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Executive Summary

Background and Context

India has set ambitious climate goals, including a 45% reduction in the emissions intensity of its gross domestic product (GDP) by 2030 (from 2005 levels) and achieving net-zero emissions by 2070. Achieving these targets will require policy interventions, including the introduction of carbon pricing mechanisms to encourage businesses and households to reduce their emissions. This study evaluates the introduction of a theoretical carbon tax in India and assesses how to balance economic growth, equity considerations, and emissions reduction. The carbon tax framework proposed in this study is a potential alternative to the Carbon Credit Trading Scheme (CCTS) that will soon be implemented in India. The study utilises the CSEP Environmentally Extended Social Accounting Matrix (ESAM) for India 2019–2020 to assess the impacts of a proposed carbon tax on the economy, consumption inequality, and the role of revenue recycling in facilitating a just transition.

Carbon Pricing Mechanisms

Carbon pricing is a key policy tool for reducing greenhouse gas (GHG) emissions. The three main approaches—the CCTS, Emissions Trading Scheme (ETS), and a carbon tax—differ in their mechanisms, coverage, impact on emission reduction, and other parameters. India has chosen to implement the CCTS, a carbon pricing mechanism based on emissions intensity. The ETS, on the other hand, sets a fixed cap on total emissions and distributes or sells allowances accordingly. The carbon tax, which is the focus of this study, applies a fixed charge per unit of emissions.

Each of these mechanisms has different features that may make it more suitable for a given context. The carbon tax offers price certainty in emissions, but not the quantum of emissions reduction and revenue generation, which can, in turn, be used to address the distributional impacts of climate-financing needs and to achieve clearer compatibility with international carbon markets. While the CCTS may not necessarily be able to provide revenues and, as an emissions-intensity-based mechanism, does not align well with international markets, it does come with several advantages for the Indian context. It aligns better with existing domestic schemes, offers more flexibility to industries that need to grow to meet demand, and can be adjusted to meet the needs of a particular sector. The ETS has a well-established history in the European Union (EU) context and has also proven to generate revenues when the permits are auctioned; however, it took almost a decade for the European Union Emissions Trading System (EU-ETS) to mature and generate these revenues. The ETS, if adopted, will be compatible with the European Union Carbon Border Adjustment Mechanism (EU-CBAM) framework.

Modelling Approach: ESAM Framework

The CSEP ESAM is a comprehensive economic framework of the Indian economy (Chadha, Sivamani, & Verma, 2023). It consists of 45 production sectors, 80 household categories, and 318 categories of labour, along with other economic agents (government, institutions, capital account, and rest-of-the- world account) to complete the circular flow of the economy. It includes environmental (pollution) accounts with data on GHG emissions by sector and households, allowing for the assessment of carbon tax impacts on emissions. The model captures inter-industry linkages (input–output relationships) and income flows, enabling analysis of how carbon tax propagates through output, income/employment, expenditure, and emissions. A limitation of the study is that it is based on the Leontief assumption of fixed cost structures, which also does not account for behavioural responses to price changes.

For this study, a carbon tax is applied to eight emissions- intensive (EI) sectors, selected in line with the sectors covered by the CCTS. These eight major emitting industrial sectors in the CSEP ESAM—aluminium, cement, fertilisers, iron and steel, paper and pulp, textiles, combustible petroleum products, and non-combustible petroleum products—account for roughly 27% of manufacturing output, 22% of manufacturing gross value added (GVA), 19% of industrial employment, and about 80% of direct manufacturing carbon dioxide (CO₂) emissions. The hypothetical carbon tax is levied at three rates (Rs 1,700, Rs 2,150, and Rs 2,600 per tonne of CO₂ emissions), reflecting low to moderate carbon price levels in line with the suggestion of the International Monetary Fund (IMF) for emerging market economies (EMEs). The tax is applied to the production of these sectors and also to the emissions from their use of coal-based electricity, ensuring downstream industries face higher costs in line with the carbon content of their inputs.

In this study, two scenarios are examined: (1) carbon tax only, where all new carbon tax revenue accrues to the government and is used to reduce the fiscal deficit, and (2) carbon tax with revenue recycling, where a portion of the revenue is recycled back to households through direct benefit transfers (DBTs). Revenue recycling is modelled via DBTs to households in the lower 50% of the consumption expenditure distribution in both rural and urban areas and across all social groups. The quantum of the transfer to each household group is equal to their reduction in consumption due to the carbon tax, effectively compensating poorer households for the carbon tax-induced higher prices in the economy. This design ensures that about 57% of India’s population, including the most vulnerable groups, receive compensation. India’s existing DBT infrastructure accounted for 182 crore transactions and disbursed Rs 2.23 lakh crore in 2024–2025, indicating that such large scale targeted transfers are administratively feasible.

