Asymmetric role of green energy, innovation, and technology in mitigating greenhouse gas emissions: evidence from India.

The primary cause of environmental degradation, which poses a danger to the long-term viability of the ecosystem, is the emission of greenhouse gases (GHG). For this reason, the Glasgow Climate Pact (COP26) established a decarbonization goal in response to this ecological concern, for which all economic players have a responsibility. India is among the participants who have a target set for them to decarbonize their economies by the year 2060 via the use of green energy and the advancement of science and innovation. Nevertheless, the asymmetrical effect of green energy, technology, and innovation on India's decarbonization program was not sufficiently explored in the prior study; hence, this research aims to fill this literature vacuum by considering India's GHG emissions from 1990 to 2020 by leveraging the non-linear autoregressive distributed lag (NARDL) model. The findings reveal the asymmetric influences of variables of interest on GHG emissions during the short and long term and under positive and negative shocks. Regarding the positive shock, long-term findings demonstrate that innovation and technical know-how grow GHG emissions and accelerate environmental degradation. However, a negative shock in innovations and technological know-how is opposed to a positive shock and improving environmental conditions. Further, positive shocks in green energy boost environmental effectiveness by reducing GHG secretions in India. In contrast, the negative shock in green energy deteriorates the environment by triggering GHG releases. These factual findings compel the Indian government to prioritize green technologies in addition to green energy generation to decouple economic growth from greenhouse gas emissions and meet rising energy demands.

Improved estimation of population distribution function using twofold auxiliary information under simple random sampling.

In this article, our main aim is to suggest enhanced families of estimators for estimating the population distribution function (DF) using twofold auxiliary evidence within the framework of simple random sampling. Numerical analysis is performed on four different actual data sets. The precision of the estimators is further investigated exhausting a simulation study. As equated with existing estimators, the suggested families of estimators have minimum mean square error (MSE) and higher percentage relative efficiency (PRE). The succeeding recommended family of estimators outperforms the first family of estimators across all data sets. These are positive indicators of its performance. The theoretical result shows that the recommended family of estimators performs better than the existing estimators. The extent of improvement in efficiency is noteworthy, indicating the superiority of the suggested estimators in terms of minimum MSE.

Automated in-season rice crop mapping using Sentinel time-series data and Google Earth Engine: A case study in climate-risk prone Bangladesh.

High-resolution mapping of rice fields is crucial for understanding and managing rice cultivation in countries like Bangladesh, particularly in the face of climate change. Rice is a vital crop, cultivated in small scale farms that contributes significantly to the economy and food security in Bangladesh. Accurate mapping can facilitate improved rice production, the development of sustainable agricultural management policies, and formulation of strategies for adapting to climatic risks. To address the need for timely and accurate rice mapping, we developed a framework specifically designed for the diverse environmental conditions in Bangladesh. We utilized Sentinel-1 and Sentinel-2 time-series data to identify transplantation and peak seasons and employed the multi-Otsu automatic thresholding approach to map rice during the peak season (April-May). We also compared the performance of a random forest (RF) classifier with the multi-Otsu approach using two different data combinations: D1, which utilizes data from the transplantation and peak seasons (D1 RF) and D2, which utilizes data from the transplantation to the harvest seasons (D2 RF). Our results demonstrated that the multi-Otsu approach achieved an overall classification accuracy (OCA) ranging from 61.18% to 94.43% across all crop zones. The D2 RF showed the highest mean OCA (92.15%) among the fourteen crop zones, followed by D1 RF (89.47%) and multi-Otsu (85.27%). Although the multi-Otsu approach had relatively lower OCA, it proved effective in accurately mapping rice areas prior to harvest, eliminating the need for training samples that can be challenging to obtain during the growing season. In-season rice area maps generated through this framework are crucial for timely decision-making regarding adaptive management in response to climatic stresses and forecasting area-wide productivity. The scalability of our framework across space and time makes it particularly suitable for addressing field data scarcity challenges in countries like Bangladesh and offers the potential for future operationalization.

A new approach for estimating variance of a population employing information obtained from a stratified random sampling.

In this article, we suggest an enhanced estimator for the estimation of finite population variance using twofold auxiliary variable under stratified random sampling. The numerical expressions for the bias and MSE are determined up to the first order of approximation. In order to effectively validate the theoretical findings, three actual data sets are included. Additionally, the application of the suggested estimators is demonstrated using a simulation study. Results of an empirical comparison among the suggested and existing estimators were investigated. To determine how good the suggested estimator, in comparison to the preliminary estimators, the MSE criterion is used. The suggested estimator has a smaller MSE and better PRE than existing estimators, according to numerical results utilizing actual data sets and a simulation analysis.

