por Shahiza Irani en 23 de enero de 2026 a las 00:00
As one of the fastest growing economies in the world, it is critical for India to build climate-resilient infrastructure. Its rapidly increasing population growth and the consequent migration to urban areas, coupled with climate risks, have made infrastructure development a key priority area in India. Thus, ensuring that existing infrastructure and future constructions integrate strategies to address climate-related risks is vital. Despite the growing recognition of the importance of legal and policy landscapes on climate-resilient infrastructure, there is a very limited number of studies on this topic, especially in India. Thus, this study sought to bridge this gap by evaluating the influence of the Indian legal and policy framework on climate-resilient infrastructure. To this end, an analytical, comparative, and evaluative approach was adopted, employing benchmarks from international legal provisions and best practices from Japan’s legal and policy systems. In addition to doctrinal legal analysis using primary and secondary sources, case studies on the Mumbai Coastal Road Project and the Chandigarh–Manali Highway were performed to assess how the extant laws operate in the field. The findings indicate that while India’s legal system is gradually incorporating climate-risk considerations into its infrastructure sector, the effects of these considerations remain constrained due to institutional co-ordination challenges and limited enforceable obligations. Therefore, streamlining climate-resilient infrastructure governance requires more robust climate change legislation and improved implementation mechanisms. The findings of this study provide useful insights for legislators and policymakers in strengthening climate resilience integration within India’s infrastructure governance framework.
Sustainability, Vol. 18, Pages 1153: A Diagnostic Framework for Socially Sustainable AI Diffusion
por Munirul H. Nabin en 23 de enero de 2026 a las 00:00
Artificial intelligence (AI) promises large productivity gains, yet growing concern surrounds its implications for social sustainability. This paper develops and empirically evaluates a simple behavioral framework in which unequal access to AI generates mutually reinforcing gaps in economic performance and social visibility, potentially undermining the long-run stability of social systems. Individuals fall into two groups—AI adopters and non-adopters—and differences in productivity and social recognition give rise to two exchange rates: an Economic Exchange Rate (EER), capturing relative economic advantage, and a Social Exchange Rate (SER), capturing relative social visibility and recognition. AI strengthens the feedback between economic success and social standing, and the joint evolution of EER and SER is stable only when the product of two feedback parameters lies below unity. When this threshold is approached, the system enters a regime of systemic disequilibrium, in which economic and social disparities expand endogenously. Using panel data for 30 economies over the period 2012–2025, we provide empirical evidence of strong mutual reinforcement between economic and social advantage, with feedback strength rising as AI diffusion accelerates. The findings suggest that unequal AI access poses risks not only to equality but to social sustainability itself. The paper contributes a diagnostic framework for socially sustainable AI diffusion, highlighting the need for policies that dampen amplification mechanisms and strengthen inclusive pathways from economic performance to social recognition.
por Varsha Vinod en 23 de enero de 2026 a las 00:00
Understanding the economic performance of heritage-rich towns requires a systematic evaluation of how heritage-related components collectively contribute to revenue generation. Existing studies often examine heritage assets, socio-cultural factors, physical infrastructure, and local economic conditions independently, resulting in fragmented insights that limit comprehensive planning for local economic development. This study develops and validates an integrated Cultural Heritage Economy Model that quantifies the influence of heritage resources, social, physical, and economic aspects on revenue generation in heritage contexts. The model is conceptualized through a structured synthesis of theoretical literature and domain-specific indicators, followed by construct operationalization, expert validation, and pilot-level assessment. Using Structural Equation Modelling (SEM-PLS), the study demonstrates strong reliability, convergent validity, discriminant validity, and significant structural relationships. The predictive relevance of the final model is further evaluated through PLSpredict, confirming its suitability for future estimation. The findings confirm that revenue generation is a product of the combined and mutually reinforcing effects of heritage, socio-cultural, physical, and economic dimensions, rather than just by the influence of heritage resources. By offering this novel, empirically grounded, multidimensional framework to estimate heritage-driven economic outcomes, this research establishes a foundational model that can guide evidence-based resource allocation, policy formulation, and long-term sustainable urban development planning.
por Hassan Samih Ayoub en 23 de enero de 2026 a las 00:00
In the era of digital transformation, organizations increasingly invest in Artificial Intelligence (AI) to enhance competitiveness, yet persistent evidence shows that AI investment does not automatically translate into superior firm performance. Drawing on the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT), this study aims to explain this paradox by examining how AI-enabled dynamic capability (AIDC) is converted into performance outcomes through organizational mechanisms. Specifically, the study investigates the mediating roles of organizational data-driven culture (DDC) and organizational learning (OL). Data were collected from 254 senior managers and executives in U.S. firms actively employing AI technologies and analyzed using partial least squares structural equation modeling (PLS-SEM). The results indicate that AIDC exerts a significant direct effect on firm performance as well as indirect effects through both DDC and OL. Serial mediation analysis reveals that AIDC enhances performance by first fostering a data-driven mindset and subsequently institutionalizing learning processes that translate AI-generated insights into actionable organizational routines. Moreover, DDC plays a contingent moderating role in the AIDC–performance relationship, revealing a nonlinear effect whereby excessive reliance on data weakens the marginal performance benefits of AIDC. Taken together, these findings demonstrate the dual role of data-driven culture: while DDC functions as an enabling mediator that facilitates AI value creation, beyond a threshold it constrains dynamic reconfiguration by limiting managerial discretion and strategic flexibility. This insight exposes the “dark side” of data-driven culture and extends the RBV and DCT by introducing a boundary condition to the performance effects of AI-enabled capabilities. From a managerial perspective, the study highlights the importance of balancing analytical discipline with adaptive learning to sustain digital efficiency and strategic agility.
por Hong Zhang en 23 de enero de 2026 a las 00:00
A low-carbon economy serves as a core pathway and pivotal engine for advancing the SDGs. Drawing on provincial panel data across 30 Chinese administrative regions spanning 2011–2023, the present study empirically examines how new infrastructure interacts with low-carbon economic development levels and their underlying transmission mechanisms by building an econometric model. Empirical results demonstrate that “new infrastructure” generates a notably positive facilitating impact on low-carbon economic development, with this influence being more pronounced in the central and western regions of China and policy pilot zones, while a rebound effect is identified in eastern China. Among various types of new infrastructure, information infrastructure and innovation infrastructure play particularly prominent roles, while integrated infrastructure shows a positive yet statistically insignificant impact. Mechanism analysis reveals that new infrastructure advances low-carbon economic progress primarily by curbing capital factor misallocation, while the elevation of the population urbanization level can amplify the facilitative impact of new infrastructure on the low-carbon economy. On this basis, it is imperative to raise investment in new infrastructure and enhance its systematic coordination with traditional infrastructure; implement differentiated layout strategies aligned with regional features; rationally steer the population urbanization process; and effectively facilitate the decoupling of carbon emissions from economic growth, thereby furnishing a robust underpinning for the full attainment of SDGs.