Analysis of aerosol optical depth and relation to covariates during pre-monsoon season (2002-2019) over Pakistan using ARIMAX model and cross-wavelet analysis.

The pre-monsoon season heavily influences the precipitation amount in Pakistan. When hydrometeorological parameters interact with aerosols from multiple sources, a radiative climatic response is observed. In this study, aerosol optical depth (AOD) space-time dynamics were analyzed in relation to meteorological factors and surface parameters during the pre-monsoon season in the years 2002-2019 over Pakistan. Level-3 (L3) monthly datasets from Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-Angle Imaging Spectroradiometer (MISR) were used. Tropical Rainfall Measuring Mission (TRMM) derived monthly precipitation, Atmospheric Infrared Sounder (AIRS) derived air temperature, after moist relative humidity (RH) from Modern-Era Retrospective analysis for Research and Applications, Version-2 (MERRA-2), near-surface wind speed, and soil moisture data derived from Global Land Data Assimilation System (GLDAS) were also used on a monthly time scale. For AOD trend analysis, Mann-Kendall (MK) trend test was applied. Moreover, Autoregressive Integrated Moving Average with Explanatory variable (ARIMAX) technique was applied to observe the actual and predicted AOD trend, as well as test the multicollinearity of AOD with covariates. The periodicities of AOD were analyzed using continuous wavelet transformation (CWT) and the cross relationships of AOD with prevailing covariates on a time-frequency scale were analyzed by wavelet coherence analysis. A high variation of aerosols was observed in the spatiotemporal domain. The MK test showed a decreasing trend in AOD which was most significant in Baluchistan and Punjab, and the overall trend differs between MODIS and MISR datasets. ARIMAX model shows the correlation of AOD with varying meteorological and soil parameters. Wavelet analysis provides the abundance of periodicities in the 2-8 months periodic cycles. The coherency nature of the AOD time series along with other covariates manifests leading and lagging effects in the periodicities. Through this, a notable difference was concluded in space-time patterns between MODIS and MISR datasets. These findings may prove useful for short-term and long-term studies including oscillating features of AOD and covariates.

Analysis of nighttime aerosols and relation to covariates over a highly polluted sub-Saharan site using Mann-Kendall and wavelet coherence approach.

High emissions of aerosols and trace gases during nighttime can cause serious air quality, climate, and health issues, particularly in extremely polluted cities. In this paper, an effort has been made to examine the variations in aerosols and trace gases over a sub-Saharan city of Ilorin (Nigeria) during nighttime. We have used Aerosol Robotic Network data of aerosol optical depth (AOD) at 500 nm, Angstrom exponent (AE) (440/870), and precipitable water (WVC). Both AE and WVC showed a decreasing trend of -0.0012% and -0.0010% per year, respectively. We also analyzed nighttime data of carbon monoxide (CO), methane (CH4 ), and ozone (O3 ) from Atmospheric Infrared Sounder and aerosol subtypes from CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation). AOD, AE, and WVC average values are found to be 0.64 ± 0.33, 0.74 ± 0.24, and 3.40 ± 0.97, respectively. As a result of northeasterly winds carrying Saharan dust during the dry season, the greatest value of AOD (1.29) was observed in February. Desert dust aerosols (37.63%) were the most prevalent type, followed by mixed aerosols (44.15%). Winds at a height of 1500 m above ground level were likely transporting Saharan dust to Ilorin. CALIPSO images revealed that Ilorin's atmosphere contained dust, polluted continental, clean maritime, and polluted dust on high AOD days. The National Oceanic and Atmospheric Administration's vertical sounding profiles showed that the presence of high AOD values was caused by the inversion layer trapping aerosol pollution. Average nighttime concentrations of CO, O3 , and CH4 were measured to be 127 ± 18, 29.7 ± 2.1, and 1822.6 ± 12.7 ppbv, respectively. The wavelet coherence spectra exhibited significant quasi-biannual and quasi-annual oscillations at statistically significant levels.

Classification of aerosols using particle linear depolarization ratio (PLDR) over seven urban locations of Asia.

Active lidar remote sensing has been used to obtain detailed and quantitative information about the properties of aerosols. We have analyzed the spatio-temporal classification of aerosols using the parameters of particle linear depolarization ratio and single scattering albedo from Aerosol Robotic Network (AERONET) over seven megacities of Asia namely; Lahore, Karachi, Kanpur, Pune, Beijing, Osaka, and Bandung. We find that pollution aerosols dominate during the winter season in all the megacities. The concentrations, however, vary concerning the locations, i.e., 70-80% pollution aerosols are present over Lahore, 40-50% over Karachi, 90-95% over Kanpur and Pune, 60-70% and over Beijing and Osaka. Pure Dust (PD), Pollution Dominated Mixture (PDM), and Dust Dominated Mixture (DDM) are found to be dominant during spring and summer seasons.This proposes that dust over Asia normally exists as a mixture with pollution aerosols instead of pure form. We also find that black carbon (BC) dominated pollution aerosols.