Epidemiological analysis of anaplasmosis in cattle from Khyber Pakhtunkhwa, Pakistan.

Background and Aim: Anaplasmosis, a tick-borne disease affecting livestock caused by the bacteria Anaplasma, poses a global concern. This study aimed to estimate the prevalence, spatiotemporal variation, and associated risk factors of anaplasmosis in cattle from the Bannu and Lakki Marwat districts of Khyber Pakhtunkhwa, Pakistan. Materials and Methods: This study used 197 cattle exhibiting clinical symptoms of anaplasmosis in natural settings. Microscopic examination was used to estimate the prevalence. Potential risk factors, such as sampling regions and months, gender, breed, and age were studied. Results: The study revealed an overall anaplasmosis prevalence of 19.79%. Bannu district exhibited a higher occurrence at 22.10%, compared to Lakki Marwat district at 17.64%. Young cattle (<2 years) demonstrated a notably higher incidence of anaplasmosis (26.78%) compared to adults (>5 years), which had a prevalence of 12.35% (p < 0.05). Female cattle (22.36%) were more susceptible than male cattle (11.11%). Prevalence peaked in June (45.71%) and was lowest in February (3.57%). Crossbred cattle had a higher prevalence (23.52%) than purebred cattle (11.47%). Conclusion: Anaplasmosis can be effectively controlled using a comprehensive approach encompassing selective breeding for resilience, targeted care of young calves and females, effective tick control during warmer months, consistent use of insecticides, and proactive risk factor management. Raising awareness among farmers through diverse channels, including media, is pivotal to bolster tick-borne disease management strategies.

Demystifying the association between economic development, transportation, tourism, renewable energy, and ecological footprint in Bay of Bengal Initiative for Multi-Sectoral Technical and Economic Cooperation region during globalization mode.

The Bay of Bengal Initiative for Multi-Sectoral Technical and Economic Cooperation (BIMSTEC) economies have yet to meaningfully contribute to accomplishing Sustainable Development Goals (SDG 7) affordable and clean energy, (SDG 8) decent work and economic growth, and (SDG 13) climate action. Dealing with this issue might require a shift or alteration of policy framework that is the major theme of this study. Consequently, this present research inspects the influence of economic growth, transportation, tourism sector development, and renewable energy on ecological footprint using panel time series from 1990 and 2019 for the BIMSTEC region. To evaluate this dynamic nexus between the mentioned environmental pollution drivers of ecological footprint, this study employed the augumented mean group (AMG) and common correlated effect mean group (CCEMG) regression estimators after detection of cross-sectional dependency. The empirical outcomes denote that economic growth and transportation sector of BIMSTEC countries increase the levels of ecological footprint. Conversely, tourism sector development, globalization, and renewable energy protect the ecological excellence in the region. Moreover, it is observed that a unidirectional causality exists from economic growth to ecological footprint, ecological footprint to transportation, tourism to ecological footprint, and globalization to ecological footprint, while bidirectional causality exists between renewable energy and ecological footprint. By observing the positive function of tourism, green energy, and globalization on sustainable environment progress, central authorities are capable to redesign policies concerning supportable efficient technologies and regulate globalization towards green programs and agenda to reduce global warming.

Impact of banking development and renewable energy consumption on environmental sustainability in Germany: Novel findings using the bootstrap ARDL approach.

This study examines the effects of banking development, economic growth and consumption of renewable energy on carbon dioxide (CO2) emissions and load capacity factor (LCF). Previous empirical studies have assessed the interrelationship between banking development and CO2 emissions; however, these studies have ignored supply-side ecological issues. To overcome this issue, this study evaluates the effect of banking development on LCF, which is considered to be one of the most comprehensive ecological proxies to date, including both biocapacity and ecological footprint (EF). Using the bootstrap autoregressive distributed lag model, the study reveals that renewable energy improves ecological quality in Germany. The results of the investigation demonstrate that the environmental Kuznets curve hypothesis is valid in Germany using CO2 emissions and LCF indicators. Furthermore, this study demonstrates that banking growth and renewable energy in Germany correlate with improved environmental quality. These findings provide policymakers with important insights. In this context, the study advises the banking industry and government authorities to leverage banking expansion to support green energy to achieve the national goal of zero CO2 emissions by 2045.

