Proper servicing and maintenance of RAC units can curtail up to 50 per cent degradation in its performance that happens with time. The servicing sector is also a source of up to 40 per cent of annual refrigerant demand in India. Thus, the ability of the servicing sector to deliver sustainability gains for the RAC sector is significant – it can help deal with refrigerant overuse for servicing, and bring operational energy-efficiency closer to the designed benchmarks.

The current requirement of skilled technicians in India is around 0.35-0.4 million, which will increase to 2 million by 2026-27, and around 5.6 million by 2046-47.

Yes, the good servicing practices are beneficial for the end-users. It improves the energy efficiency of RACs, reduces the possibility of refrigerant leakages, and enhance the life-span of RAC units. This results in significant savings in the operational cost of RACs.

Loss and damage (L&D) finance addresses irreversible impacts of climate change — arising from both slow-onset processes (such as sea-level rise) and extreme weather events (such as cyclones and heatwaves) — where adaptation efforts are no longer sufficient. These impacts cause long-term economic losses (e.g., destruction of infrastructure, homes, and crops) as well as non-economic harms (e.g., cultural loss, displacement, and health impacts). L&D finance supports the ex post activities needed for relief, recovery, rehabilitation, and reconstruction, with a focus on ensuring that vulnerable communities are not pushed further into crisis due to climate-induced shocks.

The burden sharing framework applies 11 indicators across responsibility, capability, and vulnerability — with equal weightage — to derive a balanced and evidence-based approach to mobilising and accessing finance. By analysing 150 countries, the framework identifies five differentiated groups that reflect real-world differences across historical emissions, economic readiness, and exposure to climate risks. These results inform which countries can contribute more and which require predictable, grant-based support, while recognising that all three principles remain essential to global equity.

Climate impacts have intensified and expanded to regions with limited historic responsibility and constrained economic capacity. Vulnerability, therefore, captures current and projected harm, reflecting compounding disaster exposure, socioeconomic inequalities, institutional readiness, and climate-driven risks to development. Integrating vulnerability complements responsibility and capability, ensuring that global L&D finance decisions respond to lived realities. This strengthens legitimacy, negotiating leverage for the Global South, and allocation effectiveness within the FRLD.

Under existing arrangements, contributions to L&D finance remain voluntary, fragmented, and unpredictable, limiting the pace and quality of response. A minimum allocation floor equivalent to 0.08 per cent of GDP operationalises a clear, consistent, and politically rational starting point for developed-country contributions. It enhances predictability, supports long-term planning, and provides a credible basis for negotiations — particularly as the FRLD moves into its disbursement phase in early 2026. The benchmark is also conservative, reflecting the lower-bound of L&D finance needs and aligning with established burden-sharing logic.

The FRLD represents the financial arm of the L&D governance ecosystem, alongside the Warsaw International Mechanism (overarching arm) and the Santiago Network (technical arm). The burden-sharing framework supports efforts to strengthen alignment, clarify mandates, and enable smoother access pathways, especially for the most vulnerable.

As of 2024, only about 600 electric trucks are in operation across India, the majority being heavy-duty trucks (HDTs). To accelerate the deployment, industries and government agencies have signalled demand for around 5,000 ZETs by 2027 and approximately 7,700 ZETs by 2030. These projections are supported by multi-prolonged initiatives, including NITI Aayog’s e-FAST India programme, the Bharat ZET policy Advisory framework with 30 proposed interventions, and the National Green Hydrogen Mission, which provides viability gap funding for alternate fuel vehicles. Despite these efforts, ZET adoption faces headwinds related to technology, infrastructure, costs, and operator perceptions.

The PM -Edrive initiatives allocate ₹ 11,000 crore, ₹ 2,000 crore as a capital grant for charging infrastructure development. It also sets an aggregate target of 1800 fast chargers (240 kW each) across 20 major truck corridors and 35 national highways, providing the foundation for large- scale ZET adoption. The Delhi-Agra section, NH44, is a strategic freight corridor connecting multiple logistics hubs, warehouses, and industrial zones in Faridabad, Palwal, Hodal, Mathura, Vrindavan, and Agra, as well as to widen NCR. It forms part of both the Amritsar-Kolkata Industrial Corridor (AKIC) and the Golden Quadrilateral, linking east-west and north-south national highway corridors. Recognised as a priority trucking corridor by the Ministry of Heavy Industries (MHI) and a high-traffic corridor by the Ministry of Power (MoP), it carries diverse loads, including time-sensitive perishables, bulk materials, and essential consumption goods. Its consistent truck volumes and travel patterns make it a strong candidate for piloting ZET deployment and planning EVCI.

The greenfield anchor nodes, which are large centralised charging hubs, are necessary in stretches with sparse existing installations or low-to-moderate site suitability. But these hubs require higher upfront CAPEX; they can be future-proofed through substation upgrades and renewable integration (PV+BESS). Meanwhile, retrofitting and co-locating chargers at fuel stations, warehouses, and logistic parks can expand coverage quickly and cost-effectively along high-suitability stretches. This distributed approach reduces land acquisition needs, accelerates deployment, and mitigates range anxiety. So a hybrid approach with greenfield anchor hubs at sparse existing installations and retrofitted EVCIs at high suitability stretches can provide both throughput and accessibility, anchoring broader ZET rollout.

To meet the daily charging demand of ~1000 ZETs (30% of truck movements on NH44), two deployment strategies are possible: Anchor node (hub) model: There are about five hubs with 50-75 high-capacity chargers in total. The 240 kW hub option has a higher upfront cost (~ ₹ 77.6 crore) but delivers strong throughput and long-term efficiency. The 350 kW hub option has a slightly lower cost (~ ₹ 66.3 crore) while also maintaining reliability, requiring new 33/11 kV substations and larger land parcels. Distributed network model: About 12 network sites with 36-60 chargers spread along the corridor. The 350 kW network option deployment cost is approximately ₹ 72 crore, offering accessibility at shorter intervals (10-20 km), whereas in the 500 kW network option, there is a slightly lower cost of ₹ 56 crore but with risks of underutilisation and demand fragmentation. In short, 5 hubs (240/350 kW) or 12 distributed sites (350/500 kW) could support 1,000 ZETs. The hub model is CAPEX-heavy but efficient for long-term GHG abatement, while the distributed network improves accessibility, redundancy and early adoption.

Burnt area indices are satellite data-based indicators that detect burnt fields by identifying changes in field characteristics before and after burning.

Satellite-borne sensors can only detect active fires during the overpass. Coarse spatial and temporal resolution, obstruction due to clouds and smoke, are also challenges associated with remote sensing-based CRB monitoring.

In complete burning, the entire leftover straw after harvesting is set on fire, whereas in partial burning, only the loose straw left after running the harvester is burnt.

An accurate estimate of the extent of CRB helps assess the effectiveness of CRB mitigation policies. It will also help improve the emission estimates from CRB. In turn, this will lead to improvements in the performance of air quality models that run on these estimates.