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Environmental degradation is frequently cited as one of the eminent issues in the modern era. To limit environmental degradation, prior literature discerns several macroeconomic, socio-economic, and institutional factors that affect environmental degradation. However, the relationship between geopolitical risk and environmental degradation is understudied in the previous literature. To fill this gap, the inquiry at hand aims to scrutinize the influence of geopolitical risk on environmental degradation for E7 countries while controlling the effect of renewable energy, non-renewable energy, and GDP. Further, we utilize both the ecological footprint and CO 2 emissions as proxies of environmental degradation and employ second-generation panel methods for robust findings. In addition to this, the present study uses augmented mean group (AMG) estimator to provide long-run relationship among the selected variables. The findings from the AMG estimator expound that there exists environmental Kuznets curve (EKC) for E7 countries. Moreover, renewable energy ameliorates environmental quality because it plunges both ecological footprint and CO 2 emissions. On the contrary, non-renewable energy consumption escalates both ecological footprint and CO 2 emissions. Finally, geopolitical risk tends to decrease CO 2 emissions as well as ecological footprint. Our findings deduce a few policy implications to replenish environmental quality. For instance, the share of renewables in the energy mix should be surged to ameliorate the environmental quality. Further, to control both the geopolitical risk and environmental degradation at the same time, policymakers should put forward reforms and initiatives (e.g., policies to escalate R&D, technological innovations, and tax exemptions on imports of renewables) that can help to improve environmental quality without affecting geopolitical risk. At times of low geopolitical risk, environmental degradation will surge; therefore, the rate of environmental control taxes should be increased by the policymakers.

Prior literature is substantive in highlighting the nexus between pollutant and socio-economic predictors; however, the role of human interaction has not been sufficiently explored. Thus, the present study examines the validity of the environmental Kuznets Curve (EKC) hypothesis in the presence of energy consumption, overpopulation, and human capital index in five South Asian countries. It employs fixed effects, random effects, and dynamic panel causality techniques with a set of panel data from 1972 to 2021. The baseline results validate the existence of the EKC hypothesis in the recipient panel. Nevertheless, the findings reveal that energy consumption and population density have positive effects, while human capital has negative impacts on CO 2 emissions. Furthermore, the study observes that energy consumption and per capita GDP have a significant causal link with CO 2 emissions, whereas CO 2 emissions are evident to have causality with population density and human capital index. The results are robust and suggest that the consolidation of an effective regulatory framework and technological improvements are substantial measures to improve environmental quality in South Asia. Moreover, allocating sufficient resources to uplift contemporary educational and health status would be imperative to improving environmental quality as aspired to by the Paris Agreement.

This study aims to achieve two main objectives; first, it provides a brief but critical description of the empirical literature on the environmental Kuznets curve (EKC) in terms of history, origin, micro-foundations, measurement of environmental degradation, methodologies and samples. Second, it examines the curious attraction of the EKC despite considerable criticism it has attracted over time. The motivation stems from the mixed results probably due to different econometric techniques, sample periods, country-specific factors and environmental indicators used to test EKC. The study concludes that of course, the EKC has attracted a great deal of criticism, but its survival power is undeniable. Different taxonomies of the approaches to explain income-environment nexus have been established by various commentators producing different results under different scenarios. It is still equally important among researchers to interpret the relationship between income and pollution due to its charismatic characteristics; therefore, the empirical literature on EKC continues to grow despite criticism on its validity and assumptions. However, we should not be convinced that economic growth on its own will solve environmental ills. The proposition that affluent countries will invest heavily to level off and gradually contain their environmental pollution should not be persuaded. Therefore, policymakers must not encourage unlimited economic growth to cure environmental problems.

This research combined global climate, crop and economic models to examine the economic impact of climate change-induced loss of agricultural productivity in Pakistan. Previous studies conducted systematic model inter-comparisons, but results varied widely due to differences in model approaches, research scenarios and input data. This paper extends that analysis in the case of Pakistan by taking yield decline output of the Decision Support System for Agrotechnology Transfer (DSSAT) for CERES-Wheat, CERES-Rice and Agricultural Production Systems Simulator (APSIM) crop models as an input in the global economic model to evaluate the economic effects of climate change-induced loss of crop production by 2050. Results showed that climate change-induced loss of wheat and rice crop production by 2050 is 19.5 billion dollars on Pakistan’s Real Gross Domestic Product coupled with an increase in commodity prices followed by a notable decrease in domestic private consumption. However, the decline in the crops’ production not only affects the economic agents involved in the agriculture sector of the country, but it also has a multiplier effect on industrial and business sectors. A huge rise in commodity prices will create a great challenge for the livelihood of the whole country, especially for urban households. It is recommended that the government should have a sound agricultural policy that can play a role in influencing its ability to adapt successfully to climate change as adaption is necessary for high production and net returns of the farm output.