Analyzing asymmetric ecological performance under structural change, technological innovation, and trade diversification: fresh insights from the USA.

The present research scrutinizes the influences of trade diversification, air transportation, technological innovation, and economic complexity on ecological footprint from 1990 to 2019. The findings of the both time series unit root (with and without structural break) tests confirm that none of a single variable is stationary more than the first difference. Furthermore, the Wald and nonlinear autoregressive distributed lag bound methods check asymmetry and long-term cointegration relationship between the intended variables, respectively. Moreover, this study uses the nonlinear autoregressive distributed lag model to estimate the short-run and long-run coefficients/elasticity of the ecological footprint function. Following the empirical evidence, the findings revealed that positive (negative) components in trade diversification curtail the ecological footprint in the long-run. In addition, a positive shock in air transportation leads to an increase in ecological footprint in the long-run. Nevertheless, a negative shock in air transportation exerts a significant and adverse influence on the level of ecological footprint in the long-run. Furthermore, a positive (negative) shock in technological developments significantly reduces environmental pollution in the US economy in the long-run. Besides, the outcomes from economic complexity discovered a positive shock will significantly overcome the pressure on the environment in the long-run. However, in the short-run, it is observed that negative shock in trade diversification will lead to increase the ecological footprint level in USA. Similarly, a positive shock in air transportation will lead to increase the pollution level in the short-run. In contrast, a negative shock in air transportation will lead to reduce the pressure on the environment in the short-run. Besides, in terms of policy realization, the present research recommends adopting trade synchronization, harmonic trade strategies, and investment in technological innovations to diminish the existing level of ecological footprint in the region. For sustainable development, this study put forward for instantaneously encouraging the expansion of the digital economy and reducing air pollution, accelerating the green transformation, and impelling the industrial agglomeration process in the USA.

Environmental cost of natural resources, globalization, and economic policy uncertainty in the G-7 bloc: do human capital and renewable energy matter?

The panel of G-7 economies is considered one of the most prosperous economies, endowed with abundant natural and renewable energy resources. Due to their richness in these resources, most economic development and activities, including environmental and economic aspects, depend on and are determined by energy consumption and natural resource rents. However, the increasing dependence of G-7 economies on energy consumption and natural resources raises questions about their long-term growth and ecological policies towards achieving sustainable development goals (SDGs). Therefore, the main objective of this study is to examine the influence of natural resources, renewable energy, economic policy uncertainty, human capital, and globalization on the ecological footprint in the panel of G-7 economies from 1990 to 2020. After confirming the cross-sectional dependence issue, this study applied second-generation panel data approaches to estimate robust and reliable outcomes. The estimated evidence from this study discovered that natural resources, globalization processes, and economic policy uncertainty significantly increase the level of ecological footprint in the region. In contrast, renewable energy and human capital provide feasible solutions for ecological improvement in the study area. Likewise, the interactive role of renewable energy with economic policy uncertainty significantly protects the environmental quality in the study area. Based on the estimated findings, this study recommends various achievable policy options for policymakers and the governments of these economies to ensure environmental sustainability.

Statistical inferences under step stress partially accelerated life testing based on multiple censoring approaches using simulated and real-life engineering data.

Evaluating the lifespan distribution of highly reliable commodities under regular use is exceedingly difficult, time consuming, and extremely expensive. As a result of its ability to provide more failure data faster and at a lower experimental cost, accelerated life testing has become increasingly important in life testing studies. In this article, we concentrate on parametric inference for step stress partially life testing utilizing multiple censored data based on the Tampered Random Variable model. Under normal stress circumstances, the lifespan of the experimental units is assumed to follow the Nadarajah-Haghighi distribution, with and being the shape and scale parameters, respectively. Maximum likelihood estimates for model parameters and acceleration factor are developed using multiple censored data. We build asymptotic confidence intervals for the unknown parameters using the observed Fisher information matrix. To demonstrate the applicability of the different methodologies, an actual data set based on the timings of subsequent failures of consecutive air conditioning system failures for each member of a Boeing 720 jet aircraft fleet is investigated. Finally, thorough simulation studies utilizing various censoring strategies are performed to evaluate the estimate procedure performance. Several sample sizes were studied in order to investigate the finite sample features of the considered estimators. According to our numerical findings, the values of mean squared errors and average asymptotic confidence intervals lengths drop as sample size increases. Furthermore, when the censoring level is reduced, the considered estimates of the parameters approach their genuine values.

