The GAIA Perspectives

Designing an Optimal Carbon Pricing System for India

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While the world remains divided on what should be the ideal outcome from the Glasgow Climate Summit; two clear messages are coming across: (a) a credible trajectory towards the achievement of a global compact is needed which would include sectoral strategies as well, and (b)carbon pricing mechanisms are a pre-requisite to attract clean investments and ensure stable rate of returns. Efforts towards domestic carbon pricing initiatives have also been spurred by two recent developments: firstly, carbon border adjustments are back on the negotiating in Europe, and secondly, IMF has recently submitted a proposal for an International Carbon Price Floor among large emitters. Given the heat that India will no doubt feel in the upcoming few weeks, it might be interesting to delve deeper into the carbon pricing issue in some detail from the Indian perspective.

Recent data published by the World Bank shows that internationally there are around 61 carbon pricing initiatives in place or scheduled for implementation. These initiatives cover about 12 gigatons of carbon dioxide equivalent (GtCO₂e) or about 22 percent of global greenhouse gas (GHG) emissions. While the jury remains divided on the better option between carbon taxes and ETS, India has had some experience dealing with both. It is a well-known fact that while India does not have an explicit carbon price or a market-based mechanism such as cap-and-trade, it has put in place several schemes and mechanisms that serve as an implicit price on carbon. These include the perform, achieve and trade (PAT) scheme, coal cess (now discontinued), renewable purchase obligations (RPO) & renewable energy certificates (REC), and an excise duty on petrol and diesel. India therefore stands at an important crossroad vis-à-vis how to design its carbon pricing policy.

Going back to undergraduate public finance, taxes in the fiscal system burden the economy by creating two sorts of distortions to economic activity. Firstly, the tax system distorts factor markets and lowers returns on investments, thereby reducing the overall level of economic activity and causing a contraction. Secondly, taxes distort the composition of economic activity, by providing an incentive for more activity in the informal sector that may be less productive. However, when looking at carbon taxes or similar instruments, the state of play changes a bit. Literature seems to suggest that the imposition of carbon pricing instruments may lead to large efficiency gains when revenues are used to lower other distortionary implicit taxes or fund welfare-inducing transfers, also termed as the ‘revenue-recycling’ effect. Additionally, if designed well, ‘efficient’ carbon prices, may be able to factor in externalities or co-benefits of reducing fossil-fuel use which would lead to virtuous cycles of their own (see figure 1).

Additionally, these are only the static or at best short-term impacts of carbon pricing. There are dynamic or long-term gains to be reaped while looking into the innovation compensation effects of these prices following the Porter hypothesis. This effect essentially means that firm compliances to environmental regulations (or higher carbon prices in this case) would catalyse innovation benefits for enterprises which would then be induced to improve their productivity and/or trade competitiveness to keep up.

Figure 1: Net benefits from a Pre-Existing Fuel Tax
(Source: Parry, I; Veung, C. & Heine, D; “How Much Carbon Pricing is in Countries’ Own
Interests? The Critical Role of Co-Benefits”, IMF Working Paper)

However, is carbon pricing the panacea as it sounds? While the present piece does not argue against the importance of pricing carbon to take the mitigation ambition a step farther, the gains of carbon taxes often extolled in literature definitely need to be looked at critically. The above premise of gains to the system presupposes two elements in its design- (a) an ability of the system to regularly incorporate social cost of emissions into its pricing, and (b) recycling of collected funds to improve either social welfare outcomes or add to natural capital. In fact, a paper by Ojha (2005) looked at the impact of a domestic carbon tax policy on carbon emissions, GDP, and poverty in India. Using a CGE model, the study found that a carbon tax policy would impose substantial costs in terms of lower economic growth and higher poverty. However, the fall in GDP and rise in poverty could be minimized or even prevented if either emission restriction targets were mild or tax revenues were transferred to the poor. The second option of using collected tax revenues for environmental or climate-related projects would not only add further impetus to the set climate trajectory but could also potentially ‘crowd-in’ private investment into the same. Unfortunately, on both fronts India’s previous track records have not been the greatest.

Given the above context, it is my opinion that it might be easier to scale-up the PAT scheme as a prototype for an emission trading system rather than impose a separate carbon tax given the political economy consideration. One could however also argue that this need not be an either-or decision, and both can co-exist parallelly. Let’s discuss each of these points here. The PAT scheme was designed as a means to achieve energy efficiency interventions in critical sectors cost-effectively. The process involved the identification of high energy consuming sectors and firms, setting up of consumption baselines and targets, and designing of a trading mechanism for energy saving certificates (or ESCerts) as a means for meeting targets. Given the coverage of the PAT which includes power, several energy-intensive industries, refineries and discoms as well as its dynamic mechanism for cap setting and reductions; it provides for a good base case for the emission trading design. From the fiscal policy perspective as well, the incidence of the ‘tax’ is clear and direct.

For the design to be fleshed out further, two additional critical elements need to be brought into the fold. The first refers to India’s own strategy (either independent or based on a global compact) for emission reduction and its devolution in the form of sector-specific targets. These sectoral targets form the basis for the baseline identification and future cap setting (linear or performance-based) for the aforementioned emission trading system. The second refers to the setting up of a national registry of emissions to periodically collect and verify data at a firm-level and sectoral emissions using a bottom-up approach. Mexico is currently undergoing the process of setting up a national emissions trading system and there could be lessons there for India.

