Waste Management - Data from 3 leading Indian IT companies

Essential Indicators - Waste Management

Background

Continuing with my last blog on scrapping (reading) data from XML, XBRL files, here I am presenting to you data collected on Waste Management.

The data is presented in a table in two parts as per the standard template of BRR.

The data is also plotted in simple bar charts in two parts and are at the bottom of this post. The bar charts help you grasp the numbers.

Part I Waste Management

The data is presented under following heads.

Total Waste generated (in metric tonnes)

Plastic Waste (A)

E-waste (B)

Bio-medical waste (C)

Construction and demolition waste (D)

Battery waste (E)

Radioactive waste (F)

Other Hazardous waste. Please specify, if any. (G)

MindTree has given the break up for the following.

Tube Lights, CFL Bulbs, Used Oil, Oil-soaked cotton waste - DG Filters, Printing Ink/Cartridges

Other Non-hazardous waste generated (H). Please specify, if any. (Break-up by composition i.e., by materials relevant to the sector)

Again MindTree has given the break up as follows.

Inorganic Waste, Organic Waste, Packaging Waste - Others

Total (A+B + C + D + E + F + G + H)

The total reported by Infosys in its human readable PDF format is 8,091.25, which is different. Please see the table below.

Part II Recycle, Reuse and Recover (RRR)

For each category of waste generated, total waste recovered through recycling, re-using or other recovery operations (in metric tonnes)

(i) Recycled

HCL has given a comment which says "100% recycled for battery and hazardous waste."

(ii) Re-used

(iii) Other recovery operations

Total

For each category of waste generated, total waste disposed by nature of disposal method (in metric tonnes) is reported as follows.

(i) Incineration

(ii) Landfilling

(iii) Other disposal operations

Total

The data was read using a Python script and presented below. You can read the data across the companies.

But you can not compare it as there is no common base.

Data Table

Charts Waste Management

Charts Recycle, Reuse and Recover

Conclusion

Reading multiple machine readable XML files and gathering relevant specific data using a Python script helped. The data was gathered quickly and without any errors. This is the first step. Once you have the data, then analysis of the data can be done.