A new statistical methodology using the sine function: Control chart with an application to survival times data.

Statistical methodologies have a wider range of practical applications in every applied sector including education, reliability, management, hydrology, and healthcare sciences. Among the mentioned sectors, the implementation of statistical models in health sectors is very crucial. In the recent era, researchers have shown a deep interest in using the trigonometric function to develop new statistical methodologies. In this article, we propose a new statistical methodology using the trigonometric function, namely, a new trigonometric sine-G family of distribution. A subcase (special member) of the new trigonometric sine-G method called a new trigonometric sine-Weibull distribution is studied. The estimators of the new trigonometric sine-Weibull distribution are derived. A simulation study of the new trigonometric sine-Weibull distribution is also provided. The applicability of the new trigonometric sine-Weibull distribution is shown by considering a data set taken from the biomedical sector. Furthermore, we introduce an attribute control chart for the lifetime of an entity that follows the new trigonometric sine-Weibull distribution in terms of the number of failure items before a fixed time period is investigated. The performance of the suggested chart is investigated using the average run length. A comparative study and real example are given for the proposed control chart. Based on our study of the existing literature, we did not find any published work on the development of a control chart using new probability distributions that are developed based on the trigonometric function. This surprising gap is a key and interesting motivation of this research.

Comparative Antihyperglycemic and Antihyperlipidemic Effects of Lawsone Methyl Ether and Lawsone in Nicotinamide-Streptozotocin-Induced Diabetic Rats.

Our previous study uncovered potent inhibitory effects of two naphthoquinones from Impatiens balsamina, namely lawsone methyl ether (2-methoxy-1,4-naphthoquinone, LME) and lawsone (2-hydroxy-1,4-naphthoquinone), against α-glucosidase. This gave us the insight to compare the hypoglycemic and hypolipidemic effects of LME and lawsone in high-fat/high-fructose-diet- and nicotinamide-streptozotocin-induced diabetic rats for 28 days. LME and lawsone at the doses of 15, 30, and 45 mg/kg, respectively, produced a substantial and dose-dependent reduction in the levels of fasting blood glucose (FBG), HbA1c, and food/water intake while boosting the insulin levels and body weights of diabetic rats. Additionally, the levels of total cholesterol (TC), triglycerides (TGs), high-density lipoproteins (HDLs), low-density lipoproteins (LDLs), aspartate transaminase (AST), alanine transaminase (ALT), creatinine, and blood urea nitrogen (BUN) in diabetic rats were significantly normalized by LME and lawsone, without affecting the normal rats. LME at a dose of 45 mg/kg exhibited the most potent antihyperglycemic and antihyperlipidemic effects, which were significantly comparable to glibenclamide but higher than those of lawsone. Furthermore, the toxicity evaluation indicated that both naphthoquinones were entirely safe for use in rodent models at doses ≤ 50 mg/kg. Therefore, the remarkable antihyperglycemic and antihyperlipidemic potentials of LME make it a promising option for future drug development.

What is the role of remittance and education for environmental pollution? - Analyzing in the presence of financial inclusion and natural resource extraction.

This study assessed the impact of gross domestic product (GDP), education, natural resources, remittances, and financial inclusion on carbon emissions in G-11 countries from 1990 to 2021. Based on the negative impact of pollution and the need for sustainable development, this study examined factors affecting CO2 emissions in G-11 countries using non-linear panel ARDL model. The study found that a positive GDP shock increases CO2 emissions in the short and long term, while a negative shock decreases emissions in the short term and increases emissions in the long term. Education was found to increase CO2 emissions in the long term but decrease them in the short term, emphasizing the need for education on combating emissions. Natural resources were also found to increase emissions in the long term, highlighting the need for government-defined institutions to minimize extraction effects and enforce transparency and accountability. Positive changes in personal remittances and financial inclusion were found to increase emissions in both the short and long term, suggesting the need for policies that encourage renewable energy sources and energy efficiency improvement. The study concludes that policymakers should prioritize efficient resource allocation, promote renewable energy usage, and enhance environmental awareness to achieve sustainable development goals in G-11 countries. The possible applications of this study include the use of the models to investigate the asymmetric effects on CO2 emissions. This model can be applied in future studies to examine the relationship between GDP, education, natural resources, personal remittances, financial inclusion, and CO2 emissions in other countries.

The role of monetary and fiscal policies in determining environmental pollution: Revisiting the N-shaped EKC hypothesis for China.

