NOx Emissions: A Data-Driven Analysis

NOx data analysis

Background

I have been analysing sustainability data for top 30 Indian companies. I have chosen to read the sustainability reports in XML files available in public domain. Filing of this report is mandatory. I have been looking at completeness and accuracy of the data, And what we can make out of it.In this blog I am focusing on NOx emissions.

I've been writing about it for a while now. You can refer to my last blog post here.

I've been also writing on the subject on LinkedIn. Here is the Linkedin post.

Causes of NOx Emissions:

NOx emissions, which include nitric oxide (NO) and nitrogen dioxide (NO2), are primarily produced from the reaction between nitrogen and oxygen during the combustion of fuels, such as hydrocarbons, especially at high temperatures. This typically occurs in car engines, power plants, and industrial boilers. Natural sources of NOx emissions include lightning and biogenic sources. source: wikipedia

Leading Data Points on NOx Emissions:

Nitrous oxide emissions have been measured in tonnes of carbon dioxide-equivalents over a 100-year timescale3. source: oneworldindata

Data as pulled from the files

As the table is long, I am not showing the entire table. Instead, I am showing tables with a calculated column at the end, and by splitting the table into smaller tables as shown below. The tables are split according to the units in which the data is reported. The last column is increase in FY 23 value over FY 22 value in percentage for each company.

The entire table is available on my twitter post.

As you can see, the data is reported in 18 different units by 30 companies.

• Metric Tonnes
• 0
• kg/tcs
• mg/Nm3
• Tonnes
• Metric Tonne
• Not Applicable
• ppm
• tCO2e
• Not applicable
• μg/m³
• MT
• NA
• NOx (g)
• Metric tonnes
• tons
• Kg
• Kilotonnes/year

Data analysis with % change column calculated and added at the end by me

1. Companies that have reported data in Metric Tonnes

	nameofthecompany	unitofnox FY23	unitofnox FY22	nox FY23	nox FY22	change PC
17	NTPC	Metric tonnes	Metric tonnes	657376.38	640419.16	2.65

2. Companies that have reported data in 0

	nameofthecompany	unitofnox FY23	unitofnox FY22	nox FY23	nox FY22	change PC
1	Larsen & Toubro	0	0	0.0	0.0	NaN
2	State Bank of India	0	0	0.0	0.0	NaN
3	HCL Technologies	0	0	0.0	0.0	NaN
20	IndusInd Bank	0	0	0.0	0.0	NaN
21	Bajaj Finserv	0	0	0.0	0.0	NaN
24	Bajaj Finance	0	0	0.0	0.0	NaN
25	Tata Consultancy Services	0	0	0.0	0.0	NaN
29	HDFC Bank	0	0	0.0	0.0	NaN

3. Companies that have reported data in kg/tcs

	nameofthecompany	unitofnox FY23	unitofnox FY22	nox FY23	nox FY22	change PC
4	JSW STEEL	kg/tcs	kg/tcs	1.19	1.26	-5.56

4. Companies that have reported data in mg/Nm3

	nameofthecompany	unitofnox FY23	unitofnox FY22	nox FY23	nox FY22	change PC
5	Wipro	mg/Nm3	mg/Nm3	258.60	240.80	7.39
28	Cipla	mg/Nm3	mg/Nm3	49.09	36.63	34.02

5. Companies that have reported data in Tonnes

	nameofthecompany	unitofnox FY23	unitofnox FY22	nox FY23	nox FY22	change PC
6	Reliance Industries	Tonnes	Tonnes	34337.00	36991.00	-7.17
22	UltraTech Cement	Tonnes	Tonnes	84169.11	73717.33	14.18
26	ITC	Tonnes	Tonnes	2382.00	1799.00	32.41

6. Companies that have reported data in Metric Tonne

	nameofthecompany	unitofnox FY23	unitofnox FY22	nox FY23	nox FY22	change PC
7	Tata Motors	Metric Tonne	Metric Tonne	92.0	247.0	-62.75

7. Companies that have reported data in Not Applicable

	nameofthecompany	unitofnox FY23	unitofnox FY22	nox FY23	nox FY22	change PC
8	Power Grid Corporation of India	Not Applicable	Not Applicable	0.0	0.0	NaN

