The level of emissions arising from household cooking is uncertain given the varying data sets used for emission analysis
Every winter, air pollution in Indian cities rises to alarming levels. Blame is routinely apportioned to traffic, farmers burning crop residue and emissions from power plants and manufacturing industries, among others. However, what is largely overlooked is the usage of wood, crop residue, coal and cow dung for cooking food, heating water and keeping warm. These activities also contribute to air pollution with devastating impact on health and well-being.
Household air pollution, due to biomass burning alone, leads to 0.7 million premature mortalities every year. Despite different initiatives by the government, to increase the uptake of liquefied petroleum gas (LPG), the burning of solid fuels in households hasn’t abated. A detailed analysis of solid fuel burning is not only critical for robust emission analysis but also for better policy formulation and efficient intervention.
Around 18 per cent of Indian households were using LPG as their primary cooking fuel, according to the 2001 Census. By 2011, LPG coverage increased to 29 per cent. This covered 65 per cent of urban households, but only around 11 per cent in rural India.
The Indian government introduced several programmes to increase LPG coverage, such as the Corpus Fund Programme (1992), Direct Benefit Transfer for LPG Aadhaar Linked (2013), Give It Up scheme for LPG subsidy (2015) and, more recently, the Pradhan Mantri Ujjwala Yojana (PMUY) in 2016. The PMUY programme, which provides LPG connections to families below the poverty line, had covered nearly 80 million homes by April 2021.
All these policies and programmes are based on survey data. The beneficiaries of the PMUY initiative, for example, were identified using the Socio-Economic and Caste Census (SECC, 2011). But there is no clarity on emission levels due to household cooking despite the availability of emissions data in the public domain. The reason is that emission analysis varies widely depending on the data set used. The different survey datasets reveal a 10-30 per cent variation in LPG coverage.
The impact of policy interventions-based survey data. Source: PPAC (2020), IRES (2020), Time Use survey (2020), NSSO (2018). Visualization By Ritesh Kumar and Safia Zahid / WRI India
The Petroleum Planning and Analysis Cell (PPAC), for instance, estimated that 96.9 per cent of Indian households had access to LPG in 2020. Meanwhile, the Indian Residential Energy Survey (IRES) found that approximately 85 per cent of households used LPG as the primary fuel for cooking (2019-20); the NSSO’s (National Sample Survey Organisation) 76th round pegged LPG coverage at 61.4 per cent in 2019; and the Time Use Survey pegged LPG usage at 66 per cent in 2019-20.
The PPAC LPG access data refers to active domestic connections supplied and does not consider multiple connections within a single household. The NSSO surveys a household’s principal source of energy for cooking, while IRES asks families about their usage of LPG or piped natural gas (PNG) for cooking. These are just some examples of how different sources generate usage data.
An understanding of the data generation process is crucial to ensure scientific analysis of emissions, especially as estimations have an impact on policies and action.
The PPAC data, which indicates that 96.9 per cent households have access to LPG, also shows that only 3.1 per cent of households use polluting fuels like biomass. Thus, emissions due to biomass fuel use would be considered negligible and would prompt only light policy action.
On the other hand, if we rely on the Time Use Survey data, indicating 66 per cent LPG access and 44 per cent polluting fuel use, emissions due to biomass burning would be considered high and would subsequently require extensive action to reduce their impact on health.
A poorly constructed questionnaire, a small or non-representative sample, non-responsive participants, respondent prejudice and processing mistakes can all contribute to these differences.
If there is so much uncertainty regarding the household sector, where micro-level data is available openly, then it seems fair to assume that the uncertainty will be even higher in transport, construction and other sectors. For instance, transport sector activity data, which includes the number of registered vehicles, type of fuel used, vehicle age and mobility behavior, is not available at the micro-level.
All available activity data sets should be compiled and a primary survey, followed by an uncertainty analysis, should be conducted before using data for estimating emissions in any sector. Only then will these surveys fulfill the goal of generating reliable data sets that can better inform critical decision-making.
Employing a scientific approach helps deepen our understanding of ground realities and enables the formulation and implementation of policies that ensure a healthy life and clean air for all.
Ritesh Kumar is a programme associate and Vandana Tyagi is a senior programme associate with the air quality programme at WRI India. Ajay Singh Nagpure was the director of air quality at the same organisation.
Views expressed are the author’s own and don’t necessarily reflect those of Down To Earth.
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