The equilibrium between environmental quality and economic growth is one of the contemporary objectives of fiscal and monetary policies in the case of China. In this study, we investigate the extent of the existence of the N-shaped environmental Kuznets curve (EKC) hypothesis and measure the collision of fiscal and monetary policy on carbon emissions within the economic growth perspectives that China is witnessing. This study examines the dynamic nexus between monetary supply, government expenditure, and carbon emissions in China over the spanning from 1980 to 2019. The findings demonstrate that the money supply reduces carbon emissions in the short- and long-run. Precisely, a 1-unit augmentation in monetary policy tool (money supply) will significantly reduce the pressure on the environment by 0.29332 unit in the long-run and 0.79311 unit in the short-run. In contrast, the fiscal policy instrument (government expenditure) contributes to the increase in carbon emissions. Specifically, a 1-unit increase in government expenditure will increase the carbon emission by 0.17835 and 0.48247 units in the long-run and short-run, respectively. Additionally, the result also confirmed the N-shaped EKC hypothesis. Particularly, at the initial stage of economic growth, there are 1.58659 and 4.29197 unit increas in carbon emission in the long-run and short-run, respectively. However, after taking the square of economic growth, this reduces the environmental pollution by 0.3018 and 0.81665 units in the long-run and short-run, respectively. Finally, the cubic form of economic growth shows the 0.01755 and 0.04747 units increase in the pollution level in the long-run and short-run, respectively. Moreover, the study also found the presence of a causality link between government expenditure, economic growth, and carbon emissions. These findings will aid policymakers in implementing fiscal and monetary policies that promote long-term development while lowering carbon emissions.

Evaluating the impact of digitalization, renewable energy use, and technological innovation on load capacity factor in G8 nations.

Ecosystems are in danger due to human-caused air, water, and soil pollution, so it is important to find the underlying causes of this issue and develop practical solutions. This study adds to environmental research gap by suggesting the load capability factor (LCF) and using it to look at the factors affectting environmental health. The load capacity factor simplifies monitoring environmental health by illustrating the distinction between ecological footprint and biocapacity. We examine the interplay between mobile phone users (Digitalization DIG), technological advancements (TEC), renewable energy use, economic growth, and financial development. This study assesses G8 economies' data from 1990 to 2018, using a Cross-Section Improved Autoregressive Distributed Lag CS-ARDL estimator and a cointegration test. The data shows that green energy, TEC innovation, and DIG are all beneficial for natural health. Based on the results of this study, the G8 governments should focus on environmental policies that promote economic growth, increase the use of renewable energy sources, guide technological progress in key areas, and encourage the development of digital information and communications technologies that are better for the environment.

An autoregressive distributed lag approach for estimating the nexus between CO2 emissions and economic determinants in Pakistan.

Carbon dioxide (CO2) emissions have become a critical aspect of the economic and sustainable development indicators of every country. In Pakistan, where there is a substantial increase in the population, industrialization, and demand for electricity production from different resources, the fear of an increase in CO2 emissions cannot be ignored. This study explores the link that betwixt CO2 emissions with different significant economic indicators in Pakistan from 1960 to 2018 using the autoregressive distributed lag (ARDL) modelling technique. We implemented the covariance proportion, coefficient of determination, the Durbin Watson D statistics, analysis of variance (ANOVA), variance inflating factor (VIF), the Breusch-Pagan test, the Theil's inequality, the root mean quare error (RMSE), the mean absolute percentage error (MAPE), and the mean absolute error (MAE) for the diagnostics, efficiency, and validity of our model. Our results showed a significant association between increased CO2 emissions and increased electricity production from oil, gas, and other sources. An increase in electricity production from coal resources was seen to have resulted in a decrease in CO2 emissions. We observed that an increase in the gross domestic product (GDP) and population growth significantly contributed to the increased CO2 emissions. The increment in CO2 emissions resulting from industrial growth was not significant. The increment in CO2 emissions in the contemporary year is significantly associated with the preceding year's increase. The rate of increase was very alarming, a sign that no serious efforts have been channelled in this regard to reduce this phenomenon. We call for policy dialogue to devise energy-saving and CO2 emission reduction strategies to minimize the impact of climate change on industrialization, population growth, and GDP growth without deterring economic and human growth. Electricity production from different sources with no or minimal CO2 emissions should be adopted. We also recommend rigorous tree planting nationwide to help reduce the concentration of CO2 in the atmosphere as well as environmental pollution.