8. Companies that have reported data in ppm

	nameofthecompany	unitofnox FY23	unitofnox FY22	nox FY23	nox FY22	change PC
9	Maruti Suzuki India	ppm	ppm	NaN	NaN	NaN

9. Companies that have reported data in tCO2e

	nameofthecompany	unitofnox FY23	unitofnox FY22	nox FY23	nox FY22	change PC
10	Mahindra & Mahindra	tCO2e	tCO2e	11.68	4.38	166.67
15	Kotak Mahindra Bank	tCO2e	tCO2e	0.00	0.00	NaN

10. Companies that have reported data in Not applicable

	nameofthecompany	unitofnox FY23	unitofnox FY22	nox FY23	nox FY22	change PC
11	ICICI Bank	Not applicable	Not applicable	NaN	NaN	NaN

11. Companies that have reported data in μg/m³

	nameofthecompany	unitofnox FY23	unitofnox FY22	nox FY23	nox FY22	change PC
12	Titan Company	μg/m³	μg/m³	133.15	132.9	0.19

12. Companies that have reported data in MT

	nameofthecompany	unitofnox FY23	unitofnox FY22	nox FY23	nox FY22	change PC
13	Sun Pharmaceutical Industries	MT	MT	126.0	166.0	-24.10
18	Hindustan Unilever	MT	MT	315.0	317.0	-0.63

13. Companies that have reported data in NA

	nameofthecompany	unitofnox FY23	unitofnox FY22	nox FY23	nox FY22	change PC
14	Axis Bank	NA	NA	0.0	0.0	NaN

14. Companies that have reported data in NOx (g)

	nameofthecompany	unitofnox FY23	unitofnox FY22	nox FY23	nox FY22	change PC
16	Asian Paints	NOx (g)	NOx (g)	40.28	42.43	-5.07

15. Companies that have reported data in Metric tonnes

	nameofthecompany	unitofnox FY23	unitofnox FY22	nox FY23	nox FY22	change PC
17	NTPC	Metric tonnes	Metric tonnes	657376.38	640419.16	2.65

16. Companies that have reported data in tons

	nameofthecompany	unitofnox FY23	unitofnox FY22	nox FY23	nox FY22	change PC
19	Tech Mahindra	tons	tons	0.75	3.268	-77.05

17. Companies that have reported data in Kg

	nameofthecompany	unitofnox FY23	unitofnox FY22	nox FY23	nox FY22	change PC
23	Infosys	Kg	Kg	26015.1	22907.32	13.57

18. Companies that have reported data in Kilotonnes/year

	nameofthecompany	unitofnox FY23	unitofnox FY22	nox FY23	nox FY22	change PC
27	Tata Steel	Kilotonnes/year\n	Kilotonnes/year\n	30.0	32.0	-6.25

My analysis

Analysis:

Decrease in Emissions: Several companies have shown a ▼ decrease in NOx emissions from FY22 to FY23. For instance, Bharti Airtel has reduced its emissions by 9.44%, JSW STEEL by 5.56%, and Reliance Industries by 7.17%. The most significant reduction is seen in Tata Motors with a decrease of 62.75%.

Increase in Emissions: Some companies have shown an ▲ increase in NOx emissions. Wipro’s emissions increased by 7.39%, and Mahindra & Mahindra’s emissions increased significantly by 166.67%. ITC and Cipla increased by 30% plus. UltraTech Cement and Infosys also saw an increase in emissions by 14.18% and 13.57% respectively.

No Emissions Data: Several companies such as Larsen & Toubro, State Bank of India, HCL Technologies, and others have no data available for NOx emissions. So also Power Grid Corporation of India, ICICI bank and Axis Bank didn't report on the data.

Conclusion:

The data shows a mixed trend in NOx emissions among India’s top 30 companies. While some companies have made significant strides in reducing their NOx emissions, others have seen an increase. This highlights the need for continued efforts and innovative strategies to reduce NOx emissions across all sectors. It also underscores the importance of transparency and accurate reporting in tracking progress towards sustainability goals.

Waste Management by top Indian companies

“Recycling leads with 98.22% median value”

A) Data:

I analysed waste management for BSE 30 companies based on the BRSR data they had filed with the regulator in form of XML/XBRI files. The data is for FY 23 and FY 22.

