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Developing new ornamental cultivars with improved floral attributes is a major goal in floriculture. Biotechnological approach together with classical breeding methods has been used to modify floral color, appearance as well as for increasing disease resistance. Transgenic strategies possess immense potential to produce novel flower phenotypes that are not found in nature. Adoption of Genetic engineering has supported the idea of floral trait modification. Ornamental plant attributes like floral color, fragrance, disease resistance, and vase life can be improved by means of genetic manipulation. Therefore, we witness transgenic plant varieties of high aesthetic and commercial value. This review focuses on biotechnological advancements in manipulating key floral traits that contribute in development of diverse ornamental plant lines. Data clearly reveals that regulation of biosynthetic pathways related to characteristics like pigment production, flower morphology and fragrance is both possible and predictable. In spite of their great significance, small number of genetically engineered varieties of ornamental plants has been field tested. Today, novel flower colors production is regarded as chief commercial benefit obtained from transgenic plants. But certain other floral traits are much more important and have high commercial potential. Other than achievements such as novel architecture, modified flower color, etc., very few reports are available regarding successful transformation of other valuable horticultural characteristics. Our review also summarized biotechnological efforts related to enhancement of fragrance and induction of early flowering along with changes in floral anatomy and morphology.

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In recent times, economic policy uncertainty and geopolitical risk have escalated exponentially, and these factors affect both the economy and the environment. Therefore, the objective of this study is to investigate whether economic policy uncertainty and geopolitical risk impede CO2 emissions in BRICST countries. We employ second generation panel data methods, AMG and CCEMG estimator, and panel quantile regression model. We find that all variables are integrated at I (1), and there exists co-integration among considered variables of the study. Moreover, we note that economic policy uncertainty and geopolitical risk have a heterogeneous impact on CO2 emissions across different quantiles. Economic policy uncertainty adversely affects CO2 emissions at lower and middle quantiles, while it surges the CO2 emissions at higher quantiles.On the contrary, geopolitical risk surges CO2 emissions at lower quartiles, and it plunges CO2 emissions at middle and higher quantiles. Further, GDP per capita, non-renewable energy, renewable energy, and urbanization also have a heterogeneous impact on CO2 emissions in the conditional distribution of CO2 emissions. Based on the results, policy direction was discussed.