An alternative line of thinking in this regard could be capping state level emissions and allowing them to think about the choice between emission trading or taxes and also decide upon the design, which is the approach being tried out both in China and Canada. However, the state-level proposition would need much deeper analysis taking into consideration not just the state’s energy consumption and production patterns but also its developmental imperatives while deciding the emission caps and trajectories of allowances in the future.

In sum, speculations are rife about how the climate negotiations would pan out from the developing countries’ perspective. But one thing is certain, India would need to do some soul searching, albeit reluctantly, about how to mainstream carbon pricing in its own fiscal and climate policy. Of the two options i.e., carbon tax or emission trading system, the second option seems to be preferable given the political economy of additional tax imposition and the good track record with PAT scheme.

(Views expressed are the author’s own and don’t necessarily reflect those of ICRIER.)

Are renewable capacity targets in India too ambitious?

With each passing day as we approach COP 26, the pressure on India seems to be growing to declare a date when it plans to become net-zero as far as carbon emissions are concerned. More than a hundred countries have declared their intention to turn net-zero by 2050 or so and some, like U.K. and France, have indeed legislated on this issue. Just announcing one’s intention to go net-zero is really not enough unless it is backed by action on the ground. As far as action goes, nothing much is really visible since the nationally determined contributions (NDCs) collectively will see a rise of about 3 degrees centigrade (^oC) even if the targets mentioned therein are achieved. Before one can go to net-zero, one will have to peak its carbon emissions and it is said that a gap of about 30 years between the two milestones is practical. Considering the fact that thirteen out of the twenty G20 countries have already achieved their peak carbon emissions between 1990 and 2017, it is only natural that they reach net-zero not by 2050 but much ahead of that. The G20 countries, incidentally, account for about 85% of the carbon emissions.

When questioned about its efforts towards climate change, India has always explained that it is the only country to have an NDC which is 2^oC compliant. This, of course, is history since, on re-assessment, India is now being termed as 4^oC compliant, coming in the category of ‘highly insufficient’. Having said that, one should not read too much in to these categorisations since they are highly subjective. It is said that India has slipped from to 2^oC to 4^oC compliant because it is yet to announce its net-zero date! It is India’s contention that not only was its NDC quite ambitious, it is well on its way to achieve the targets, well ahead of schedule. India has already achieved a reduction in emissions intensity of 28% over the 2005 level, against a target of 33% to 35% by 2030. Further, India’s share in generating capacity through non-fossil sources (including large hydro and nuclear) has almost reached its target of 40% (August 2021) which actually was to be attained by 2030.

India has no doubt gained a huge mileage in international fora by upgrading its renewable capacity target to 175 GW by 2022. The earlier target was 22 GW (by 2022) under the Jawaharlal Nehru National Solar Mission (JNNSM). The government deserves a pat on the back for revising the target manifold, which at the time of being announced in 2015, seemed audacious. The government has also announced that for 2030, the target would be 450 GW. Whether these targets are achievable or even feasible needs to be analysed closely especially keeping in view the experience over the last five years or so.

First, while the targets are being announced by the central government, most of the implementation is carried out by the private sector which has to liaise with state level agencies. Some of the states have been busy reopening power purchase agreements (PPAs) already signed which has shaken off investor confidence. Further, there are delays in payment of dues by the distribution companies which at times have resulted in the developer going bankrupt. This has led to a drop in the pace of installation of solar projects. Second, one is not aware how the targets of 175 GW and 450 GW have been arrived at. It is highly unlikely that it is based on any model which would ensure a harmonious growth between the economy at large and the targets laid down for renewable capacities. Given this, there is a big question whether the Indian economy will be in a position to absorb such large renewable capacities especially with the economic slowdown witnessed since the last two years. Third, considering the fact that we are about a 100 GW of renewable capacity today, reaching a target of 450 GW by 2030 would mean that we would need to add almost 40 GW on a yearly basis from now till 2030. This certainly seems to be a tall order going by the achievements in the past. In fact, a dip in the pace of addition has already been seen since 2018 on account of various factors and it is quite apparent that we are going to miss the target of 175 GW by 2022. Fourth, India’s solar program is totally dependent on imports of solar panels, mainly from China. India’s manufacturing cost is not only 20% to 25% more expensive, we do not have the requisite manufacturing capacity that is required. India’s domestic industry has the capacity to manufacture only 3 GW of solar cells and about 15 GW of modules annually whereas the demand is about 20 GW. There have been periods when imports from China have been disrupted in the past, adversely affecting our targets. In fact, there is a disruption on at this very moment because of the energy crisis in China. As a result of this, cost of equipment has gone up further from 26 cents per watt to about 28 cents per watt which has rendered many projects unviable for which PPAs have already been signed. The point being made here is that the Indian renewable program is highly dependent on foreign suppliers and vagaries on this score will adversely affect meeting our targets.

To conclude, irrespective of whatever decision the government takes regarding going net-zero, what is very palpable is that we are unlikely to meet our targets of 175 GW by 2022 and 450 GW by 2030. One can hope that the recently announced production linked incentive (PLI) scheme for the solar industry bears fruit and the expected 10 GW of additional domestic capacity, which again may be an optimistic estimate, is actually set up. This would be complimented with the imposition of basic customs duty of 40% which would be imposed from April 2022.

(This article was first published in Times of India Online on 29th October 2021)

PROMOTING SOLAR IRRIGATION SYSTEM THROUGH PM-KUSUM: GOOD OR BAD FOR INDIA?

Agriculture is a critical sector of the Indian economy. Its contribution to the country’s GDP is however diminishing gradually over time. Currently agriculture contributes only around 17-18 percent as per the India Economic Survey 2018. Given that, the sector generally employs over half of the workforce and serves food to the whole nation, its importance cannot be stressed enough.