Implications of the Carbon Tax

1. Emissions and Climate Targets
   Even a moderate carbon tax applied to select sectors contributes to emissions reduction. The range of reduction in total CO2 emissions from the economy relative to the baseline is 1.02%–1.55%, with higher tax rates achieving larger reductions. Emissions from the targeted EI sectors fall by an even greater margin (1.49%–2.27%), reflecting the direct impact of the tax. These are notable impacts, given that the tax covers only part of the industrial sectors and uses modest rates for emerging market economies. In terms of India’s climate pledges, the carbon tax could further lower the GDP emissions intensity by 0.8%–1.2%, on top of the approximately 37% reduction already achieved since 2005.
2. GDP and Growth
   The carbon tax has a modest impact on economic output. Due to the tax, the GDP is projected to be about 0.22%–0.33% lower than the baseline. This marginal contraction reflects slight reductions in production and consumption due to higher costs. The impact is relatively small in magnitude, suggesting that a carefully designed carbon tax need not derail India’s economic trajectory if phased out gradually.
3. Fiscal Outcomes
   A key benefit of introducing a carbon tax is the generation of government revenue. At the Rs 2,150/tCO2 rate, the carbon tax yields roughly Rs 1.27 lakh crore in the first year, leading to a significant improvement in India’s fiscal position. In the carbon tax-only scenario, the fiscal deficit-to-GDP ratio declines from a baseline of 5.01% to about 4.40%. With the revenue recycling case, the deficit still improves, albeit to a smaller extent (approximately 4.57%). The revenue thus creates much-needed fiscal space that could help finance climate action and other development activities.
4. Sector-Specific Effects
   The carbon tax raises production costs for carbon-intensive industries, leading to modest reductions in their output and emissions. Notably, the mining sector sees reduced activity due to lower demand for coal and minerals from taxed industries like steel and cement. However, services and low-emission manufacturing sectors remain largely unaffected in the short term.
5. Employment and Incomes
   Across all sectors of the economy, total employment is projected to decline by 0.95%–1.44% in the carbon tax scenario, with the largest losses in secondary (manufacturing) sectors. Rural employment is more adversely impacted than urban, reflecting the concentration of industrial and mining jobs in rural areas. However, revenue recycling via DBTs helps offset these job losses, particularly in the primary sector (e.g., agriculture), as transfers increase rural household consumption. With DBTs, overall job losses are reduced to 0.67%–1.01%.
6. Household Consumption and Inequality
   The carbon tax alone slightly increases consumption inequality, as poorer households spend a larger share of their income on primary- and secondary-sector goods. The national Gini coefficient rises marginally in the carbon tax case, with rural areas more affected due to their higher consumption of energy-intensive goods. However, with revenue recycling, inequality falls below baseline levels, especially in rural regions, thus demonstrating that targeted transfers can both protect vulnerable groups and stimulate the economy without undermining the tax’s environmental goals.

Global Experiences in Environmental Fiscal Reform (EFR)

International experiences reinforce that well-designed environmental taxes can be compatible with equity and growth. Brazil invested oil royalty revenues in health and education, helping to reduce regional inequalities. Ireland and Canada have implemented carbon taxes with revenue recycling to support vulnerable households. India’s reforms in removing fuel subsidies between 2010 and 2017 demonstrate that environmentally harmful subsidies can be phased out and redirected toward cleaner alternatives with public health and fiscal benefits. These experiences underscore the need for transparent, equitable use of environmental revenues.

Policy Recommendations

Based on this study, the following policy changes are proposed to help India meet its climate, growth, and equity goals.

1. Adopt a Moderate, Phased Carbon Tax: Introduce carbon pricing at modest initial rates in key emitting sectors, balancing climate targets with economic growth needs. This could begin with alignment to the CCTS framework and gradually expand over time.
2. Implement Revenue Recycling for Equity: A significant portion of carbon tax revenues could be used for DBTs to vulnerable households, mitigating regressivity and boosting inclusive consumption. A carbon price accompanied by employment protection, particularly in regions dependent on fossil fuels, would help attain a more just transition.
3. Embed within a Broader EFR Strategy: Rationalise fossil-fuel subsidies, strengthen monitoring, reporting, and verification (MRV) systems, and integrate environmental goals into fiscal policy at national and subnational levels.

India’s climate transition can be fiscally prudent and socially equitable if it combines moderate carbon pricing with the redistribution and reinvestment of revenues. This integrated approach aligns with India’s dual imperatives of development and decarbonisation.

The post Carbon Taxes in India: Balancing Growth, Equity, and the Net-Zero Transition first appeared on CSEP.

Executive Summary

Universal Health Coverage (UHC), a cornerstone of global health policy, aims to ensure that everyone, everywhere has access to quality, affordable healthcare without suffering financial hardship. While financial protection through health insurance is a crucial aspect of UHC, the effectiveness of such schemes hinges on the underlying strength and equity of the healthcare system itself. This paper examines the case of India, a nation actively pursuing UHC applying health insurance model as key strategy, to investigate the critical question: Can India achieve UHC while simultaneously addressing the diverse healthcare needs of its population, particularly the most vulnerable segments?

India’s path towards UHC presents a unique challenge due to its fragmented healthcare system, characterised by an under-resourced public sector and a rapidly growing, largely unregulated private sector. This study utilises data from the National Sample Survey (NSS) 75th round (2018) and the National Health Accounts (NHA) (2019–20) to explore healthcare utilisation patterns, out-of-pocket expenditures (OOPE), and the distribution of government health benefits across different income groups and social categories in India.

Unequal Access and Reliance on Private Care

The analysis reveals a significant reliance on private healthcare facilities for both inpatient and outpatient care, despite the associated high OOPE. While the poorest income quintile shows a higher likelihood of utilising public facilities for inpatient care, a considerable proportion (38%) still opt for private care. For outpatient care, the preference for private facilities is even more pronounced, with over 60% of individuals in the poorest two quintiles choosing private providers. This trend persists across social groups as well, indicating a pervasive reliance on private care irrespective of socio-economic background.