B) Introduction:

In an era where sustainability is no longer a choice but a necessity, understanding how companies manage their waste has become crucial. This post delves into the waste management practices of BSE 30 companies, based on the BRSR data they filed with the regulator for FY 23 and FY 22. We’ll explore the different types of waste generated, the proportions of each type, and how these companies are recycling, reusing, and recovering waste.

The PDF with visualisations is available in my LinkedIn post.

And my earlier blog post on waste management for the last year. 

C) Part I Waste Management

The data is presented under following heads. Total Waste generated (in metric tonnes)

Plastic Waste (A)

It was a small percentage for almost all companies.

E-waste (B)

This component of the total was hundred percent for following companies. IndusInd Bank, Bajaj finance, Bajaj Finserv, HDFC bank. It was close to hundred percent for Axis bank.

Bio-medical waste (C)

It was almost 0 for all the companies.

Construction and demolition waste (D)

It looks like this activity had picked up during the year FY 23. It was close to hundred percent for Tata Consultancy Services. It was more than 50% for L&T, Infosys and NTPC.

Battery waste (E)

Tech Mahindra, HCL Technologies and Bharati Airtel reported more than 30% for this category.

Radioactive waste (F)

Thankfully, it was zero for almost all companies. An IT company reported a very small percentage.

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

It was more than 50% for the two pharmaceutical companies.

Other Non-hazardous waste generated (H).

Please specify, if any. (Break-up by composition i.e., by materials relevant to the sector)

Other non-hazardous waste occupies big part of total waste generated for almost all companies. Its median value was 71.25%. Classifying as such under others doesn't give much information. Sustainability leaders and regulators should ponder over this point.

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

Let me explain with an example. In case of Wipro for year FY23, the total waste generated was 4.48K metric tonnes. 55% of it was other non-hazardous waste, 34.4% was construction and demolition waste, and the rest was made up of e waste, plastic waste, battery waste, biomedical waste, and other hazardous waste. and c) Wipro e-waste was 5.9% of its total waste, whereas that of Infosys was only 3.9% of its total waste.

Median value of total waste generated was 24,068.8 in metric tonnes. The highest for a company was 20.23 MMT.

D) 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)

Three companies reported zero for the total waste recovered in FY23 & in FY22 (and no data on sub categories of recovery ) and similarly 2 companies in FY22. Is this the case of missing data?

(i) Recycled

Waste recycling appears to be preferred approach. Its median value in FY23 was 98.22% which is quite impressive.

These companies reported hundred percent recycling for both the years: L&T, Tata consultancy services, JSW steel Ltd, State Bank of India and Tata motors. This is really impressive.

(ii) Re-used

In FY23 more than 50% of companies reported 0% on reuse. While others had a very small number for reuse.

But two companies out for doing hundred percent re use mainly power grid Corporation of India and Titan company.

(iii) Other recovery operations

In FY23 more than 50% of companies did not opt for “other recovery, methods” for recovery which is good because “other method” wouldn’t give any information.

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

Four companies reported zero for total waste disposed (and no data on sub categories) in FY 23 and similarly, seven in FY 22.

(i) Incineration

Two companies reported hundred percent, and one came close to hundred percent in FY 23 under this category and one in FY 22.

Five companies reported more than 50% under this category for both the years.

(ii) Landfilling

Landfilling appears to be more preferred choice. 41.01% was the median value for it. It was 67.38% in FY22. 90 to 100% of waste (of the total waste disposed in FY23) was sent to landfill by eight companies, out of 30.

(iii) Other disposal operations

Six companies reported hundred percent under this category. Similarly, four FY 22.

As I said earlier, this doesn't clarify, what was the method.

Total

If I categorise the total waste generated into a) total recovered and b) total disposed, then I get the following analysis.

Median value of total waste recovered in FY23 in % was 71.81. This is a good number. The focus appears to be recovering of the waste.

The data was read using a Python script. Reading multiple machine readable XML files and gathering relevant specific data using a Python script helped.You can read the data across the companies. But you cannot compare it as there is no common base.

E) Conclusion

In conclusion, the analysis of BSE 30 companies’ waste management practices reveals a diverse landscape. While some companies have shown impressive strides in recycling and reusing waste, others have room for improvement. The high percentage of ‘Other Non-hazardous waste’ across companies calls for more transparency and specificity in waste categorization. Furthermore, the zero recovery reported by some companies raises questions about data completeness.

