National Institute of Technology Karnataka

Purpose This study aims to explore the effectiveness of green finance (GF) dynamics, public–private investments and GF policies in advancing renewable energy through a literature review. Furthermore, this study seeks to identify the key barriers to green financing of renewable energy and proposes strategies to overcome these obstacles. Design/methodology/approach This study used preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines for document search using relevant keywords in the Scopus database. Bibliometric analysis was performed using R Bibliometrix and VOSviewer. Further screening guided by themes based on the abstract screening constructed during the study resulted in key findings, actionable implications, future research avenues and policy-level suggestions. Findings The bibliometric analysis indicated a notable increase in research activity post-2017, with China being the leading contributor. This study highlights that GF plays an important role in supporting renewable energy and sustainability, but its impact is highly uneven across countries and becomes effective only when strong structural and institutional conditions are present. Geopolitical risks, inflation and financial development maturity shape GF effectiveness. Mechanisms such as utility modification, social learning and capacity building enhance the GF’s impact, while challenges such as fossil fuel dependence and energy overuse remain. Public–private partnerships, financial instruments and policy interventions are vital for creating a favorable investment environment. Green bonds and energy performance contracts are promising, but transparency is crucial for preventing greenwashing. Robust environmental regulations, fiscal flexibility and support for technology transfer and project-level funding are essential for effective GF implementation. Originality/value This study is an initial attempt to integrate GF and renewable energy. It explores trends in academic research and examines the effectiveness of GF instruments, policies and public–private partnerships in advancing renewable energy and reducing carbon emissions.

The full potential solar photovoltaic (PV) energy is not utilised by the mining industry due to heavy dust deposition and harsh operating condition. This study aims to quantify the seasonal impact of dust deposition on PV performance and determine the optimal cleaning frequency for optimize the performance of solar PV Panel in an operational mining condition. Additionally, an innovative automated dry dust cleaning system is designed, developed, and validated in the active mining field to evaluate its operational efficiency and durability. A comprehensive study of twenty-six week was conducted to monitor the variations in dust deposition density and output energy of PV panel across three distinct seasonal phases. Among these, pre-summer phase showed the highest dust deposition density of 5.98g/m2 and due to this maximum output power (Pmax) of dusty panel reduce by 63.50%. This phase experienced an intense mining activity and dry weather condition. Therefore, a cleaning schedule of 3 to 4 days is recommended to effectively utilise the potential of PV panel. The other two phases such as dry winter and early monsoon experienced comparatively lower average reduction of 35% to 40% in maximum output power. A moderate cleaning scheduled of 6 to 7day is recommended for these two phases Further, the developed dust cleaning system is validated in the mining field under three different environmental phases and an average recovery of 40% in Pmax is obtained across three phases. This presents the effectiveness of developed dust cleaning system under real operating condition. The outcome of this research highlights the need of dynamic cleaning schedule for effective utilisation of solar energy in mining industry. The integration of smart dust sensors and AI based predictive modelling of developed cleaning system for sustainable utilisation of solar energy in mining and allied industries.

This study develops vehicle and time-specific crash rate prediction models for rural highway curves using high-resolution geometric and speed data. A 30km segment of State Highway-1 in Karnataka, India, encompassing 32 horizontal curves, served as the study site. Detailed data collection included 10 years of crash records, traffic volume count, LiDAR-based geometric features, and spot speeds recorded from laser speed cameras. Distinct models were built for motorized two-wheelers (MTW), passenger cars (CAR), heavy commercial vehicles (HCV), and for both daytime and nighttime conditions. The study offers a novel contribution by incorporating nighttime crash rate modelling rarely addressed due to challenges in data availability, and by developing disaggregated models for multiple vehicle classes. A backward stepwise regression (BSR) approach with square root transformation was employed, ensuring model transparency and interpretability. Sight-distance deficiency consistently emerged as the most influential predictor of crash rate, highlighting the critical role of visibility on curved segments. Validation through Leave One Out Cross Validation (LOOCV) confirmed acceptable predictive performance (R² = 0.43-0.80), with residuals exhibiting normal distribution. The findings underscore the importance of curve geometry and visibility in crash risk and provide actionable insights for design audits and safety interventions on rural highways.