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Introduction: Road traffic injuries stands as a major concern globally, as they result in significant loss of life, economic impact, and erode trust in government and societal safety. The influence of weather on road traffic safety is undeniable, impacting road conditions, individuals, and vehicles. However, the specific influence of weather on road traffic casualties has seldom been explored. Method: This study assesses the effect of weather factors on road traffic casualties in China from 2006 to 2021. Vector error correction models (VECM) were utilized to determine the Granger causality between weather factors and covariates. Furthermore, panel autoregressive distribution lag models (ARDL) were applied to quantify the association between weather factors and road traffic casualties. Results: The findings indicate that rainfall and temperature exert a short-term negative impact on casualty risk, which intriguingly becomes positive in the long term. A standout discovery is the significant role of health investments, which are shown to reduce casualty numbers in both the short and long-terms. In the long run, the gross domestic product significantly enhances casualties, while expressway mileage notably decreases them. Conclusions: These results demonstrate the significant influence of weather on road traffic casualties and highlight the critical roles played by factors such as gross domestic product, health investment, and expressway mileage. The evidence presented in the study underscores the urgent need for more effective strategies to mitigate road traffic casualties. Thus, some effective measures are proposed to reduce road traffic casualties. This study is conducive to the improvement of traffic in severe weather in China and provides guidance for traffic management departments.

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The existing scholarly discourse surrounding the energy transition has long operated on the assumption of perfect displacement of non-renewable energy. However, an evolving set of studies highlights an intricate web of inefficiencies and complexities that prevent the perfect displacement of fossil fuel energy with renewable energy production. Since this could carry serious implications for the environmental targets of several economies, it is crucial to accurately and continuously measure the actual extent of fossil fuel displacement. Within this framework, this study empirically investigates the extent of non-renewable energy displacement by renewable energy for a balanced panel of seven Asia-Pacific (APAC) countries between 1989 and 2015. The outcome function also controls for globalisation, real GDP per capita, and crude oil prices. After implementing the necessary diagnostics, the panel cointegration establishes a significant long-run relationship among the selected variables. The PMG-ARDL estimation indicates that renewable energy production and globalisation significantly reduce the fossil fuel energy production, whereas real GDP per capita and crude oil prices induce it positively. However, the coefficient of renewable energy production is only - 0.39, indicating that more than 2.5 units of renewable electricity are necessary to displace a unit of non-renewable energy. As such, this study concludes that the current energy transition in Asia-Pacific region is not perfect. These results are robust to the usage of the FGLS estimation technique. The study suggests the adoption of a new energy transition that allows greater displacement of fossil fuel energy as well as gradual reduction in overall energy use.

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Using the QARDL approach and data from January of 2010 to May of 2022, we explore how renewable energy consumption affects CO2 emissions in the USA. Long-term analysis reveals a negative link between these variables, while only lower quantile levels show short-term statistical significance. Integrating technical innovation (measured by patents) in our QARDL model shows substantial reduction in CO2 emissions, with varying effects over time. Interestingly, only renewable energy consumption, not technical innovation, significantly impacts CO2 emissions at lower quantile levels. These findings emphasize the crucial role of renewable energy in reducing both short-term and long-term CO2 emissions and offer policymakers valuable insights for shaping effective energy strategies to combat emissions and promote sustainability in the USA.

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This study aims to focus on identifying how unemployment and inflation and their related aspects affect Palestinian economic growth. The study employed the Autoregressive Distributed Lag (ARDL) model employing annual time series data for estimate and result analysis. The ARDL model was estimated in both long and short run as follows; on one hand, the results showed that there is a potential negative impact of aspects-related unemployment on economic growth in Palestine over the long-term. On the other hand, over the long term also, it can be noticed that there is a positive impact of aspects-related inflation on economic growth in the state of Palestine. In the short term, a significant negative effect of unemployment (UN) was discovered on economic growth, while there was no effect of inflation (INF), despite being positive. The error-correction model (ECM) has been adopted in this model, and the results revealed that the ECM co-efficient [ECM (-1)] was positive and equaled to (0.0183) and therefore was not significant. Based on the results obtained, the study recommended that there a need to stabilize inflation through effective price control mechanisms that can enhance macroeconomic conditions, increase investor confidence, stimulate economic growth, and work to create opportunities for the full use of human resources by stimulating self-employment to engage in entrepreneurial activities according to the qualifications of graduates. Finally, it equips population, especially youth, with relevant skills to increase productivity and reduce unemployment.