In addition to the above, agriculture has an important role to play in defining India’s climate mitigation and adaptation strategies. On one hand, agricultural production is highly climate-sensitive and dependent on the vagaries of nature, on the other, for certain states this sector has turned into a power guzzler. In these aforementioned states farmers rely predominantly on diesel and electric pumps for irrigation purposes, contributing to India’s total Green House Gas (GHG) emissions. Both diesel and electric pumps use fossil fuels and pollute the environment by emitting toxic fumes and GHGs respectively. Statistics reveal that approximately 14.33 million electric and 6.26 million diesel pump sets have been functioning in India since 2010-11. Excessive usage of these pump sets has led to uncontrolled groundwater extraction and has depleted groundwater reservoirs to dangerous levels. In India, about 89 percent of the groundwater extraction is accounted for irrigation that serves around 34 percent of the total cropped area. This implies huge groundwater extraction for smaller irrigated areas, which is another area of concern for India along with GHG emission. Thus, to correct the Water-Energy-Food (WEF) interaction and to achieve an environmentally sustainable economy, greater reliance on technological innovations and enlightened policymaking is required.

Government of India’s Response:

To combat climate change induced agricultural stresses, Government of India launched the Pradhan Mantri Kisan Urja Suraksha Utthan Mahabhiyan (PM-KUSUM) scheme in February 2019 for farmers for installation of solar pumps with a total central assistance of INR 34422 crore. The scheme is implemented under the aegis of the Ministry of New and Renewable Energy (MNRE) and has the following three components:

Components	Details
Component A	10,000 MW of decentralized ground or stilt-mounted, grid-connected solar power plants up to 2 MW by individuals or group farmers, cooperatives or panchayats (subject to the interest showed to the distribution companies, DISCOMs and availability of sub-station surplus capacity)
Component B	17.5 lakh standalone off-grid solar pumps of individual pump capacity up to 7.5 HP to replace existing diesel pumps
Component C	“solarizing” 10 lakh existing grid-connected pumps of individual pump capacity up to 7.5 HP by outfitting them with solar panels and allowing owners to sell excess electricity back to DISCOMs

Meanwhile, a study by HWWI (2005) shows that India has a high potential for solar PV water pumping irrigation system of 9-70 million, supporting the move of the central government towards solarization of irrigation systems.

Advantages:

The solar irrigation system is helpful for both farmers and governments in terms of their cost management.

Farmers’ Side – Often it is found that in rural areas, the supply of electricity gets highly disrupted during day time, mainly because of the higher electricity demand in urban areas. This leads to lower irrigation facilities, and thus, declining crop production. This in turn, reduces farmer’s income. Consequently, despite having electric pumps, farmers have to rely on diesel pumps to maintain their basic livelihoods. A study done by the Indo-German Energy Programme shows that though the upfront cost for diesel pump sets is lower as compared to Solar PV pumps, higher maintenance cost and fuel prices make diesel pumps more expensive for farmers.
Electricity Supplier’s Side – Almost free electricity supply for agricultural use along with commercial theft of electricity create huge financial burden on the state government and electricity suppliers, i.e., DISCOMs.
Government’s Side – Besides the DISCOMs, state governments have also got benefits from solarization of irrigation system as electricity cross-subsidy has cost them a significant amount. Further, solar irrigation through PM-KUSUM scheme can help India to achieve the goal of “Doubling Farmers’ Income” by increasing crop production through better irrigation facilities and also by selling the excess electricity generated from Solar PV panels to the grid.

Disadvantages:

Although solar irrigation makes the economy better by taking care of the ‘energy’ component in the WEF nexus, it may deteriorate the ecosystem. For instance, given that India is the World’s 13^th most water-stressed country, excessive ground water extraction because of consumption and exploitation for irrigation purposes may further exacerbate the water imbalance in the ecosystem. Specifically, the Western states (Punjab, Rajasthan) and some Southern states (Andhra Pradesh, Karnataka) are more water-stressed than their Eastern counterparts (e.g. West Bengal, Bihar) in India.

Water Stressed Punjab vs. Water Abundant Bihar:

To illustrate this point further, it would be useful to look into states where solar irrigation systems have been adopted in large numbers such as in Rajasthan, Punjab, Uttar Pradesh, Gujarat, and Bihar. While most of these states are water-stressed, Punjab is facing extreme groundwater scarcity. Two prominent staple foods – rice and wheat (water-intensive crops) are produced in high quantities in Punjab. In fact, Punjab ranks 3^rd and 2^nd for rice and wheat production respectively in the country. Although Bihar is rich with respect to groundwater level, it ranked 6^th for both these crops. A comparison of groundwater levels between Punjab and Bihar can be seen from Figure 1 & 2.

Figure 1 and Figure 2 depict that in Punjab groundwater depth is too high (10-40 m) during both pre- and post-monsoon periods. Increasing depth in the groundwater level implies rising water scarcity. By contrast, the situation is much different in the case of Bihar (generally, 2-10 m, even < 2 m in some places during the post-monsoon period). Despite having a low groundwater table in Punjab, the state contributes a higher share in the rice production of the country. One of the reasons for this higher rice production in Punjab may be the high Minimum Support Price (MSP) for paddy, which recently again hiked by 3.7 percent to INR 1,815 per quintal. This MSP hike for paddy in Punjab can lead to further exploitation of groundwater for maximizing rice production.