Environmental sustainability through renewable energy and banking sector development: policy implications for N-11 countries.

Climate change has become an alarming condition for developed and developing countries. The main reason for this is the use of non-renewable energy (RE) in economic sectors. Therefore, the world economies are now replacing non-renewable with RE. The role of BANKs is fundamental for this transition because they can fund clean energy projects. Several studies probed the economic growth andCO2e association and ignored the role of BANK development. Therefore, this work investigates the influences of banking sector development on CO2 emissions (CO2e) in the next eleven countries. This work included other factors of GDP, clean energy, and non-RE in the model by taking annual data from 1990 to 2020. Robust econometric techniques are used to highlight the empirical outcomes. The cross-sectional autoregressive distributed lag approach shows that BANK development is a coproduction factor toward CO2 production in the presence of economic development and renewable and nonrenewable energy. More credits from banks to the private sector are enhancing economic activity and increasing energy consumption. This means that a 1% increase in BANK development will increase CO2e by 0.01%. A 1% increase in RE is lowering CO2e by 0.31%. Therefore, this work highlights the importance of BANK development in creating sustainable development. The BANK can fund clean energy projects. Policymakers can utilize the BANK to encourage green investments in household and corporate sectors. The use of green technologies will ultimately bring a cleaner environment and sustainable development to N-11 countries. This work is helpful for policymakers in that they can utilize the banking sector to launch greener projects to attain sustainable development goals.

Analyzing the validity of load capability curve: how economic complexity, renewable energy, R&D, and communication technologies take their part in G-20 countries.

Intense anthropogenic contamination of the air, water, and soil inspires scholars to examine the causes of pollution and provide remedies to assure environmental sustainability. Therefore, researchers in this study are driven to investigate the causes of the severe air, water, and soil contamination that has resulted from human activity and to offer recommendations for achieving environmental sustainability. This research contributes to the ecological works by suggesting the load capability curve (LCH) hypothesis and using the load capacity factor (LC) to investigate components influencing climatic quality. The LC enables thorough climatic value examination when comparing ecological footprint and biocapacity. Information and communication technologies (INF), development and research (R&D), renewable energy (RE) usage, and disposable income are all examined, considering their effects on the load capacity factor. This analysis utilizes the cross-sectionally augmented autoregressive distributed lag estimator and the Westerlund cointegration on data for the G-20 countries from 1995 to 2018. Empirical evidence suggests that renewables, R&D spending, economic complexity, and INF all benefit environmental quality. This study cannot support the LCH hypothesis, which states that increasing income worsens ecological conditions up to a certain point but then aids in improving environmental quality afterward. Based on the findings, G-20 governments should prioritize environmental policies that boost economic growth, spread renewable energy, prioritize research and development spending, and assist the implementation of green INF infrastructure.

Analyzing the impacts of renewable energy, patents, and trade on carbon emissions-evidence from the novel method of MMQR.

Achieving sustainable development necessitates proactive measures to mitigate the economy's negative impact on environmental standards. A new empirical association between renewable energy patent innovation and net international trade on carbon emissions in ASEAN countries from 1990 to 2021 is presented, along with its significance. Using present panel data techniques, this study investigates the connections between these factors. Second-generation cointegration and unit root tests, as well as a novel method of Moments Quantile Regression, are used in the econometric procedure. Compared to standard quantile regression, this method is more resistant to outliers and provides an asymmetric relationship between the variables. The findings show that trade increases carbon emissions in countries with medium to high emissions, that patent innovation contributes to increasing emissions, and that renewable energy mitigates carbon emissions in countries with low to medium emerging economies. Our results are consistent with other specifications, including quantile regression canay (Canay 2011), fully modified, dynamic, and fixed effect regressions, proving the EKC hypothesis. These countries need to prioritize greener products and adopt advanced manufacturing technologies to reduce carbon emissions from consumption. However, as prosperity increases, it also leads to higher consumption-based carbon emissions, worsening ecological damage in the region. Implementing policies like trade synchronization and increasing investment in patent innovations are proposed in this study to lower the current level of carbon emissions.

How human capital and energy prices play their role to enhance renewable energy: defining the role of innovations and trade openness in G-11 countries.

