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Wednesday, September 23, 2026

Renewable Energy with Storage Can Match Coal’s Reliability and Operating Profile at a Lower, Fixed Price: Evidence from India's Market Test

by Amol Phadke, Nikit Abhyankar, and Umed Paliwal.

Despite dramatic declines in clean-energy costs, more than \$1 trillion investments in new coal and gas plants is under consideration worldwide, largely because conventional plants are assumed to be the only economical way to provide reliable, round-the-clock power, such as that required by data centers. India’s recent “thermal-mimic” auction directly tested this assumption by requiring renewables paired with storage to match the reliability and operating profile of conventional plants. Several major developers offered to provide this service at prices below those of conventional power. This calls for reassessing planned coal and gas power investments, especially because renewables combined with storage are also faster to deploy, modular, and cleaner. It also raises a broader question: would consumers, especially large industrial users, be better served by procuring low-cost clean power directly in a more competitive and less regulated market, rather than relying on monopoly utilities to purchase power on their behalf?

Despite dramatic progress in renewable energy and battery storage, more than a trillion dollars of investment in new coal and gas plants remains under consideration globally

The transformation in renewable energy over the past decade has been extraordinary, both in scale and cost. Solar PV and battery costs have fallen by nearly 70-80%, while global deployment has accelerated rapidly. In 2025 alone, the world added more than 600 GW of solar capacity, and solar continues to attract hundreds of billions of dollars of investment annually (IEA, 2026). India is at the forefront of this transformation, adding almost 50 GW of solar in 2025 and overtaking the United States to become the world’s second-largest solar market after China. Solar tariffs in India are now around ₹2.5/kWh, while solar + 4 hours of storage tariffs have reduced to as low as ₹2.9/kWh (SECI, 2025).

There is also growing operational evidence that batteries can support grid reliability at scale. Batteries are already supplying several gigawatts during critical peak periods in Texas, while California now has more than 21 GW of battery resources, increasingly shifting abundant daytime solar generation into the evening peak.

And yet there is an important paradox: power systems around the world are still planning enormous investments in new coal and gas generation. The United States has more than 250 GW of proposed fossil-fuel generation, predominantly gas. China and India are considering more than 200 GW and 100 GW, respectively, of additional coal capacity. Large technology companies such as Meta, despite having net-zero emissions commitments, are choosing to build gas generation capacity at scale. Taken together, these projects could represent well over a trillion dollars of new thermal investments, locking in massive greenhouse emissions for decades.

Why do planners still reach for coal and gas power plants?

The rationale for continued investment in fossil-fuel generation, despite record-low renewable-energy and storage costs, rests largely on two concerns.

First, battery storage today is typically deployed with two to four hours of duration—enough to shift inexpensive midday solar generation into the evening peak. But many major loads, including data centers and industrial facilities, require electricity around the clock. The conventional argument is therefore that storing enough solar energy to supply power through the 10 to 16 hours when solar output is low or zero would be prohibitively expensive.

Second, solar paired with storage is often assumed to be inherently less reliable than coal or gas because of weather variability, including periods of persistent cloud cover. Under this view, renewables and short-duration storage can supply an increasing share of electricity, but the system still requires conventional “firm” resources capable of delivering power whenever needed.

This question is especially important for India. Industry already accounts for a large share of electricity demand and requires substantial round-the-clock supply. Cooling demand increasingly extends well into the evening, while rapid growth in data centers, advanced manufacturing, electric mobility, and other new loads will add further demand for reliable, affordable 24×7 power.

India has already demonstrated that solar plus storage can economically shift cheap midday electricity into the evening peak. But the harder question is different: can renewable energy and storage reliably supply power through the 10–12 hours when solar generation falls to zero—and do so at a cost competitive with new thermal power plants?

How India designed a market test for firm renewable power

That is what makes the Solar Energy Corporation of India’s (SECI) recent 1,000 MW “thermal-mimic” FDRE-RTC auction, among the first of its kind globally at this scale, so important.

Rather than asking developers simply to supply renewable electricity or meet a short evening peak, SECI asked them to bid for a product designed to replicate the operating profile and contractual availability of a conventional thermal power plant. Developers would combine renewable generation and storage to provide firm, dispatchable power under a 25-year contract—similar in scale and duration to a large thermal power purchase agreement (PPA).

The auctioned profile closely follows the way India’s thermal fleet operates today: delivering the most electricity during the evening, night, and early morning, while backing down during solar-rich midday hours.

Figure 1: Average hourly net load, thermal-fleet operation, and thermal-mimic generation profile

Generators must supply at least 90% of contracted capacity during six hours nominated by the buyer within the 6 p.m.–10 a.m. window, at least 70% during the remaining non-solar hours, and 50–60% during solar hours. Performance is measured in every 15-minute block, with shortfalls penalized at 1.5 times the contract tariff. This binding 15-minute performance requirement was particularly important because earlier FDRE contracts allowed developers considerably more flexibility in how they met their delivery obligations. The thermal-mimic auction therefore represents a more stringent test of whether renewable energy and storage could reproduce the operating profile of firm conventional generation.

What did the market discover?

The answer was striking: “thermal-mimic” firm renewable power cleared at ₹5.25–5.26/kWh, fixed in nominal terms for 25 years. At this price, renewable energy combined with storage is cheaper than new conventional firm power in India.

This gives India something it did not have before: a competitively discovered market benchmark for renewable power designed to perform much like conventional firm generation. For utilities planning new capacity, the relevant comparison is therefore no longer between intermittent renewables and coal, but between different technologies capable of meeting the same underlying power requirement.

Is the price sustainable?

A natural question is whether ₹5.25/kWh reflects a replicable market price or simply an unusually aggressive outlier bid.

Figure 2: Results of the thermal mimic auction with winning bidders in green and brown, while red shows bidders that did not win the auction

The auction results provide considerable reassurance. Sixteen developers participated, with seven securing capacity and all winning bids falling within the narrow range of ₹5.25–₹5.26/kWh. NTPC Renewable Energy, the renewable arm of India’s largest thermal power generator, bid only 3% above the winning tariff, while ReNew, one of India’s largest private renewable developers, bid less than 1% above it. The close clustering of bids from two very different and large developers provides further evidence that the winning price was not an outlier. It is consistent with the underlying economics created by rapidly falling solar and battery costs.