Several factors contribute to this preference for private care. The study highlights quality concerns, long wait times, unavailability of specific services, and the distance of public facilities from home as key drivers pushing individuals towards the private sector, particularly for outpatient care. Financial constraints are identified as a less significant deterrent, raising concerns about the effectiveness of purely insurance-based approaches to UHC in addressing access barriers.

The Burden of Out-of-Pocket Expenditures

The study underscores the substantial financial burden imposed by OOPE on Indian households. Compared to other BRICS nations, India exhibits the highest OOPE as a percentage of current health expenditure (50.6%). The poorest quintile bears a disproportionate share of this burden, with OOPE constituting 8% of their annual per capita consumption expenditure – a level considered catastrophic by the World Health Organization (WHO). This financial strain is exacerbated by the opportunity cost of lost wages and other associated costs of seeking care, pushing vulnerable households further into hardship.

Regression analyses reveal that social determinants, including income, education, age, and place of residence, significantly influence OOPE. The type of provider emerges as a critical factor impacting the magnitude of OOPE, with expenditures in private facilities averaging nine times higher than those in public facilities. This reinforces the concern that reliance on private care, while addressing immediate access barriers, can exacerbate financial vulnerability.

Analysing the Distribution of Government Health Benefits

A Benefit Incidence Analysis (BIA) is employed to assess the equity of government health expenditure distribution. The findings reveal a contrasting picture for inpatient and outpatient care. While the government inpatient care system appears largely equitable and pro-poor at the national level, the outpatient care system exhibits a pro-rich bias.

A deeper dive into state-level data reveals significant variations. In EAG states (characterised by lower incomes), both inpatient and outpatient benefits distribution favour the poor. However, in non-EAG states (higher-income states), the benefits are skewed towards the richer quintiles, indicating a pro-rich system. This disparity underscores the need for targeted interventions to address regional inequalities in healthcare access and benefits distribution.

Implications for Achieving Universal Health Coverage in India

The study highlights the limitations of focusing solely on financial protection through health insurance as a means to achieve UHC in India. The fragmented nature of the healthcare system, persistent reliance on private care, high OOPEs and inequitable distribution of government benefits pose significant challenges to realising equitable access to quality care for all.

Based on the findings, the study recommends a multi-pronged approach to address these challenges and facilitate progress towards UHC in India. Key recommendations include:

• Strengthening the Public Healthcare System: Increasing the density of public healthcare facilities, particularly in underserved areas, is crucial to improve access and reduce reliance on private care. Ensuring adequate staffing, essential medicines, and diagnostic services at all public facilities will enhance the quality of care and encourage greater utilisation.
• Improving Efficiency and Quality of Care: Addressing long waiting times, streamlining referral processes, and enhancing the responsiveness of healthcare providers are essential to improve patient experience and reduce forgone care. Investing in quality improvement initiatives will build trust in the public healthcare system and incentivise utilisation.
• Expanding Coverage for Outpatient Care: Incorporating outpatient care under the health insurance model will alleviate the financial burden on vulnerable households, encourage early treatment-seeking behaviour, and prevent the escalation of health conditions.
• Targeted Interventions to Address Regional Inequalities: Implementing targeted programmes and resource allocation strategies to address healthcare disparities between EAG and non-EAG states is crucial to ensure equitable access and benefits distribution across all income groups and regions.

Achieving UHC in India requires a comprehensive strategy that goes beyond financial protection and focuses on strengthening the public healthcare system, improving the quality and efficiency of care, expanding coverage for outpatient services, and addressing regional disparities. By addressing the underlying weaknesses of the healthcare system and prioritising equitable access for all, India can ensure that the pursuit of UHC translates into tangible improvements in the health and well-being of its entire population.

The post Health-Seeking Behaviour and Equity in Public Health Expenditure in India first appeared on CSEP.

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Executive Summary

India’s socio-economic development and pathways for sustainable growth require a substantial understanding of its energy requirements. Policymakers must have access to comprehensive and reliable data on the country’s energy scenario to make better-informed decisions for India’s transition to a net-zero emissions economy by 2070. This paper offers a robust database of India’s energy landscape by validating and reconstructing the energy balance (EB) and computing emissions data. Policymakers can utilise this framework to assess the impacts of policy interventions on energy consumption and emissions in the country’s energy transition efforts.

Key Takeaways

1. The Energy Balance (EB) is an accounting framework that maps the supply and demand of all forms of energy in a country. It provides data on energy flows in physical and common energy units for comparison.
2. This study identifies various gaps and inconsistencies in data on India’s energy. It reconstructs and validates these statistics by cross-referencing data from other sources, including relevant Indian ministries, to provide a more representative picture of India’s energy landscape. For example, in this paper, the statistical differences in coal and petroleum products have been minimised by refining conversion factors.
3. The results of the study reveal trends in energy consumption and emissions from energy combustion. Non-coking coal, primarily used in power generation, has remained the dominant energy resource in India recently and is responsible for approximately 60% of all combustion emissions. Meanwhile, energy from renewable sources has slowly increased its share in total energy supply.
4. Electrification of machinery is an important step in reducing emissions. This paper presents a framework for estimating the impacts of policies on energy and emissions. It showed that electrification can lead to substantial reductions in emissions, especially when the machinery currently operates on fossil fuels with low efficiencies. A greener power grid would further help reduce emissions in the country.

