In the decade gone by, no discussion on emerging technology trends has been complete without the mention of Artificial Intelligence (AI). It has now become a mainstream technology, touching all aspects of our lives – work, commute, entertainment, shopping, travel, and much more. Technology and environment are often projected on the opposite sides of the sustainability debate. In this article we offer a different perspective and explore how is big data used in environmental science.
Can Data Science Save the Environment?
Climate change is here and now. Cities are running out of water, glaciers are melting, species are getting extinct and our non-renewable energy sources are getting depleted. We are probably the first generation of humans to feel it’s adverse effects and the last generation with the time to do something about it. The billions of smart devices, fastest of cars, sophisticated space missions, miniature robots to clear up our veins, and astronomical market caps of tech giants would be of little use if there are no humans to reap their benefits. Here are some ways AI can help us build a better, sustainable tomorrow.
Smart City Projects
This is arguably the biggest, all-encompassing use case of harnessing data science to help the environment. If we effectively leverage machine learning for sustainability, we can have cities with green buildings, utilizing recycled water for outdoor use, minimal carbon footprint, sustainable power management through demand prediction, focus on renewable energy sources, and a lot more. It will take the burden off existing infrastructure and end a never-ending, unsustainable construction race as we are witnessing across the world.
Waste Management
Data science and the environment might seem unrelated concepts at first. However, a closer look at each opens up unlimited possibilities in sustainability and waste management. World Bank estimates we generate 2.01 billion ton municipal solid waste annually, out of which at least 33% damages the environment. Waste segregation is a major bottleneck to recycling and a large portion of it happens manually. AI, machine learning, and data science can be leveraged to predict waste categories by reading images and weight data. This can improve our waste segregation processes, both in terms of speed and accuracy. As self-learning systems consume more data, they get smarter and more accurate in estimating future trends.
Traffic Management
Passenger and commercial traffic is a major contributor to environmental pollution, primarily through emissions and noise. AI-based Intelligent Traffic Management Systems can help control traffic signals by analyzing past data and setting signal timers accordingly. Machine learning models can read camera images and detect traffic violations, speed limit violations, helmet-less driving, blocking free left turns, wrong side driving, lane violations, etc. These are some of the many use cases in leveraging machine learning for sustainability.
Management of Food Resources
With the ever-increasing world population and growing life expectancy, agriculture sector around the globe is under tremendous pressure to increase production. United Nations studies indicate nearly half of all fruit and vegetables produced globally are wasted each year. In developed countries such as The US alone, 30% of all food produced (worth USD 48.3 billion) is thrown away. The damage is compounded when we consider the corresponding wastage of resources used to produce it – water, soil, labor, electricity, transport, energy, etc. AI and data science are powerful tools to minimize this waste, once we learn how is big data
Challenges
Sustainable development needs a wholesome approach with all participants and stakeholders buying into it. There are regulatory, technological, and operational challenges in getting it implemented on the ground, some of them being:
Heterogeneous Data
We have a lot of data which is scattered, unformatted, and heterogeneous. In an ideal world, our systems demand their data be cleaned at source and fed into a universal format which is easy to process, analyze, present, communicate, and integrate. If we want data science to help the environment, we need to realign our data strategy to develop a homogenous ecosystem where all participants pull towards the shared goal.
Investment
Government agencies and private players need to join hands in realizing the sustainability dream. It requires expertise, resources, commitment, and coordination throughout the value chain. Given the scale of our cities, this is a major challenge that can be solved by education and leadership oversight.
Regulatory coherence
Environment-focused startups and private players around the world are developing solutions to enable us take initial steps towards a green, clean, and sustainable future. They need the buy-in and direction from regulators. Agile policy makers with government backing is the need of the hour, as the environmental clock is ticking every day.This list is by no means exhaustive and application of AI to improve our environment is only limited by our imagination. In a piece focusing on how does AI help the environment, Nature magazine reckons AI could help achieve 79% of the Sustainable Development Goals (SDGs).
English 
German 
Arabic 
French 
Dutch 
Spanish