How reliable is the project?

The remaining question is whether such a system can maintain the required output during difficult conditions, particularly monsoon periods, unusually cloudy days, and successive days of weak solar generation.

Paliwal et al. (2026) tested this using ten years of hourly weather data across ten Indian states. They find that for every 1,000 MW contracted, they find that a configuration of about 3 GW of solar and 12 GWh of battery storage in Rajasthan can deliver the thermal-mimic profile at an all-in cost below the ₹5.25/kWh auction price. In states with weaker solar resources or stronger monsoon effects, roughly 10–20% more solar is required, while storage remains around 12 GWh; even there, the modeled costs remain within about 7% of the auction price.

Earlier studies had already shown that this type of system could be technically feasible. (Chojkiewicz et al., 2025; Ember; IRENA). What the SECI auction adds is market evidence: major developers are now willing to put binding commercial bids behind that technical proposition. Projects of this scale remain rare globally. For example, Masdar’s 1 GW 24/7 clean-energy project is another prominent example.

A recent CSEP analysis cautions that low storage-auction tariffs can understate the cost of firm power when contracts allow monthly averaging or leave difficult hours to the buyer; under a much stricter every-hour firmness requirement, it estimates costs of roughly ₹8.3–11.8/kWh (Vijay and Tongia, 2026). The thermal-mimic auction addresses much of this concern by specifying output in 15-minute blocks every single day, and with explicit penalties for shortfalls, rather than relying on annual or monthly energy targets.

Three additional advantages not priced in the “thermal-mimic” market test

Recent analysis by Paliwal, Abhyankar and Phadke 2026 explains how this result is achievable and highlights three particularly important advantages beyond just lower prices for comparable performance

1. The cost advantage is significantly understated: ₹5.25/kWh stays fixed for 25 years while conventional power costs rise

The ₹5.25/kWh auction price is not only below the starting price of recently contracted coal power, it is fixed in nominal terms for 25 years. That makes the contract a long-term hedge against fuel-price and freight cost inflation.

Figure 3: Actual and projected utility power purchase costs, average realized revenue of NTPC, recent coal PPA prices, and thermal-mimic auction price

Data sources: PFC, Reports on Performance of Power Utilities, 2009-10 to 2024-25 editions (power purchase cost; FY2010-12 reconstructed from expenditure annexures); NTPC annual reports FY2012-FY2026 (average realized tariff, standalone). Projections: seven TBCB coal PPAs MarNov 2025 (11.9 GW, Rs 5.38-6.30, average 5.81; line anchored on the Rs 5.84 midpoint per SERC adoption orders: UPERC 2228/2025, MPERC 121/2025, WBERC, BERC 36/2025, AERC)

As shown in the figure, India's average power-purchase cost has been rising at 4-5% per year - from about ₹2.7/kWh in FY2010 to ₹5.4/kWh in FY2025 (solid blue line). NTPC's average realised tariff, predominantly reflecting its coal-based generation fleet, increased at a similar rate from about ₹2.6/kWh in FY2011 to ₹4.8/kWh in FY2026.

More importantly, several new coal power purchase agreement (PPA) prices already start above the thermal-mimic price. For example, the seven competitively procured coal PPAs signed in 2025 opened at ₹5.4–6.3/kWh (average of Rs 5.8/kWh). Coal tariffs are not fixed for the contract duration. They contain a fixed cost component (which typically includes depreciation, interest, maintenance etc) and a variable or fuel cost component that escalates over time. Assuming the variable cost increases at 2.6% per year (according CERC tariff norms), the average new coal PPA price could be as high as ₹7.4/kWh by 2050 (dotted red line). The thermal-mimic contract price, by contrast, remains fixed at ₹5.25/kWh throughout (solid golden line). For the 1 GW contract size operating at ~70% capacity factor, this is equivalent to an annual saving of Rs 350 - 1,300 Cr/yr, with a nominal NPV of over Rs 5,700 Cr over 25 years (assuming 10% discount rate).

The difference becomes even clearer in real terms. A nominal tariff fixed at ₹5.25/kWh over 25 years is equivalent to roughly ₹3.79/kWh in real 2026 rupees (assuming 4% annual inflation). In other words, the real cost of power under the contract declines every year. The same is true in dollar terms: ₹5.25/kWh is about \$55/MWh at ₹95 per dollar today; with a 3% annual depreciation of the rupee compared to USD (similar to long-term historical trends), it would fall to roughly \$41/MWh by 2036 and \$27/MWh by the final year of the contract.

The key comparison, therefore, is the fixed price for 25 years versus a coal tariff that starts higher and remains exposed to fuel-cost escalation.

2. Shorter lead times and modularity reduce the costs of overbuilding or underbuilding amid rapid but uncertain demand growth

A second major advantage is the combination of rapid deployment and modularity. The auction requires projects to be commissioned in less than two years, while conventional power plants typically require much longer development and construction periods. The results also show that developers are willing to offer similar tariffs for projects as small as 100 MW, roughly one-tenth the scale of a large coal plant.

Firm solar-plus-storage capacity can therefore be added incrementally as demand materializes. This reduces the risk of committing prematurely to large, indivisible assets that could leave the system with costly excess capacity or supply shortfalls if demand differs from forecasts. Such flexibility is especially valuable given the deep uncertainty surrounding the scale, timing, and location of AI-driven electricity demand.