Energy Resources of India

Data on India’s energy resources can be classified as primary or secondary in nature (i.e., if it is available naturally or requires some processing) and renewable or non-renewable. The major energy resources in India include:

• Coal: This is the largest contributor to emissions from India’s energy sector. The country has abundant resources which are used for power generation, though higher-grade coking coal is imported for use in the steel industry.
• Crude Oil and Petroleum Products: India is highly dependent on crude oil imports to produce petroleum products domestically. Petroleum products contribute significantly to emissions, particularly from the transport sector. Electrification of this sector would result in lower demand for petroleum products, and hence lower import demand of crude oil.
• Renewable Energy Sources: Solar, wind, and hydro are rapidly growing in India’s energy mix and will drive the country’s decarbonisation efforts.
• Natural Gas: India consumes relatively lower amounts of natural gas, though it is important for non-energy uses, such as feedstock for fertilisers and other manufacturing sectors.

Policy Recommendations

1. Broader and Validated Data Collection:

a. Disaggregating energy consumption data by type of consumer, state, and household income levels can provide policymakers with a clearer picture of energy access and usage.
b. Data on biomass usage are important for a comprehensive understanding of the energy landscape in India.
c. India’s diverse energy landscape requires state-specific energy strategies that consider locally-available resources and consumption patterns. Policies can be tailored to help states meet their unique energy needs while contributing to national climate goals.

2. Electrification and the Role of Renewables:

a. Electrification of machinery will help reduce emissions, particularly for less-efficient sectors.
b. Investments in grid infrastructure and energy storage will help accommodate more renewable energy and make electrification more effective in decarbonisation.

3. Energy Losses:

a. Large energy losses in transmission, distribution, and transformation need to be addressed, and incentives can be provided to help reduce these losses.

This study underscores the critical importance of accurate and comprehensive energy statistics for understanding and modelling India’s energy landscape and transition. Through validating, disaggregating, and extending existing energy balances with data on emissions from energy combustion, this study provides a more reliable dataset for policy analysis. As India charts its path towards net-zero emissions, robust energy and emissions data will be essential for designing targeted, inclusive, and sustainable interventions that achieve the country’s long-term goals.

The post Mapping India’s Energy and Emissions Landscape first appeared on CSEP.

With greater additions of renewable energy (RE) to the generation mix, ensuring reliability of the power system will be a challenge. Resource Adequacy (RA) is an assessment to determine whether a power system has sufficient resources to meet the demand for electricity at all times. While resource adequacy has always been of concern, the addition of non-dispatchable and non-controllable resources, such as RE, to the system is making it more challenging to ensure resource adequacy. Recognising this challenge, the Ministry of Power (MoP) recently issued guidelines that give a recommended framework for ensuring resource adequacy. The MoP framework relies on three reliability metrics to ensure resource adequacy: (1) loss of load probability (LOLP); (2) planning reserve margin (PRM); and (3) normalised energy not served (NENS). This paper argues that, while the framework is a good starting point, it requires significant modifications to effectively address the challenges posed by a rapidly transforming power sector increasingly reliant on RE. 

The MoP framework and similar ones used in other parts of the world were developed for power systems driven by fossil fuels. This paper suggests several modifications that will make it more suitable for the RE-rich system of the future. While reliability is paramount for power sector planners, minimising its cost is also crucial. This paper proposes modifications to the MoP framework with this dual goal in mind, and most of the recommended modifications to the framework fall into one of two categories: (1) those that enhance reliability; and (2) those that increase the cost-effectiveness of the resource adequacy measures. We also suggest measures to improve implementation by making a few changes to the roles and responsibilities of the institutions involved in the current framework. 

For enhancing reliability, we recommend that, because LOLP is not a very intuitive metric, planners use an alternative metric—the number of loss of load hours (LOLH) in a year. In addition, we point out that the depth (in MW) and duration of individual generation shortfalls, and the frequency of shortfalls, are important because they affect the level of distress consumers experience from outages. Therefore, we recommend that, in addition to LOLH, the following metrics, along with their probability distribution over the year, be assessed in any RA planning exercise: (1) duration of individual shortfalls; (2) depth of shortfalls in MW; and (3) frequency of shortfalls given by the metric, loss of load events (LOLEv).

For enhancing reliability, we recommend caution when using PRM and capacity credits because these are much more appropriate for fossil-fuel-driven power systems and not for RE-rich systems of the future. Another major drawback of using PRM and capacity credits is that it assumes that failures or generation shortfalls at individual plants are independent of each other. However, that is not always the case. Common-mode or correlated failures or shortfalls can occur, particularly during extreme weather events. Extreme weather events can lead to shortfalls across entire regions. One example is the extreme winter storm in Texas, USA in February 2021. The forecast for the winter had predicted there would be reserves of 28% of the expected peak load after accounting for planned and estimated unplanned outages. But the peak load exceeded the forecast, and 32% of the generation capacity failed to operate, leading to widespread blackouts and extreme distress for people.

RA planning must also account for extreme weather events. As such events become more frequent, we recommend that the proposed system be stress-tested through modelling a few potential high-impact, low-probability events. Changing weather patterns and the increasing frequency of extreme weather events will also affect future patterns of RE generation and electricity demand, and these are likely to be very different from the past. Therefore, we suggest that planning models not rely on historical data alone, particularly for RE generation patterns and electricity demand. We suggest that, instead, electricity planners collaborate with climatologists to develop better forecasts of weather patterns.

