The UN agenda of Sustainable Development Goals (SDGs) 2015–2030 is a holistic approach. Universities play an important role in dissemination of quality knowledge, developing the skills and attitudes of a large number of youth across the world. Though the emphasis on Education for Sustainable Development (ESD) started as early as 1992, yet Universities adopted the concept of Green Campus integrating the environmental, social and economic aspects of sustainability quite recently. In developing countries including Pakistan, the Green Campus Initiatives (GCI) have not been implemented in the majority of the Universities. Northern Pakistan comprising Azad Jammu & Kashmir (AJ&K) and Gilgit Baltistan (GB) faces multiple challenges including Climate Impacts at the top. The fragile ecosystem of the region requires more sustainable initiatives at the University and community levels. In this research, the readiness of the seven universities located in Northern Pakistan have been assessed for GCI on the basis scanning of the websites and questionnaire survey of the relevant stakeholders. The results have shown that there is little commitment of resources for sustainability from senior management, lack of awareness in faculty & staff and less research focus on the related themes of green campus. The co-curricular activities in universities are not linked with sustainability and there are no incentives for faculty, staff and students to this end. It has been recommended that Green Campus Framework may be developed for Pakistani Mountain Universities, with commitment from leaders of the universities and allocation of sufficient resources for development of sustainable campuses. The Higher Education Commission of Pakistan (HEC) needs to allocate special funds for promoting GCI across universities in Pakistan.

Smart electric meters play a pivotal role in making energy systems decarbonized and automating the energy system. Smart electric meters denote huge business opportunities for both public and private companies. Utility players can manage the electricity demand more efficiently whereas customers can monitor and control the electricity bill through the adoption of smart electric meters. The study examines the factors affecting the adoption intention of smart electric meters in Indian households. This study draws a roadmap that how utility providers and customers can improve the smart electric meters adoption. The study has five independent variables (performance expectancy, effort expectancy, social influence, environmentalism, and hedonic motivation) and one dependent variable (adoption intention). The sample size for the study is four hundred and sixty-two respondents from Delhi and the National Capital Region (NCR). The data was analysed using structural equation modelling (SEM). The results of this study have confirmed that performance expectancy, environmentalism, and social influence have a significant impact on the intention of adopting smart electric meters. Therefore, utility providers can improve their strategies to attract more customers to adopt smart electric meters by focusing more on the performance of smart electric meters and by making them environmentally friendly. This research offers meaningful insights to both customers and utility providers to make energy systems decarbonized and control energy consumption.

Infrastructure investment has long been held as an accelerator or a driver of the economy. Internationally, the UK ranks poorly with the performance of infrastructure and ranks in the lower percentile for both infrastructure investment and GDP growth rate amongst comparative nations. Faced with the uncertainty of Brexit and the likely negative economic impact this will bring, infrastructure investment may be used to strengthen the UK economy. This study aims to examine how infrastructure funding impacts economic growth and how best the UK can maximize this potential by building on existing work.
The research method is based on interviews carried out with respondents involved in infrastructure operating across various sectors. The findings show that investment in infrastructure is vital in the UK as it stimulates economic growth through employment creation due to factor productivity. However, it is critical for investment to be directed to regional opportunity areas with the potential to unlock economic growth and maximize returns whilst stimulating further growth to benefit other regions. There is also a need for policy consistency and to review UK infrastructure policy to streamline the process and to reduce cost and time overrun, with Brexit likely to impact negatively on infrastructure investment.

Taxus cuspidata Sieb. ET. Zucc. is a taxus of Taxaceae, a rare third-order relict species distributed in northeastern China, and a wild endangered plant species protected by national level I. Taxol (paclitaxel, trade name taxol) and cephalomannine (cephalomannine) are all diterpenoids contained in the genus Taxus, with broad-spectrum anti-tumor activity and unique anti-cancer mechanism. In this study, the distribution of paclitaxel and cephalomannine in the leaves of Taxus cuspidata in different parts and different growth stages was discussed. The results showed that the content of two substances in the leaves of the majority of the crowns was lower than that of the biennial and tertiary there were no significant differences in the contents of two substances in the two-year and three-year-old

foliage. There was no significant difference in the contents of the two layers in the three levels of the noodles, and

the content of the male was slightly higher than that of the dark. The content of paclitaxel in the leaves of natural

northeast yew was the highest at dormancy period, and the content of flowering and fruit was not much different. The

content of Cephalotaxin was the highest in dormancy period, and that of cephalosporin the content of paclitaxel and

cephalomannine in each plant were significantly different. There was significant difference between the two plants.

New technologies always have an impact on traditional theories. Finance theories are no exception to that. In this paper, we have concentrated on the traditional investment theories in finance. The study examined five investment theories, their assumptions, and their limitation from different works of literature. The study considered Artificial Intelligence (AI) and Machine Learning (ML) as representative of financial technology (fintech) and tried to find out from the literature how these new technologies help to reduce the limitations of traditional theories. We have found that fintech does not have an equal impact on every conventional finance theory. Fintech outperforms all five traditional theories but on a different scale.

To study the environment of the Kipushi mining locality (LMK), the evolution of its landscape was observed using Landsat images from 2000 to 2020. The evolution of the landscape was generally modified by the unplanned expansion of human settlements, agricultural areas, associated with the increase in firewood collection, carbonization, and exploitation of quarry materials. The problem is that this area has never benefited from change detection studies and the LMK area is very heterogeneous. The objective of the study is to evaluate the performance of classification algorithms and apply change detection to highlight the degradation of the LMK. The first approach concerned the classifications based on the stacking of the analyzed Landsat image bands of 2000 and 2020. And the second method performed the classifications on neo-images derived from concatenations of the spectral indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Building Index (NDBI) and Normalized Difference Water Index (NDWI). In both cases, the study comparatively examined the performance of five variants of classification algorithms, namely, Maximum Likelihood (ML), Minimum Distance (MD), Neural Network (NN), Parallelepiped (Para) and Spectral Angle Mapper (SAM). The results of the controlled classifications on the stacking of Landsat image bands from 2000 and 2020 were less consistent than those obtained with the index concatenation approach. The Para and DM classification algorithms were less efficient. With their respective Kappa scores ranging from 0.27 (2000 image) to 0.43 (2020 image) for Para and from 0.64 (2000 image) to 0.84 (2020 image) for DM. The results of the SAM classifier were satisfactory for the Kappa score of 0.83 (2000) and 0.88 (2020). The ML and NN were more suitable for the study area. Their respective Kappa scores ranged between 0.91 (image 2000) and 0.99 (image 2020) for the LM algorithm and between 0.95 (image 2000) and 0.96 (image 2020) for the NN algorithm.