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This paper examines the portfolio carbon footprint of Indian banking system. To this end, we combine the emissions data from India's Biennial Update Reports to UNFCCC and Reserve Bank of India's bank database from 2010 to 2016; apply Network for Greening the Financial System's backward-looking methodology and Partnership for Carbon Accounting Financials Standards in conjunction with Leontief's Input-Output technique to estimate the weighted average carbon intensity (WACI) of bank's loans portfolio at three levels – System level, Bank group level and Spatial level. Our major findings include-first, at system level, the carbon footprint of Indian banks shows a rising trend over the study period with the aggregate WACI of bank loans increasing by 9.4% from 332.5 tCO 2 e/US$ million in 2010 to 363.7 tCO 2 e/US$ million in 2016. Second, at the group level, while the financed emissions of public sector banks have plateaued, for private sector banks they have nearly doubled during the study period, suggesting the increased exposure to carbon intensive assets. Third, on the spatial level, top five regions by WACI include Delhi, Maharashtra, Gujarat, Tamil Nadu, and Telangana in descending order. • First study to estimate carbon footprint of bank loans for India. • Combines environment input-output analysis with PCAF standard. • Analyses carbon footprint at three levels – system level, bank group level and spatial level. • Carbon footprint of Indian banks show a rising trend over the study period. • Financed emissions of public sector banks plateaued, of private sector banks doubled. [ABSTRACT FROM AUTHOR]

As the world struggles to cope with the pressing matters of global warming, rising temperature, and environmental degradation, the need for sustainable practices has become more urgent than ever for individuals and other establishments. Organizations across industries recognize the importance of adopting sustainable practices, not only to meet societal expectations but also to achieve long-term success. This research paper studies the influence of sustainable marketing on customer loyalty, focusing on the consumer electronics sector in India. A mixed-methods approach incorporating descriptive and causal research methods was used; with data collected from 302 participants from India's capital, Delhi and its nearby regions. Structural equation modeling was employed to establish multivariate relationships and Smart PLS 4 was used to analyze the data. The results suggest that sustainable marketing practices positively influence brand image and loyalty. Additionally, brand image acts as an intermediary between sustainable marketing and customer loyalty. These results provide valuable insights for managers aimed at fostering customer loyalty through sustainable practices, particularly in the consumer electronics sector. [ABSTRACT FROM AUTHOR]

This paper studies a collection centre location problem for e-waste acquisition from households. The proposed location-allocation model is a bi-objective mixed-integer non-linear model that maximises waste collection and minimises the cost of collection. The model considers that both drop-off and pick-up policies are in place, i.e., people can either drop their e-waste at a collection centre or request doorstep pick-up of their e-waste. The model considers that people's decision to drop off their e-waste at the collection centres depends on the financial incentive offered by the firm and the distance of the collection centre. However, in the case of pick-up, no incentive is provided, and it is considered that the households' willingness to return depends on people's concern for environmental degradation. The proposed model decides the location of the collection centre, the assignment of customer nodes, the incentive offered, and the e-waste collected using both pick-up and drop-off methods. Instances generated using a real case of Delhi, India, demonstrate the feasibility and validity of the proposed model, and the epsilon constraint method is employed to handle its multi-objectivity. Furthermore, sensitivity analysis is used to assess the relative importance of the parameters on the model output. The study offers valuable theoretical and practical implications. • A bi-objective mixed-integer non-linear program model is proposed. • Both pick-up and drop off strategy are simultaneously considered. • Drop off is modelled as a function of incentive and distance. • The ε -constraint method is employed to solve the proposed bi-objective mixed-integer non-linear program model. [ABSTRACT FROM AUTHOR]