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The endeavor to implement the 2030 Agenda of national and international stakeholders became increasingly impetuous, considering the wide range of uncertainties and risks. The new humans-centered development model built on the prominence of environmental and social values seeks to reinforce communities' resilience and mitigate environmental risks, leaving no one behind. For this to happen, solid and effective institutions, the right environmental policies, and a safe statutory labor framework are the sine qua non. In this study, we evaluated the effects of informality, institutional quality, and renewable energy consumption on ecological footprint of two groups of emerging countries from Europe and Asia from 2002 to 2018. Our results by PMG-ARDL approach highlight dissimilarities between the two groups, showing greater heterogeneity. In this sense, informality is found to have positive and significant effects on ecological footprint in the long run in emerging European countries. In contrast, the effect is negative for emerging Asian countries. In the short run, the effects are less critical in the studied countries between the two groups. Institutional quality variables impacted environmental quality differently. In this sense, it is detrimental for policymakers to consider concerted measures to decrease institutional vulnerabilities and reduce the level of the informal economy. The outcome of this study concurs with a complete awareness of the importance of informality and institutional quality to mitigate social and environmental risks conjunctively.

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Studying historical response of crops to weather conditions at a finer scale is essential for devising agricultural strategies tailored to expected climate changes. However, determining the relationship between crop and climate in Mississippi (MS) remains elusive. Therefore, this research attempted to i) estimate climate trends between 1970 and 2020 in MS during the soybean growing season (SGS) using the Mann-Kendall and Sen slope method, ii) calculate the impact of climate change on soybean yield using an auto-regressive distributive lag (ARDL) econometric model, and iii) identify the most critical months from a crop-climate perspective by generating a correlation between the detrended yield and the monthly average for each climatic variable. Specific variables considered were maximum temperature (Tmax), minimum temperature (Tmin), diurnal temperature range (DTR), precipitation (PT), carbon dioxide emissions (CO2), and relative humidity (RH). All required diagnostic-tests i.e., pre-analysis, post-analysis, model-sensitivity, and assessing the models' goodness-of-fit were performed and statistical standards were met. A positive trend in Tmin (+0.25 °C/decade), and a negative trend in DTR (-0.18 °C/decade) was found. Although Tmax, PT, and RH showed non-significant trends, numerical changes were noted as +0.11 °C/decade, +3.03 mm/decade, and -0.06 %/decade, respectively. Furthermore, soybean yield was positively correlated with Tmin (in June and September), PT (in July and August), and RH (in July), but negatively correlated with Tmax (in July and August) and DTR (in June, July, and August). Soybean yield was observed to be significantly reduced by 18.11 % over the long-term and by 5.51 % over the short-term for every 1 °C increase in Tmax. With every unit increase in Tmin and CO2 emissions, the yield of soybeans increased significantly by 7.76 % and 3.04 %, respectively. Altogether, soybeans in MS exhibited variable sensitivity to short- and long-terms climatic changes. The results highlight the importance of testing climate-resilient agronomic practices and cultivars that encompass asymmetric sensitivities in response to climatic conditions of MS.