3. Significant environmental benefits

Carbon emissions impose costs on every country, including the country that emits them. India has contributed relatively little to historical emissions, but it is highly exposed to climate damage. One study estimates India’s domestic social cost of carbon at \$86 per tonne of CO$_2$, with a 66 per cent uncertainty range of \$49 to \$157, the highest central estimate among the countries studied. Assuming coal emissions of 0.9 tonnes/MWh and an exchange rate of ₹95 per dollar, this corresponds to domestic damages of roughly ₹4 to ₹13/kWh, with a central estimate of about ₹7/kWh. These estimates are uncertain, but the policy implication is clear. Even if India disregards the damage its emissions impose on other countries, the avoided damage within India should be included when comparing coal with clean power. (Ricke et al., 2018)

Coal generation also imposes substantial local air-pollution costs. Cropper et al. (2021) estimate that premature mortality caused by air pollution from India’s coal-fired power plants imposes damages of ₹0.73/kWh, using a value of statistical life of ₹10.3 million. The authors describe this as a lower-bound estimate because it includes premature mortality but excludes morbidity and other effects of air pollution, including impacts on neurological development, worker productivity, crop yields, and visibility. Chakravarty and Somanathan (2021) estimate average air-pollution mortality damages from coal generation in India at 2.03 US cents/kWh, equivalent to ₹1.40/kWh using the authors’ 2018–19 exchange rate of ₹69 per USD. A reasonable conservative estimate is therefore that premature-mortality damages alone add roughly ₹1/kWh to the social cost of coal generation in India, with total local air-pollution damages likely higher.

Would it create significant import dependence on China and how to mitigate those risks?

One common criticism of renewable energy plus storage systems is their dependence on Chinese imports, particularly for battery cells. While India has developed a robust solar panel manufacturing base, its battery manufacturing and supply chains remain underdeveloped, and the country is indeed heavily reliant on China. But the relevant question is not simply whether that dependence exists. It is more nuanced such as how large the exposure is, where in the value chain it lies, how much leverage does China have etc.

First, India’s import dependence is concentrated in battery cells. Battery-cell prices have fallen dramatically over the past decade (from roughly \$400–500/kWh in 2015 to around \$50/kWh in 2025) due to the technological progress, manufacturing scale, and substantial excess production capacity in China. As a result, battery cells now account for only about 15-20% of the upfront capital investment in a firm clean-power project (Paliwal et al., 2026).

Their share is even smaller when measured against the project’s full lifecycle cost. Imported cells account for only about 10% of total lifecycle costs. Financing, domestically produced solar equipment, labour, construction, and other storage-system components and services account for the remaining roughly 90%, much of which represents domestic value creation. For example, of the thermal-mimic auction price of Rs 5.25/kWh, the imported-cell component would be only about Rs 0.5/kWh or so. The macroeconomic exposure is thus modest relative to the economic gains.

Second, dependence on imported batteries is different from dependence on imported fuels like coal, oil, or gas. Fuels are consumptive and must be continuously replenished. If supply stops, electricity production can stop. A battery is a capital asset that operates for many years. A disruption in cell imports would affect the construction of new projects, not the operation of existing ones. This significantly reduces the leverage of exporting countries and gives India time to find alternative suppliers, expand domestic production, or modify deployment plans.

The main security risks arise from the electronics and software surrounding the cell. India can import cells while retaining domestic control over pack assembly, inverters, battery-management systems, energy-management systems, firmware, communications, operational data, and remote access. It should also build recycling capacity and maintain some domestic cell manufacturing, as it has sought to do with solar equipment.

What are the implications? What more needs to be done?

First, governments and utilities should not commit to large fleets of new coal and gas plants without allowing renewables and storage to compete against the same performance requirements. This does not imply that renewables and storage will win in every location or for every operating profile. It means that the presumption in favour of coal and gas is no longer justified. All-source competition should determine which portfolio can provide the required reliability at the lowest cost.

Second, this competition must compare full costs. A fixed-price contract has value when fossil-fuel costs are exposed to inflation. Modularity has value because utilities can procure capacity in smaller increments as demand emerges, reducing the risks of overbuilding and underbuilding. Short lead times also have value when demand is growing but uncertain. Environmental damage should be priced rather than treated as free. Utilities should specify the quantity, operating profile, and commissioning date they require. Any resource that meets these requirements at the lowest total cost should win.

Third, these changes weaken the case for monopoly utility procurement. Large, slow, and scale-intensive power plants once favoured centralised planning and a single buyer backed by a distribution monopoly. Firm, round-the-clock power can now be assembled from modular solar and storage projects with much shorter lead times. The wires network remains a natural monopoly because duplicating distribution infrastructure is wasteful. Power generation, procurement, and retail supply do not require the same monopoly. Competitive suppliers can buy and sell power, while distribution utilities operate the network, provide last-resort service, and protect small and vulnerable consumers. ERCOT shows that competitive markets can support rapid investment in renewables and storage, although its design cannot simply be copied. Prayas (Energy Group) has outlined pathways for India, while proposed amendments to the Electricity Act also point towards greater competition. In the United States, PJM and CAISO should examine which elements of the ERCOT model, including faster interconnection and stronger market signals, can be adapted to their systems. GridLab’s recent work provides one such pathway. A wires-focused utility may also become financially stronger as electrification expands demand for network services.

Fourth, the gains from low-cost clean power extend far beyond the electricity sector. Time-varying prices can encourage flexible consumers to use electricity when clean supply is abundant and inexpensive. This can make industrial heat, hydrogen, steel, aluminium, transport, and other activities cheaper to electrify or decarbonise. Power-market reform is therefore not merely an electricity-sector reform. It can provide the foundation for lower-cost industrialisation and a cleaner economy.

References

Assam Electricity Regulatory Commission (AERC) (2025). Order dated 22 October 2025 on procurement of 3,200 MW of coal-based thermal power by APDCL. Tariff approval for the Assam thermal-power procurement cited in Figure 3. Cited in MPERC, Order in Petition No. 121/2025, paragraph 73.

Bihar Electricity Regulatory Commission (BERC) (2025). Order in Case No. 36/2025: Adoption of tariff for long-term procurement from the 2,400 MW Pirpainti Thermal Power Station. Final order, 27 August. Patna: BERC.

Chakravarty, S., and E. Somanathan (2021). There is no economic case for new coal plants in India. World Development Perspectives, 24, 100373. DOI: 10.1016/j.wdp.2021.100373.

Chojkiewicz, E., N. Abhyankar, U. Paliwal, and A. Phadke (2025). Declining costs make solar plus storage economical for industrial captive power. iScience, 28(11), 113763. DOI: 10.1016/j.isci.2025.113763.