In Punjab, around 72 percent of the total cropped area is under rice production, of which 99.7 percent area was irrigated in 2015-16. Within the state, Bhatinda, Ferozpur, Ludhiana, and Sangrur have higher productions of both rice and wheat, where almost 100 percent cropped area is under irrigation for both. Around 32 and 65 percent of farmers used diesel and electric pumps respectively for irrigation during 2011-12. While in Bhatinda around 40.7 and 70 percent of farmers used to rely on diesel and electric pump sets respectively, the corresponding figures for Ferozpur were 38.4 and 54.6 percent, for Ludhiana 56 and 67 percent, and for Sangrur 5.8 and 86 percent respectively. Among these four districts, Ferozpur has the highest share of diesel pump sets usage (9.5%). Thus, the solarization of even diesel pumps alone in Punjab can lead to higher extraction of groundwater as farmers get independent access to electricity.

Limitations of Standalone Solar Pump Sets:

The Honourable Finance Minister during the Union Budget 2020 announced a plan for the expansion of PM-KUSUM by setting up an agricultural credit targets at INR 15 lakh crore to set up stand-alone solar pumps to 20 lakh farmers and grid-connected 15 lakh solar pumps to 15 lakh farmers. Stand-alone off-grid solar pumps have an evident disadvantage of water wastage because of the unstoppable water extraction while generating power. This results in continuous groundwater extraction due to power generation at day time and also disturbs the soil moisture which is very crucial for crop production. Thus, the stand-alone off-grid pump does not seem to be a good option, especially in water-scarce states like Punjab, at least till further improvements and innovations in the power storage capacity are made.

Capacity of a Solar Pump and Its Cost:

In addition to the aforementioned pros and cons, the advantage or disadvantage of a solar pump depends on its capacity. This further depends on a number of factors such as depth or distance from the water source, solar radiation, type of a pump (AC or DC), and maintenance, among others. There are three types of pumps: brushless direct current (DC) pumps, DC positive displacement pumps, and AC centrifugal pumps. Among these, the brushless DC pumps are the most efficient and have low maintenance costs, although they are the most expensive of the lot. In general, DC pumps are more efficient as they can operate during fluctuations and hence cost more than AC pumps. The discharge rate from a solar pump declines as the distance from water source increases (Benghanem et al. 2014). Thus, regions with lower water tables like Punjab will need solar pumps with higher capacity. Therefore, farmers in Punjab need to have access high capacity and high efficiency pumps such as 3HP or 5HP DC pumps which can provide at least 3-inch delivery of water in a minute and take minimum time (1.5-2 hours) to irrigate the total cropped area under rice production (i.e., 2970 hectares) in Punjab. On an average, the water discharge rate of a 3HP and 5HP DC solar pump is 1.65 – 0.63 lac litres and 2.64 – 1 lac litres per day for a depth of water of 20-40 meters respectively, where water discharge declines with the depth of groundwater level. However, for installation of a solar pump with a 3000-watt solar panel, even after governments’ subsidy, farmers will have to pay a hefty amount to the tune of roughly INR 51,000 (for 3HP DC) and INR 75,000 (for 5HP DC).

Further, water discharge from the solar pumps vary with solar radiation, which in turn varies with the time of the day, month, and location. In states like Jammu and Kashmir, Himachal Pradesh, Uttarakhand, etc. where the number of cloudy days is more than the number of sunny days, it is difficult to implement the solar irrigation system. Meanwhile, proper maintenance and servicing of solar panels and pumps require adequate training of farmers which is hardly available in India. Although farmers used to get a five-years annual maintenance contract of solar pumps from insurance companies, it only covers the maintenance costs of day to day operation, cleaning, insurance, and security. However, the pump manufacturer is not responsible for any kind of breakage, damage, theft or malfunctioning due to misuse and alterations. Pump set thefts are quite a usual occurrence in the rural areas. Moreover, regular dusting of solar panels is required which is very difficult to do in a state like Rajasthan where sandstorms are a regular phenomenon.

Limitations of Grid-Connected Solar Irrigation System:

Apart from off-grid solar irrigation (component B), the other two components (A & C) of the PM-KUSUM scheme have been largely successful in areas where infrastructure needed for grid-connection is already present. In these cases, water wastage would not be an issue as farmers have an incentive to sell excess power to the grid at a reasonable rate. But in a state like Bihar, where share of electrified rural households is low possibly due to households’ inability to pay as well as lower accountability of DISCOMs, grid-connected solar irrigation schemes may not be successful. However, the solar irrigation system through a community-based pay-as-you-go model has shown significant success, where a community owns a solar pump and members of the community pay according to their usage. This success may be because of the absence of government subsidy on-grid electricity in rural areas and also because of the presence of greater number of marginal and small farmers.

Limitations of Subsidy-Based Schemes:

Further, innovations such as a solar irrigation system can only be successful when the government provides subsidised-schemes like PM-KUSUM. However, subsidization is not a good practice in the long run. A similar situation was witnessed in Punjab when MNRE reduced the subsidy from 80 percent (during 2000-01 to 2003-04) to 30 percent (during 2003-04 to 2012-13) as the cost of the solar panel started declining, installation of solar water pumps reduced drastically from 1,850 to almost zero. It increased to 105 solar pump installations in 2015-16 due to an increase in government subsidy of 40 percent from an earlier 10 percent in 2000-01.

Lastly, the solar irrigation system under PM-KUSUM scheme, especially the off-grid solar irrigation system may be successful in a water-rich state like Bihar, it is not that ideal for a water-scarce state like Punjab. This is particularly because solar pumps are inefficient beyond the maximum water level of 50 meters and the groundwater level in Punjab has already reached at 20-40 meters. Hence, the adoption of a solar irrigation system is a state-specific, even district-specific sensitive agenda, and governments in each layer should implement this scheme very cautiously.

