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As the world economies have become more integrated and with the global economy subsequently growing, there is increasing concern regarding how such trends will affect the developing and developed nations’ trade, trade in services, gross domestic product (GDP) growth, and climate change. In fact, the relationship between globalization and the environment has become quite contentious in policy circles. In part in response to these controversies, a burgeoning amount of academic attention has emerged that examines the globalization linkage between trade and economic growth as an after effect (positive and negative). Although there have been advances in the thinking about these relationships, significant challenges still persist. In light of the above, this chapter talks about the evolution of globalization, prevailing benefits and also caters to the views of some famous economists like Stiglitz, Simon Kuznets. The crucial aspects include the review of globalization on the basis of certain parameters like Trade (as % of GDP), Trade in services (as % of GDP), per capita CO₂ emissions and per capita GDP that have impacted the trends of both developed and developing nations. It arrives at the position that many current and proposed national and multilateral environmental policies are in a possible conflict with current and proposed trade and investment rules. When climate policy and global trade rules are combined, the nature of their linkage is often a function of both domestic and international politics. This also implies that, despite the worldwide awareness of climate change, the address of climate change in trade will become increasingly significant for reducing carbon footprints.

The global economic fallout following the unexpected onset and rapid spread of COVID-19 pandemic worldwide, in early 2020, has necessitated international and national action plans towards new normal models of realignment in enterprise bottom-line and management. In 2020, ‘Supporting Small Business through the COVID-19 Crisis’ was declared the lead theme of the MSME Day – June 27 – by the UN. A ‘COVID Response Alliance for Social Entrepreneurs’ was launched by an affiliate of the World Economic Forum (WEF). Drawing inspiration from the ‘small business’ focus of the UN MSME Day declaration and the ‘social entrepreneurship’ perspective of the WEF, the study seeks to draw few perceptions and conclusions in the post-COVID economic recovery context of India, where Micro, Small and Medium Enterprises (MSMEs) are observed to be a key driver of development, thanks to an add-on supportive package in the wake of the COVID-19 economic crisis. It is found that the package fails to provide a direct push for promotion of social enterprises/entrepreneurship in the Indian MSME sector, as there is no focused policy approach on leveraging ‘entrepreneurship resources’. Hence, the general trend of the sector continues to be dominated by the ‘for-profit first’ concern rather than a fair blend of ‘social value creation first’, with ‘profit’. Discourse on social entrepreneurship and action-oriented rehabilitation tools proposed in the Covid context globally have failed to reorient the dominant outlook of social enterprises in India – business as a tool for achieving social impact – to social impact as a spontaneous/positive outcome from business. The study highlights the lapses on the ground, of theoretical formulations, despite their couching in Covid contexts, and the need for a more institutionalised enabling environment for social value creation, impact investment and social stock exchange in the social enterprise ecosystem.

Breast cancer (BC) is one of the leading cancer in the world, BC risk has been there for women of the middle age also, it is the malignant tumor. However, identifying BC in the early stage will save most of the women’s life. As there is an advancement in the technology research used Machine Learning (ML) algorithm Random Forest for ranking the feature, Support Vector Machine (SVM), and Naïve Bayes (NB) supervised classifiers for selection of best optimized features and prediction of BC accuracy. The estimation of prediction accuracy has been done by using the dataset Wisconsin Breast Cancer Data from University of California Irvine (UCI) ML repository. To perform all these operation, Anaconda one of the open source distribution of Python has been used. The proposed work resulted in extemporize improvement in the NB and SVM classifier accuracy. The performance evaluation of the proposed model is estimated by using classification accuracy, confusion matrix, mean, standard deviation, variance, and root mean-squared error.

The experimental results shows that 70-30 data split will result in best accuracy. SVM acts as a feature optimizer of 12 best features with the result of 97.66% accuracy and improvement of 1.17% after feature reduction. NB results with feature optimizer 17 of best features with the result of 96.49% accuracy and improvement of 1.17% after feature reduction.

The study shows that proposal model works very effectively as compare to the existing models with respect to accuracy measures.