Cropper, M., R. Cui, S. Guttikunda, N. Hultman, P. Jawahar, Y. Park, X. Yao, and X.-P. Song (2021). The mortality impacts of current and planned coal-fired power plants in India. Proceedings of the National Academy of Sciences, 118(5), e2017936118. DOI: 10.1073/pnas.2017936118.

International Energy Agency (IEA) (2026). Technology: Solar PV and wind. In Global Energy Review 2026. Paris: IEA.

International Renewable Energy Agency (IRENA) (2026). 24/7 renewables: The economics of firm solar and wind. Abu Dhabi: IRENA, May.

Madhya Pradesh Electricity Regulatory Commission (MPERC) (2025). Order in Petition No. 121/2025: Adoption of tariff for long-term procurement of 3,200 MW plus 800 MW under the greenshoe option from new power stations in Madhya Pradesh. Final order, 15 December. Bhopal: MPERC.

NTPC Limited (various years). Annual reports. FY2012-FY2026 editions cited in Figure 3 for standalone average realised tariffs. Report collection.

Paliwal, U., N. Abhyankar, and A. Phadke (2026). Closing the Credibility Gap: Solar-Plus-Storage Delivers Coal-Equivalent Reliability at Lower Cost in India. Working paper, March. India Energy and Climate Center, Goldman School of Public Policy, University of California, Berkeley.

Paliwal, U., A. Phadke, and N. Abhyankar (2026). India's Renewable Energy Breakthrough: Coal-Like Reliability at a Lower Fixed Price. Working paper, August. India Energy and Climate Center, University of California, Berkeley.

Power Finance Corporation (PFC) (various years). Report on Performance of Power Utilities. 2009-10 to 2024-25.

Ricke, K., L. Drouet, K. Caldeira, and M. Tavoni (2018). Country-level social cost of carbon. Nature Climate Change, 8, 895-900. DOI: 10.1038/s41558-018-0282-y.

Solar Energy Corporation of India (SECI) (2026). Request for selection: 1,000 MW firm and dispatchable renewable energy round-the-clock power (SECI-FDRE-RTC-V). Tender SECI000240; SECI/C&P/IPP/13/0020/25-26, 10 March, with amendments.

Uttar Pradesh Electricity Regulatory Commission (UPERC) (2026). Order in Petition No. 2228/2025: Approval of the power supply agreement and adoption of tariff for procurement of 1,500 MW from Mirzapur Thermal Energy (UP) Private Limited. Final order, January. Lucknow: UPERC.

Vijay, R., and R. Tongia (2026). Electricity Storage is Getting Quite Cheap - But Firmer Storage Isn't as Cheap as Sometimes Believed. Centre for Social and Economic Progress (CSEP), 7 September.

West Bengal Electricity Regulatory Commission (WBERC) (2025). Order in Case No. OA-513/24-25: Adoption of competitively discovered tariff for 1,492 MW contracted capacity from a new 2 x 800 MW greenfield thermal power plant. Order, 28 May. Kolkata: WBERC.


The authors are researchers at India Energy and Climate Center, University of California, Berkeley

Friday, September 04, 2026

Regulating the regulators: Assessing regulation-making frameworks in India's financial sector

by Natasha Aggarwal and Renuka Sane.

Indian regulators, like the Securities and Exchange Board of India (SEBI) and the Reserve Bank of India (RBI), routinely wield quasi-legislative powers. For example, Section 30 of the Securities and Exchange Board of India Act, 1992 empowers the SEBI Board to make regulations. According to its 2024-25 annual report, SEBI issued 104 consultation papers, 61 amendments to its regulations, one new set of regulations, 14 master circulars, and 154 "policy measures." Such regulatory interventions can significantly influence markets and affect economic outcomes. Yet the processes by which regulators design, consult on, and review delegated legislation have been fragmented and, in large part, left to each regulator's discretion.

Traditional safeguards on delegated legislation in India, i.e., parent statutes requiring regulations to be laid before Parliament, prior publication requirements under the General Clauses Act, 1897, and judicial review, provide important accountability functions, but do not regulate the internal process by which a regulator formulates its regulations. They do not require a regulator to identify the problem warranting intervention, weigh alternatives, or assess costs and benefits.

In 2013, the Financial Sector Legislative Reforms Commission proposed provisions to govern regulation-making and, through the Financial Sector Development Council Resolution of 24 October 2013, financial sector regulators agreed to comply with these procedures. Since then, six financial sector regulators (the Insurance Regulatory and Development Authority of India (IRDAI), Insolvency and Bankruptcy Board of India (IBBI), International Financial Services Centres Authority (IFSCA), Pension Fund Regulatory and Development Authority (PFRDA), SEBI and RBI) have each adopted some form of instrument governing how they make regulations, ranging from non-binding concept notes to regulations. More recently, the Economic Survey (2024-25) recommended strengthening regulatory impact assessment; the Securities Markets Code, 2025 proposes statutorily mandating public consultation and periodic review at SEBI; and in March 2026 the Standing Committee on Finance recommended a mandatory regulatory impact assessment framework for the IBBI.

In this backdrop, our paper, 'Regulating the regulators: Assessing regulation-making frameworks in India's financial sector', evaluates the regulation-making frameworks adopted by these six regulators against three principles of good regulation-making - consultation, evidence-based regulation-making, and periodic review - and then assesses a randomly selected 2025 consultation paper issued by each regulator against indicators derived from these principles and from each regulator's own framework.

We find that while all six financial regulators have adopted some form of instrument, these instruments vary considerably in legal form, substantive scope, and analytical ambition. Regulators operating under more demanding frameworks are more likely to clearly identify the regulatory problem in their consultation documents. However, this relationship is not linear: stronger frameworks do not consistently produce stronger performance on more analytically demanding requirements. No regulator, including those whose frameworks expressly require it, included a cost-benefit analysis in its consultation paper, and no regulator assessed available alternatives to direct regulation. Every regulator failed to comply with at least one of its own procedural requirements.