As we move forward, it’s clear that more consistent and detailed reporting, coupled with innovative waste management strategies, will be key to achieving our sustainability goals. Let’s hope that this analysis sparks conversations and actions towards better waste management in our corporate sector.

CSR Data from from Business Responsibility Report

CSR data from Business Responsibility Report (BRR)

Background

CSR data was read from the BR reports in XML and XBRL formats for year 2021-22 using a Python script. The IT companies covered were MindTree, Infosys and HCL - all leaders in sustainability and CSR work.

CSR projects

MindTree provided information on all its projects. The information contained names of NGOs, brief description on projects and counts of beneficiaries. Some companies do not mention names of NGOs. The last column is count of beneficiaries.

MindTree CSR Projects

	Company	NGO	Project	Value
0	MT	CURE India	Clubfoot treatment for new-born Children	400
1	MT	SPASTN	Reaching inclusive educationand comprehensiverehabilitation to the doorstep	62
2	MT	APD	Reaching inclusive educationand comprehensiverehabilitation to the doorstep	178
3	MT	AMBA	Job-Oriented Training ofIntellectually Disabled Youthsfor Employment	200
4	MT	Sparsh Foundation	Early Corrective Surgeries	29
5	MT	Centurion University	Skill Development trainingfor hearing and speechimpaired youths	60
6	MT	Goonj	Medical Support for Missed-Out Communities (Leprosy,Trans-genders, HIV patientsetc.)	2000
7	MT	IDL	Education Continuity Supportfor Visually Impaired Children	50
8	MT	BMST	Thalassemia disabled people –blood transfusions support	50
9	MT	Bal Bhavan	Disabled Friendly Park	0
10	MT	Mindtree - NCPEDP Helen KellerAwards	None	15
11	MT	SSK	Literacy Enhancement	280
12	MT	Gubbachi	Transform FoundationalLearning	90
13	MT	Dream to Reality (D2R)	None	22
14	MT	Agastya	Home Lab Kit	8000
15	MT	Sikshana Foundation	Sikshana @ Home	141966
16	MT	BRDO	Yuva Jyoti	957
17	MT	Goonj	Not Just Piece of Cloth(NJPC)	2500
18	MT	Mindtree - OxyBus	None	107
19	MT	SankalpTaru	MyTree Mindtree	5000
20	MT	Olympics Gold Quest	Paralympics Support	10
21	MT	National Agro Foundation	Integrated WatershedCommunity Development Program(IWCDP)	2001

Beneficiaries

Infosys and HCL, each provided total beneficiaries count. MindTree provided beneficiaries count per project. So I added all the beneficiaries to arrive at a total beneficiaries count. Interestingly beneficiaries count is not available in PDF report of HCL.

Beneficiaries Bar Chart

Number of beneficiaries by company

Aspirational Districts

About Aspirational Districts

Launched in January 2018, the Aspirational Districts Programme (ADP) aims to quickly and effectively transform 112 most under-developed districts across India. The progress is measured across 49 Key Performance Indicators (KPIs) under 5 broad socio-economic themes - Health and Nutrition, Education, Agriculture and Water Resources, Financial Inclusion and Skill Development and Infrastructure. Please click NITI Ayog webpage to know more about the programme from the website of NITI Aayog.

Spend on Aspirational Districts - Data Table

CSR spend in INR by states and districts in India

Aspirational Districts - Bar Chart of total spend

Please see the chart below.

CSR spend in INR on aspirational districts - an important metric for government of India

Aspirational Districts - Interactive Locations Map

Please click on the map link to see the interactive locations map.

The green markers represent locations of Infosys. Blue markers represent HCL locations. If you click a marker, you will see the name of the district.

Total spend on CSR projects

The information on total spend was not available.

Summary

The numerical CSR data that was pulled from BRR report in XML format was shown above and was very insightful. Textual information was also available to be read and captured.