In order to enhance cost-effectiveness of measures to ensure resource adequacy, we recommend that there be a re-evaluation of the economic justification for the selected RA criteria, such as an LOLP of 0.2% and NENS of 0.05% over the year. This is because the relationship between reliability and cost is highly non-linear, and a small relaxation in the reliability metrics can lead to a significantly larger reduction in costs. In addition, because consumers are indifferent to the cause of any outage, we suggest that it will be good to compare, on an energy basis (say, GWh), the outages caused by bulk system outages versus those caused by distribution network faults. If the outages, in GWh terms, due to distribution network outages are much larger than bulk power system outages, then it would be an indication that we are overspending on grid resource adequacy and underspending on upgrading the distribution network.

For enhancing cost-effectiveness, we also recommend that instead of developing just a single plan and subjecting it to various uncertainties, planners should evaluate a few alternate plans so that the preferred plan is the one that best balances value and risk. Furthermore, because good resource planning can reduce system costs considerably, we recommend that there be sufficient time and training for effective long-term resource planning. More specifically, we recommend that long-term resource plans be required only once every two years, as usually done in the US, instead of requiring it to be completed in two months as mandated in the MoP framework.

The MoP framework puts the onus of resource adequacy planning on the discoms alone. We think that, given the extent of consumer migration to other suppliers, it may be fairer and also cost-effective that all load serving entities (LSEs) be required to do resource adequacy planning. Furthermore, to capture synergies between all LSEs, including discoms, in the approval process for resource plans, SERCs should review them in a holistic manner for the entire State to ensure that electricity is delivered in the most optimal manner for the entire State.

We recognise that some of these changes to the RA framework may increase the complexity of the RA process, and some could also be challenging for discoms to carry out. Discoms in India are just beginning to consider resource planning. Therefore, we have recommended a gradual transition to our recommended framework. However, it is important that these changes are not ignored because doing so could lead to decreased reliability and increased costs that consumers will have to pay for electricity service. Power procurement costs constitute 70–80% of the costs of electricity that consumers pay, and effective resource planning can help significantly reduce those costs. Neglecting these recommended changes could lead to the entrenchment of outdated practices, making future framework revisions more difficult. 

The post Strengthening the Resource Adequacy Framework for an RE-Rich Future first appeared on CSEP.

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Executive Summary

Aggregate Technical and Commercial losses (AT&C losses) have consistently remained a major problem in the distribution space of the electricity sector in India. AT&C losses are a widely discussed issue, with conventional wisdom blaming high AT&C losses as the root cause of Distribution Company (DisCom) financial problems. However, recent trends show improved AT&C losses. In this paper we examine the losses in detail, breaking the composite AT&C loss figures into constituents to understand if these improvements are sustainable and address the financial problems of DisComs.

A high AT&C loss does not inherently mean financial losses for the DisCom—the impact depends on the target set by the regulator. Regulators allow a certain level of AT&C losses, and these costs are passed through to consumers. However, the excess losses (i.e., beyond the specified mark) incurred by the DisComs pose the real threat. Although the AT&C losses have improved from a high of 30.47% (in FY2007) to 15.79% (in FY2023), in financial terms they are still high, more so when we multiply percentage per unit losses by the rising volume and higher prices.

Even at the improved level of 15.79% (2023), the excess AT&C loss costs around Rs 0.21 per each unit of energy (kilowatt-hours or kWh) sold by the DisComs. The same excess AT&C losses, cumulatively over a period of 17 years (FY2007–FY2023), constituted over one-third of the total cash basis financial gap suffered by all public sector DisComs put together. Therefore, the improvement observed in AT&C loss in percentage terms is not a reason to feel relieved. To address the problem of financial gap suffered by public sector DisComs, AT&C loss is an urgent issue that needs to be tackled upfront.

Digging into its constituents, AT&C loss comprises of two components: technical loss (also called billing loss) and collection loss. Billing loss is the amount of energy (in kWh) lost in the network, i.e., from the point of input at the DisCom periphery to the delivery point of the end-consumer. Billing loss happens due to network physical losses as well as theft of electricity. Theft includes stealing electricity by laying bare hooks onto the transmission conductors, withdrawal of energy by an un-registered consumer from distribution lines and poles, meter tampering etc.

In contrast, the collection loss indicates loss due to DisComs’ inability to collect money against the bills raised to the consumers and is measured in rupees. Collection loss also includes loss due to drawl of electricity under the subsidised consumer category–but using it for commercial purposes, etc. Collection losses span both types of non-payments–by the end-consumer and the state government in case it had promised a subsidy. However, collection loss also includes another form of theft such as unauthorised use of electricity (using a domestic connection for commercial purposes), drawl through tampered meters etc.

Is Steady Improvement of AT&C Losses Good Enough?

Since FY2007, both the components of AT&C loss have been improving in percentage terms. While the billing losses have improved from a whopping 26.2% (in FY2007) to 13.28% (in FY2023), the collection losses improved from 5.83% to 2.89%. Irrespective of the improvement in percentage terms, it is the excess losses’ beyond the mark specified by the regulator and its impact in financial terms that matters most.

This improvement in billing losses can be seen where the FY2023 loss was observed to be Rs 4,730 crore, while the cumulative billing loss (FY2007-FY2023) beyond the normative target was Rs 74,766 crore.

Although the gap between normative billing loss and the billing loss achieved substantially reduced over the period, still there exists significant scope for further correction of the current normative mark of billing loss from 12.58% to around 4%.1. More than ten public sector DisComs have already achieved less than 10% billing loss, and this is good enough signal for regulators of other DisComs to bring down the normative mark much further. Putting it all together,there is enough scope for billing losses to come down from the current level of 13.28%.

