As individuals, we are awash in an ever-expanding array of digital devices that accumulate and communicate (and sell) data on our functional needs and personal desires. Similarly, businesses and entire societies have embraced digital technologies for tasks ranging from retail purchasing to online banking.
And, yet, while confronting a rapidly changing and deteriorating natural environment, digital technology management for environmental sustainability remains a hodgepodge of disconnected, at times antiquated, methods of data collection and analysis.
The scale of environmental data creation is experiencing a massive explosion. It includes data from about 5,000 earth-orbiting satellites, rapidly increasing drone operations and about 20 billion scattered sensors capturing data from citizen science initiatives to regulatory compliance. In addition, underground well logs, soil characterization analyses, underwater probes, point-source monitors for air and water discharges, wildlife biomonitoring and chemical signatures add to our challenge of putting environmental data to effective use.
Applications of digital technologies that provide both a granular and system-level view of sustainability issues continue to gain momentum.
There are three reasons why environmental sustainability is a lagging indicator for digital technology applications. First, many current data collection and evaluation methods were established at different times for different purposes. Second, they are insufficiently open, accessible or rapid in their delivery of data. Third, many traditional methods of data collection and analysis frequently fail to convert data into value that provides sufficient insight for rapid decision making.
These failures are illustrated by the practice of continuing to rely upon visual inspection of water lines, bridges and other infrastructure to provide the initial indications of system failure. By contrast, smart technologies embedded within water delivery systems, highways and rivers can provide earlier, more accurate and rapid warnings that natural or physical systems are experiencing stress, thereby enabling decision-makers to take pre-emptive actions to forestall a breakdown.
Standardizing and scaling
Applications of digital technologies that provide both a granular and system-level view of sustainability issues continue to gain momentum. Farmers download apps to adjust planting and harvesting cycles, conserve precious water supplies and compare crop prices in the marketplace. Digital devices adjust heating and cooling within buildings to reduce pollution and save money. And millions of drivers use traffic apps that provide real-time data to avoid congestion and choose the fastest routes.
Standardizing and scaling the collection and use of digital data can transform environmental decision-making in five major ways. They include:
Implementing smarter and timelier sustainability decisions. In 2018, the National Oceanic and Atmospheric Administration (NOAA) introduced a seafood traceability program for 13 imported species groups (comprising more than 1,100 unique species) that are vulnerable to illegal, unreported and unregulated fishing fraud. Data collected enabled NOAA to trace fish and fish products from the point of harvest or production to their entry into the U.S. market. To date, the Seafood Import Monitoring Program data has greatly strengthened audits of chain of custody records and has yielded significant new insights into seafood supply chains. Going forward, new technology upgrades will better enable both the fishing industry and NOAA to use predictive analytics to better identify risk factors to fish species such as overfishing.
Improving accountability for environmental progress. The Environmental Defense Fund (EDF) has used a satellite, MethaneSAT, to collect data on methane emissions from oil and gas operations in roughly 50 major regions, accounting for more than 80 percent of global oil and gas production. The satellites pinpoint exact locations, chemical signatures and the amount of methane released into the atmosphere. By constructing such a global, transparent data collection and analysis system, EDF fosters direct accountability for private or state-owned companies responsible for the releases and makes such information rapidly available to governments, investors and other stakeholders. In many instances, this practice has led to subsequent collaboration to mitigate methane pollution.
Mobilizing drivers for change. Constructing digital technology platforms, whose data and analysis can be shared across public, private and nongovernmental institutions, greatly expands the number of knowledgeable parties who can drive environmental sustainability decisions. For example, future research projects increasingly will focus on documenting pollution burdens in low-income communities adjacent to large industrial operations to better document whether their residents experience disproportionate health risks. The information from such research can better inform the setting of pollution standards, permitting, facility siting and public health planning decisions.
Strengthening value chain decision-making. The many commercial relationships involved in extracting, manufacturing, storing, transporting and consuming products has yielded a highly complex, at times dysfunctional, system for estimating the movement of materials and emissions across multiple chains of custody. This problem has proven especially vexing for issues such as the estimation of Scope 3 carbon emissions or the management of global plastic wastes. Experience with "track and trace" information technologies to prevent the diversion of chemicals into weapons, or to prevent counterfeit pharmaceutical products, has yielded robust systems widely used across business value chains in collaboration with regulatory authorities. IBM’s PRISM (Plastics Recovery Insight and Steering Model) collaboration with the chemical industry’s Alliance to End Plastic Waste aims to organize multitudes of data systems to track the movement of plastic waste globally so as to improve waste management, recycling and other options for communities, regulators and citizens.
Digitizing for decarbonizing. The growing electrification of the transportation system represents an enormous opportunity to harvest many kinds of environmental data to decarbonize the movement of people and goods. Armed with an extensive array of sensors and other digital tools, individual consumers, auto and truck manufacturers and government regulators can access common data sets for lowering carbon footprints for functions ranging from planning individual trips for shopping or family visits, updating national and regional air quality standards and modeling future carbon emissions reductions.
Significant challenges stand in the way of digitizing environmental decision-making. These range from lack of sufficient investments, bureaucratic inertia and human resistance to changing long-established practices. Other major challenges include:
- Environmental data exists in a wide variety of forms from many sources. Not all collected data can be used in decision-making. New strategies are needed to account for the fact that environmental data originates from a variety of systems comprising the geosphere, hydrosphere, biosphere, atmosphere and human behavior.
- Data integration is important. The opportunity to integrate environmental data with other decision endpoints — public health, economic choices, demographics and public and private investments — is both valuable and large. Ensuring greater data consistency to enable such interoperability is a first step towards meeting this challenge.
- There are many significant data gaps. These limit our ability to forecast the time, location and duration of storm surges; measure the progress towards achieving the U.N. Sustainable Development Goals; and assess the contribution of individual sources of PM 2.5 pollution to subsequent health effects.
- New standards are needed. Standards for peer reviewing digitalized data must be established to maintain public confidence in establishing health and environmental regulations. As data sources expand beyond traditional scientific protocols for epidemiology, human clinical studies and animal toxicology into a greater reliance upon citizen science, meta-analysis of different data sets and data from a variety of electronic sensors, new thinking is required to define "scientific quality" for a new era of environmental decisions.
- Digitized environmental data requires a different type of data governance. Because more environmental data will originate from new data sources, often managed through open-source collaboration, all relevant stakeholders must share and have a voice in how data is collected and managed. This development transcends national boundaries as climate change, water scarcity, migration of plastic waste and refugees from food scarcity and environmental degradation have redefined traditional boundaries of the "environment."
- Data privacy and security are paramount issues. Abuses of digital data (banking and credit record fraud, ransomware of critical infrastructure) are widespread. A high priority must be assigned to securely collecting, storing and retrieving environmental data, while designing rigorous and well maintained safeguards against the potential for data manipulation, fabrication and misuse.
The new era of digital technology creates many opportunities for significant new collaborations and innovations. Giving an amplified voice to both nature and people can transform our understanding of both environmental sustainability problems and the solutions to them. A big leap forward through digital technology is vitally important to avoiding the worst outcomes of accelerating climate change and other measures of an increasingly compromised planet.
An expanded version of this column appeared in a separate article co-written by the author and several colleagues in Environmental Law Reporter, June 2021.