We need nimble policy to keep pace with technology

Dr. Ian Brettell, Biodiversity Policy Officer at the lab, muses on how we can make sure that the latest technology benefits nature and biodiversity.

Technological progress is outstripping our ability to control it. Breakthroughs in AI seem to be released almost every week, risking the pollution of our infosphere with AI-generated video, audio and text that cannot be distinguished from content created by humans. And for better or worse, their effects are not restricted to the cyber realm. 

The dynamic we’re observing is the ability of powerful technology to beget even more powerful technology, leading to exponential increases in power and access. This is concerning, because every new technology that can be applied to solving our most profound problems can equally be used to create new ones.

Yet the institutions we rely on to keep us safe from technological excess are no longer fit for purpose. E.O. Wilson’s famous quote captures it succinctly: “The real problem of humanity is the following: We have Paleolithic emotions, medieval institutions, and godlike technology.”

There is accordingly an urgent need to update our institutions by constructing nimble decision-making apparatuses that can respond rapidly to new technological developments, and to determine which policy settings are most likely to lead to beneficial outcomes.

Avoiding the pitfalls of putting a price on nature

In the ecosystem conservation and restoration space, improvements in AI, DNA sequencing, remote sensing, and drone technologies have enabled an unprecedented ability to monitor the health of ecosystems at a simultaneously fine and global scale. These means of accurately measuring nature create the opportunity to put a price on it, with the goal of internalising nature into our economic system, thereby making it more expensive to do anything that degrades our environment, and generating income for actions that restore it. 

This is not a new idea, but given the current state of nature, previous attempts at implementing it are generally considered to have failed. With the growing awareness that our natural systems are now teetering on a precipice, stakeholders are once again debating how a ‘price on nature’ should be structured so that it ensures real and significant uplifts in biodiversity. Should they be used to offset “unavoidable” biodiversity loss elsewhere? If not, would there be a sufficient “voluntary” market to sustain the system? Should there be a secondary market? Should the credits be unitised based on some standardised biodiversity metric, or based on land area? And how does one even measure biodiversity? (Our approach is the SEED Biocomplexity Index, which you can read more about here.)

Credit: Mikhail Nilov / Pexels

How can we create effective biodiversity credit markets?

At present, there is simply not enough information to answer these questions with any certainty. When scientists are faced with uncertainty, they run experiments, which can overcome the truism that correlation does not equal causation. By controlling for or randomising all variables other than the variable of interest – here, the form of the biodiversity credit system – one can infer that the differences in the variable of interest actually causes differences in the outcome

Public policy experiments have been used for decades to assess the effectiveness of universal basic income, education reforms, and measures for sustainable urban development. These kinds of experiments can be implemented now to determine which biodiversity credit system models are most effective. For example, governments can set up a number of small-scale schemes designed in slightly different ways while controlling for other variables, and then compare the outcomes.

Although this process will take several precious years to reap reliable results, and some uncertainties will inevitably remain when comparing the outcomes across ecoregions or jurisdictions, they will certainly inform the creation of more robust schemes than would a purely speculative design process. The C40 Cities Climate Leadership Group is a strong example of a coalition that uses its distributed structure to attempt different methods for combating climate change at a smaller scale, before sharing their results and knowledge with their wider network. It is a model that could be expanded within and across nations.

Using public policy tools to determine the right policy settings

Another option would be to design ‘primary’ legislation that can be more easily amended by ‘secondary’ legislation, which does not require the same degree of parliamentary scrutiny or approval. The proposed law for establishing the Australian Nature Repair Market – the first national biodiversity credit market in the world – follows this model. The primary legislation sets up the system’s framework, then the details on which land use practices are eligible to produce credits are to be fleshed out, and flexibly amended, through secondary legislation by a standing panel of experts.

A more recent concept is a ‘regulatory sandbox’, where companies are permitted to run small-scale, controlled experiments in the real world, under the supervision of a regulator. The process was first developed in the finance sector for testing new investment tools, and has been recommended by the EU Council as an innovation-friendly regulatory framework that can help address disruptive challenges in the digital age. Perhaps most importantly, this structure fosters a deep engagement between regulators and innovators. In relation to ecosystem protection and restoration, a regulatory sandbox may be appropriate for testing novel methods of regenerative agriculture; monitoring, reporting and verification (MRV) techniques; and novel biodiversity credit and funding schemes.

Credit: Restor

Restor – the tech platform with built-in experiments to compare land management

Finally, one of the most exciting features of Restor, an ETH spin-off founded by Prof. Tom Crowther, is that the restoration projects on the platform (~140,000 and counting) represent a natural experiment. Within a given ecoregion, the projects that are applying the same land management practice can be grouped together and compared against other groups of projects that are applying different practices. These comparisons will be able to tell us about which land management practices are most effective at improving biodiversity, and this information can be disseminated to the wider network. You can learn more about our approach to deriving insights from this huge network here. Eventually, AI could be used to coordinate the experiments and direct income back to the local communities that manage those areas in return for their insights and restoration outcomes.

To make these analyses even more robust, we are actively engaging with policymakers to draw more large-scale projects – like national parks – onto the platform. If you are interested in collaborating on this project, please get in touch. To get through the meta-crisis, policy is key – we need to update our institutions now to ensure that we can harvest the fruits of technological progress without destroying the orchard.