It is crucial to switch from nonrenewable energy to renewable energy sources to improve environmental quality. For this reason, innovations play a crucial part, yet the use of renewable energy is insufficient. This work contributes to the literature by Using both aggregate and disaggregated data, this study investigates the relationship between energy consumption, trade openness, human capital (HCA), and innovations in G11 nations. This study analyzed yearly data from 1990 to 2020 using empirical methods of augmented mean group (AMG) and cross-sectional autoregressive distributed lag (CS-ARDL) techniques. The findings highlight the significance of human capital, energy pricing, and innovations in fostering the growth of renewable energy in G-11 nations. A 1% increase in GDP and trade openness will enhance total energy consumption by 0.47% and 0.07%. GDP and trade are also increasing non and renewable energy consumption. A 1% increase in human capital and eco-innovations are increasing renewable energy by reducing non-renewable energy consumption. In addition, the G-11's rising commerce and GDP have increased their reliance on energy sources that rely on fossil fuels. The authors of this study suggest raising HCA as a means of encouraging the G-11 to use cleaner forms of energy. The need to bolster renewable energy to achieve a cleaner environment in the G-11 countries.

How institutional quality and renewable energy interact with ecological footprints: do the human capital and economic complexity matter in the Next Eleven nations?

Changes in the economy and human conduct have contributed to one of today's most urgent challenges: environmental pollution. This study's overarching objective is to evaluate the following Next Eleven nations (N-11) ecological footprints (EF) with their natural resources (NR), economic complexity (EC), renewable energy (RE), and foreign direct investment (FDI). The data from 1995 to 2018 are used with the panel data estimations. The complexity of an economy is found to influence the EF. For this purpose, the cross-sectional autoregressive distributed lag method is appropriate. The analysis shows that a higher degree of economic complexity was associated with a larger ecological impact. Moreover, this correlation was the highest among all the variables considered. However, the consumption of natural resources and the economies' complexity enhance environmental conditions. The key recommendation from the study's conclusions is to improve R&D activities to build environmentally friendly technology and clean energy infrastructures and to change to a clean industry pattern. Meanwhile, strategic initiatives are offered to legislators depending on the stability of institutional quality.

The impact of economic growth, tourism, natural resources, technological innovation on carbon dioxide emission: evidence from BRICS countries.

The main objective of this manuscript was to investigate the relationships among economic development, tourism, the use of natural resources, technical advancement, and carbon dioxide emissions in the BRICS group of nations. Data from the panel was gathered from 1995 to 2018. Modern methodology tools including the CS-ARDL tests, Westerlund cointegration tests, and panel data unit root tests have been used in this study. Results of the models show that all the variables were transformed to the first difference to make it stationary. The Westerlund model test results suggest that dependent and independent variables have robust cointegration. Results of the CS-ARDL models reveal that all the variables signed, and significance are aligned with the economic theory. It indicates that except for tourism, the rest of the variables like technical innovation, natural resources, and economic growth have positive and significant effects on carbon dioxide emissions both in the short and long runs. Additionally, a 1% rise in economic growth, technical innovation, and natural resources over the long term would raise carbon dioxide emissions in the BRICS economies by 1.79%, 0.15%, and 0.10%, respectively. However, a 1% increase in tourism would result in a 0.39% decrease in carbon dioxide emissions among the nations in the panel data set. Therefore, the promotion of sustainable tourism and advancement in technological innovation is highly important in these countries, so the high impact of environmental degradation pressure may reduce to some extent. An in-addition comprehensive set of policies should be made on encouraging low-carbon transportation, promoting sustainable tourism certification, boosting local produce, reducing waste management, and provide education and awareness campaigns to tourists.

Defining the roles of socio-economic factors and bureaucratic quality to minimize the impacts of fossil fuels consumption on CO2 emissions in G-20 countries.

G-20 nations are committed to reducing CO2 emissions considering their commitments to the United Nations. Therefore, this work investigates the associations between bureaucratic quality, socio-economic factors, fossil fuel consumption, and CO2 emissions from 1990 to 2020. To counter the problem of cross-sectional dependence, this work applies to cross-sectional autoregressive distributed lag (CS-ARDL). The valid second-generation methodologies are applied, and the results cannot be found in the environmental Kuznets curve (EKC). Fossil fuels (coal, gas, oil) exert a negative impact on environmental quality. The direct impact of bureaucratic quality and socio-economic factors are suitable to lower CO2 emissions. A 1% increase in bureaucratic quality and socio-economic factors will lower CO2 emissions by 0.174% and 0.078% respectively in the long run. The indirect effect of bureaucratic quality and socio-economic factors is significant in reducing the CO2 emissions created by fossil fuels. The wavelet plots also validate these findings that bureaucratic quality is important to lower environmental pollution in 18 G-20 member countries. Considering the findings, this research presents important policy instruments that there is a need to bring clean energy sources into the total energy mix. For this purpose, it is important to improve bureaucratic quality to speed up the decision-making process for clean energy infrastructural development.