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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]

Abstract: This paper presents a methodology to measure the environmental co-benefits of transport initiatives, defined here as carbon emissions in conjunction with local air pollution. An evaluation tool was developed and then tested on the case of the Delhi metro. The metro is an extensive rail project spanning the Indian capital, which is also the world's first rail based Clean Development Mechanism (CDM) project. However, it has also been a flashpoint in urban policy-making in the city in recent years. This analysis identifies the co-benefits based on the current situation as well potential co-benefits based on increased ridership and altering mode share contributions. The paper then discusses the challenges faced in the quantification process and the practical implications of achieving increased co-benefits. The paper highlights issues of data quality as well as data access, whilst identifying that factors peripheral to the project itself can have a critical effect in achieving co-benefits from large scale transport infrastructure projects in developing countries. [Copyright &y& Elsevier]

Abstract: Despite growing interest in, and government support for, ISO14001 certification across Asia in recent years, little is known about issues surrounding the uptake and the effectiveness of environmental management systems in this region. There is a particular void of academic work in relation to ISO certification in India and the country's large number of small and medium enterprises; a field of inquiry this paper seeks to advance. Specifically, as a means of providing a basis for classification of firms for an analysis of the state of ISO14001 in India we test the adequacy of critical factors such as nature of business, size, year of establishment and internationalisation among small and medium enterprises and industry best companies. An analysis is presented of secondary data derived from the websites of a sample of ISO14001 certified firms located in the cities of Delhi and Noida. The data show that in the area of manufacturing the highest number of ISO14001 certifications can be recorded and that established small and medium enterprises with high turnover are more likely to adopt ISO14001 certification compared to low turnover firms. The findings inform the status of environmental management systems in India and have implications for government policy intended to foster ISO14001 uptake by Indian firms. [Copyright &y& Elsevier]

Accelerated urbanization and industrialization have led to poor air quality, which threatens human health with various lung ailments. Monitoring, modeling, and forecasting air quality would be a prudent way to promote awareness and defend human beings from the adversities of air pollution. The air quality of a region is monitored through various air quality monitoring stations built in and around it. The air quality data collected from these stations are highly dynamic, nonlinear, and hold intensely stochastic spatiotemporal correlations in them. Deep learning algorithms that are capable of extracting a higher level of abstraction in data can efficiently capture the spatiotemporal features in it. In this paper, we propose a hierarchical deep learning model named DL-Air that embodies three components for air quality forecasting. The first component, the encoder, encodes all spatial relations in the data. The second component, STAA-LSTM, a proposed variant of LSTM, identifies all temporal relations and the level of association between the identified spatiotemporal relation and the forecast. Also, the STAA-LSTM predicts the future spatiotemporal relations in the latent space. The third component, decoder suitably decodes these relations to obtain the actual forecast. The proposed framework was extensively evaluated for forecasting the real-world air quality data of Delhi. DL-Air shows better performance with around 30% reduced RMSE and MAE, 37% reduced AAD, 11% improved R2 and 8% improved accuracy in AQI category prediction than the best performing baseline approaches. Also, the predictive performance of DL-Air is found to be consistent across all seasons in Delhi. Image 1 • Air pollution monitoring and forecasting is the primary step for air pollution control which is mandatory for sustainable development. • Precise forecasting of the stochastic and highly nonlinear air quality data imposes the need for extracting the spatiotemporal relations in them and their associations on the forecast. • A hierarchical deep learning framework named DL-Air is proposed for performing accurate forecasting of the air quality of Delhi, the capital city of India. • The STAA-LSTM proposed and incorporated in DL-Air estimates the impact of the extracted spatiotemporal features over the forecast in latent space. • DL-Air shows better predictive performance than the best performing baseline approaches. [ABSTRACT FROM AUTHOR]

Passive photonic radiative cooling is used to make sky exposed surfaces sub-ambient with recent development in metamaterials throughout the day and night cycle. Daytime radiative cooling depends on the intensity of solar radiation, and both daytime and nighttime radiative coolings have a function of environmental factors like ambient temperature, humidity, wind velocity, cloud fraction, etc. Hence, the passive radiative coolers have location-specific performance and need analysis individually for a location before implementation. In this paper, the potential of passive radiative cooling for Indian cities of various climatic conditions covering maximum climate diversity is evaluated for different (ideal, broadband and selective) emissive radiative cooler surfaces. The effects of various environmental parameters on the cooler performance are studied. The cooling energy-saving potentials of various photonic radiative coolers as roof envelope integrated with the conventional air conditioning system are evaluated as well. Results reveal that the selective radiative cooler performs better in low humidity locations, namely Jaisalmer and Delhi, with maximum temperature drops of 20.6 °C and 17.3 °C, respectively. However, it is less likely to recommend for monsoon season and high humid cities. The cooling load reduction of 36% observed for low humidity locations with an integrated system. • Potential of photonic radiative cooling for various Indian climates is evaluated. • Effects of various solar-environmental parameters on cooler performance are studied. • Energy saving potentials of various radiative coolers as roof envelope are evaluated. • Selective cooler is suitable for low humidity and broadband one is for high humidity. • Cooling load reduction of 36% is observed for low humidity location with integration. [ABSTRACT FROM AUTHOR]