Economic growth and trade openness are closely linked with energy consumption and hence have environmental consequences. Many studies have investigated the relationship between these variables. Two weaknesses in empirical literature on energy-growth nexus are prominent. First majority of the studies are conducted on different groups of countries; however, no study has focused the top energy-consuming countries despite their immense importance in the context of energy-growth nexus. Second, this literature cannot simultaneously capture time and frequency domains, short- and long-run dependence, and lagging and leading effects among the variables. Furthermore, environmental impacts of increased energy consumption emerging from trade base economic growth are less studied. This study employs wavelet transformed coherence method to examine dependence partners of energy consumption with economic expansion and trade openness in top 10 energy-consuming countries. This methodology avoids the unrealistic assumption of stationarity of the variables due to favorable scaling tool and unveils the time frequency dependence among variables with more reliability as it accounts for the seasonality, cycles, or trends extracted from the transformation change over time. Furthermore, this technique has the novelty to handle data when its transformation from one-dimensional to bi-dimensional time-frequency sphere is allowed. Findings reveal a positive influence of economic growth and trade on energy consumption in many countries. The wavelet transformed coherence indicates short-run coherence among energy consumption and economic growth of all the top 10 energy-consuming countries. Long-run dependence among energy consumption and economic growth exists in case of China, India, Brazil, and South Korea with mostly leading role of energy consumption over economic growth. The findings of the study reiterate the importance of energy consumption in the development of these economies and suggest that energy policies aimed at improving efficiency in the production and consumption of energy will not hurt economic growth.

The importance of sentiment analysis in the rapidly evolving financial markets is widely recognized for its ability to interpret market trends and inform investment decisions. This study delves into the target-level financial sentiment analysis (TLFSA) of news headlines related to stock. The study compares the performance in the TLFSA task of various sentiment analysis techniques, including rule-based models (VADER), fine-tuned transformer-based models (DistilFinRoBERTa and Deberta-v3-base-absa-v1.1) as well as zero-shot large language models (ChatGPT and Gemini). The dataset utilized for this analysis, a novel contribution of this research, comprises 1476 manually annotated Bloomberg headlines and is made publicly available (due to copyright restrictions, only the URLs of Bloomberg headlines with the manual annotations are provided; however, these URLs can be used with a Bloomberg terminal to reconstruct the complete dataset) to encourage future research on this subject. The results indicate that the fine-tuned Deberta-v3-base-absa-v1.1 model performs better across all evaluation metrics than other evaluated models in TLFSA. However, LLMs such as ChatGPT-4, ChatGPT-4o, and Gemini 1.5 Pro provide similar performance levels without the need for task-specific fine-tuning or additional training. The study contributes to assessing the performance of LLMs for financial sentiment analysis, providing useful insights into their possible application in the financial domain.

Background The high prevalence of tobacco use in Pakistan poses a substantial health and economic burden to Pakistani individuals, families, and society. However, a comprehensive assessment of the key risk factors of tobacco use in Pakistan is very limited in the literature. A better understanding of the key risk factors of tobacco use is needed to identify and implement effective tobacco control measures. Objectives To investigate the key socioeconomic, demographic, and psychosocial determinants of tobacco smoking in a recent large nationally representative sample of Pakistani adults. Methods N  = 18,737 participants (15,057 females and 3680 males) from the 2017–18 Pakistan Demographic Health Survey, aged 15–49 years, with data on smoking use and related factors were included. Characteristics of male and female participants were compared using T-tests (for continuous variables) and χ2-tests (for categorical variables). Multivariable logistic regression models were used to identify gender-specific risk factors of tobacco use. The Receiver Operating Characteristic Curve test was used to evaluate the predictive power of models. Results We found that the probability of smoking for both males and females is significantly associated with factors such as their age, province/region of usual residence, education level, wealth, and marital status. For instance, the odds of smoking increased with age (from 1.00 [for ages 15–19 years] to 3.01 and 5.78 respectively for females and males aged 45–49 years) and decreased with increasing education (from 1.00 [for no education] to 0.47 and 0.50 for females and males with higher education) and wealth (from 1.00 [poorest] to 0.43 and 0.47 for richest females and males). Whilst the odd ratio of smoking for rural males (0.67) was significantly lower than that of urban males (1.00), the odds did not differ significantly between rural and urban females. Finally, factors such as occupation type, media influence, and domestic violence were associated with the probability of smoking for Pakistani females only. Conclusions This study identified gender-specific factors contributing to the risk of tobacco usage in Pakistani adults, suggesting that policy interventions to curb tobacco consumption in Pakistan should be tailored to specific population sub-groups based on their sociodemographic and psychosocial features.

