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An effective climate economy needs valid remote sensing data

Drawing parallels between the emerging climate economy and the Internet age, Josh Gilbert, CEO and Founder of geospatial AI company Sust Global, explains how lessons learnt in the 1990s can solve the environmental misinformation problem.

green and brown rocky mountain under white clouds during daytime

The Internet age of the 1990s marked a pivotal moment in human history, comparable to the industrial revolution or the invention of the printing press. This digital revolution introduced unparalleled speed and scope in communication, broke geographical barriers, and democratised access to information. 

While the Internet has transformed businesses and society, it has also introduced new problems. For example, online banking has transformed the way we manage our finances, but a single cyber-attack can now compromise millions of customer accounts within minutes. Regulators and tech companies have been tasked with developing regulatory frameworks and practices to navigate new and complex societal issues related to privacy, personal data, and misinformation. 

Today, we see similar risks emerging with climate change and the climate economy. Products and services that have the potential to combat climate change effectively and improve lives are also introducing unforeseen challenges. Scientific consensus is that climate change will be the biggest exogenous shock to the global economy, but misinformation through greenwashing and the politicisation of climate science threaten the introduction of crucial mitigation and adaptation measures. 

In order to thrive on a changing planet, we need to learn from the mistakes of the Internet age and build a fit-for-purpose climate economy. Through remote sensing and AI, we can provide an objective source of truth, ensuring that misinformation doesn’t derail our efforts against climate change.  

Defining the Climate Economy 

The ‘climate economy’ refers to the evolving economic landscape shaped by climate change – an environment where every decision will need to be climate-informed. The climate economy is more than specific hardware and instruments – or “cleantech” (e.g. solar energy, direct carbon capture) – it is a broader climate-awareness across every aspect of the global economy.

We are seeing a paradigm shift, driven by both the tangible effects of a changing climate and new regulatory measures, which will affect every existing process and organisation regardless of size or industry.  

photo of outer space

The Risk of a Stunted Climate Economy

The climate economy is at a critical juncture, hamstrung by a climate data gap – a deficit in reliable, easily interpretable climate data that users can trust. This gap increases the risk of maladaptation and makes it difficult to identify genuine climate initiatives. Failure to adapt could cost the global economy $1.7 trillion per year by 2030

In the financial sector, greenwashing is a growing concern –  green bond issuance exceeded $500bn in 2021, but many of the environmental claims made by bond issuers are unsubstantiated, meaning their climate impact is often questionable. Just as we struggle with managing the flow of misinformation in the Internet age, regulators are scrambling to develop and impose minimum standards in emerging green markets to ensure their integrity. 

Forest carbon projects in particular highlight the risks of bad data and maladaptation. Many large corporations are investing in forest carbon projects that remove CO2 from the atmosphere, seeing them as a means to ‘offset’ their own emissions. However, many of these projects don’t capture as much carbon as they advertise due to flawed data and methodologies for measuring sequestration. Projects are also often situated in areas prone to wildfires, meaning that well-intentioned initiatives can have a net-negative carbon impact

Better data and analysis are essential for steering the climate economy in a more environmentally and economically effective direction. 

Building Trust with Reliable Data

With so much at stake, the call for a credible foundational layer of data becomes paramount. Reliable data forms the bedrock of informed decisions, policy-making, and driving innovation towards a sustainable future. 

Take the example of climate risk modelling. Public and private sector organisations rely on climate risk models to inform climate adaptation and mitigation strategies. Existing approaches to modelling patterns of climate change in coming decades are mainly aimed at serving the academic community – broad brushstrokes showing how Earth is changing at the macro scale.

However, entities like businesses, governments, and financial institutions increasingly need to integrate climate as a risk factor in their analysis of specific portfolios, companies and address-level sites and projects. This is vital for a range of activities, including corporate risk management, sustainable finance, forest management, community resilience planning and more.

three batteries sitting side by side on a green surface

Current climate risk models are not fit for this purpose. Firstly, they lack the geospatial resolution required to inform decision-making at the local level. Secondly, they are based on historical data and do not account for the accelerating pace of climate change. This means that they significantly underestimate climate risk under 21-st century climate conditions. Thirdly, they are not backtested or hindcasted often enough, and when they are, the information isn’t made available to those using the models. This creates a “blackbox” where users cannot understand or trust the models, preventing real world integration into their workflows.

So how do we ensure that the data we rely on is trustworthy?

Remote sensing data—primarily obtained via satellites—offers a direct window into the Earth’s state. This includes capturing real-time observations of deforestation rates, sea-level rise, temperature anomalies, greenhouse gas emissions and more. By utilising machine learning techniques, observational satellite datasets can be combined with climate simulations to correct the biases of existing climate models according to the most recent data observations, and downscale their resolution. 

These AI-powered models can be rigorously validated by back-testing against unseen data, and their performance can be benchmarked against models created without remote sensing. With these enhanced models, we have the best possible understanding of our current and future climate. This will enable us to make informed decisions about how and where we implement new climate action solutions, and to combat greenwashing by validating any claims regarding a product or project’s environmental impact.

More on remote sensing, data and climate technology:

Look up, off world: The satellite tech driving climate action

Can Treefera restore trust in offsetting with AI reforestation, conservation?

Images: Ben Griffiths (top) / NASA (middle) / Jorge Ramirez (bottom)

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