality rates. Also, health expenditures and female labor force participation rates had a negative effect, and carbon dioxide production had a positive effect on infant mortality rates; therefore, policies to increase paid maternity leave for mothers, as well as policies to increase maternal employment, are proposed to increase fertility while increasing the health of infants.

Compact Mid-Infrared Gas Sensing Enabled by an All-Metamaterial Design.

The miniaturization of mid-infrared optical gas sensors has great potential to make the "fingerprint region" between 2 and 10 µm accessible to a variety of cost-sensitive applications ranging from medical technology to atmospheric sensing. Here we demonstrate a gas sensor concept that achieves a 30-fold reduction in absorption volume compared to conventional gas sensors by using plasmonic metamaterials as on-chip optical filters. Integrating metamaterials into both the emitter and the detector cascades their individual filter functions, yielding a narrowband spectral response tailored to the absorption band of interest, here CO2. Simultaneously, the metamaterials' angle-independence is maintained, enabling an optically efficient, millimeter-scale cavity. With a CO2 sensitivity of 22.4 ± 0.5 ppm·Hz-0.5, the electrically driven prototype already performs at par with much larger commercial devices while consuming 80% less energy per measurement. The all-metamaterial sensing concept offers a path toward more compact and energy-efficient mid-infrared gas sensors without trade-offs in sensitivity or robustness.

Role of political risk to achieve carbon neutrality: Evidence from Brazil.

The current research assesses the impact of political risk on carbon dioxide (CO2) emissions in Brazil while controlling the role of financial development, GDP growth, trade openness, and technological innovation. In doing so, the quarterly dataset from 1990 to 2018 is utilized with Bayer and Hanck cointegration, dynamic ordinary least square (DOLS) and canonical correlation regression (CCR), and frequency-domain causality tests. The cointegration test revealed a long-run association amongst the variables of interest. Furthermore, the outcomes from the DOLS and CCR revealed that increasing financial development, technological innovation, trade openness, and real growth increase CO2 emissions while a better political environment reduces environmental pollution.

The carbon dioxide neutralizing effect of energy innovation on international tourism in EU-5 countries under the prism of the EKC hypothesis.

Mitigation of carbon dioxide emissions has become an utmost important global agenda, keeping into consideration the associated environmental hardships. As a result, it is important to unearth the factors which can neutralize carbon emissions to transform the world economy into a low-carbon one. Against this backdrop, this study explores the carbon dioxide neutralizing effects of economic growth, international tourism, clean energy promotion, and technological innovation in the context of five European Union (EU-5) nations during the 1990-2015 period. This study's main contribution is in terms of its approach to test the interaction effect between foreign direct investment (FDI) inflows and energy innovation on carbon dioxide emissions. The econometric analysis chronologically involves the employment of unit root, cointegration, causality, and regression methods. Overall, the findings support the inverted-U-shaped economic growth-carbon dioxide emissions nexus to verify the Environmental Kuznets Curve (EKC) hypothesis. Besides, the Pollution Haven Hypothesis in the context of the selected panel is also verified as higher FDI inflows are seen to boost the carbon dioxide emission levels. The results also confirm that energy innovation moderates the harmful effect of air transport (a proxy for international tourism) on carbon dioxide emissions during the developing stage of the tourism industry. On the other hand, renewable energy promotion is found to curb carbon dioxide emissions. These findings suggest that the European governments need to enhance investments in their respective renewable energy sectors and simultaneously ensure the development of clean industries, which can collectively help these nations become carbon-neutral in the future.