THE COVID-19 OUTBREAK IN INDIA: GEOGRAPHIC TRAJECTORIES AND DISTRICTS’ VULNERABILITY

The COVID-19 global pandemic is an erratic phenomenon and has raged for about six months, affecting 212 countries and territories globally and two international conveyances (https://www.worldometers.info/coronavirus/), in all afflicting over 6.4 million people and causing nearly 380,000 deaths till date. In India, number of covid cases crossed the 2,00,000-mark on 3^rd June 3, 2020. As per the Ministry of Health and Family Welfare (MoHFW, GoI), nearly half of this number (1,01,497) are active cases and 1,00,303 have been cured or discharged. India has so far recorded 5,815 deaths. Maharashtra continued to lead the tally of the states with highest caseload with more than 72,000 positive covid cases. Nonetheless, this deadly corona virus and the stringent lockdown measures implemented to contain it have exposed the marked inequalities that put certain populations more at risk of adverse outcomes than others. As the lockdown gets lifted, state/district or local government need to keep in mind which districts are most vulnerable, and may consequently require effective surveillance, monitoring, and adequate protective measures. What does vulnerability mean in the context of the outbreak? Suggested precautionary measures such as physical distancing, self-quarantine, frequent hand washing with sanitizer or face masking, among others are neither accessible nor affordable especially for those residing in concentrated poverty, congested and derelict residential environments, with meagre access to essential goods and services. While there are certain common and well recognized risk factors for COVID-19 infection such as aging and co-morbidity which apply to individuals, we are really unaware about risk factors and resultant vulnerability at the levels of a community and dis-aggregated local geographies. Yet these vulnerabilities are as likely as individual-level factors to result in higher risk of infection and unwanted outcomes.

With no vaccine yet available, the prospect of a viral return in this coronavirus’ original or mutated form casts further threat, more so on populations and areas that are most at risk from such an outbreak. Therefore, tracing the geographic trajectories of COVID-19 outbreaks and measuring the local level vulnerability using a multidimensional approach would certainly provide a nuanced understanding regarding the identification of regions that are at the greatest risk from its further spread, if the rising trend of the COVID-19 outbreak in India continues unabated over the coming days. Such attempts are extremely crucial towards gaining a comprehensive understanding of the pandemic’s likely impact on a region (in health, economic and societal terms), not only in light of the current ongoing situation, but to also gauge possible scenarios and ascertain target areas for pre-emptive resource allocation in the near future.

Geographic trajectories of the COVID-19 outbreak

If district wise numbers of COVID-19 cases, as reported by the MoHFW (GoI), are mapped for different dates, one can understand how this deadly virus is spreading across space and time and feasibly demonstrate each stage of the pre and ongoing lockdown phases. These dates are- a) Pre-lockdown Phase (on 23rd March); b) Early lockdown Phase (29th March); c) Mid-lockdown Phase (10th April); d) Late lockdown Phase (18th April), and e) Unlock Phase-1 (1^st June).

COVID-19 cases were first diagnosed in India in late January, 2020 and crossed 60,000-mark by the end of the second week of May, 2020. With this highly contagious disease transmitting primarily through international tourists and returning travellers from aboard, the initially affected areas were mostly large cities that have international airports (or are regions adjoining such places) or are major tourist destinations. This is made apparent by the district-wise spread pattern of the COVID-19 virus (Figure 1). In the first phase (Figure 1A), cases were reported from western India (around Mumbai and Ahmadabad- two of the main commercial hubs of the country), from around New Delhi (the national capital) and Ladakh (popular tourist destination as well as a prominent base of the Indian Army) and from the southern states of Kerala (which has a high number residents who migrate/travel for work to the Gulf region), Tamil Nadu, Andhra Pradesh and Karnataka (all of which have major metropolitan centres that are important commercial hubs- Chennai, Hyderabad and Bengaluru). Further intensification of the above pattern was observed in the next time step on 29th March, 2020, with the adjoining regions of the above areas reporting substantial numbers of COVID-19 cases (Figure 1B). Broad swathes of the eastern part of the country still remained mostly unaffected, except for Kolkata (the major regional metropolitan centre) and its adjoining areas.

In the next phase on 10th April, 2020, two large contiguous zones in northern India and western-central-south India reported high numbers of COVID-19 cases (Figure 1C). The northern entity manifested in a broad swath from western Rajasthan, through Haryana, Delhi, western Uttar Pradesh and Punjab, up to Ladakh and parts of Jammu and Kashmir. The west-central-southern entity, while contiguous in nature had two broad trends- one portion of it stretched into the interior of the country from west to east across Maharashtra and Madhya Pradesh, the other followed the alignment of the eastern and western coastal plains, with one arm trending from Gujarat down to Kerala via Maharashtra, Goa and Karnataka’s littoral districts and the other incorporating the coastal tracts of Andhra Pradesh and Tamil Nadu (with the adjoining parts of Telangana and Karnataka). Even at this comparatively advanced stage of the pandemic in India, the eastern portion of the country reported markedly lower numbers of cases, with only a few centres in Gangetic West Bengal, coastal Orissa and along the Ganga valley in eastern Uttar Pradesh and Bihar. The pandemic’s footprint in the north-eastern states of India was by and large negligible. The next time step map of 18th April, 2020 (Figure 1D), showed the merging of the two entities described above, with infilling of their intervening districts (i.e. reporting of cases from areas that did not have any before) and a rise in the numbers of cases of those districts already afflicted previously. This resulted in a near-continuous stretch of the nation, from Kashmir to Kanyakumari, along the central and western corridors and down the eastern and western coastal belts, reporting COVID-19 cases. By now the country’s eastern part had noted a rise in cases (with its epicenter at Kolkata). This manifested as a narrow line of districts along the densely populated Ganga plains in Uttar Pradesh and Bihar, which merged into the larger/more contiguous zone further west. The final phase unlock-1 map of 1^st June, 2020 (Figure 1E) glaringly discerned that about 92% districts (i.e. 590 of 640) reported at least one positive covid cases and in addition to districts from north and south-western Indian states (e.g. Delhi, Rajasthan, Western MP, Gujarat, Maharashtra, Karnataka, Telengana, Andhra Pradesh and Tamil Nadu), the entire eastern India are now dotted with increasing caseload and seems to be emerged as a COVID-19 hotspot in coming weeks. Other factors aside, huge influx of reverse migration from north, and south-western states coming back to their original homes carrying infection with them are largely responsible for further spreading of coronavirus in these socioeconomically vulnerable regions.