GHG emissions data from 3 leading Indian IT companies

GHG Emissions plus SOx, NOx, and Particulate Matter (PM) Data - From 3 leading Indian IT companies

Background

Section-I

Details of greenhouse gas emissions (Scope 1 and Scope 2 emissions) and its intensity:

Description of Scope 1 and Scope 2 Emissions

"Scope 1 emissions are direct greenhouse (GHG) emissions that occur from sources that are controlled or owned by an organization (e.g., emissions associated with fuel combustion in boilers, furnaces, vehicles). Scope 2 emissions are indirect GHG emissions associated with the purchase of electricity, steam, heat, or cooling. Although scope 2 emissions physically occur at the facility where they are generated, they are accounted for in an organization’s GHG inventory because they are a result of the organization’s energy use." - Source EPA

Greenhouse gas emissions (Scope 1 and Scope 2 emissions) and its intensity

Unit for Data on Total Scope 1 and Scope 2 emissions is Metric tonnes of CO2 equivalent.

The data was read using XML/XBRL for Scope 1 and 2 emissions. It was inline with data read manually from PDF file.

A) Scope 1: Infosys gave break up of Total Scope 1 emissions (Break-up of the GHG into CO2, CH4, N2O, HFCs, PFCs, SF6, NF3, if available) in its PDF format document.

C) Scope 1 and 2 per Rs Turnover and F) Scope 3 per Rs Turnover: HCL reported it per Rs million, whereas Infosys per Rs Cr. MindTree reported in per Rs hence its ratio was very small and was shown as zero.

This was also observed in intensity calculations related to electricity in my earlier blog post.

D) Scope 1 and 2 intensity MindTree reported it in Metric tonnes of CO2 equivalent per square feet. The other two companies did not.

Please see the data table below.

Description of Scope 3 Emissions

"Scope 3 emissions are the result of activities from assets not owned or controlled by the reporting organization, but that the organization indirectly affects in its value chain. Scope 3 emissions include all sources not within an organization’s scope 1 and 2 boundary. The scope 3 emissions for one organization are the scope 1 and 2 emissions of another organization. Scope 3 emissions, also referred to as value chain emissions, often represent the majority of an organization’s total greenhouse gas (GHG) emissions."

"The GHG Protocol defines 15 categories of scope 3 emissions, though not every category will be relevant to all organizations. Scope 3 emission sources include emissions both upstream and downstream of the organization’s activities."

Figure Scope 1, 2 and 3 expalined (Source EPA)

Scope 1, 2, and 3 emissions explained by EPA, USA

"Emissions-wise, Scope 3 is nearly always the big one."

G) Scope 3 intensity: MindTree reported it in Metric tonnes of CO2 equivalent per square feet. The other two companies did not.

E) Scope 3: The script returned NaN for Infosys even though the number was typed in and present. I suspect the number was typed and formatted manually by adding thousand separators. I had to replace that number without thousand separators and it worked.

I keep highlighting issues on organising data in spreadsheets. Please refer to my earlier blog on it.

Data Table for Scope 1, 2, and 3 emissions

GHG Scope 1, 2, 3  emissions data of leading Indian companies

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.

Water consumption data of 3 leading Indian IT companies

Water Consumption Data of 3 leading Indian IT companies. 

Background:

Kindly refer to my last blog post which was about scrapping energy data, from xml file containing Business Responsibility Report in machine readable XBR language, of 3 leading Indian companies namely MindTree, Infosys and HCL.

About this post:

This post reads data from the same xml file and XBRL on how much water was drawn from various sources and how much was consumed from water consumption data of 3 leading Indian IT companies. 

The two visualisations at the end show all the data (in absolute and percentages) in bar charts for ease of reading and understanding. 

Let us begin. The xml and XBRL file was read using Python script and data was plotted using Matplotlib.

Section - I)

Water withdrawal by source (in kilolitres) 

i) Surface water

Apparently only MindTree used surface water. 29% of the total water consumption of MindTree was from surface water.  The other two companies didn’t.

(ii) Groundwater

It is the other way round. MindTree didn’t use ground water, but the other two companies did. Infosys usage was 45% of its total water consumption. It was 9% of usage in case of HCL.

(iii) Third party water 

Third party water was used by all. From the human readable BRR, you can see the break up of third party water for MindTree namely water from municipal corporation, private sources and packaged water. The other two companies didn’t give any further break up.  

MindTree usage was 64%, Infosys used 86% whereas it was 33% for HCL.

(iv) Seawater / desalinated water

The usage of it was zero or not applicable for all the three companies. 

(v) Others

In case of MindTree (6% of its total) and Infosys (5% of its total) this source was rainwater. In case of HCL (22% of its total) it included rainwater plus municipal water. 

Total volume of water withdrawal (in kilolitres) (i + ii + iii + iv + v )

This was total of all the above sources.

