Data, Information and Knowledge in
Environmental Impact Assessment
(picture sourced from http://2.bp.blogspot.com/-R-O6cSRsQVI/Uscxh6VjdQI/AAAAAAAACFk/Ytf2RB6KLJs/s1600/Data+Information+Knowledge+Dots.JPG)
In Environmental Impact Assessment (EIA), we take decisions on the project, the preferred alternative and the required environmental management measures.
To improve our understanding and take appropriate decisions, transforming data into information and into knowledge is required.
It is often hard, however, to differenciate between data, information and knowledge (DIK). Sometimes these terms are used interchangeably.
EIAs are often weak because they rely mostly on data, less so, on information and least of all on available knowledge. Most EIA report contains only data. I would attribute this to the lack of proper training.
What is data? Data is a fact that is not significant on its own, as it does not relate to other data. Data is essentially a raw entity. It simply exists and has no significance beyond its existence.
Data can exist in any form, usable or unusable. It does not have any meaning in itself.
Data may answer a very basic what question e.g. what are the concentration levels of air pollutants in an urban area? As an answer, we provide values of SO2, NOx, and RSPM at 10 locations over 5 years.
What is information? Information is data that is related and is therefore to be viewed in a certain context. Information is processed or transformed data that relates to who, what, where and when, providing a foundation to the overall understanding. Information is data that is useful.
A comparison of air pollutant concentrations with air quality standards leads to information on violations. Air Quality Index developed on the basis of data on concentrations of air pollutants is another example. Air Quality Index provides information on the overall status on air quality.
While information may become the input for making or evaluating decisions, the level of understanding may limit the completeness and correctness of the decision. To improve the understanding further, we require knowledge.
What is knowledge? Knowledge is the application of information. Knowledge addresses how and why, in addition to who, what, where and when. Knowledge often contains a set
of instructions and know-how. Knowledge links all the relevant information together along with experience to help us take a better decision. Knowledge is often expert resident and tacit. It needs to be captured. When done so, it forms an asset for the organization.
Application of knowledge is critical to EIA as the assessment requires information from multi-disciplinary sources and responses of diverse stakeholders.
Let us revisit the case of urban air quality impact assessment. Here we need data on air pollutant concentrations. This data needs to be processed to draw information on the extent of violations. Information on the percentage of time the violation occurs, magnitudes of these violations, and the extent to which violations have been contiguous in time needs be processed for each pollutant. This information improves the understanding on the status of air quality and possible air quality related impacts. See the Table below as a dummy illustration for Mumbai, India.
|Pollutant||% Violations over 1 year||Magnitude of Violations (Maximum)||Contiguous Period of Violations|
|SO2||20||150% exceedence on July 12 at Andheri||3 days between May 10 to 12 at Bandra|
|NOx||10||200% exceedence on February 15 in Bhandup||2 days between April 5 and 6 in Dadar|
|RSPM||40||350% exceedence on November 1In Mulund||9 days between October 28 to November 6 in Sion|
To assess the impact on health of people due to such violations, we need to process information on all the pollutants (to address the cumulative effect), assemble more pieces of information such as age, sex, exposure (indoor, travel & work patterns) of the people, coupled with research and field studies as evidence. This exercise is essentially taken up to understand the pattern and apply this knowledge to take decisions.
Knowledge represents a pattern that connects and generally provides a high level of predictability as to what is described or what will happen next. For example, we can come up with knowledge to speculate what could happen to the health of people over the next 10 years as result of deteriorating air quality in the city. We also need here a pool of experts who could share their experience on health impact assessment, especially the damage in economic terms, with supporting data and information. This may require holding of a specialized knowledge workshop. If in this workshop, gaps in the knowledge get identified, then these gaps would need to be addressed through directed research. EIA reports should therefore not simply state the impact and its significance, but the certainty of assessment based on DIK. EIAs should provide leads to the research required.
