Research methodology and data analysis
The study covered in this book is based on both primary and secondary sources of data. The secondary sources include published reports of the World Health Organization (WHO), Census of India, Central Pollution Control Board (CPCB), Planning Commission of India, and National Health Estimates. The study also takes into consideration the data and papers published in renowned national and international journals and newspapers. The book incorporates the mapping of green spaces using Global Positioning System (GPS), satellite images and Geographic Information System (GIS).
To identify the green spaces, the study uses satellite images of Landsat (Landsat 7, October 2002 and Landsat 8, April 2016). Landsat 7 (2002) and Landsat 8 data (2016) with 30 metres of spatial resolution were taken into consideration. Landsat 7 bands: are Band 1 (Blue), Band 2 (Green), Band 3 (Red), Band 4 (NIR). Landsat 8 bands are Band 2 (Blue), Band 3 (Green), Band 4 (Red), Band 5 (NIR), Band 6 and Band 7, to make the bands composite. After getting the images geo-registered, a supervised classification technique was performed. After the Land Use and Land Cover (LULC) map preparation, Normalised Difference Vegetation Index (NDVI) was also calculated to understand the density of greenness. From the LULC map, the vegetation class was derived, coupled with Agricultural class and Park class. For validating the vegetation class, NDVI calculation was conducted, because NDVI is the most generalised index of plant ‘greenness’. Hence, the output maps show the classes of vegetation, agriculture and parks for the years 2002 and 2015. A land use and NDVI map of 2016 was also prepared.
The study uses maps to show the variation in temperature and pollution level at different locations of Delhi. Besides maps, the study uses descriptive analysis by using relevant statistical diagrams like bar diagrams, pie diagrams and trend lines to represent the data.
The spatial analysis of temperature variation and air pollutants (mainly PM2.5, PM10 and CO,) was done on the basis of data collected through manual air quality monitor (AQM). Data were collected at 35 different locations of the city. The sample was thus determined by keeping in view various sectors and green and non-green spaces of Delhi. Care was taken to identify the 35 locations in such a way that they covered various categories of land use like residential, commercial, market, traffic junctions, water bodies, and semi-natural areas like forest or ridge. The GPS instrument was also used in order to acquire the absolute location (cardinal points). Data were collected twice in the month of June, between 10th and 15th, 2016 (11:00 am to 3:00 pm). Data (temperature, PM2.5, PM10 and CO,) were recorded at 35 selected locations and then mapped to check the relevant role of green spaces and type of economic activities in the extent of pollution. Data were also collected for temperature and relative humidity to calculate heat stress by using the heat stress calculator (WBGT, UTCI). The data were then represented on the map using ARC GIS for better understanding.
The AQM employed works with principle of light scattering (PM2.5 and PM10) and Non-Dispersive Infra-Red (NDIR) (CO,). NDIR means that when a beam of IR light is emitted from a light source, it does not disperse between the source of the light and the detector. An NDIR gas sensor specifically measures the abundance, or concentration, of gases. The employed AQM range is from 0 to 1,000 pg/m5 for PM 10 and 0-500 pg/m! for PM2.5 and 400 to 3,000 ppm for CO,. The accuracy level is +5 per cent FS for all parameters.
To analyse the health economics, a primary survey was conducted for a total of 900 households. The survey was been conducted taking 100 households from each nine zones or administrative divisions (2011) of Delhi on the basis of random sampling, by taking into consideration different socio-economic backgrounds of the respondents. Since the sample size was not based upon any pre-hoc power calculations, this was a sample of convenience.
One of the relevant sections of the questionnaire was based on healthcare cost, health insurance and willingness-to-pay study. All the answers have been kept confidential, processed statistically and used only for a scientific study. A pilot survey of 50 questionnaires was done using the random sample method, to understand the bidding amount for willingness to pay for health insurance. Thus, bids were introduced in the questionnaire after the responses.
The health satisfaction level has been analysed by considering both physical health and mental health. The level of environmental awareness has also been evaluated, based on responses related to eco-friendly products and services.
To analyse the health economics, especially the willingness to pay for health insurance, the method chosen for this study was a bidding game and open-ended questions. Valuations using the bidding format (BID) were elicited by face-to-face negotiation. All respondents were asked whether they would be prepared to pay at least some amount (payment principle question). Those who responded positively were asked to state the maximum amount they would be willing to pay (WTP) per month on the basis of the BIDS given to them. During the survey, five WTP BIDS - for Rs. 200, Rs. 500, Rs. 1,000, Rs. 2,000 and Rs. 2,500 - were given for providing green insurance. Respondents were given one bid value, to which they could respond with either a ‘Yes’ to accept that they were WTP the proposed amount, or a ‘No’ to refuse to pay the proposed amount. That is, each individual was given one bid chosen randomly. Responses were discrete for this dichotomous choice question; therefore, the Ordinary Least Square Method was unsuitable to estimate the valuation function. In this case, Logit Models have been used for computational ease. Later, Multiple Regression Analysis was conducted to find the relation between various socio-economic variables and willingness to pay for health insurance.
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