Bringing down the billing losses helps bridge the financial gap that distribution sector is currently suffering from. At the current power purchase costs, assuming DisComs achieve a moderate target of close to 6%, the financial value of this 7.28% of billing loss reduction can bring down the DisCom’s p power purchase cost by around Rs 33,000 crore every year, from FY2030 onwards (at the current power puchase prices). Given the escalation in power procurement costs over the period, if the billing loss is not improved, the loss in rupee terms is likely to increase further. As such, it is needless to say that any reduction in DisComs’ expenditure brings down the tariff burden on the consumer.

In this context, this paper focuses its analysis on billing losses and the way forward for its improvement.

Critical Issues That Helped Loss Improvement

What is the path forward to reduce losses further? This paper is aimed at addressing a range of questions for public utilities across India:

1. Given the billing loss improvement achieved since FY2007, what is the level of investment (or channels of revenue) that facilitated such improvement?

2. What have been the roles of Government (through schemes) as well as the Distribution Companies (DisComs) (through capital expenditure and repairs and maintenance) in facilitating such improvement?

3. How do investments through ‘repairs and maintenance’ and ‘capital expenditure’ complement each other?

4. Can the efficacy of investment be measured? If not, what are the challenges?

5. Regarding DisComs, is there any saturation effect between high and low loss areas? Stated another way, where would we expect the maximum bangfor-buck improvement?

6. What does it take to achieve the ultimate goal of matching the best figures achieved by a public DisCom?

7. What are the policy implications based on the inevitable heterogeneity across and within DisComs?

Challenges in Measuring Efficiency of Investments

Progressively tighter targets for billing losses require a combination of steps by DisComs; some are based on intangibles (including political will), but many loss reductions require investments in grid strengthening, IT infrastructure, manpower, etc. Another challenge is the ongoing evolution of the grid, which is growing in reach, changing consumer mix, change in demand, among other factors.

Measuring billing losses and the efficacy of investments made is complex for two main reasons. Firstly, the data on billing efficiency are never 100% accurate because of the lack of universal metering (and meter reading). The overwhelming majority of agricultural consumption is unmetered, and its accounting is heavily assumption-based. Historically, there was a wider lack of metering across a large chunk of consumers, and so some older data are also questionable. Secondly, measurement of investments made, and its efficacy is also quite challenging.

Given DisComs are cash-strapped, there is a greater reliance on many Central Government schemes for capital expenditures, some of them explicitly geared towards loss reduction (e.g., Restructured Accelerated Power Development & Reforms Programme (R-APDRP)). Even for other investments like the Rajiv Gandhi Gramin Vidyutikaran Yojana (RGGVY) scheme for rural electrification, the investment went not just for new wires but also for increasing the capacity of existing rural networks, which ultimately facilitates lowering billing losses. Most of the investments being dual or multi-purpose, breaking down the investment into identifiable components that directly improve billing losses is challenging. At the same time, it remains to be seen if well-accepted regression techniques provide any insights.

The Way Forward

Given the criticality of the objectives and the challenges as explained above, this paper therefore, makes recommendations on the following lines:

1. Regulators should consider a tightened billing loss improvement trajectory to bring down losses from the current 13.28% to reach the benchmark 6%, i.e. 7.28% improvement over a seven-year period (they could consider a lesser range as well, depending on a host of factors including consumer mix, geographic terrain etc.).

2. Given the track record of past investments, there is a requirement for greater Central Government allocations and capital expenditure as well as ‘repairs & maintenance’ expenditure by the DisComs.

3. Owing to the criticality of government support through multi-objective schemes in improving networks, the schemes should be designed for a longer duration, while customising the terms and conditions to meet the heterogeneous nature of DisComs which also have varying loss levels.

4. As measurement of losses suffers from inherent challenges, distribution transformer (DT)-level and feeder-level (in that order) metering should be taken up as a priority.

5. Loss due to theft is part of billing losses, and it can be safely assumed that efforts towards modernisation of network coupled with efforts of the on-ground staff must have improved the loss due to theft by a considerable measure. Given the opacity of data, the exact measure and improvement in loss due to theft is not examined in this paper. With this backdrop, continuation of efforts towards mitigation of theft is suggested.

The post DisCom Billing Losses: Moderate Improvements, but Miles to go first appeared on CSEP.

This paper examines the economics of producing and using green hydrogen in India, focusing on the 2030 timeframe. Green hydrogen is intended to decarbonise ‘hard-to-abate’ industries, such as fertiliser and steel, and certain end-use applications in transport, such as shipping and long-distance road freight. 

Green hydrogen is produced by the electrolysis of water using renewable or “green” electricity. In our analysis, we link green hydrogen production costs with the cost and availability of renewable energy (RE) generation, which is measured by its capacity utilisation factors (CUFs). We also calculate the premium, if any, of using green hydrogen compared to energy-basis equivalent costs of fossil fuels for a range of applications. 

