The advent of massive data sets and advanced analytical techniques is changing information technology in fundamental ways. In the past, data was used primarily to confirm hypotheses. But today, data is increasingly used to generate hypotheses, indicate trends, identify anomalies, and make predictions. This new paradigm that goes by the name “Big Data” is impacting research in diverse fields and business in diverse industries, energy and environment being no exception. This is driven by the proliferation of sensors measuring physical properties, the availability of vast networks of human data, the algorithms to extract information and the computing power to do this in real time.
The “industry” often uses the 5 V’s to characterize Big Data:
The trade-off between “volume” and “veracity” is usually tilted towards the former, as large quantities of data can more than make up for a lack of precision.
Handling large volumes of data means that actions can be coordinated and monitored right along value chains, allowing for efficient oversight of products and externalities. In particular, it enables large firms to understand, measure and act upon major environmental impacts they have that are outside their direct control, i.e. those caused by their suppliers of physical inputs and services.
There are important examples of leading firms that have developed big data-based systems to understand the impact of their business right along the value chains they operate in, particularly Nike,Ikea and Hitachi. The last of these has created an online platform that makes it easier for its suppliers to report on their compliance with sustainability criteria, a process that is believed to encourage accountability among small suppliers and that facilitates overall monitoring of the chain.
Besides these cases, British Telecom (BT) seems to have gone furthest, at least judging by the information available online. The firm collaborated with the Carbon Trust to study the total carbon footprint of its business and concluded that 92% of all emissions in its value chains were outside its direct control, with two thirds of all emissions originating in the operations of 17,000 suppliers. It also identified so-called carbon ho tspot areas where there were business opportunities to cut costs and carbon emissions.
Apart from the example of cooperation between BT and the Carbon Trust, big data have been used to reduce negative environmental externalities by two major firms in the information technology sector: Cisco and IBM.In May 2014, Cisco signed partnership agreements on big data management fo renvironmental purposes with three local governments in Denmark, a country that is a leader in this area. The municipalities of Copenhagen, Albertslund and Frederikssund have signed memoranda of understanding with the firm to develop the digital infrastructure of tomorrow, the “Internet of Everything”, a concept similar to that of the Internet of Things.
The strong green profile of these three municipalities was the main reason for Cisco’s decision to enter into these partnerships. In particular, Copenhagen has set itself the goal of becoming the world’s first CO2-neutral capital by 2025 via the CPH 2025 Climate Plan. The agreement includes technical solutions to improve service to citizens and meet the environmental targets set for 2025. In particular, in Copenhagen and Albertslund it should be possible to validate and scale the cities’ solutions through a single network, including solutions such as outdoor lighting, parking, mobile services, traffic light systems, location-based services, sensor-based cloudburst mitigation, physical infrastructure monitoring and control and intelligent energy technologies.
At the other environmental extreme, big data-based instruments are also being used in Beijing to reduce the impact of negative growth externalities. The municipality of the Chinese capital announced on 15 July 2014 that it had reached a 10-year agreement with IBM, called Green Horizon, to use its advanced weather forecasting and cloud computing technologies to solve the problem of smog. Since handling pollution and fog problems requires improvements to data collection and monitoring and prediction capabilities, Beijing reported that it had set up an early warning system, using data from 35 monitoring stations, which could flag up acute pollution episodes three days in advance, enabling traffic volumes to be adjusted in time.At the same time, since China has sought to reduce the proportion of coal-fired power generation, the IBM cloud computing analysis system will optimize and adjust renewable energy source goals. In particular, the demonstration project of the State energy network in Zhangbei, Hebei, shows that the IBM system of supply and demand management can cut energy wastage rates by between 20% and 30%. The project is also expected to create business opportunities in the sectors of renewable energy and pollution control.
In yet another shift crucial to sustainability, this time in the management of natural resources,Google announced in June 2014 that it was planning to use Skybox satellites in the near future to update Google Maps and increase their accuracy. According to Wired, this will make it possible to estimate the dynamic of natural resource reserves in real time.One example is the measurement of Saudi oil reserves from space using Skybox Imaging. Because oil is typically stored in tanks with floating lids to avoid losses from evaporation in the gap between the top of the oil and the lid, Skybox satellite images have been used to estimate the volume and level of oil in each tank by observing the movement of the lid. Increasing integration between Skybox and Mapbox will simplify access to and analysis of images of this type .
In summary, major examples in recent years that involve firms such as BT, Cisco, IBM and Google show that big data is now being used as a sustainability instrument in real life situations both to optimize environmental outcomes and to prevent extreme situations from arising.
Extending its philosophy of sustainability, ETI Dynamics too has initiated “Big Data” centric capability applicable to its sectors of interest, primarily in water and energy.