Total volume of water consumption (in kilolitres)

The water consumption was equal to water withdrawal for MindTree and Infosy. But water consumed was 97% and was lower compared with water withdrawn in case of HCL. 

Water intensity per rupee of turnover (Water consumed / turnover) 

Unlike last time, I did not do any calculation on my own. Infosys reported WI with Rs Crore in the denominator, while HCL reported the WI with Rs Million in the denominator. WI of HCL can be compared with that of Infosys by multiplying it by 10. MindTree reported zero.

Water intensity (optional) 

MindTree reported it by using area in the denominator but the other two companies didn’t. 

Section - II)

Water Discharge

Water discharge has to be reported on all the parameters mentioned above for water sources with additional information on discharge mentioning if the discharge was treated before discharge.

  

MindTree reported zero liquid discharge for all its sites by 100% recycling. 

Similarly Infosys reported no discharge in any of these categories. Infosys  treated Waste water generated in sewage treatment plants and reused for purposes like landscaping, HVAC applications and flushing. 

HCL reported a discharge of 23,453.06 kilolitres that was sent to third parties with no treatment.

Chart showing water consumption absolute numbers

Chart showing water consumption percentage wise

Conclusion

Thus it is convenient to read and extract data from xml and xbrl machine-readable Business Responsibility Report (BAA). This approach reduces the errors introduced in manually copying and pasting the data. This was the very purpose of introducing this format. 

Energy Consumption Data from Business Responsibility Report (BRR) and XBRL

Introduction:

Each BR Report is available in human readable form contained in a PDF file. It is also available in machine readable XBR Language contained in a XML file. Both these versions are available NSE website and also on the websites of respective participating organisations.

In this post I will mention the benefits of using the latter. As an example; I will use only energy consumption data of 3 leading Indian IT companies.

What is BRR?

In 2012, the Securities and Exchange Board of India (SEBI) mandated the top 100 listed companies by market capitalisation to file Business Responsibility Reports (SEBI-BRRs/ BRR) through the Listing Agreement. 

These disclosures were intended to enable businesses to engage more meaningfully with their stakeholders, and encourage them to go beyond regulatory financial compliance and report on their social and environmental impacts. 

The requirement for filing BRRs was extended to the top 500 listed companies by market capitalisation from the financial year 2015-16. In March 2019, the updated NVGs were released as the ‘National Guidelines for Responsible Business Conduct’ (NGRBCs).

In December 2019, SEBI extended the BRR requirement to the top 1000 listed companies by market capitalisation, from the financial year 2019-20.

BRR was changed to Business Responsibility and Sustainability Report (BRSR) with the introduction of BRSR in May 2021. Reporting is mandatory for the top 1,000 listed companies (by market capitalisation) from FY2022–23, while disclosure is voluntary for FY2021–22. Click SEBI website for more information.

What is XBRL?

XBRL (eXtensible Business Reporting Language) is a language for electronic communication of business and financial data which is revolutionising business reporting around the world. It offers major benefits to all those who have to create, transmit, use or analyse such information. XBRL has been developed by XBRL International, a not-for-profit consortium of over 600 companies and agencies which is promoting its worldwide use. Please click XBRI org for additional information.

Key benefits of XBRL and XML file format:

Automate the data capture process:

The benefit of using the machine readable XBRL  is as follows. You can automate the process of capturing the data from XBRL  from one or many XML files. And then bringing in data in to a report you want to prepare. Once you have the data you can manipulate the data. This must be the reason the report is made machine readable by regulators. Regulators can capture data from hundreds of such reports to prepare their report. 

Get the precise specific data points:

With XBRL  you can capture only the specific data points you want for further study. My example below will show how specific data points related to energy consumption were captured. 

You can manually read, copy and capture the data as well. But if you want to capture data from many files, then the process becomes tedious and prone to error. It is also difficult to estimate time to complete the manual task. 

An example:

By writing a script (a few lines of code in Python) you will be able to get the precise information you wants. You can bring that information in a report by automating this task.  

Here I have scrapped energy usage data of three leading Indian IT companies for year 2021-22. This post is for educational purpose only. 

Section - I) covers essential and leadership indicators from the BRR report. 

Section - II) covers ratios from the BRR report and prepared by me. 

These ratios can be compared across the companies.