Indian legal frameworks

From 2016 onwards, Indian regulators have progressively formalised how they make their own regulations: IRDAI led the way with a concept note in 2016, followed by IBBI in 2018, IFSCA in 2021, and PFRDA in 2024. In 2025, IFSCA issued an updated and expanded framework for making regulations and subsidiary instructions, SEBI adopted regulations for making, amending, and reviewing regulations, and RBI opted for a non-binding policy framework rather than enforceable regulations.

While all regulators now subject regulation-making to some framework, they diverge along two axes: legal form and substantive scope. On legal form, IBBI, PFRDA, IFSCA, and SEBI have adopted regulations, signalling a commitment to enforceable constraints; RBI and IRDAI, by contrast, have adopted non-binding approaches, suggesting either a desire to retain discretion or a reluctance to subject internal processes to enforceable standards. On scope, PFRDA's framework is narrowly confined to the making of regulations; IBBI, SEBI and IRDAI expand this to include amendments; IFSCA moves further by bringing "subsidiary instructions" within its fold; and RBI adopts the broadest scope, extending its framework to directions, guidelines, notifications, and other instruments. In this context, IFSCA stands out as the strongest: it uses binding regulations, rather than non-binding frameworks, to govern its regulation-making process, and their applicability extends beyond regulations and amendments to subsidiary instructions.

On consultation specifically, most regulators (IRDAI, IFSCA, SEBI, RBI and IBBI) make public consultation mandatory, typically for a minimum of 21 days; PFRDA alone makes it optional, albeit with a longer 30-day window. IRDAI, IFSCA, IBBI and PFRDA publish stakeholder comments and provide responses to them, while SEBI and RBI provide responses but do not publish comments, limiting external visibility into the range of views considered.

On evidence-based regulation-making, the picture is fragmented: IRDAI and IFSCA require both a problem statement and a statement of regulatory intent; SEBI requires only regulatory intent; PFRDA and IBBI require a problem statement but not regulatory intent. Only RBI's framework requires an impact assessment, and only PFRDA and IBBI mandate cost-benefit analysis.

On periodic review, IBBI has the most frequent cycle (three years), followed by IFSCA (five years) and RBI (five to seven years); SEBI and PFRDA require review but specify no timeline, and IRDAI's concept note is silent on review altogether.

Evaluation of consultation papers

We evaluated one randomly selected 2025 consultation paper (for IRDAI, an exposure draft) issued by each regulator, against indicators drawn from the principles of good regulation-making and from each regulator's own framework. Notably, the RBI did not issue a formal consultation paper in the relevant period; the document evaluated for RBI is a circular proposing amendments to its directions, reflecting a broader pattern of the RBI using directions and circulars to make substantive regulatory changes.

The IRDAI Exposure Draft states the objective of its proposal and describes the key features of the framework, but does not clearly explain the problem it seeks to address, does not consider alternative approaches to regulation, and does not include a cost-benefit or impact analysis.

The IBBI Discussion Paper, for each of its three proposals, includes a statement of the problem, a proposed solution, and the draft regulation, but does not identify and assess available alternatives to direct regulation, does not include a cost-benefit analysis, and does not comply with the IBBI Regulations' requirement of an economic analysis, guidance from international standard-setting bodies, or the statutory provision enabling the proposed regulations.

The IFSCA Consultation Paper does not comply with any of the principles of good regulation-making, other than relying on market data as evidence of growth; it refers to fund management entities facing unspecified "operational hassles" without elaborating on what these are, and does not specify the statutory provision enabling the amendments or include guidance from international standard-setting bodies, both required under its own regulations.

The PFRDA Consultation Paper performs comparatively better: it identifies the problem to be addressed, assesses how existing frameworks contribute to the problem, and relies on evidence. However, it does not identify and assess available alternatives to direct regulation or include a cost-benefit analysis, and does not comply with several of its own regulations; it does not specify the statutory provision enabling the proposed regulations, attach a draft of the proposed regulations, include the required economic analysis, include guidance from international standard-setting bodies, or specify the manner of implementation.

The SEBI Consultation Paper does not comply with any of the principles of good regulation-making other than identifying the problem to be addressed; it does not assess how existing regulations contribute to the problem, identify alternatives, or include a cost-benefit analysis, an outcome that closely mirrors the design of SEBI's own framework, which requires only a statement of regulatory intent.

The RBI Circular likewise does not comply with any of the principles other than a rather broad articulation of the problem, and does not comply with the RBI Policy because it does not specify the statutory provision enabling the proposed regulations, or include an impact analysis or guidance from international standard-setting bodies.

Analysis

The results reveal a gap between the formal existence of regulation-making frameworks and their actual operationalisation in consultation documents. There are failures at two levels: compliance with general principles of good regulation-making and compliance with each regulator's own procedural requirements.

More broadly, regulators operating under more developed procedural frameworks, particularly IBBI and PFRDA, which explicitly require problem identification, perform better on basic problem-definition indicators. Both clearly identify the regulatory problem, and PFRDA goes further by examining whether existing regulations contribute to it. In contrast, SEBI and RBI, whose frameworks impose minimal analytical obligations, produce consultation documents that are largely limited to statements of regulatory intent, with little substantive justification. Second, there is inconsistent articulation of the regulatory problem, even at a basic level. While some regulators - such as IBBI, PFRDA, SEBI, and RBI - identify a problem, others (notably IRDAI and IFSCA) fail to do so clearly. Even where a problem is identified, it is often thinly specified and not linked to evidence or to failures in the existing regulatory framework.

However, stronger frameworks do not necessarily translate into stronger performance on more analytically demanding requirements. Despite formal mandates, both IBBI and PFRDA fail to include the economic analysis required by their own regulations. IBBI omits cost benefit analysis, while PFRDA fails to provide economic analysis, draft regulations, international benchmarking, and implementation details. No regulator assesses alternatives to direct regulation or conducts a cost benefit analysis. The absence is uniform and not explained by framework design alone: even regulators whose own frameworks require economic analysis (IBBI, PFRDA, and RBI) fail to provide it. Their universal absence suggests that regulators do not treat regulation as one option among many, but as the default response. As a result, consultation processes are narrowed: stakeholders are invited to comment on how to regulate, but not whether regulation is justified in the first place. This significantly weakens accountability and the quality of regulation-making.