Which districts are more vulnerable to virus outbreak than others?

The COVID-19 vulnerability index is developed from a PCA of 15 interlinked indicators, identified based on existing research measuring the socio-economic, demographic, health, hygiene, and environmental dimensions of COVID-19 vulnerability. For instance, in general, densely populated areas may raise the risk of physical contacting with the people. As the COVID-19 is a highly contagious disease, population density is an unavoidable measure (Rocklöv & Sjödin, 2020). Cities or urban areas are the early centre for COVID-19 spreading, therefore percent urbanization is taken. Overcrowded households (percent HH with HH size 5+) are more vulnerable to COVID-19 than others. Percent women with below 10 years of schooling are taken as proxy for the lack of awareness about COVID-19 spreading (Zhong et al., 2020). Lack of basic hygiene practice leads more infection and smoking in any form may also elevate the risk of infection of COVID-19 (WHO, 2020). Percent of under-5 children or adult women lacking nutrition support- undernourishment or micro-nutrient deficiency in the body raises the risk of infection from any communicable diseases (França et al. 2009). Both lower temperature and humidity increases the risk of COVID-19 spreading (Sajadi et al., 2020; Wang et al., 2020). The economically well-off section can make better arrangements for rigid interventions such as lockdown, while the poorest and poor may go out for their food. Accessing the food from community kitchens, receiving food items from donations, buying essential commodities from the public distribution systems (PDS), among others, may exacerbate the probability of infections. Prevalence of NCDs and percent elderly– comorbidities and aging are also highly positively linked with coronavirus infection (Dowd et al., 2020).The higher value of the index indicates higher vulnerability of the district to COVID-19 and vice-versa. This disaggregated level vulnerability risk mapping would facilitate policy makers with some indication on which districts are likely to be most vulnerable to a COVID-19 outbreak and specifically where should the Government target its resources and accordingly plan a data driven intervention strategy.

A closer look at the spatial visualization of district vulnerability score reveals an interesting pattern (Figure 2). Most of the districts in Bihar, Jharkhand, West Bengal, Odisha, Madhya Pradesh, Chhattisgarh and Gujarat, and adjoining districts in Rajasthan and Maharashtra show high vulnerability scores. Moderate vulnerability is seen in northern districts of Karnataka, Eastern Maharashtra, Telangana, Andhra Pradesh and Eastern districts of Tamil Nadu. Finally, the districts of Kerala, Himachal Pradesh, Haryana, Uttarakhand, Punjab, Jammu & Kashmir, and most districts of the Northeastern states show relatively low vulnerability scores. The vulnerability index scores independent of the reported number of cases clearly suggest that a large number of districts are already in a precarious condition. The districts with higher vulnerability scores are typically characterized by poor socio-economic conditions, chronic poverty, and weak health systems. High-vulnerability districts are those where COVID-19 is likely to spread rapidly, once it is introduced, while also remaining undetected for longer periods. There is an 84% overlap of the places most at risk in these states with those demarcated under the Union Government’s ‘Transformation of Aspirational Districts’ programme, i.e. 104 of the discerned 125 highly to very highly vulnerable districts is also aspirational districts.

The relatively well-off states and districts have so far reported more cases, and imposed stringent lockdown that have caused huge job loss and catastrophic financial impact on poor people, particularly informal migrant workers. On examining the distribution of vulnerabilities together with that of reported cases, it is suggested that the outbreak will rapidly spread to districts with higher vulnerability scores (Figure 3). This may be attributed to the effect of reverse migration, as people from high vulnerable districts usually migrate to the more urbanized, well-off and industrialized districts or large cities for livelihood, and now are returning home because of limited or no work opportunities and higher cost of living.

Conclusion

This short analysis highlighted the initial centres of the COVID-19 pandemic in India, how its incidence occurred, its spread over time and concentrations. By tracing the geographic trajectories of the virus’ outbreak, it pinpointed the areas where possible community transmission has occurred and which need targeted measures to control the situation. Geo-spatial visualization was based on the data garnered about the cases occurring in the initial, middle and last stages of the five-phase lockdown underway in India. However, subsequent reports of the pandemic’s spread largely validate the estimation of the areas that were most likely to be affected in the forthcoming days (i.e. the case of the virus gaining a foothold and then spreading in various clusters of Eastern India, around Kolkata). Vulnerability index based risk mapping can be a useful tool to gauge where it is most critical to be cautious and to protect and priorities strengthening health system capacity. The most vulnerable districts are also where the epidemic will have the most devastating impact. The districts emerging as most vulnerable are frequently those that are poorest, with the weakest health systems and which are home to the most marginalized populations. Aspirational districts have a higher magnitude of vulnerability to COVID-19. Discerning such locations can allow targeted resource allocation by the Governments to combat the next phase of this pandemic in India. By and large, vulnerability provides a lens to anticipate the fallout of the epidemic which we cannot afford to ignore.

