According to Niti Aayog, the demand for energy in India is set to grow from the current level of 5,311 TWh to 18,635 TWh by 2047, an increment of 3.5 times. As demand for energy observes an unprecedented increase, it becomes pertinent to explore novel sources of energy, such as distributed renewable energy systems (DRES).

The domino effect set in motion by COVID-19 is a blunt reminder that under-prepared systems will have to bear devastating shocks. Climate change is one such inexorable force that not only amplifies existing risks but also creates new risks to natural and human systems.

India is blessed with abundant sunlight. Tapping even 0.1% of the incident solar energy in 2019-20 would have given us nearly 3.5 times the total energy generated from all sources in the year! Currently, India's solar-energy generation utilises only about 0.001% of the incident solar energy. Leveraging this immense potential is crucial to power the country via clean energy.

In February 2015, the Government of India announced the ambitious target of installing 175 GW of renewable energy (RE) by 2021-22. This target comprises 100 GW solar, 60 GW wind, 10 GW biomass and 5 GW small-hydro. Today, the total installed RE capacity is expected to cross 70 GW and account for around 20% of the country's power generation capacity.

One consequence of the recent demonetisation was a push towards the digitisation of the economy - a move that will create transparency in the financial system. But this digital push must be accompanied by greater security of digital transactions to deal with the tsunami of cybercrimes that is bound to follow. India's enforcement mechanisms, laws and policies must be re-examined immediately to ensure that the theft of data or money is dealt with severely, swiftly and transparently.

One of the undisputed benefits of the recent demonetisation exercise has been the concerted push towards digitisation of cash transactions. Concomitant with this development has been the rapid growth in the use of smart devices, primarily mobile phones. Though digital payment systems have been in existence for a while, the last few weeks have witnessed an explosive growth in their use. While many welcome the idea of trackable, transparent and frictionless monetary transactions, there are significant risks associated with moving to these systems.

Emergency and disaster management has become a widely researched area in the last decade. The use of Information and Communication Technologies (ICTs) has been highly advocated for addressing the obstacles and improving decision-making in the event of a disaster. A number of ICT support systems and frameworks, both conceptual and application-based, have evolved over time to support the highly time and collaboration intensive task of emergency and disaster management. The use of ICTs like GIS has helped the relief worker to a great extent.

In this article, we suggest the use of the Gini Index to measure the inequality in infrastructure services provided within an urban region. We develop a functional form for the Lorenz Curve, the basis of measurement of the Gini Index. The sparse nature of data available to measure the distribution of infrastructure services within an urban region results in a challenge in estimating the Lorenz Curve.

Emergency and disaster management involves four stages: Mitigation (Planning), Preparedness, Response and Relief. Concerned authorities at different levels (central, state, district, etc.) play different roles and have different responsibilities in each phase of emergency and disaster management. GIS-based tools have been shown to be useful in the response and recovery phases of an emergency. In this paper, we propose how GIS-based simulation tools can be used in the Mitigation and Preparedness phases of emergencies and disasters.

With the increase in simulation of urban environments for the purpose of planning, modelling vehicular traffic has become important. While empirical evidence on traffic flow is relatively sparse, models representing the same are being increasingly used for planning urban roads and environments. In this paper, a simple explicit model is proposed to approximate the speed versus density of vehicular traffic flow.