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In an asymmetric information environment, investors diversify their investments to minimize risk and maximize their wealth. Such diversification ranges from one market to another market and from one country to another country. Investors prefer foreign portfolio investment over foreign direct investment because of the economy's turmoil, changes in macroeconomic indicators, and market liberation. This study analyzes the dynamic relationship among stock market volatility, foreign portfolio investment, and macroeconomic indicators (foreign exchange rate, interest rate, and Gross Domestic Product) using the dynamic long-run Auto-regressive Distributed Lag (ARDL) model concerning the Pakistan environment. This study also considers the impact of multiple structural breaks, representing variables' endogenous and exogenous shocks. The secondary data is used from Oct. 01, 2009, to Sept. 30, 2019, with monthly frequency. The results indicate a co-integration between SMV, FPI, FXR, IR, and GDP. In short-run analyses, the error correction term is statistically significant, while in the long run, the SMV, FPI, and FXR are not impacted. As no evidence of volatility has been found between SMV and FPI, unidirectional or bi-directional policies can be devised to further attract the new FPI for strengthening the foreign reserves, the balance of payments, and other macroeconomic variables. Additionally, investors should update their knowledge based on considering the endogenous and exogenous shocks on the SMV.

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The continuous increase in government expenditure in the last three decades without a commensurate improvement in all known indicators of development has generated heated debates among scholars as to the justification for the persistent rise in the annual expenditure of the government. Therefore, this study examined the effects of government sectoral spending on human development in Nigeria using annual data spanning the period 1986-2021. This study contributed to the literature by examining the effects of government sectoral spending on human development using a robust human development index that captures the multifaceted state of economic development in terms of educational attainment, life expectancy and per capita income, unlike previous studies that concentrated on aggregate government spending and used the gross domestic product as an indicator of development. Surprisingly, however, results from the Autoregressive Distributed Lag (ARDL) model employed indicated that both in the short and long run, there is no link between government sectoral spending and human development in Nigeria. Although, outcomes from ECMs suggest that government sectoral spending may affect human development in the long run.

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Carbon dioxide emissions (CO2e) which is caused by energy use contributes to the global average surface temperature increase by 1.5 °C as compared to the mid-1800s which is causing a certain change in the climate and becoming an adverse effect on health and economy. The relationship between health status, CO2e, and energy use has yet to be thoroughly investigated in the top 20 highest emitting economies. The data from 2000 to 2019 is analyzed by using advanced techniques of cross-sectional augmented distributed lag (CS-DL) and cross-sectional augmented autoregressive distributed lag (CS-ARDL) which take into consideration crucial elements of panel data, namely dynamics, heterogeneity, and cross-sectional dependence. Moreover, cross-sectional augmented error correction method (CS-ECM) and the common dynamic process of the augmented mean group (AMG) are applied for robustness checks. The empirical findings revealed that (i) CO2e weakens the health status only in the short-run, whereas health expenditure improves the health status in the both short- and long-runs, while economic growth is not contributing to the health status in the both short- and long-runs; (ii) health expenditure and economic growth only help to mitigate CO2e in the long-run, whereas energy use causes CO2e in the both short- and long-runs; (iii) energy use causes high economic growth in the both short- and long-runs, whereas CO2e aids economic growth in the short-run but is extremely damaging to economic growth in the long-run, while in the both short- and long-runs health expenditure is not aiding the economic growth. This study provides policy recommendations on improving human health by advocating massive health expenditures, CO2e easing, promoting renewable energy use or low-emission energy, and steering the economy toward green economic growth.

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Considering the alarming rate of environmental degradation, all countries are looking for solutions to close their environmental gaps in order to ensure long-term sustainability. In order to achieve green ecosystems, economies seeking clean energy are motivated to embrace environmentally friendly practices that can support resource efficiency and sustainability. In this sense, the current paper addresses measuring the link between CO2 emissions, economic growth (GDP), renewable and non-renewable energy (RE), tourism, financial development, foreign direct investment, and urbanization in the United Arab Emirates (UAE). The aim of this paper is to empirically evaluate the link between CO2 emissions and macroeconomic factors in the UAE. The UAE was chosen as the country for the case study as the UAE is one of the world's richest oil-based economies and one of the countries with the highest per capita income, adopts sustainable technologies, and has signed the Paris agreement supporting the transition to clean energy. To verify the environmental Kuznets curve (EKC) for UAE, the timespans for the years 1990-2021 have been chosen according to data availability. The long-run coefficients supported the EKC hypothesis of an inverted U shape for income and CO2 emissions, according to the findings. Notably, urbanization and financial development both reduce pollution while foreign direct investment increases environmental pollution. The study recommended the creation of more environmental policies to promote sustainable business operations and nationwide green awareness, increase the use of clean energy technology, reduce energy intensity, and achieve a net zero carbon target.