Moreover, every regulator, without exception, fails to comply with at least one of its own procedural requirements. The most consistent gap is the failure to specify the statutory provision enabling the proposed regulation - a basic transparency requirement met only by IRDAI. This omission raises concerns about the legal legitimacy of the proposed regulation, as stakeholders are not informed of the source of regulatory authority. IBBI omits the economic analysis mandated by its framework. PFRDA fails to include draft regulations, economic analysis, implementation guidance, and international benchmarks. IFSCA omits both the problem statement and international benchmarks required under its framework. RBI, similarly, does not provide the impact analysis or international benchmarking contemplated by its policy. These are not merely formal deficiencies. The absence of draft regulatory text, as in the case of PFRDA, prevents stakeholders from engaging with the legal substance of the proposal, limiting consultation to broad regulatory intent. The absence of economic or impact analysis means that the regulatory choice cannot be independently assessed.

Across all six regulators, consultation papers ostensibly function as instruments for presenting pre-determined regulatory proposals, rather than as vehicles for reasoned, evidence-based decision-making.

Reforms

We propose five reforms: (i) legislative amendments to parent statutes that clearly define the scope of regulators' quasi-legislative powers and the processes governing their exercise; (ii) regulatory impact assessment should be made mandatory and comprehensive; all consultation papers should be required to identify the problem or market failure to be addressed, assess whether existing regulations contribute to it, consider available alternatives including non-intervention, and include a cost-benefit analysis; (iii) constituting Regulations Advisory Committees of domain experts, legal scholars and market participants at all regulators; (iv) requiring periodic review of regulations at defined intervals with a clear methodology specifying which regulations are to be reviewed, against what criteria, and within what timeframe; and (v) leveraging technology (for instance, dashboards tracking active consultations and regulators' responses, and automated tools that flag missing elements in consultation papers before publication).


The authors are researchers at TrustBridge Rule of Law Foundation.

Envisioning the INR as a floating exchange rate

by Rounak Hande, Rajeswari Sengupta and Ajay Shah.

The Question

A central question in macroeconomic policy is the exchange rate regime. In the long-run, India's economic strategy should move to a combination of inflation targeting, floating exchange rate and an open capital account. In more than three decades since the economic reforms of 1991, only one of these milestones has been achieved--RBI today is an inflation targeting central bank. For IT to be fully effective, it must be accompanied by a floating exchange rate. However, we in India are used to the idea that the RBI actively intervenes in the FX market to stabilise the USD/INR rate. It is important to ask, What might a genuine floating exchange rate look like if the RBI did not intervene? In other words, If RBI were to the USD/INR what SEBI is to the Nifty, what would that world look like?

When it comes to government price controls on commodities, e.g. wheat, there is a ready way to visualize what a reformed India would look like: the Indian price of wheat would be the world price of wheat. But what about the exchange rate? If the required reforms took place, and we got to a market determined rupee, what would it be like? In this article, we present a reasonable depiction of what the exchange rate regime would be like, if the RBI did nothing on the currency market. This also helps us understand how much currency volatility Indian firms and households need to prepare for if the RBI were absent from the market.

The period of INR as a float

When we look back into India's history, we find that there was one period when trading by the RBI on the currency market dropped to near zero levels. We treat this as a natural experiment to gain insights into what the INR would look like without government control. Our first task is to establish the start and end dates of that period.

We start with three long time-series graphs: (i) RBI's spot market trading volume in USD, (ii) RBI's spot market trading volume relative to reserve money, and (iii) RBI's open position on the currency forward market.

Figure 1: The long time-series of spot price trading volume by RBI, in billion USD

Figure 2: The long time-series of spot price trading volume by RBI, expressed as per cent of M0.

Figure 3: The long time-series of the RBI's currency forward position, in billion USD.

In all these graphs, we can spot one remarkable period, from June 2009 to October 2011, where an important reform of the exchange rate regime took place, and the RBI stepped out of the currency market. Let's zoom into that period. To obtain greater clarity, we focus on the period from June 2006 to October 2014, adding three years to each side.

Figure 4: Spot price trading volume by RBI, in billion USD (June 2006 to Oct 2014).

Figure 5: Spot price trading volume by RBI, expressed as per cent of M0 (June 2006 to Oct 2014).

Figure 6: RBI's currency forward position, in billion USD (June 2006 to Oct 2014).

In these pictures, we see a middle period -- 28 months from June 2009 to October 2011 -- when currency trading by the RBI was very low. The RBI's trading volume in these months was not always 0. In choosing these endpoints, we set a limit where the RBI's gross monthly trading volume stayed below 1 percent of M0.

Examining the characteristics of this period gives us insights into what a floating exchange rate in India might look like.

Characteristics of the INR as a float

In this section we describe the characteristics of the INR in the period from June 2009 to October 2011. For the sake of comparison, we use the methodology described in Sengupta and Shah (2026) to establish the dates of two other exchange rate regimes. We will now focus on three such regimes:

  • The natural experiment of the INR as a float: 1st June 2009 to 31st October 2011.
  • The recent period of a tight USD peg : 1st September 2023 to 16th December 2024.
  • The present exchange rate regime: 27th December 2024 to 28th August 2026 (latest available data).

For each of these periods, we examine (a) The volatility of the USD/INR rate (b) The parameter estimates obtained from the exchange rate regression (see Google colab notebook associated with Sengupta and Shah (2026)) and (c) Deviations from market efficiency as seen in variance ratios.

Metric Float (June 2009–Oct 2011) The USD peg (Sept 2023–Dec 2024) Current ERR (Dec 2024–Aug 2026)
Volatility:
     USD/INR vol (%) 7.40 1.43 4.98
The exchange rate regression:
     USD 0.66*** 0.89*** 0.79***
     EUR 0.20** 0.05 0.11
     JPY -0.15** -0.01 -0.08
     GBP 0.04 0.05 0.25
     R-sq 0.75 0.98 0.76
     RSE 0.77 0.18 0.67
Variance ratio tests:
     VR(5), daily 0.99 0.67 0.96
     p value 0.82 0.03 0.61
     VR(4), weekly 1.07 0.64 0.76
     p value 0.93 0.03 0.14

We summarise our findings as:

  • In the popular discourse on the INR, a lot of attention is given to the raw USD/INR volatility. At present, it is running at 4.98 percent. During the period of the USD-peg, it had fallen to 1.43 percent. We see that under the float, it was 7.4 percent. In other words, if the RBI did not intervene in the currency markets, the USD/INR volatility that the economy could experience is around 7-7.5 percent. Later in the article, we speculate on how things might work out if the INR were to return to a float under the present conditions and we argue that the volatility could be lower.