Green hydrogen is an emerging technology globally, and India plans to increase its domestic production from a few kilo-tonnes at present to 5 million tonnes per annum (Mtpa) by 2030. Currently, India produces about 6 Mtpa of hydrogen from fossil fuels (mostly by steam reforming of natural gas, i.e., grey hydrogen), which is used primarily for fertiliser production and oil refining. While the cost of green hydrogen is expected to decline in the coming years from its current range of 4–6 $/kg, it is unlikely to reach the oft-stated target of 1 $/kg by 2030 in India. Based on forward-looking assumptions about electrolyser efficiency, we estimate that the input cost of RE for green hydrogen production alone would be at least 1.4 $/kg in 2030 (even after factoring in rupee depreciation), which would be about two-thirds of the total production cost. Other costs include electrolyser capital expenditure (capex) and operation and maintenance (O&M) costs, including those of pure water supply. Incentives, such as a waiver of inter-state RE transmission charges and capital subsidies of up to 0.55 $/kg for green hydrogen production, under the National Green Hydrogen Mission of the Government of India, could potentially help bring the total costs under 2 $/kg. 

Cheaper and more efficient electrolysers are important to lower the cost of green hydrogen production. Achieving high electrolyser utilisation (i.e., CUF) will be necessary for a faster payback of electrolyser capex (i.e., improved amortisation costs), which requires a steady supply of RE. There is an explicit trade-off between RE cost and CUF, and the most cost-effective RE supply is obtained from hybrid (wind + solar) power plants with oversizing, i.e., a total RE generation capacity much larger than the nameplate capacity of the electrolyser. Based on high CUF solar and wind capacity, using 2019 actual RE output data for India as a benchmark, we find that the lowest cost of producing green hydrogen is achieved when the capacity of RE generation (with wind to solar in the ratio 2:1) is about twice that of the electrolyser, resulting in over 60% electrolyser CUF. If electrolyser capex is higher, a higher CUF will be required to achieve the lowest production cost.  

Considering only the cost of green hydrogen production, however, ignores the costs associated with handling, storing, transporting, and using hydrogen, which are significant compared to other fossil fuels due to the low volumetric energy density and high chemical reactivity of hydrogen. 

To determine the cost-efficiency of replacing fossil fuels with green hydrogen, we suggest using the marginal cost of CO2 abatement ($/tonne-CO2), which considers end-use efficiency and the carbon-intensity of alternative fuels, as a more useful metric than $/kg-H2. We calculate abatement costs for the most commonly referred end-uses of green hydrogen: steelmaking, fertiliser, oil refining, transport, and heating/cooking. Even at an optimistic price of 2 $/ kg-H2 in 2030, we find that abatement costs across applications range between 70–175 $/tonne-CO2, depending on whether green hydrogen displaces inexpensive but carbon-intensive domestic coal or price-controlled natural gas in India. This is very high compared to alternative abatement options, particularly electrification. It is also important to note here the significant effect of energy taxes on fuel costs. 

Decarbonisation by displacing coal-based electricity with RE in the grid is more cost-effective (i.e., has a lower marginal cost of CO2 abatement) than displacing other fossil fuels elsewhere with green hydrogen, some of which are less carbon-intensive than coal (e.g., natural gas). Direct electrification of possible end-uses will also result in higher system efficiency due to reduced conversion losses (for instance, battery electric vehicles have a much higher roundtrip efficiency than hydrogen fuel-cell vehicles). This is a crucial consideration, as the production of the targeted 5 Mtpa of green hydrogen will require approx6 Benchmarking Green Hydrogen in India’s Energy Transition Expensive but Important for Some Uses imately 115 GW of dedicated RE capacity (under optimistic technology assumptions). Integration of RE into the grid and electrification of all viable end-uses in transport and industrial heating should, therefore, be prioritised as a more cost-efficient mitigation option. 

In the medium-to-long term, green hydrogen will be needed to decarbonise sectors where alternative solutions are unlikely to be available, such as fertilisers, steelmaking, and refining—all of which use fossil fuels as chemical feedstocks. This will also reduce dependence on the import of natural gas and coking coal in the future. In the short term, we suggest promoting the use of green hydrogen in applications with relatively low marginal abatement costs, such as oil refining, as a steppingstone towards developing a green hydrogen ecosystem in India. In oil refining, switching to green hydrogen would not require significant changes in downstream processes and is, therefore, less capital-intensive compared to other processes, such as Haber-Bosch synthesis for fertilisers or iron ore reduction for steel. 

Finally, we emphasise that defining the conditions for “green” electricity is essential to ensure that green hydrogen and its derivatives, thus produced, have low or zero carbon emissions. This is especially important if the products are to meet international emission standards. Current green hydrogen standards in India allow electricity “banking” with the electricity distribution company (DisCom) for up to 30 days, where an RE generator can overproduce RE at some times of the day and feed it into the grid and reclaim it from the DisCom when RE is not available. This means that some of the electricity consumed for electrolysis may not actually come from renewable sources, and the hydrogen so produced may have significant carbon emissions. The conditions to define “green”, hence, should be based on the additionality, deliverability, and timing of the RE supply. This is key to determining the cost and availability of RE, which disproportionately affects the cost of green hydrogen production and, thus, the cost of decarbonisation. 

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Benchmarking green hydrogen in India’s energy transition

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Mathematical modelling programs have become indispensable in climate science and policy research, providing projections of greenhouse gas emissions and economic output for the analysis of climate change mitigation and adaptation strategies. These programs, including Integrated Assessment Models (IAMs) and Energy System Models (ESMs), facilitate evidence-based policymaking at national and international levels. This report provides a descriptive overview of selected models, highlighting their diverse applications and accessibility. IAMs such as REMIND, GCAM, IMAGE, WITCH and MESSAGE, which have been notably used in the development of the IPCC’s Shared Socioeconomic Pathways (SSPs), and ESMs such as TIMES and OSeMOSYS are discussed, along with India specific models such as IESS 2047, Rumi/PIER and EPS India. The report outlines the technical attributes and features of the models, such as sectoral coverage, economic growth assumptions, modelling algorithms, optimisation methods, etc., with an emphasis on the usability and scalability of the models. Inter-model comparison tables are provided to help assess the suitability of a model for a desired application. The report also acknowledges the limitations and uncertainties in the models. Recommendations include increasing transparency and accessibility to improve the usability and integration of these tools.