References

Coudhry V, Avindandan V (2020) Why COVID-19 Outbreak in India’s Slums Will Be Disastrous for The Urban Poor. https://www.outlookindia.com/website/story/opinion-covid-19-outbreak-in-indias-slums-will-be-disastrous-for-the-urban-poor/350335 (accessed on 8th April 2020).

Dowd JB, Rotondi V, Adriano L, Brazel DM, Block P, Ding X, Liu Y, Mills MC (2020) Demographic science aids in understanding the spread and fatality rates of COVID-19. medRxiv (preprint) https://doi.org/10.1101/2020.03.15.20036293

França, T. G. D., Ishikawa, L. L. W., Zorzella-Pezavento, S. F. G., Chiuso-Minicucci, F., da Cunha, M. L. R. S., & Sartori, A. (2009). Impact of malnutrition on immunity and infection. Journal of Venomous Animals and Toxins including Tropical Diseases, 15(3), 374-390. http://dx.doi.org/10.1590/S1678-91992009000300003

Rocklöv J, Sjödin H (2020) High population densities catalyze the spread of COVID-19. Journal of Travel Medicine 1-2:taaa03. doi: 10.1093/jtm/taaa038

Sajadi MM, Habibzadeh P, Vintzileos A, Shokouhi S, Miralles-Wilhelm F, Amoroso A (2020) Temperature and latitude analysis to predict potential spread and seasonality for COVID-19. SSRN. https://dx.doi.org/10.2139/ssrn.3550308

Wang J, Tang K, Feng K, Lv W (2020) High Temperature and High Humidity Reduce the Transmission of COVID-19. SSRN. Populations and Evolution. arXiv:2003.05003 [q-bio.PE]

WHO (World Health Organisation) (2020) (World Health Organisation) (2020) Newsroom/Coronavirus (Covid-19). https://www.who.int/news-room/q-a-detail/q-a-coronaviruses (accessed on 8th April 2020)

Zhong, B. L., Luo, W., Li, H. M., Zhang, Q. Q., Liu, X. G., Li, W. T., & Li, Y. (2020). Knowledge, attitudes, and practices towards COVID-19 among Chinese residents during the rapid rise period of the COVID-19 outbreak: a quick online cross-sectional survey. International Journal of Biological Sciences, 16(10), 1745. doi:10.7150/ijbs.45221

Housing for all 2022: Progress and performance

Of late India is experiencing significant transitions in its key socioeconomic and demographic indicators. Manifestation of messy and hidden urbanization (Ellis & Roberts, 2016) in India poses a serious challenge to the government particularly in the context of realizing the potential of its cities for prosperity and livability. Consequently, it fails to adequately address congestion constraints stemming from the strain that colossal urbanization put on existing land, housing, physical infrastructures, civic amenities and services including the environment. As per governments’ estimate, as many as 18.78 million families were in acute housing deficit and high level of shelter deprivation across urban India (MoHUPA, 2012). In June 2015 the incumbent union government launched its comprehensive mission oriented and ambitious flagship national housing programme called ‘Pradhan Mantri Aawas Yojana (Urban) – Housing for All (HFA)’ to cater to the escalating unmet ownership housing needs of the Economically Weaker Sections (EWS) and the Low Income Groups (LIG), including the Middle Income Group (MIG) living in urban areas. Under the stewardship of the Ministry of Housing and Urban Affairs (MoHUA), this giant housing scheme is expected to build 12 million new houses across all states/UTs by March 31, 2022 in a phased manner. It has four verticals namely in-situ slum redevelopment using land as a resource, credit linked interest subsidy (CLSS), affordable housing in partnership (AHP) and beneficiary-led individual house construction or enhancement (BLC).

Physical and Financial Progress of PMAY (U)

Total number of estimated urban housing shortage during 2001 and 2007 has been around 10.57 million and 24.71 million respectively (Bhan, et al., 2017). These numbers glaringly portray the face of ‘housing poverty’ in urban India. Though the implementation of Rajiv Awas Yojana (RAY) – a flagship housing scheme under JnNURM (Jawaharlal Nehru National Urban Renewal Mission) programme had been partially successful in the last decade, the total housing shortage to be addressed in PMAY (U) programme is still huge (18.78 million).

Almost five years after the government launched the PMAY (U) programme, just one third of the houses (31%) have been completed under the programme against the sanctioned number of houses, while another 59% houses are grounded for construction. Out of ₹1.63-lakh crore Central assistance sanctioned for the programme, the government has released only ₹64,000 crore till February 10, 2020. This implies a rather slow pace of implementation and the physical and financial progress of this housing scheme has not being very impressive (Table 1 & 2). Nonetheless, more disaggregated data on the financial and physical progress of the scheme for each of the vertical components including other details, like economic categories, is not available in the public domain for in-depth analysis. This is indeed a serious lacuna in making an effective assessment of the performance of the programme.

The government plans to construct 1.63 lakh crore houses under this very mission. As per recent statistics released by the MoHUA on February 2020, as many as 21566 projects have been sanctioned for construction of 10,308,595 houses under this programme, of which 6,155,024 houses are grounded for construction and 3,215,666 houses are completed.

