Climate Change and Pollution Control

Mapping environmental factors

Maps of flood vulnerability, species abundance, water supply, and other environmental factors reflect current and historical conditions. However, all these conditions are changing as a result of climate change, and the future will be much different from the past. Advanced computing enables projections with much greater geographic granularity than in the past. This would be extremely useful to land and infrastructure planners in designing structures with an expected lifetime of many decades.
Professor Michael B Gerrard, Director, Sabin Center for Climate Change Law, Columbia Law School

The Tower of Babel

Lawyers, scientists and AI professionals are building the Tower of Babel when collectively addressing pollution control and climate change. It is vital to identify the essential features of AI and big data to be applied in environmental control policy where law plays a part, and develop a common language of understanding so that control outcomes can be a jointly understood and committed mission.
Professor Mark Findlay, Director, Centre for AI and Data Governance, Singapore Management University, Honorary Fellow, BIICL

The impact of Covid-19 on environment protection

Lawyers need a hand from the big data analysts for an assessment of data provided by environmental scientists on the COVID-19 short and long-term, direct and indirect impact on environment protection. This is crucial for outlining the tendencies in developments in climate change law from the perspective of the measures that States undertake to combat the pandemic.
Professor Maria Kenig-Witkowska, University of Warsaw, ClientEarth

Appropriate implementation of AI techniques

Since environmental pollution is a typical case of negative externalities, hence a source of market failure which requires regulatory intervention, environmental law should be viewed as an inherent element of the main corpus of economic law. The evolution of such an approach could mostly benefit from the appropriate implementation of AI techniques.
Professor Christos Gortsos, National Kapodistrian University of Athens, President of the Academic Board of the European Banking Institute

When machine learning steps away from human programmers

The VW emissions scandal should have been a wakeup call for environmentalists. Algorithms embedded in systems can cause harm (especially machine-to-machine systems). What happens when machine learning develops agency and steps away from its human programmers? How will the law handle that future and how do we prepare?
Dave Rejeski, Environmental Law Institute, Former Director, Science and Technology Innovation Program, Woodrow Wilson Center

Let's kick-start collaboration

Environmental data scientists have the skills to aggregate and intelligently combine complex heterogeneous datasets to disentangle causal mechanisms and provide uncertainty. To kick-start collaboration with environmental lawyers we should develop end-to-end roadmaps for three real-world examples ensuring the data pipelines are trustworthy, interpretable and reproducible.
Dr Scott Hosking, Senior Research Fellow, Environmental AI and Data Science, British Antarctic Survey, NERC, The Alan Turing Institute

Bridging the gap

Environmental problems and risks are becoming more global while the world still lacks a powerful international legal enforcement framework beyond national jurisdictions. Advancing technologies can help bridge the gap, clarify causation, and thus contribute to the establishment of such systems to better tackle issues such as climate change.
Boya Jiang, Legal Researcher, China Office, ClientEarthClientEarth