An Overview of Achieving Sustainability with Big Data Practices

The importance of sustainability can’t be stressed enough, especially in the long term. But how do we know if we’re making progress and how can we make progress? Big Data is one answer. In this article, we’ll take a brief look at how Big Data and Data Science can help in achieving sustainability.

The relevance of sustainability

Before we jump into Big Data‘s role in achieving sustainability, here’s a brief reminder of what sustainability is.

EnvironmentalScience.org defines sustainability as “the study of how natural systems function, remain diverse and produce everything it needs for the ecology to remain in balance, and takes into account how we might live in harmony with the natural world around us, protecting it from damage and destruction.”

In other words, sustainability mainly “focuses on meeting the needs of the present without compromising the ability of future generations to meet their needs” and it consists of three pillars: environment, economy, and society.

When discussing sustainability, the first thing that usually comes to mind is the environment. Environmental sustainability, as most of us would imagine, is all about doing what it takes to protect and conserve nature’s best gift.

But, as we can see from the three pillars idea, it’s not the only form of sustainability that matters, although we’ll be putting focus on this pillar for simplicity in this article.

Process Street defines economic sustainability as the “growth inclusive of practices that support the preferred percentage of its population below its preferred minimum standards of living level, without negatively impacting social, environmental, and cultural aspects of the community.”

The United Nations (UN) Global Compact claims that social sustainability is about identifying and managing positive and negative business impacts on people, encompassing social areas such as human rights, labour rights and corporate governance.

The descriptions of these pillars alone are pretty self-explanatory in stressing the importance of sustainability to the environment, economy and society as a whole.

There’s no denying that the responsible application of Big Data in sustainable development can more effectively measure the progress of sustainability efforts. Big Data and machine learning mechanisms are required to deal with the complexity of the data related to sustainability, from its variety of formats to its variety of sources.

How does Big Data help in sustainability?

Sustainability-related data usually stem from the collection, analysis and connection of data from fires, droughts, rains, earthquakes and other physical components with data from phone calls, social media activity, transportation, households’ light intensities and other social components.

The data is obtained mainly from satellite photos and public databases and such data collection usually require public-private partnerships. Since sustainability data come in all shapes and sizes, Big Data can help sustainability professionals make sense of all the data in 2 major ways:

Operations

Big Data is useful in helping manufacturers understand the environmental footprint of their operations. Every single part of their supply chain including raw material sourcing, equipment utilisation and waste disposal can be measured and analysed more easily.

One example of this is the fashion upcycling company, The Renewal System, which takes discarded apparel and textiles and turns them into renewed products, upcycled materials or recycling feedstock.

Data is collected on everything that flows through the system and is given back to their brand partners to help them improve the production and design of future products.

Co-founder of The Renewal Workshop, Nicole Bassett told TechRepublic that the data collected by The Renewal Workshop falls into two buckets.

1. First bucket: focuses on what is required to make the product, such as the amount of materials, water, energy and toxic chemicals used, along with the amount of greenhouse gases emitted.
2. Second bucket: focuses on the upcycling of the product, such as the ease of re-using the materials of the garment or the amount of time required to repair certain elements of a garment. This helps fashion brands understand the repairability of their products.  

This data is then built into a lifecycle analysis system to calculate the environmental impact of each garment from the front end of producing it to the back end when it is upcycled or repaired. 

Environmental conservation projects

Big data is also useful in assessing environmental risks. Environmental conservation organisations can take advantage of the various data collected about nature to understand its conditions and figure out what needs to be done to protect it.

For example, World Resources Institute’s (WRI) Global Forest Watch (GFW) combines the latest technology with partnerships to enhance forest information.

In a working paper by University of Oxford’s Smith School of Enterprise And The Environment, it’s said that the WRI team partnered with Google to cut costs, run algorithms, and add cloud technology for GFW.

GFW also combines satellite technology and crowdsourcing to generate a mapping application that is used by non-profit organisations, governments and other organisations interested in these monitoring efforts for subjects like chimp habitats, boundaries of protected areas and borders.

Once an environmental problem is identified, publicly available data at the highest resolution is used and turned into digestible formats like maps and rating systems. These formats are shared publicly so that people can identify problematic areas and who is involved, and then do something to fix it.

Big Data for Sustainability

The capacity of Big Data Science to improve analysis and decision-making appeals to various types of organisations including those involved in ensuring sustainability.

Organisations can use Big Data to gain insights about natural conditions and resource usage, and then use the insights to review and revise their efforts or practices to ensure that they fulfill their sustainability goals.

The question then arises as to whether Big Data-driven sustainability applications are actually successful in achieving sustainability.

The answer is yes if it prove its capability in showing a soup kitchen if communities are well-fed and which communities are hungry, so that it can direct its energy to feeding the ones who really need the help.

The answer is yes if it can show a forest watch organisation if forests are staying lush and green, and which forests are being cut down, so that it can direct its energy to saving the endangered forests from complete destruction.

There are so many ways Big Data can make a big difference to sustainability initiatives and the methods mentioned above are just a few examples in achieving sustainability with Big Data if used effectively.

What are the pillars of sustainability?

– Environmental sustainability
– Economic sustainability
– Social sustainability

What are the two main ways of using Big Data for sustainability?

– Monitoring and improving the resource usage in the supply chain of products.
– Assessing the conditions of the environment to decide how to best heal and protect it.