Therefore to assess the significance of impacts for coming up with appropriate management measures, we need a structured approach to DIK. What is required is a careful planning of the data elements, data processing logic/algorithms with tools, followed by application of knowledge. The figure below shows such a desirable progression.
In most EIAs unfortunately, data is either incomplete, irrelevant or outdated. Processing of data to draw meaningful information is seldom done – sometimes due to lack of training and tools. Further, weak information structuring and lack of appropriate expert input leads to poor application of knowledge. Quality of EIA thus suffers. The decisions taken are often ‘foggy’ and ‘inappropriate’. See the Figure below. See Figure 2.
We need good examples to show how proper planning and processing of DIK helps improve the quality of EIA.These examples can serve as guides for the purpose of training and EIA review. Do we have such examples? I will be very interested to know. I would be most grateful if the readers to this blog are willing to share.
Thanks for insights into EIA Process thro’ DIK. And though I am familiar withe EIA related solely to EC, partial approach of DIK is followed (for Mines EIA etc.) however ground- proofing of that is not followed up- except for large projects (where 6 monthly reports are to be submitted to Regional Office, MoEF) and if these reports are analyzed to check the progress of ‘K’ the basic study of EIA for EC would be feasible. However Project Proponent is not interested in this aspect nor does RO of MoEF bother about this aspect. (something similar to Environmental Statement being submitted to SPCB every year with no ‘K’ aspect studied by Corporate nor any use/ analysis made by SPCB.
Agree with you. How long do we continue doing these rituals of reporting?
There is total disconnect in DIK as far as present EIA studies for EC are considered. Only exception is air quality impact prediction in the case of coal based power plants. We should discourage all data collection, compilation and visualisation unless it leads to K for decision making.
Thanks Vijay. Will be great if you pass me some good examples. cheers
Knowledge to do what? is the question. The context is decisive. If knowledge is understanding ‘in order to fix’ a problem. then just data and information is enough. Ironically when knowledge is generated ‘in order to’, then one problem when fixed, gives rise to a new problem.
But when more fundamental questions are raised, the context of knowledge is understanding for its own sake, then fundamental premises can be challenged. Why do we need fossil fuel to travel? why do we need motorized transport? how else can we meet the same want? and that is when innovations happen.
Hence we need knowledge to go back to ‘nothing’ and create new possibilities for life and living.
This is where the knowledge takes us to Wisdom
The cycle is often therefore looked at as DIKW
If the TOR for EIA Study of very small project ( eg Mining of sand & stone from 5ha area ) is similar to large scale project ( Coal mining) then the dilution of all elements of DIK is inevitable.
Indeed, This is a typical case where we apply “one size fits for all” that does not work! .
Scoping is an important process here which is to be used to customize the data and information needs.
Knowledge that is contextual and appropriate plays an important role in decision making.
Small projects are however by no means less impacting.
A very good discussion especially in today’s context.
Having said that the challenge that is faced by all systemic reporting is that the existing technologies have matured to high end visualization but fails in SenseMaking which in other words is data finding data. That’s the first challenge. Secondly,the concept of business dynamics has been ignored for long. However, variety, velocity and veracity of data has increased by leaps and bounds. So in effect a decision maker today looks at a jazzy report and takes a gut-based decision which is neither fed back into the system nor being tracked.
Combining this two calls for proper machine learning algorithms wherein the decision maker’s dashboard changes from a traffic light report to traffic light plus plus. The first plus showing similar condition in the past and the next one showing decisions made and their systemic impact under those conditions. The extent of inferring from those should still be left to the decision maker as gut still has ots value😊
Well presented article. Connecting the dots is knowledge. But, half knowledge (K) is dangerous.So, awareness on it’s need and importance is vital. My observation is two things work well in India. 1. Carrots ( incentives) 2. Sticks ( rules and penalties). If the government could come with any of that it would encourage the K. In the current market lingo, I call it as ‘data analytics’. I guess this would be included in the next generation of EIA and ESG assessments.