Energy is regarded as an engine of economic growth and an important ingredient of human survival and development, but it can lead to deterioration of environmental quality. The study investigates the energy environmental Kuznets curve (EEKC) during the 1990–2017 period for 144 countries using models for total energy, renewable energy, and non-renewable energy consumptions. We employ panel mean and quantile regressions, accounting for individual and distributional heterogeneities. It is found that the EEKC sustains among the higher middle-income countries while it cannot be verified at some lower-income quantiles due to the heterogeneous nature of the different groups of countries. The relationship between economic growth, total energy, and non-renewable energy consumption is positive and non-linear. The quantile estimations revealed mixed (positive and non-linear, inverted U-shape, U-shape, and N-shape) EEKC. The maximum and minimum turning values of GDP per capita for total energy consumption (is 43,201.58 and 89,630.49), for renewable energy consumption (53,535.07 and 89,869.41), and for non-renewable energy consumption (42,188.16 and 89,487.71). Urbanization and population growth had positive impacts on energy consumption while these effects become more significant as moving from low to high-income quantiles. The study implies that while the developed nations can adopt energy-efficient policies without compromising on the growth momentum and environment, this might be not recommended for the developing nations and it would be preferable for these countries to “grow first and clean up later.” The study indicates the importance of the developed nations to support the developing countries to achieve economic growth along the EEKC by transferring energy-efficient technologies.

The willingness to pay (WTP) plays a central role in directing appropriate policy regarding ambitious renewable energy targets. Based on this discrepancy, this study intends to investigate the willingness to pay (WTP) for Turkish citizens regarding green electricity by using a one-way analysis of variance (one-way ANOVA). The interviews were conducted comprising 2500 households in 12 major metropolitan cities of Turkey, which is based on the contingent valuation method and consists of 26 questions. The results indicate that for a 20% share of renewable energy, middle-income groups are willing to pay higher than lower and upper-income groups. Moreover, highly environmentally conscious people tend to pay more for a 20% share of green energy. On the other hand, high-income groups and old age groups indicated a positive and high willingness to pay for a 30% share of renewable energy (RE) sources. In addition, primary school and undergraduate educational groups recorded highly significant results for willingness to pay. The results also indicate that Turkish citizens are willing to pay 9.25 Turkish liras (TL) per month for a 20% share and 4.77 Turkish liras per month for a 30% share of renewable energy in total energy production.

Neuroimaging has made it possible to measure pathological brain changes associated with Alzheimer's disease (AD) in vivo. Over the past decade, these measures have been increasingly integrated into imaging signatures of AD by means of classification frameworks, offering promising tools for individualized diagnosis and prognosis. We reviewed neuroimaging-based studies for AD and mild cognitive impairment classification, selected after online database searches in Google Scholar and PubMed (January, 1985–June, 2016). We categorized these studies based on the following neuroimaging modalities (and sub-categorized based on features extracted as a post-processing step from these modalities): i) structural magnetic resonance imaging [MRI] (tissue density, cortical surface, and hippocampal measurements), ii) functional MRI (functional coherence of different brain regions, and the strength of the functional connectivity), iii) diffusion tensor imaging (patterns along the white matter fibers), iv) fluorodeoxyglucose positron emission tomography (FDG-PET) (metabolic rate of cerebral glucose), and v) amyloid-PET (amyloid burden). The studies reviewed indicate that the classification frameworks formulated on the basis of these features show promise for individualized diagnosis and prediction of clinical progression. Finally, we provided a detailed account of AD classification challenges and addressed some future research directions. •We reviewed Alzheimer’s disease neuroimaging-based classification studies.•We covered structural MRI, fMRI, DTI, amyloid-PET, FDG-PET, and multimodalities.•The reported studies were validated through appropriate cross-validation.•We categorized the studies based on feature extraction methods.•We discussed challenges, disparities in experimental conditions and future directions.