Spatial Variations in the Progress of PMAY (U) Implementation

A further look at its geographic variation reveals an interesting picture. For instance, more than 60% of the total approved houses are situated in the large states of – Andhra Pradesh (20.07 lakh), Uttar Pradesh (15.74 lakh), Maharashtra (11.77 lakh), Madhya Pradesh (7.84 lakh) and Tamil Nadu (7.68 lakh).

More than 80% of the total approved houses in Arunachal Pradesh, Gujarat and Telengana have been grounded for construction followed by West Bengal, Kerala, Madhya Pradesh, Tamil Nadu, Himachal Pradesh and Chhattisgarh where construction of 75% approved houses is underway (Figure 1).

**Figure 1: Approved houses grounded for construction**

When it comes to completion of approved housing construction, just two states namely Gujarat (58.21%) and Kerala (55.45%) achieve the 50% mark among bigger states. House completion rate is hovering around 40-45% in West Bengal, Telengana, Odisha and Madhya Pradesh and no other bigger state have achieved the 50% mark yet (Figure 2). By and large, progress of such a celebrated housing programme across states seems to be disappointing. Nonetheless, it is crucial to remember how India’s affordable housing puzzle challenges the scheme’s ability to achieve the target by 2022.

Challenges for the Effective Implementation of PMAY (U)

Without a doubt PMAY (U) is a mass housing programme involving colossal budgetary commitments and other efforts and as of now the number of houses approved under this flagship scheme has been phenomenal, and seemingly it can achieve the anticipated 2022 goal. However, there are certain constraints impeding PMAY (U) from achieving its full potential. First, the quantum of new houses to be built to mitigate the existing housing shortage is massive. Given the current success rate of PMAY (U) implementation, with 32.16 lakh houses built so far, it will take a lot of time to address the housing deficit. Second, shortage of urban land for new housing construction is the biggest issue in speedy implementation of the programme. Though, land issue can be averted partially through in-situ slum redevelopment with the partnership of private developers using land titles, it remains ineffectual in many small cities where land prices are low and private players are reluctant to invest due to high risk of cost recovery. Third, absence of appropriate documents is another challenge for effective implementation of PMAY (U). Central assistance under BLC needs the beneficiary to have reliable and clear land titles and documentation which is rarely available to slum dwellers (Bhan, 2017). Dismal state of land and property records among urban poor creates frequent and widespread issue of unclear land titles, thereby hindering effective implementation of the scheme. However, some bigger states like Andhra Pradesh, Gujarat, Telengana, Maharashtra, Uttar Pradesh, among others where updation of land records have been successful, PMAY (U) also made commendable performance. Fourth, chronic economic poverty. Due to lack of adequate regular income support and intangible collaterals, many housing poor families are unable to get loans and therefore remain deprived in terms of accessing the concession for housing construction under CLSS component of PMAY (U). Scope for monetary assistance enlisted in PMAY (U) is thus largely unreached. Fifth, neglected rental market. There are 10 million houses that have remained unoccupied as per the census of 2011 estimation across urban India, which accounts for roughly half of the total urban housing deficit. Due to distorted rental market in India and stringent rent control act, homeowners often prefer to leave their dwelling lying empty, rather than letting it out on rent, leading to underutilization of valuable resources.

Way forward

Housing is a state subject, therefore, efficient and effective implementation of PMAY (U) and its expected level of performance is largely contingent on the very nature of state-centre political relationship. Nonetheless, in order to mitigate the policy bottlenecks that PMAY (U) faces now, following measures can be useful.

a. In order to adequately harness the scope of monetary assistance enlisted under the BLC and CLSS verticals of PMAY (U), states, in coordination with the union government, need to adopt an easy and hassle free process of property document updation and develop a system of clear land titles so that the poor people can get appropriate documents to their land and property.

b. Urban poor lacking legal land title for in-situ upgrading of dwelling should be given greater tenure security through effective policy measure that would encourage them to invest more in housing construction and enhancement.

c. Finally, there is a desperate need for a balanced approach to housing issues by strongly considering rental housing development, coupled with fair policies backed by the robust demand for affordable rental housing in urban India, using relevant levers to spur rental housing. Sustainable social rental housing in partnership with private and foreign players and the strong engagement of state and local governments might be the way forward. These are essential for not only catering to the colossal housing demand but also to ensure equitable housing outcomes in terms of quantity, usage, quality and affordability among diverse city residents. There is also an emerging need for vacant housing surveys, as is being carried out in developed nations, to gain a more nuanced understanding for the sustainable use of existing resources.

References

Bhan, G., Deb, A., and Harish, S. (2017). Understanding inadequacy: The view from urban India. Available at: http://iihs.co.in/knowledge-gateway/wp-content/uploads/2017/10/Understanding-Inadequacy.pdf (accessed on: 20/04/2019).

Bhan, G. (2017). From the basti to the ‘house’: Socio-spatial readings of housing policy in India, Current sociology, 65(4): 587–602.

Ellis, P., and Roberts, M. (2016). Leveraging Urbanization in South Asia: Managing Spatial Transformation for Prosperity and Livability. South Asia Development Matters. Washington, DC: World Bank. doi: 10.1596/978-1-4648-0662-9.

MoHUPA (Ministry of Housing and Urban Poverty Alleviation). (2012). “Report of the Technical Group on Urban Housing Shortage,” (TG-12) (2012–17), Ministry of Housing and Urban Poverty Alleviation, National Buildings Organization, Government of India, New Delhi.