Today India has about 4 million trucks on the road and these carry about 70% of the country’s domestic freight. With the freight activity of trucks projected to grow by more than two times by 2050 and trucks already responsible for about half the well-to-wheel CO₂ emissions from on-road transport, adopting zero-emission technologies such as battery electric and fuel-cell electric trucks is critical to decarbonizing the transport sector and achieving India’s climate goals.  

But a successful transition to zero-emission trucks will require extensive data, including about truck travel patterns and operations. In India, the data that’s currently available to the public has gaps and is not in a form that’s useful for researchers and policymakers. Fortunately, there are several ways to begin to fill these gaps. 

It’s not difficult to see where the data challenges come from. For one, trucking in India is largely unorganized: 80% of the operators have small fleets of fewer than 10 trucks. Beyond the sheer multiplicity of truck operators, small operators tend not to log data about their operations like larger fleets do. For another, there is limited data published by the national government. The Ministry of Road Transport and Highways (MoRTH) maintains the national register of vehicles registered by regional transport offices and it provides up-to-date information on the registration of different types of vehicles disaggregated by fuel type, vehicle classes, and region of registration. MoRTH also publishes an annual road transport yearbook that reports national-level freight activity in tonne-km estimated as a function of gross domestic product, but researchers find that these estimates are significantly overestimated. Furthermore, data on the age of vehicles, annual vehicle activity, load factor, and energy consumption is scarce. This results in a wide range of baseline energy consumption estimates and projections for India’s truck fleet.  

International examples offer ways to improve. The European Union implemented a regulation in 2012 that mandates the collection of various data on road freight transport through regular surveys about fleet operators, their operations, and goods transported. In addition, the EU-funded European Transport Policy Information System (ETISplus) project consolidated various datasets into a new reference dataset of road freight transport at various levels: major socio-economic region (NUTS1), states (NUTS2), and district or county (NUTS3). This was instrumental in estimating truck movement on the European highway network and developing recommendations for electric vehicle charging infrastructure deployment targets in the European Union for 2030.  

Similarly, in the United States, highway statistics compiled by the Federal Highway Authority contain annual average daily traffic count at different road sections from state transport agencies through the Highway Performance Monitoring System (HPMS), which uses equipment such as loop detectors and laser sensors. This dataset has supported research on county-level zero-emission truck charging and highway hydrogen refueling needs. 

With these in mind, here are some opportunities that we see for India. First, there are more than 1,000 toll plazas on national and state highways that already use FASTag, a Radio Frequency Identification (RFID)-based electronic toll collection system that was launched in 2014. Additionally, the MoRTH is conducting pilots for an automatic number plate recognition system and plans to introduce GPS-based toll collection to replace toll plazas in the long term. Toll tax collection regularly captures data on daily traffic disaggregated by different vehicle segments. The National Highway Authority of India (NHAI), under the MoRTH, is responsible for developing, managing, and maintaining national highways, and this data could potentially be captured and processed by the NHAI or an expert agency to estimate traffic counts disaggregated by vehicle types at different road sections and at different times of the day. The NHAI or MoRTH could then publish and maintain this dataset in the public domain. Figure 1 shows what the data flow process could look like. 

Figure 1. Traffic count data that already exists at toll plazas through FASTag and how it could be processed and published.

Second, India implemented an electronic way bill (e-way bill or EWB) system under the Goods and Services Tax (GST) regime and it essentially contains the details related to the shipment or consignment of cargo. The consignor generates the bill for transporting goods of more than INR 50,000 in value and it contains a great deal of information on the origin and destination, mode of transport, vehicle type, and goods transported. (The goods exempted from GST are also exempted from the e-way bill system.) More recently, the e-way bill system was integrated with the FASTag and Vahan (national vehicle registry by MoRTH) systems to facilitate the real-time tracking of truck movement to curb tax evasion. This data could be processed by either NHAI or an equivalent expert agency to estimate traffic volume counts and origin and destination matrices in a way that’s useful for researchers and policymakers. MoRTH can then manage the dataset and publish it at regular intervals. 

The Directorate General of Commerce Intelligence and Statistics (DGCI&S), under the Ministry of Commerce, already manages and publishes data on the interstate movement of goods via rail and air, and acknowledges the wide data gap on the interstate movement of goods by road. The data generated by the e-way bill system can help bridge that gap. Figure 2 shows a potential roadmap for such a data-collection system using e-way bills. 

Figure 2. Truck movement data that could be collected through EWB.

Third, as part of PM GatiShakti, the national master plan for multi-modal connectivity, a Unified Logistics Interface Platform (ULIP) was launched to enable seamless data sharing among government and private entities that are directly or indirectly involved in the Indian logistics eco-system. It enables real-time inventory management and monitoring of cargo movements for shippers, identifies demand for transporters, and serves as a planning tool for policymakers to improve logistics in India. Thus, there are already a few different avenues for road freight data collection in India and what’s left is to make the data available in the public domain.  

The future of clean trucking in India hinges in part on our ability to effectively gather, analyze, and leverage truck data from multiple sources. At present, independent research groups are carrying out small-scale surveys in select geographies to fill data gaps and inform policies. This is a highly inefficient use of time and resources.  

As India transitions to zero-emission trucks, truck travel patterns and operations data become critical for designing new vehicles, effectively deploying supporting refueling infrastructure, and crafting a variety of policies and programs for decarbonization. Government bodies and agencies could collaborate to address the information gap, and only once it’s bridged can we unlock the full potential of India’s trucking industry. 

