An assessment of bilateral debt swap financing indispensable for economic growth and environment sustainability: a policy implication for heavily indebted countries.

Bilateral debt swap is an innovative global financing mechanism designed to support heavily indebted countries (HICs). It is a debt-restructuring process involving donor countries forgiving debt owed by HICs in exchange for commitments to undertake projects on environment and socio-economic development. It is a unique approach designed to help heavily indebted countries get back on their feet. Effective debt swap financing can lead to both economic growth and environment sustainability, but they are challenging to implement. This study examines the impact of bilateral debt swap financing on economic growth and environment sustainability. For the purpose, we have used debt swap index developed with Kaiser-Meyer-Olkin (KMO) methodology. KMO widely used approach of Principle Component Analysis (PCA) to solve the problem of "over-identification" and make strong correlation among endogenous variables of interest. In order to validate the nexus empirically between bilateral debt swap financing with economic growth and environment sustainability, we have employed the Two-Step System Generalized Method of Moments (SYS-GMM) approach in 25 countries for the period of 2002 to 2021. This modern econometric method addresses endogeneity issues and controls for unobserved heterogeneity in panel data. At the same time, the technique addresses the simultaneity problem, reverse causality, and remove selection bias. Findings of the study shows that effective bilateral debt swap financing can boost economic growth and environment sustainability by investing domestic resources for targeted activities along with reduced debt burden. Empirical results reveal that 1% change in debt swap financing can lead to a maximum of 0.23% growth in the economy and 0.28% improvement in environment sustainability. However, it is important to note that in most empirical specifications, results are inconclusive. One possible reason for this is often ineffective debt swap practices coupled with inadequate monitoring and evaluation in HICs. Policymakers should focus on enhancing debt swap policies to promote economic growth and environment sustainability.

Symmetric and asymmetric analysis of trade and environment in Pakistan.

The study examines how trade influences the environment via the effects of scale, composition, and technique. The study is conducted for Pakistan based on yearly data between 1980 and 2020. The study takes into both symmetric and asymmetric analyses of trade and environment. The symmetric analysis is carried out through the autoregressive distributed lag (ARDL) model. ARDL ignores the nonlinear association between the variables and captures only the linear relationship among variables whereas nonlinear ARDL captures the asymmetry. Results of the symmetric analysis show that scale and composition effect positively contribute to CO2 emission while technique effect is inversely related to CO2 release. Openness to trade accelerates the CO2 discharge. The findings also confirm the presence of the pollution haven proposition. Moreover, natural resource abundance increases emissions while better governance reduces the emission. The asymmetric analysis illustrates that there is a nonlinear association between trade openness and carbon dioxide emission. The rise and decline in trade are found to be positive and insignificant respectively. Moreover, the findings of asymmetric analysis follow the findings of the symmetric analysis in direction. The study suggests that emissions can be reduced by using environment-friendly technology and by improving governance.

Decomposing the effect of trade on environment: a case study of Pakistan.

Using time-series data from 1984 to 2019, the study examines the vigorous trade-environment relation in Pakistan. Pakistan is an interesting case study in which trade liberalization has expanded economic activity while also increasing environmental pollution during the last two decades. As a result, determining whether trade and industrial operations have contributed to environmental degradation is crucial. Our first goal is to look at how trade affects the environment in terms of scale, composition, and technique. The second step is to look into the pollution haven theory. The study uses a new approach to measuring trade openness called composite trade intensity, which differs from the traditional approach. The dynamic autoregressive distributed lag (ARDL) simulation framework, which was recently developed, was employed. The findings show that the scale impact raises CO2 emissions while the technique effect helps to lessen them, proving the existence of an environmental Kuznets curve (EKC) hypothesis. The composition impact contributes to increased pollution in the environment. Through the expansion of pollution-intensive export businesses, trade openness degrades environmental quality over the long as well as in the short term. The notion of a pollution hypothesis has also been proven. The quality of the environment deteriorates as a result of urbanization, whereas it improves as a result of good governance. Economic growth, trade openness, urbanization, and CO2 emissions have bidirectional causality, according to frequency domain causality findings. Based on our empirical findings, the study concludes that individual efforts, as well as collective efforts at the international level to reduce carbon emissions, are critical to solving the problem of environmental degradation and making the world a completely peaceful place.

Impact of good governance and natural resource rent on economic and environmental sustainability: an empirical analysis for South Asian economies.

Good governance and natural resource rent are important pillars of sustainable development. The paper explores the role of governance and natural resource rent in enlightening the economic, social, and environmental sustainability. To achieve this objective, panel data for six selected South Asian countries from 1996 to 2020 is used. The second-generation unit-root test of Pesaran and panel unit root test of structural break proposed by Karavias and Tzavalis (Computat Stat Data Anal 76: 391-407, Karavias and Tzavalis, Comput Stat Data Anal 76:391-407, 2014) are utilized to examine the stationarity of variables and results confirm that variables are stationary at first difference. We used the first-generation cointegration test, i.e., Pedroni (1999), Kao (1999), and (Westerlund, Oxford Bull Econ Stat 69:709-748, 2007) and second-generation cointegration given by (Westerlund and Edgerton, Oxford Bull Econ Stat 70:665-704, 2008) test to confirm the co-integration and make long-run analysis by using fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) models. For robustness we also estimated cross-sectionally augmented autoregressive distributed lag model which is important to deal with heterogeneous slope coefficients and unobserved common factor. To check whether the residuals of the model were cross-sectionally dependent or not, we used Breusch and Pagan (1980) and Pesaran (2004) tests and confirmed the heterogeneity among sample countries by using (Pesaran and Yamagata, J Econometr 142:50-93, 2008) homogeneity test. The coefficients of long run analysis conclude that governance improves the environment by reducing greenhouse gas emissions (GGE) and is positively and significantly related to growth and social sector. Moreover, gross domestic product and trade openness are positively related to economic and social effect, whereas natural resources rent has a positive association with GGE. But the results confirm that with good governance, the natural resource rent can decrease greenhouse gas emissions. The recommendations of the study for policy purposes focus on that the governance will reduce GGE emissions and increase social and economic development and the countries should use more environment-friendly sources.

Bio-Imaging-Based Machine Learning Algorithm for Breast Cancer Detection.

Breast cancer is one of the most widespread diseases in women worldwide. It leads to the second-largest mortality rate in women, especially in European countries. It occurs when malignant lumps that are cancerous start to grow in the breast cells. Accurate and early diagnosis can help in increasing survival rates against this disease. A computer-aided detection (CAD) system is necessary for radiologists to differentiate between normal and abnormal cell growth. This research consists of two parts; the first part involves a brief overview of the different image modalities, using a wide range of research databases to source information such as ultrasound, histography, and mammography to access various publications. The second part evaluates different machine learning techniques used to estimate breast cancer recurrence rates. The first step is to perform preprocessing, including eliminating missing values, data noise, and transformation. The dataset is divided as follows: 60% of the dataset is used for training, and the rest, 40%, is used for testing. We focus on minimizing type one false-positive rate (FPR) and type two false-negative rate (FNR) errors to improve accuracy and sensitivity. Our proposed model uses machine learning techniques such as support vector machine (SVM), logistic regression (LR), and K-nearest neighbor (KNN) to achieve better accuracy in breast cancer classification. Furthermore, we attain the highest accuracy of 97.7% with 0.01 FPR, 0.03 FNR, and an area under the ROC curve (AUC) score of 0.99. The results show that our proposed model successfully classifies breast tumors while overcoming previous research limitations. Finally, we summarize the paper with the future trends and challenges of the classification and segmentation in breast cancer detection.