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Sustainable economic development involves both improving the socio-economic quality of life in the present and improving environmental conditions for future generations. The main objective of the study was to investigate the impact of the determinant macroeconomic variables, including economic complexity, construction, energy consumption, and housing sector expenditures, on environmental changes in Iran over the period 1991 to 2019; using the autoregressive distributed lag (ARDL) model. The results show that at the macro level, an increase in the economic complexity index can reduce pollution in both the short and long term. The effects of further expansion of the housing sector and household energy consumption on pollution are positive and significant in both the short and long run. The error correction model (ECM = 0.65) shows that 65% of the imbalances in each period are corrected in the next period. Based on the obtained results, it is recommended to take serious measures to improve the structure of production and increase the country's economic complexity index; in other words, to produce goods with higher knowledge and technology and apply policies to improve the level of household energy consumption leading to air pollution reduction. Jel: Q40, Q48, Q50, Q52.

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We explore the impacts of economic and financial dislocations caused by COVID-19 pandemic shocks on food sales in the United States from January 2020 to January 2021. We use the US weekly economic index (WEI) to measure economic dislocations and the Chicago Board Options Exchange volatility index (VIX) to capture the broader stock market dislocations. We validate the NARDL model by testing a battery of models using the autoregressive distributed lags (ARDL) methodology (ARDL, NARDL, and QARDL specifications). Our study postulates that an increase in WEI has a significant negative long-term effect on food sales, whereas a decrease in WEI has no statistically significant (long-run) effect. Thus, policy responses that ignore asymmetric effects and hidden cointegration may fail to promote food security during pandemics.

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This paper examines for the first time the relationship between CO2 emissions and the consumption of renewable and non-renewable energy in Uzbekistan, spanning the period 1985-2020. The analysis uses the Autoregressive Distributed Lags (ARDL) model to estimate the long-run dynamic multipliers and short-run elasticity coefficients of energy consumption variables. Economic factors, such as GDP, are excluded in the analysis as they may cause multicollinearity problems. The empirical results document that in the short- and long-run, hydropower (renewable) energy consumption negatively impacts CO2 emissions per capita, showing a unidirectional causal effect. As regards with non-renewable energy consumption, natural gas and oil energy consumption have a positive impact on CO2 emissions per capita both in the short and long run. Coal consumption positively impacts CO2 emissions in the short run, while it is negative in the long run. Policy measures to enhance the collection of energy from additional renewable energy sources, in particular hydropower, should be taken into account in order to increase the share of renewable energy, and thus, to compensate non-renewable energy consumption which is the main contributor to CO2 emissions. Moreover, solar and wind energy should be explicitly taken into consideration as an additional renewable energy source, which has the lack of attention by policymakers. Furthermore, policy actions, such as the involvement of the private sector into renewable energy projects and the implementation of effective carbon tax policies, could be further options to reduce CO2 emissions.

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This study aims to identify the role of socio-economic and female indicators on child mortality in Bangladesh from the data of 1975 - 2019. A number of econometric techniques of time series analysis like Augmented Dickey-Fuller, Autoregressive Distributive Lag bounds and pair-wise Granger causality tests have been applied to ascertain the desired outcomes. The Augmented Dickey-Fuller test has confirmed that neither series is integrated at level two and the Autoregressive Distributive Lag bounds testing approach has shown the cointegration and short-run and long-run relationship between the variables. Total fertility rate and urbanization have a positive effect, and female education, female life expectancy at birth, and economic growth rate have a negative effect on the child mortality rate. The pair-wise Granger causality test has shown the unidirectional and bidirectional causal relationship among the studied variables. All the outcomes are theoretically consistent and the policy recommendations are made based on findings.