Last week, we released a wide-ranging analysis estimating that more than 150,000 jobs could be needed in the United States to deploy “behind-the-meter” charging infrastructure for electric light-duty vehicles (LDVs) and medium- and heavy-duty vehicles (MHDVs) through 2032. The term “behind the meter” refers to the customer’s side of the electricity meter and the term “front of the meter” is used when talking about the utility’s side, where there’s infrastructure such as substations, transformers, and feeder lines (Figure 1).

For HDVs specifically, the new study estimated that the Environmental Protection Agency’s (EPA) proposed HDV Phase 3 greenhouse gas (GHG) standard could generate as many as 16,000 jobs by 2032, or about 10% of the national total. But that’s only part of the jobs story.

As we’ll explore here, when all the jobs to construct the infrastructure to channel megawatt-scale power to chargers at private depots and public charging plazas for battery electric trucks and buses are considered, the utility-side infrastructure in front of the meter is likely to require a workforce an order of magnitude larger than the workforce building out customer-side infrastructure.

Figure 1. Battery-electric MHDV charging infrastructure ecosystem.

Let’s look at a preliminary, top-down jobs estimate based on available national-level data. It’s sensitive to assumptions about how individual chargers are configured into charging stations, how expensive utility grid upgrades are at each charging station, and how utility investments translate into jobs in the economy.

Still, we make generally conservative assumptions and the eventual number of jobs created could be larger. First, while the total number of chargers is based on a projected level of zero-emission vehicle adoption supported by the EPA HDV GHG Phase 3 proposal, in previous analysis we found that market forces, aided by Inflation Reduction Act (IRA) incentives, can support a larger number of zero-emission MHDVs and may draw even greater investments in charging infrastructure. Second, we do not fully account for possible infrastructure investments upstream from the distribution substation to support the largest multi-megawatt installations with peak loads greater than 10 MW.

We arrived at the job estimates in Figure 2 by first aggregating the nameplate capacity of 100 kW, 350 kW, and 1 MW chargers into a total number of hypothetical charging stations. The cost of grid upgrades and connection costs for charging stations were taken from previous ICCT research and utility upgrade cost estimates by the National Renewable Energy Laboratory (NREL). Next, we converted dollars invested in distribution grid capacity into a total number of direct and indirect jobs in the United States required and supported by these investments; this is based on an economic impact analysis of a utility’s substation transformer upgrade costs and other high-level utility infrastructure economic impact studies (here and here). Direct jobs are those related to the core construction and electrical work, for example installing substations and laying feeder lines; indirect jobs are upstream manufacturing, administrative, and other jobs not immediately involved in utility upgrade activities.

Under the most optimistic level of electrification likely to occur with the proposed EPA HDV Phase 3 GHG rule, we project more than 493,000 overnight 100 kW chargers, nearly 17,000 fast 350 kW chargers, and around 12,800 ultra-fast 1 MW chargers by 2032. We estimate up to $21 billion would need to be invested in distribution grid capacity to support these chargers, also by 2032.

These calculations, combined with the behind-the-meter jobs our colleagues estimated, suggest approximately 262,000 direct and indirect full-time equivalent jobs would be necessary to support the most optimistic rates of electrification to meet the EPA proposal by 2032 (Figure 2). More than 94% of these jobs come from what would be needed for utility-side infrastructure deployment. These front-of-the-meter jobs are wide-ranging and include substation construction, laying conduit, wiring, installing transformers and meters, laying feeder lines and their foundations, and manufacturing electrical grid components and assembly of these assets.

Figure 2. Estimated direct and indirect jobs created from infrastructure investments in MHDV electrification under the most optimistic rates of electrification to meet the EPA Phase 3 GHG proposal by 2032.

Billions of dollars in public investments are already funding charging infrastructure deployment at the federal and local levels. Private sector investments from companies such as TerraWatt Infrastructure, WattEV, Forum Mobility, and GreenLane reflect this growing industry.

Our estimates suggest the vast majority of charging infrastructure job creation will occur not in the manufacturing and installation of chargers themselves, but in the distribution grid assets that power the chargers. Finalizing the EPA Phase 3 proposal would generate significant momentum toward this job creation and the potential is even greater when accounting for the additional market potential shaped by IRA incentives. It’s key that utilities and regulators not only recognize the potential in constructing infrastructure assets in front of the meter, but that they begin planning to deliver front-of-the-meter assets and prepare their workforce in a time frame consistent with the EPA Phase 3 proposal and beyond.

About this event

The ICCT in collaboration with NITI Aayog is organizing a one-day workshop on Low Emission Zones (LEZs) in Indian cities. LEZs, designated areas where certain vehicles, particularly those with high emissions, are restricted or prohibited, have proven effective in reducing air pollution worldwide. Additionally, LEZs play a crucial role in promoting the adoption of electric vehicles, aligning with NITI Aayog’s proactive advocacy in this area.

Our workshop, in association with the Raahgiri Foundation & SUM Network, is scheduled for February 19, 2024 in New Delhi, and aims to raise awareness about LEZ benefits, discuss best practices for LEZ implementation in Indian cities, and formulate a roadmap for future actions.

The workshop will include discussions on the following topics:

1. The benefits of LEZs for air quality and public health
2. Case studies of successful LEZs from around the world
3. Experiences in implementing LEZs in Indian cities
4. Legal pathways for developing LEZs in India
5. The role of technology in supporting LEZ implementation

The workshop will, we believe, significantly contribute to ongoing efforts to improve air quality and enhance EV adoption in Indian cities.