Antimicrobial Usage and Detection of Multidrug-Resistant Staphylococcus aureus: Methicillin- and Tetracycline-Resistant Strains in Raw Milk of Lactating Dairy Cattle.

Staphylococcus aureus is a prominent cause of food-borne diseases worldwide. Enterotoxigenic strains of this bacteria are frequently found in raw milk, and some of these strains are resistant to antimicrobials, posing a risk to consumers. The main objectives of this study were to determine the antimicrobial resistance pattern of S. aureus in raw milk and to detect the presence of mecA and tetK genes in it. A total of 150 milk samples were obtained aseptically from lactating cattle, including Holstein Friesian, Achai, and Jersey breeds, maintained at different dairy farms. The milk samples were checked for the presence of S. aureus, and it was detected in 55 (37%) of them. The presence of S. aureus was verified by culturing on selective media, gram staining, and performing coagulase and catalase tests. Further confirmation was performed through PCR with a species-specific thermonuclease (nuc) gene. Antimicrobial susceptibility testing of the confirmed S. aureus was then determined by using the Kirby-Bauer disc diffusion technique. Out of the 55 confirmed S. aureus isolates, 11 were determined to be multidrug-resistant (MDR). The highest resistance was found to penicillin (100%) and oxacillin (100%), followed by tetracycline (72.72%), amikacin (27.27%), sulfamethoxazole/trimethoprim (18.18%), tobramycin (18.18%), and gentamycin (9.09%). Amoxicillin and ciprofloxacin were found to be susceptible (100%). Out of 11 MDR S. aureus isolates, the methicillin resistance gene (mecA) was detected in 9 isolates, while the tetracycline resistance gene (tetK) was found in 7 isolates. The presence of these methicillin- and tetracycline-resistant strains in raw milk poses a major risk to public health, as they can cause food poisoning outbreaks that can spread rapidly through populations. Our study concludes that out of nine empirically used antibiotics, amoxicillin, ciprofloxacin, and gentamicin were highly effective against S. aureus compared to penicillin, oxacillin, and tetracycline.

Influence of COVID-19 vaccination on the dynamics of new infected cases in the world.

The initial COVID-19 vaccinations were created and distributed to the general population in 2020 thanks to emergency authorization and conditional approval. Consequently, numerous countries followed the process that is currently a global campaign. Taking into account the fact that people are being vaccinated, there are concerns about the effectiveness of that medical solution. Actually, this study is the first one focusing on how the number of vaccinated people might influence the spread of the pandemic in the world. From the Global Change Data Lab "Our World in Data", we were able to get data sets about the number of new cases and vaccinated people. This study is a longitudinal one from 14/12/2020 to 21/03/2021. In addition, we computed Generalized log-Linear Model on count time series (Negative Binomial distribution due to over dispersion in data) and implemented validation tests to confirm the robustness of our results. The findings revealed that when the number of vaccinated people increases by one new vaccination on a given day, the number of new cases decreases significantly two days after by one. The influence is not notable on the same day of vaccination. Authorities should increase the vaccination campaign to control well the pandemic. That solution has effectively started to reduce the spread of COVID-19 in the world.

A hybrid deep leaning model for prediction and parametric sensitivity analysis of noise annoyance.

Noise annoyance is recognized as an expression of physiological and psychological strain in acoustical environment. The studies on prediction of noise annoyance and parametric sensitivity analysis of factors affecting it have been rarely reported in India. A hybrid ConvLSTM technique was developed in the study to predict traffic-induced noise annoyance in 484 people based on ambient noise levels, as well as survey information. Ambient noise levels were obtained at different locations of Dhanbad city using sound level meter at varying intervals, viz. 09AM-12PM, 03PM-06PM, and 08PM-11PM. The proposed method was compared with some well-known neural network techniques such as K-nearest neighbors (KNN), artificial neural network (ANN), recurrent neural network (RNN), and long-short-term memory (LSTM). The experimental results indicate that the proposed method outperforms other techniques and can be a reliable approach for prediction of noise annoyance with an accuracy of 93.8%. It can be concluded from noise maps that the noise levels in all locations of the Dhanbad city were higher than 70 dB(A) and noise sensitivity is the most important input variable of traffic-induced noise annoyance, followed by honking noise, education, exposure hours, LAeq, sleeping disorder, and chronic disease. The study shall facilitate in developing a decision support tool for prediction of noise annoyance and promoting implementation of suitable public policy in urban cities.