Search

Receivers and intermediary establishments typical to urban areas have unique freight behaviour and are understudied, particularly in developing countries. This paper uses establishment-based freight survey data to examine their freight generation patterns in the Chennai metropolitan area, India. Three sets of Simple Linear Regression (SLR) models, three single predictor proportional odds logit models, one multiple linear regression model, and one multiple predictor proportional odds logit model with business size (employment, area, and operational age) and indicators of establishment category as regressors are developed each for both freight production and freight attraction. Partial proportional odds logit models are developed in a few cases to overcome the limitations of proportional odds logit models. The best SLR model varied with the establishment category for freight production and attraction. The establishment area model is the best among single predictor proportional odds logit models. The multiple predictor proportional odds logit models marginally improved the fit over single predictor models. The proportional odds logit model results show that the establishment category has a greater impact on freight generation levels than business size variables. Since earlier studies rarely focused on receivers and intermediary establishments, policymakers may benefit from the developed models and study insights while estimating freight demand and developing freight policies. • Receivers and intermediary establishments' freight generation is studied. • Partial Proportional Odds (PO) logit models are developed for freight attraction. • Area model is the best single predictor PO logit model for freight generation. • Establishment type affects freight generation more than business size variables. [ABSTRACT FROM AUTHOR]

Despite several research evaluating the connection between sustainability and the bottom line of firms, the existing research is inconclusive. In addition, most studies focus on multinational firms based in the United States or Europe, while research in the context of emerging economies is scant. This paper endeavours to investigate the strength of the association between Environment, Social and Governance (ESG) disclosure scores and the financial performance of 272 Indian firms listed on National Stock Exchange for the period 2015 to 2021 by employing panel data regression techniques. The results indicate that organisations having better ESG performance outperform their industry peers financially and are valued more by the market. Moreover, the data indicates that financial markets for lenders consider ESG disclosure when determining creditworthiness. In accordance with this, businesses can examine their operations and become more aligned with eco-friendly methods. Even though ESG disclosure appears to be associated with improved financial success, this is not the case across all industries and during COVID-19 pandemic. This study is relevant due to the growing significance of sustainability and the role of emerging nations in international commerce. It helps managers prioritize resource allocations to ESG-related activities that may affect financial performance differently across industry sectors, thus adding to the literature on the financial effects of ESG disclosures, particularly industry-specific aspects. This research represents a pioneering effort by assessing the impact of ESG disclosure on the profitability, market value, and cost of financing of Indian firms using both aggregation and disaggregation methods. [ABSTRACT FROM AUTHOR]

In the emerging landscape of Industry 5.0, the integration of innovative technologies such as Cognitive Digital Twins (CDTs) is pivotal for steering industrial processes towards sustainability and inclusivity, particularly in developing nations striving to align with Sustainable Development Goals (SDGs). The study explores the transformative capabilities of CDTs in Industry 5.0, concentrating on India's quest for inclusive and sustainable industrialization in alignment with SDGs. The inclusion of the TOE-HOT framework enriches the research's analytical foundation, providing a nuanced perspective to investigate the multifaceted dynamics of technology, organization, environment, and human aspects within the sphere of industrial evolution. By employing the Best-Worst Method (BWM), Interpretive Structural Modelling (ISM), and MICMAC analysis, the study delves deep into the synergies between industrial strategies, technology, and sustainable development. The findings reveal the paramountcy of the Technological Dimension, emphasizing CDTs strategies like real-time optimization and data analytics, which are instrumental in dynamically adjusting manufacturing processes and fostering alignment with market demands. The research also stresses the roles of various CDTs strategies like strategic alignment, change management, job security, skill augmentation, and environmental compliance in industrial sustainability and inclusivity. The paper concludes with invaluable insights for policymakers, industry leaders, and researchers, emphasizing the necessity for a holistic, interconnected approach to fully harness the potential of CDTs in realizing the aspirations of Industry 5.0 and contributing to India's sustainable industrial advancement by 2030. [ABSTRACT FROM AUTHOR]

Literature has highlighted the existence of a gap in clean fuel usage between the social groups in most emerging nations across the world, which is detrimental to global efforts to achieve Sustainable Development Goal (SDG) 7 (access to clean energy) and SDG 10 (reducing inequalities) and just energy transition with recognition justice. This paper, assesses the clean fuel gap across social groups in India using three rounds of representative data (2004, 2014, and 2018) by applying the logit model, exogenous switching treatment effect regression (ESTER), and the Blinder–Oaxaca Decomposition Model. Such an analysis is instrumental in identifying the clean energy usage gap and its causes, thereby providing insight to policymakers to design policies to achieve just energy transition and carbon neutrality by 2060. The results show a substantive clean fuel gap between social groups in India, which is a cause of concern for just energy transition. Clean energy utilization gaps among social groups are alarming across geographical locations and consumption quartiles, highlighting the importance of recognition justice for just energy transition. The results show that in rural areas, only 19.6 % of the General Caste households and 14.5 % of the disadvantaged social groups (Scheduled Tribes, Scheduled Castes, and Other Backward Class) in quartile one use clean energy, while in urban areas, the shares are 60.4 % and 46.3 %, respectively. To achieve a just distribution of the benefits of energy transition, energy policies should focus on disadvantaged social groups and simultaneously target General Caste households from a lower economic status and those residing in rural areas, indicating the need for targeting the neglected regions. [Display omitted] • Clean energy use has steadily increased among all social groups in India, indicating some improvement in energy justice. • Clean energy access gap among social groups has declined, but this gap still persists. • Scheduled Castes and Scheduled Tribes are the most disadvantaged social groups in terms of clean energy. • All social groups from low-income families have limited access to clean energy. • All social groups from households with low education have limited access to clean energy. [ABSTRACT FROM AUTHOR]