Global electric vehicle adoption: implementation and policy implications for India

It starts at home: non-economic factors influencing consumer acceptance of battery storage in Australia

Homeowners play a critical role in the uptake of low-carbon technologies, yet little is known about the factors that underlie market acceptance of residential battery storage. This research integrates social-psychological, demographic and behavioural factors into a holistic model that predicts market acceptance. Previous research has indicated that social factors play a crucial role in the adoption of rooftop solar. Still, the influence of subjective norms on battery storage, a relatively invisible technology, has yet to be fully understood. An online survey from homeowners in Australia, a mature renewable energy market, is used to provide insights into market acceptance that are relevant to international energy markets. A two-step econometric model, using factor analysis and ordered logistic regression, was used for data analysis. The results show that subjective norms, moral emotions and an environmental self-identity are positively associated with market acceptance. Demographic factors, such as younger age and higher levels of education, predict market acceptance. Motives such as technical interest, autarky and load-shifting behaviours are also relevant. Several recommendations for policymakers and practitioners are offered to improve the acceptance of battery storage, including interventions that exploit social parameters and appeal to consumer psychology.

Transforming air pollution management in India with AI and machine learning technologies

A comprehensive approach is essential in India's ongoing battle against air pollution, combining technological advancements, regulatory reinforcement, and widespread societal engagement. Bridging technological gaps involves deploying sophisticated pollution control technologies and addressing the rural-urban disparity through innovative solutions. The review found that integrating Artificial Intelligence and Machine Learning (AI&ML) in air quality forecasting demonstrates promising results with a remarkable model efficiency. In this study, initially, we compute the PM2.5 concentration over India using a surface mass concentration of 5 key aerosols such as black carbon (BC), dust (DU), organic carbon (OC), sea salt (SS) and sulphates (SU), respectively. The study identifies several regions highly vulnerable to PM2.5 pollution due to specific sources. The Indo-Gangetic Plains are notably impacted by high concentrations of BC, OC, and SU resulting from anthropogenic activities. Western India experiences higher DU concentrations due to its proximity to the Sahara Desert. Additionally, certain areas in northeast India show significant contributions of OC from biogenic activities. Moreover, an AI&ML model based on convolutional autoencoder architecture underwent rigorous training, testing, and validation to forecast PM2.5 concentrations across India. The results reveal its exceptional precision in PM2.5 prediction, as demonstrated by model evaluation metrics, including a Structural Similarity Index exceeding 0.60, Peak Signal-to-Noise Ratio ranging from 28-30 dB and Mean Square Error below 10 μg/m3. However, regulatory challenges persist, necessitating robust frameworks and consistent enforcement mechanisms, as evidenced by the complexities in predicting PM2.5 concentrations. Implementing tailored regional pollution control strategies, integrating AI&ML technologies, strengthening regulatory frameworks, promoting sustainable practices, and encouraging international collaboration are essential policy measures to mitigate air pollution in India.

Green hydrogen: Paving the way for India's decarbonization revolution

The potential of green hydrogen to make India self-sufficient and energy-independent is reviewed in this study. We integrated technological advancements, economic analysis, and policy frameworks to provide a comprehensive overview of the green hydrogen landscape in India. This review examines cost reductions in electrolyzer technology, the potential for renewable energy integration, and the socio-economic benefits of green hydrogen adoption. Additionally, the study proposes innovative policy measures tailored to India's unique conditions, such as targeted subsidies and incentives for green hydrogen production and use. The research highlights significant cost reductions and increased renewable power generation as key factors contributing to the economic viability of green hydrogen in India. It underscores the importance of large-scale production and advancements in electrolyzer technology. Furthermore, the study emphasizes the necessity of clear regulatory frameworks, infrastructure development, and financing to support the deployment of a green hydrogen economy in India. By implementing a strategic roadmap for green hydrogen, India can reduce its reliance on fossil fuels, lower greenhouse gas emissions, and become a major player in the global green hydrogen market. The proposed policy measures and technological advancements are crucial for successfully adopting and deploying green hydrogen, ensuring energy self-sufficiency and long-term economic sustainability for India.

Promoting the Diffusion of New Energy Vehicles under Dual Credit Policy: Asymmetric Competition and Cooperation in Complex Network

This study aims to investigate the impact of dual credit policy on the diffusion of new energy vehicles (NEVs) from the perspective of complex interactions among heterogeneous manufacturers. Thus, the market competition and credit cooperation relationships, constituting the complex interrelated system in NEV diffusion, are considered in this paper. To this end, we established a double-layer complex network to depict the asymmetric competition and cooperation structure, and developed an evolution game model on network, revealing the diffusion rule and trend of NEVs among manufacturers. Simulation results show that the existence of credit cooperation relationship can effectively improve the diffusion of NEVs, especially when implementing cooperative strategy that prioritizes helping manufacturers with low sale profits. Such a cooperative strategy is effective for maintaining high diffusion of NEVs even under low NEV credit’s trading price. Meanwhile, the monopoly competitive structure characterized by scale free is harmful to NEV diffusion. However, credit cooperation can eliminate the by-effect of monopoly structure greatly by adopting the strategy of distributing by sale profits. In addition, manufacturers are advised to learn from their competitors during the evolutionary process, which should be the focus of manufacturers to maximize the NEV diffusion under small world competitive structure.