Conservation technology is vital to understanding and addressing climate change, perhaps the biggest challenge our planet collectively faces. In this case study from WWF’s Attila Steinegger, he discusses how we can make tech-based strategies like carbon credits more effective and sustainable as our ability to understand the natural world evolves.

How Can Technologies Drive Sustainability of the Voluntary Carbon Market?

By Attila Steinegger

The natural world is deteriorating at rates unparalleled in human history. We are in the midst of the sixth mass extinction and are consequently facing the irreversible loss of plant and animal species, habitats, and vital ecosystems upon which our modern world depends. 

Meanwhile, a growing number of countries, cities and businesses are making pledges to get to net zero emissions, which also leads to an explosion of interest in carbon credits and the voluntary carbon market. McKinsey estimates that annual global demand for carbon credits could reach up to 1.5 to 2.0 gigatons of carbon dioxide (GtCO2) by 2030 and up to 7 to 13 GtCO2 by 2050 (Exhibit 2). Depending on different price scenarios and their underlying drivers, the market size in 2030 could be between $5 billion and $30 billion at the low end and more than $50 billion at the high end (McKinsey).

For those who are not that familiar with carbon credits: A “carbon credit” (also known as a “carbon offset”) is an electronic and serialized unit that represents one ton of CO2 equivalent that is reduced, avoided, or sequestered from projects, which can be purchased on the voluntary carbon market. While the voluntary purchase of carbon credits can be an impactful practice, some organizations and experts believe that using carbon credits for carbon neutrality or net zero claims is inappropriate and may be equivalent to greenwashing. For instance, WWF cautions businesses on claiming “carbon/climate neutrality” for either the business or its products, because it could signal that a company’s work on climate is done when a company or its products’ entire footprint hasn’t actually been eliminated (WWF).

In order to provide high quality carbon credits, projects which aim to protect and restore our nature and forests require a feasible monitoring, reporting, and verification process (MRV). However, these MRV activities are time, labor, and cost intensive, and have been shown to be subjective. Therefore, the potential for impact and scale of leveraging advancements of emerging technologies is promising. The World Economic Forum (WEF) presented a diverse set of digital technologies, which can be applied together to deliver decarbonization (see illustration below).

Digital MRV for Forest Projects 

Based on this overview, the combination of Sensing & Control Technologies and Decision Making Technologies looks very promising for applying in MVR of forest carbon stock. There are three key aspects that are important for the use of remote sensing in such projects. 

One aspect is financial; using available and accessible technology and sensors to lower the cost and upfront capital requirements for forest owners to get certified, especially in low and middle-income countries. 

The second aspect is reducing subjectivity in estimating carbon stock and increasing trustworthiness and transparency in the carbon offsetting certification protocols.

And lastly, the solutions need to be scalable due to the urgency of financing forest restoration, especially in tropical regions (ETH). Satellite imagery is increasing in quality and availability and, combined with state-of-the-art deep learning and lidar, promises to soon map every tree on earth and to enable forest above-ground biomass and carbon to be estimated at scale. Compared to current manual estimates, these advancements reduce time and cost and increase transparency and accountability, thus lowering the threshold for forest owners and buyers to enter the market (ETH).

Risk and Limitations of Digital MRV 

As already mentioned, technological advancements reduce time and cost and increase transparency and accountability, thus lowering the threshold for forest owners and buyers to enter the voluntary carbon market. Nevertheless, these algorithms risk additionally contributing to a systematic overestimation of carbon stocks, not reducing it, and are not really applicable for smallscale forests, below 10,000 ha (ETH). A recent benchmark study by ETH Zurich shows that all of the available global Above-Ground Biomass (AGB) maps have a tendency to overestimate the ground truth measurements up to a factor of ten. These are not encouraging results, showing that these maps are far from being accurate enough to be used in remote sensing of forest carbon stock at a small scale (ETH).

This also leads to the conclusion that technology’s current state bears the risk of creating misleading data regarding the impact of conservation projects. As a result, the volume of carbon credits for the voluntary carbon market is increased by low quality carbon credits, which eventually lead to a lower price. 

What’s next?

Considering the benefits of technologies for digital MRV, it is important that we leverage and scale digital solutions in the MRV process. However, it is also crucial to reduce the mentioned risks and limitations of these technologies. Therefore, it is good to see developments by various organisations like the ForestBench Consortium’s effort to create equitable benchmarks for MRV of Nature-Based Solutions with machine learning, which can help to tackle these issues and drive transparency. In addition, some certification bodies of carbon credits are increasingly aware of the technological risks and limitations, and are therefore also working on establishing guidelines and benchmarks to drive transparency and accountability of digital MRV.

What does that mean for conservationists? 

So, what does that mean for the community of restoration and reforestation experts? How could they actively take a step toward driving sustainability with Digital MRV? 

1) Get started with Digital MRV - As mentioned earlier, digital technologies can reduce time and cost and increase transparency and accountability in the MRV process. Therefore, it is important to get started and to think about where, when, and how technologies for digital MRV can be applied. To do that, it is crucial to integrate these thoughts into the full process of planning and organizing restoration and reforestation projects; for instance, in a specific monitoring and evaluation (M&E) concept. The use of technologies for digital MRV is usually discussed too late or only when the project has already started. Because of this late start, it is frequently difficult to implement new solutions and tools at a later stage due to issues with availability of financial and personal resources.

2) Make or Buy - Depending on individual requirements for a digital MRV solution, a makeor-buy decision must be made at a certain point. In that case, a short period of research is worthwhile, particularly when it comes to MRV for reforestation and restoration projects, where there are already many potential solutions. One such example of an existing platform is Restor. A global hub for nature restoration, thousands of local communities, NGOs, governments, and businesses share and monitor their projects on the Restor platform. 

3) Demand Transparency - Another major action point is demanding transparency from digital MRV and its technology providers. Most providers of digital MRV solutions rely on, among other things, the use of remote sensing data and machine learning algorithms. However, these algorithms are mostly not fully transparent and comprehensible for the users. 

To strengthen the sustainability of such solutions, the comparison to a benchmark can be required (e.g. by certification bodies), assessing the accuracy of the algorithms and thus reducing the risk of wrong estimations.

In addition, the technology providers can also be asked to publish the footprint of its solution by creating a holistic view on the entire lifecycle of its product (e.g. use of satellites/drones, platform hosting, data processing, data storage, etc.).

Conclusion & Acknowledgements 

Restoration of forests is one of our most important climate mitigation strategies. And by reducing the overestimation of carbon credits, we can allow every man on earth who owns a tree to participate in climate action. Biodiverse and sustainable forestry can provide hope that reaches far beyond the confines of the machine learning community alone (ETH). 

I am very thankful for the strong collaboration and insights by ETH Zurich. Special thanks belong to David Dao, Kenza Amara, Gyri Reiersen and the entire ReforesTree-Team for their support and for re-using their work in this article.

Download the Sustained Effort Series

This article is from our latest editorial series, Sustained Effort: Community Thoughts on Conservation Tech Sustainability. 

Our series Sustained Effort brings together conservation tech users and makers to share their own perspectives on this topic. Through these case studies, we'll consider the current challenges of working sustainably in our field, but more importantly, how we can all take realistic, practical, and effective steps toward not only lessening our negative impact right now, but discovering larger steps toward the longterm, system-wide change needed to make conservation technology truly sustainable for our planet.

The entire Sustained Effort series is now available to download here on WILDLABS.