    The numbers also suggest that during the current exchange rate regime (27th December 2024 to 28th August 2026), the machinery of the RBI's currency policy seems to have delivered only a small decline in volatility of about 2.4 percentage points on an annualised basis.

  • In the exchange rate regression, during the period of the USD peg, the USD coefficient was statistically significant with a value of 0.89, and no other currency was significant. At present, the USD coefficient has come down to 0.79, but still, none of the other currencies have statistically significant coefficients.

    In contrast, during the period of INR float, the USD coefficient was smaller at 0.66, and other currencies were significant too. This suggests that the Indian economic engagement with the outside world is not merely with the US. In the float period, USD, EUR and JPY were all statistically significant, with coefficients of 0.66, 0.2 and -0.15. This gives us an undistorted sense of the currencies that matter for the Indian economy.

  • Another important statistic from the exchange rate regression is the residual standard deviation (or RSE, residual standard error). It shows the size of the prediction error of the exchange rate regression. A lower RSE means the model fits the data well. Therefore, during the period of the USD peg, the residual standard deviation was only 0.18. In contrast, under the INR float the RSE was 0.77. In the current regime, the RSE stands at 0.67.

  • In the exchange rate regression, during the period of the USD peg, the R-squared was 0.98. A high R-squared value implies that almost all of the variation in the INR was accounted for by the currencies in the regression model. At present, the R-squared has fallen to 0.76. During the INR float, the R-squared had a very similar value, 0.75. In other words, despite active trading by the RBI in the currency market in the present period, there is not much of a difference in the R-squared.

    This yields insights into deciphering a floating exchange rate regime from the data. A floating exchange rate does not necessarily mean an R-squared value close to 0. It means that the central bank does not intervene and lets the exchange rate respond freely to market forces. In a floating regime, the R-squared value can still be high because it reflects the natural, underlying co-movement of the rupee with major currencies of the world (and not just the USD) under conditions of globalisation. India is deeply interconnected with these countries through trade and financial flows, and is exposed to the same global shocks. Some co-movement is therefore entirely consistent with a genuine float.

  • The variance ratio test is a simple tool to examine serial correlations. A floating exchange rate is expected to be an efficient market, with no discernible serial correlation, and no exploitable profit opportunities for trading based on time-series characteristics. This does work out correctly in the float period. In the daily data, the 5-period variance ratio was 0.99, and indistinguishable from 1, whereas in the weekly data, the 4-period variance ratio was 1.07 and indistinguishable from 1. Under the USD peg, the two variance ratios were 0.67 and 0.64, with statistically significant deviations from non-forecastability. In the present arrangement, the variance ratio at 4 weeks is away from 1.

A useful variant of the exchange rate regression, introduced in Kumar et. al. (2020), differentiates between the USD coefficient when faced with a USD appreciation vs. a depreciation thereby highlighting asymmetric intervention by the RBI. We now turn to these estimates.

Metric Float (June 2009–Oct 2011) The USD peg (Sept 2023–Dec 2024) Current (Dec 2024–Aug 2026)
USD (App) 0.52*** 0.87*** 0.68***
USD (Dep) 0.83*** 0.91*** 0.88***
EUR 0.20*** 0.05 0.12
JPY -0.15*** -0.01 -0.09
GBP 0.04 0.05 0.24
R-sq 0.77 0.98 0.77
RSE 0.74 0.18 0.66

During the period of the USD peg, it is not surprising to see statistically significant values of the USD coefficient close to 1, for both USD appreciation and USD depreciation. This makes sense because when the RBI is pegging the INR to the USD, it is expected that currency interventions would take place on both sides of the market, regardless of which way the USD is moving. In the present exchange rate regime, the INR responds more strongly to a USD depreciation (with a coefficient of 0.88) than it does to a USD appreciation (with a coefficient of 0.68). This implies that the RBI now intervenes asymmetrically, letting the INR move more freely when the USD appreciates (i.e. the INR depreciates) but managing the INR more when the USD depreciates (i.e. the INR appreciates). This is consistent with existing studies documenting the RBI's asymmetric intervention patterns (Patnaik and Sengupta, 2022). RBI prefers buying dollars (preventing INR appreciation) over losing reserves (preventing INR depreciation).

Interestingly however, we find asymmetric coefficients in the floating period too, with a response of 0.83 when the USD depreciates but a coefficient of 0.52 when it appreciates. This is puzzling because in a float, there should be no asymmetry between these coefficients, both of which should be equally low. Further research is therefore required to understand the market-based sources of this asymmetry.

Conclusion

In the strategic view of macroeconomic policy, the long-run answer for India lies in graduating from one milestone -- inflation targeting -- to two more milestones -- a floating exchange rate and an open capital account.

At every stage in the journey of Indian economic reforms, the prospect of getting the government out of price determination has raised alarms in the minds of some people. When the proposals to remove price controls for steel or cement were made, there was shock and unhappiness in the minds of many people. These things are often easier done than said, because the price system works rather well. It solves the resource allocation problem, and prices move continuously in a way that provides good incentives to private persons.

In this article we have shown one tangible period, of 883 days, in which the RBI stayed away from the currency market and there was a genuine floating exchange rate. This period can be utilised for many other research projects. Using the insights from this period, we are now able to offer a thumb rule to judge the extent of government management of the exchange rate in India in terms of three numbers. For example, we can compare the values observed today of (i) the USD/INR volatility of 4.98 percent, (ii) the USD coefficient of 0.79, and (iii) the RSE of 0.67 vs. the values observed during the float: (i) the USD/INR volatility of 7.4 percent, (ii) the USD coefficient of 0.66, and (iii) the RSE of 0.77. This gives us a sense of how much government control of the exchange rate is present today.