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COVID-19 has acutely arrested the attainment of sustainable development goals (SDGs). Internal mobilization of resources got slimmed as the government's expenditure on health and social safety nets have increased. External sources are also constricted owing to the uncertainties over the cross-border investment and economic recovery process of the countries. A government study in 2017 projected that Bangladesh, on average, would need an additional USD 68.83 billion from internal sources and USD 11.03 billion from external sources since 2021 to accomplish its SDGs by 2030. Using autoregressive distributed lag (ARDL)forecasting techniques, this paper re-estimated future flows of all SDGs funding sources, e.g., fiscal revenues, private sector investment, non-government organizations (NGOs), public-private partnerships, foreign direct investments, and foreign grant still 2030 under the purview of the COVID-19. Revised allocation estimated by this study reveals that private investment and NGOs would need to contribute higher than the 2017 estimation during 2021-2025 while only private investment needs to be higher during 2026-2030.

Le COVID-19 a gravement freiné la réalisation des objectifs de développement durable (ODD). La mobilisation interne des ressources s'est amoindrie à mesure que les dépenses du gouvernement en matière de santé et de sécurité sociale ont augmenté. Les ressources externes sont également restreintes en raison des incertitudes liées aux investissements transfrontaliers et au processus de redressement économique des pays. Une étude menée par le gouvernement en 2017 prévoyait que le Bangladesh aurait besoin, en moyenne, de 68,83 milliards de dollars supplémentaires provenant de sources internes et de 11,03 milliards de dollars provenant de sources externes à partir de 2021 pour atteindre ses ODD d'ici 2030. À l'aide de modèles de prévision autorégressifs à retards échelonnés (ARDL), cette étude a réestimé les flux futurs de toutes les sources de financement des ODD, par exemple, les recettes fiscales, les investissements du secteur privé, les organisations non gouvernementales, les partenariats public-privé, les investissements directs étrangers et les subventions étrangères jusqu'à 2030, le tout dans le contexte de la COVID-19. Selon cette étude, qui a recalculé l'allocation nécessaire, le secteur privé et les organisations non gouvernementales devraient augmenter leur contribution par rapport à l'estimation de 2017 pour la période 2021-2025, tandis que seul l'investissement du secteur privé doit augmenter pendant la période 2026-2030.

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This study creates a US state-level asymmetric J­curve hypothesis testing map with Canada. The map may visually present how a US state policymaker manages bilateral trade balances with Canada. Green-colored US states support the evidence of the asymmetric J­curve hypothesis, while red-colored and gray-colored states do not. The main empirical finding indicates that the asymmetric J­curve hypothesis is supported for only 15 US states and D.C., shown in green on the map. This suggests that policymakers of these US states may have more sustainable and manageable bilateral trade policies with Canada. If so, policymakers in red/grey US states should reevaluate their bilateral trade policy regulations, especially those related to taxation, budgetary frameworks, energy prices, and other relevant factors that can impact consumer-producer prices and thereby create competitive state-level real exchange rates. By doing so, they may achieve the anticipated positive outcomes of the J­curve effect to export more.

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The aim of this paper is to assess the impact of energy poverty on health in the EU-27 countries for the period from 2003-2020 using Panel Autoregressive Distributed Lag models and generalized ridge regressions. Arrears on utility bills exerts positive long-run effects on capacity to keep the home adequately warm, current health expenditures, and self-perceived health as bad or very bad, but a negative long-run influence on energy import dependency. In the long-term, the population being unable to keep their home adequately warm positively affects self-perceived health as bad and very bad and negatively influences number of cooling days. Current health expenditure has a long-run influence on self-perceived health as bad and very bad and the number of heating days. Positive short-run impacts were observed for energy import dependency, arrears on utility bills, and number of heating days on current health expenditure and the population unable to keep their home adequately warm. People at risk of poverty or social exclusion in different zones had a significant impact on energy poverty indicators. A separate analysis is made for those EU states with the highest energy import dependency and the implications of the results are discussed.

