The climate crisis is no longer a distant event that might happen in the future. It is here, bringing new and previously unimaginable challenges. Temperatures are rising, rainfall patterns are shifting, and extreme events such as record-high temperatures and heavy rainstorms are becoming more common.

This year in India, the month of February was the hottest so far since 1901. According to a study in The Lancet, published in July 2021, with two decades of data (2000-2019), more than five million people died on average each year worldwide because of extreme temperatures.

Over the past decade, digitalisation has been expedited across all industries. This has enabled businesses to store, manage, and remotely retrieve large amounts of both critical and non-critical data. In addition, improvements in the precision of global navigation systems, expansion of trade and commerce, increased access to quality education, and other data-intensive procedures have contributed to an elevated quality of life and heightened productivity.

With an increasing number of countries setting strong net-zero targets, there is a global push for the gradual discontinuation of fossil-fuel usage and the large-scale adoption of renewable energy (RE). In this context, green hydrogen is seen as the fuel of the future, with various governments and energy companies betting on it.

In recent years, blockchain technology has made significant strides across various industries, from finance to supply chain management.

Recent studies show that 67% of public transport commuters in India shifted to private vehicle ownership after the initial Covid-19 crisis (during 2021-22). Around 96% of these new vehicles were internal combustion engine vehicles (ICEVs) and will be in use for at least a decade, creating around 18.8 MtCO2 emissions annually.
Our analysis infers that if all of the newly added vehicles in India (during 2021-22) were electric vehicles (EVs), around 9.5 MtCO2 of emissions could have been avoided annually.

The Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS) is a national poverty alleviation programme that provides 100 days of guaranteed wage employment to rural households whose adult members demand unskilled manual work. The scheme includes more than 260 activities (called ‘works’) that are categorised into natural resource management (NRM)-based and non-NRM-based rural development works.

In this study, we combined state-of-the-art data modelling techniques (machine learning [ML] methods) and data from state-of-the-art low-cost particulate matter (PM) sensors (LCSs) to improve the accuracy of LCS-measured PM2.5 (PM with aerodynamic diameter less than 2.5 microns) mass concentrations. We collocated nine LCSs and a reference PM2.5 instrument for 9 months, covering all local seasons, in Bengaluru, India. Using the collocation data, we evaluated the performance of the LCSs and trained around 170 ML models to reduce the observed bias in the LCS-measured PM2.5.

To effectively manage air pollution, we need to measure it accurately and at high spatial resolution. However, maintaining a dense network of regulatory instruments is financially and technically burdensome for low- and middle-income countries. A hybrid approach that combines non-conventional, less expensive, short-term stationary, and mobile deployments may be a cost-effective solution. In the city of Bengaluru, India, we adopted such a hybrid measurement approach to generate high spatial resolution air pollution maps.

MGNREGS is a poverty alleviation programme implemented pan India. As an employment guarantee scheme, it successfully captures information on the number of jobs and assets created. However, assessment of the multiple climate co-benefits arising from assets created through the programme has been a blind spot. While there have been many studies that attest to the climate co-benefits of MGNREGS works, they have been limited in their geographical scope, resulting in constrained reporting of the accrued benefits.