This natural experiment, of a country that graduated to a floating exchange rate and then retreated from it, gives us insights on how to interpret the estimates from the exchange rate regression and the toolchain of Zeileis et. al (2010).

Looking into the future, when economic policy reforms take place in India, we believe the USD/INR volatility under a true floating exchange rate will be lower than this value of 7.4 percent, for two reasons:

  1. There is one important difference between the float period of 2009-2011 and the future: Inflation Targeting. That RBI movement to a floating exchange rate was incomplete because it was not accompanied by inflation targeting. In some sense, that was a particularly unfortunate event as the rupee lost its nominal anchor during that period. In the future, things will be better because now the nominal anchor is 4 percent CPI inflation.
  2. Another important difference concerns the liquidity of the USD/INR spot and derivatives markets. In the 2009-2011 period of INR float, these markets were less developed. We estimate that in that period, the total turnover(onshore and offshore) was about USD 40 billion per day. By now, things have improved, with a huge increase in INR activity outside India. Now the total turnover (onshore and offshore) is about USD 140 billion per day. This bigger market delivers greater stability. Hence, we can speculate that in the future, things will be better in terms of USD/INR volatility.

References

Kumar, S H, Balasubramaniam, V, Patnaik, I and Shah, A (2020), "Who cares about the Renminbi?", Working Paper, December 2020.

Patnaik, Ila and Rajeswari Sengupta (2022) "Analyzing India's Exchange Rate Regime", India Policy Forum, National Council of Applied Economic Research, vol. 18(1), pages 53-85.

Sengupta, R and Shah, A (2026), "Words and deeds in the Indian exchange rate", The Leap Blog, May 19, 2026.

Zeileis, A, Shah A, and Patnaik, I (2010) "Testing, monitoring, and dating structural changes in exchange rate regimes", Computational Statistics & Data Analysis, Volume 54, Issue 6.


Rounak Hande and Ajay Shah are researchers at XKDR Forum, Mumbai and Rajeswari Sengupta is a researcher at IGIDR, Mumbai.

Wednesday, September 02, 2026

The curious case of definition and adjudication of front running in India

by Natasha Aggarwal, Amol Kulkarni and Bhavin Patel.

One of the core legislative mandates of the Securities and Exchange Board of India (SEBI) is to prohibit fraudulent and unfair trade practices (FUTP) relating to securities markets. A practice commonly classified as FUTP is front running. It is generally understood as a set of two trades or positions: first, a trade or position taken in advance of a large order, and second, a squaring off of the initial trade or position after the large order, to benefit from the price movement it causes.

SEBI issued the SEBI (Prohibition of Fraudulent and Unfair Trade Practices Relating to Securities Market) Regulations, 2003 (the PFUTP Regulations) to deter and sanction front running and other FUTPs.

Neither the SEBI Act nor the PFUTP Regulations define front running. SEBI has, however, defined it elsewhere: in guidelines, glossaries, master circulars and consultation papers, and in a number of adjudicatory orders.

In our working paper, Front running: The law and enforcement of an ill-defined violation, we set out the legal elements that a violation under the PFUTP Regulations requires: fraud, manipulation and unfair trade practice. These elements should inform any definition of front running. We find that the characteristics of front running laid down by SEBI in its other regulatory instruments and adjudicatory orders are inconsistent with these legal requirements.

Through an empirical study of 33 SEBI adjudicatory orders on front running between 2019 and 2024, we show that SEBI's enforcement practice is also disconnected from the codified law, and does not engage with its key requirements.

This proliferation of inconsistent definitions and interpretations causes a range of problems:

  • It creates confusion, and weakens the certainty and predictability of the law.
  • It gives the regulator untrammelled discretion in deciding whether a violation has taken place.
  • It raises concerns about separation of powers: the boundary between the regulator's law-making and adjudicatory functions is erased and re-drawn erratically, at cost to the integrity of each function.

The result is a lack of doctrinal clarity about what front running means, and a set of conflicting articulations of the violation with no clear grounding in the codified law.

We suggest that the term be defined clearly, either in the parent statute or in the PFUTP Regulations. There is now a nearly three-decade history of enforcement, which should be enough to identify the ingredients of this violation.

The Securities Markets Code Bill, 2025 (the SMC) is an opportunity to write such a definition into the parent law. This would take the power to define the violation away from the regulator, restore the integrity of SEBI's separate functions, and meet the requirements of separation of powers. In its present form, though, the SMC may fall short: we show the gaps and confusions that remain in its treatment of fraud, and its failure to define fraudulent or unfair trade practice, or front running, explicitly.

Part of the problem may lie in the absence of separation of powers within SEBI. The regulator can write subordinate legislation, investigate alleged violations, and adjudicate and sanction them, all without a strict separation between its quasi-legislative, investigative and adjudicatory arms.

This may incentivise SEBI to write broad, all-encompassing regulations in its quasi-legislative capacity, and then to widen their scope further to suit its quasi-judicial needs.

The regulator might defend this by pointing to the constantly changing methods used by fraudsters. International experience suggests otherwise: methods change, but the key constituents of securities fraud stay broadly constant.

In India, a lasting solution may require Parliament, not SEBI acting under its delegated powers, to define terms such as front running in the parent legislation.

Front running is only one example of an inconsistently defined and adjudicated practice within the broader category of FUTP. There are likely others. This points to the need for a wider review of how fraud, manipulation and unfair trade practice are defined and adjudicated under Indian securities law, with the aim of achieving consistency and certainty in interpretation and enforcement.

References

Front running: The law and enforcement of an ill-defined violation, TrustBridge working paper.

SEBI (Prohibition of Fraudulent and Unfair Trade Practices Relating to Securities Market) Regulations, 2003, Securities and Exchange Board of India.

Securities Markets Code Bill, 2025, Bill No. 200 of 2025.


Natasha Aggarwal, Amol Kulkarni and Bhavin Patel are researchers at TrustBridge Rule of Law Foundation.