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The rapid growth of environmental pollution and the destruction of eco-systems force every individual economy to focus on environmentally friendly economic development. This research explores economic growth, energy use, foreign direct investment, agriculture, industrialization, and urban population growth with environmental sustainability proxied as CO2 emissions from 1971 to 2018. Econometric methods are employed for different purposes as the unit root for the stationary check. ARDL determines the long-run relationship, while the Decoupling Index examines the growing speed of variables and CO2 emissions, and VECM has been used for short- and long-run causalities. The study's findings confirm the long-run impact of all environmental pollution variables as ECM-1 is negatively significant. The short-run causality test shows CO2 emissions because of economic growth (GDP = > CO2 ≠ GDP), energy use (ENU = > CO2 ≠ ENU), and foreign direct investment (FDI = > CO2 ≠ FDI) at a 1% level. In contrast, CO2 emissions are not the Granger cause of GDP, ENU, and FDI. Economic growth, energy use, and foreign direct investment will increase CO2 emission, not vice versa. The study findings suggest that governments should move toward adopting green technology by implementation of green fiscal policies.

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This paper asymmetrically analyzes the impact of energy consumption and oil price fluctuations on the economic growth of the MENA net oil-exporting and importing nations from 1990 to 2019 using panel nonlinear autoregressive distributed lag (PNARDL) model developed by (Salisu and Isah, Econ Model 66:258-271, 2017). The findings revealed that for the net-oil exporting countries, the impact of nonrenewable energy on economic growth is nonlinear in both terms, where in the both terms, high consumption of nonrenewable energy is influencing economic growth and its low consumption is limiting it. Furthermore, the impact of renewable energy is linear and it is influencing and limiting economic growth in both terms respectively. Moreover, the impact of oil price fluctuations on economic growth is linear in the long run and nonlinear in the short run, where in the long run, increase in it is not influencing economic growth but in the short run, while its decrease has no effect. For the net-oil importing countries, the impact of nonrenewable energy on economic growth is nonlinear in both terms, where in the long run, high consumption of nonrenewable energy is influencing economic growth but in the short run, it is discouraging it; however, in both terms, low consumption of nonrenewable energy has no effect. In addition, in the long run, the impact of renewable energy is nonlinear but linear in the short run; however, none of its impacts is significant in both terms. Also, the impact of oil price fluctuations on economic growth is linear in both terms and in the both terms, it is influencing economic growth. Nonetheless, for all the variables, the impacts are higher in the net-oil exporting countries. Policy recommendations were provided.

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Electric power industry, as one of the main industries leading to the increase of China's carbon emissions, accounts for about 40% of the total carbon emissions. It is of great practical significance to study the influencing factors of carbon emission decoupling index in power industry and put forward relevant policy suggestions. Based on the decoupling index of China's electric power industry from 1995 to 2018, this paper explores the influence of each index on the decoupling index through the autoregressive- distributed lag model. It turns out that the policy will significantly change the rate of change of carbon emissions and the rate of economic growth, but the impact of the policy is extremely short-lived; power generation structure, environmental regulations, and total lighting value at night play a positive role in promoting the decoupling index, while thermal power fuel efficiency and power generation conversion ratio play a negative role in inhibiting the decoupling index. In addition, the influence of power generation structure, environmental regulations, and the total value of night light on decoupling index also has a lagging and cumulative effect. Therefore, we propose targeted policy recommendations for policy formulation, green development, and low carbon construction in China's power industry from different perspectives based on the findings of the study.

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