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Overview of climate-economy and energy system models – Hindustan Times

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The standard SAM framework has been expanded with an Environmentally-extended SAM (ESAM) that integrates data on emissions, thus enabling us to assess climate policy interventions. The ESAM includes three pollution types: air emissions, wastewater generation, and land degradation. The ESAM constructed in this paper includes 45 production sectors, 318 categories of labour, 80 household categories, and 3 environmental pollution categories. The labour factor of production is further disaggregated by region, social group, occupation, education, and gender. Households are disaggregated based on regions, social groups, and annual consumption expenditures. The ESAM is used to compute output, labour income, and employment multipliers, while the environmental extension provides emissions multipliers.

This study tackles the existing research and data gaps regarding pollution generation by incorporating sector-specific data from India’s greenhouse gas inventory. It attempts to introduce a novel approach to categorise labour in this ESAM, which can be used to investigate the questions of income inequality across regions, social groups, educational attainment, and gender, amongst others.

The post Developing an Environmentally-extended Social Accounting Matrix for India 2019-20 first appeared on CSEP.

Abstract:

In this technical note, the India Input-Output Table 2015–16 is used to compute the direct and indirect uses of energy sources required by the production sectors of the economy through the construction of an Energy Input-Output Table. The analysis shows the energy intensities of each of these sectors. This enables the computation of energy savings due to increased energy efficiencies, across sectors and at an economy-wide level. The implications of climate change policies may be analysed through a General Equilibrium model that considers the backward and forward linkages of one sector with all other sectors of the economy.

Executive Summary:

India’s food and energy needs are going to expand in future. Climate change has links with both. India announced its National Action Plan on Climate Change (NAPCC) in June 2008, followed by submission of the Intended Nationally Determined Commitments (INDC) to the UN Convention on Climate Change (UNFCCC) in October 2015. With its objective of reducing energy intensity, India shall face constraints on the use of fossil fuels for energy generation. Increased greenhouse gas (GHG) emissions will adversely impact air quality and hence human health. Climate change will lead to adverse weather conditions in different parts of the country and affect agricultural production. Volatile monsoons and melting glaciers will impact water flows across the perennial rivers and lead to rising seawater levels, affecting water and soil. Lifestyles will change in tandem, as will consumption baskets (though differentially across agroclimatic zones and states). Therefore, there is an urgent need for research on the consequential impact of climate change on the economy and society.

Different sectors, both energy and non-energy, are affected through their inter-sectoral economic linkages. Economic impacts can be measured at the sectoral or the overall economy level using general equilibrium (GE) models. The GE models address the effects of a change in one sector on all sectors through sectoral linkages. Analytical studies can also be done at the single-sector level. Examples of such studies include emissions from, say, power generation or steel manufacturing. However, such studies limit the understanding of inter-sectoral linkages. Hence, it is pertinent to use a GE accounting framework based on the input-output structure that considers the backward and forward linkages of one sector—say production of petroleum products—with all other sectors of the economy. Other examples include understanding the impact of sectoral output changes, capital formation, employment, trade, income levels, and changing consumption baskets. While the data are available to conduct such studies at the national level, these can also be done for the sub-national or the agroclimatic zones, subject to data availability.

An economy’s input-output transactions tables provide the necessary information on sectoral production, intermediate and final consumption, taxation, and income levels. The tables provide the accounting framework of the cost structure of intermediate sectoral production and the estimates of final consumption, capital formation and trade. These tables help compute income and employment multipliers at the sector level and the inter-sectoral forward and backward linkages measures. The input output information can be extended to account for the full circular flow of money and activities—across the household, corporate, and government—to a Social Accounting Matrix (SAM). Climate change issues can be incorporated in the input-output tables and SAM to provide detailed environmental indicators. The extended tools are referred to as the energy input-output (EIO) table, environmentally extended input-output (EEIO) table, and environmentally extended SAM (EESAM). While the EIO accounts for the energy flows across sectors of an economy, the EEIO extends this further with data on GHG emissions and water consumption. The EEIO measures the emissions and primary natural resources embodied within the goods and services of an economy (Institute for Prospective Technological Studies, 2011).

Some of the extant literature addresses the issues of energy and emissions within an input-output and SAM framework. Miller and Blair (2009) provide detailed chapters on these issues. Hikita et al. (2007) extended India’s input-output table for 1993–94 and 1998–99 for environmental analysis to estimate CO₂ emissions. Goldar et al. (2011) discuss the prioritisation of India’s green export portfolio using the environmental input-output approach. Pohit and Pal (2014) worked on an environmentally-extended SAM (or EESAM) for India’s climate change policy.

The EEIO tables and EESAM are amenable to computable general equilibrium (CGE) models for addressing and answering some critical policy questions. These include the impact of changing consumption baskets, energy requirements and cropping patterns on climate change. The models can also analyse the climate change implications of energy efficiency, renewable energy, environmental tax reform and carbon leakage, and border carbon adjustment in a multi-country framework.

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