Investors need accurate carbon emissions data to make good decisions as they seek to affect change by shifting capital from brown to green companies and through shareholder activism by exerting pressure internally on corporate management to move toward greener policies.
Data drive the process. Remember the old adage: garbage in, garbage out (GIGO). Today, only roughly half of all emitting companies around the world report their emissions, and those who do report do not follow a single standard, giving rise to data inconsistencies. Data providers estimate the missing data for companies that do not report their emissions using a variety of estimation models. Estimated data are noisy data regardless of the model used.
Investors, however, only have estimates to assess much of the investment risks and opportunities related to carbon emissions.
Because they have no other option, investors use the estimated data. The wide use of estimated data reveals a tacit assumption that estimated emissions data are a satisfactory substitute for company-reported emissions. Our new research debunks this view.
After analysing carbon data from four major carbon data providers over the seven-year period from 2010 to 2016, we find that estimated emissions data are 2.4 times less effective than reported data in identifying the 5 per cent worst emitters. These 5 per cent of emitters are responsible for more than 80 per cent of total global emissions.
Data providers also evaluate forward-looking emissions. If accurate, this information would be extremely valuable to investors because it would identify future risks and opportunities. To be helpful for investors, forward-looking information should have the power to predict future changes in carbon emissions. Unfortunately, we find they do not have predictive power.
Why are estimated data on carbon emissions often inaccurate? The problem is not that data providers are sloppy in their estimates, to the contrary, we believe they are doing the best they can. Instead, the problem is that the data providers do not have transparency into the activities of the companies and can only base their estimates on crude proxies.
Specifically, we find the estimates predominantly reflect company size and industry information. Consequently, the estimated data do not help investors identify the green companies in brown sectors, further undermining investor actions.
As the realisation grows that in the coming decades our economy will experience changes in adapting to this new reality, much of the assets currently being used by today’s emitters can become stranded, that is, prematurely depreciated and ultimately worthless. Inversely, companies able to adapt to the new green reality will find huge opportunities. Investors bear responsibility in helping the economy transition to a greener future by providing capital to green companies and helping the brown companies to reform. They need accurate data to effectively play this crucial role.
Unfortunately, investors do not currently have the data accuracy they need to wield the power they have in allocating capital. The environment is a public good. Companies are polluting the environment in an effort to generate profits. Inexplicably, today these companies are not even required to report their emissions and investors are left with only guesstimates to guide important ESG-related investment decisions.
Our research indicates that mandatory reporting under a single global standard is likely to be the only possible solution. Until we have mandatory reporting, investors can and should incentivise companies to voluntarily report their carbon emissions. This will go a long way in addressing the GIGO problem investors face today. Absent accuracy, investors’ well-meaning actions can quickly lose efficacy and expose them to excessive and underappreciated risks.
Vitali Kalesnik, partner and director of research for Europe, Research Affiliates
This article is based on the working paper “Green Data or Greenwashing? Do Corporate Carbon Emissions Data Enable Investors to Mitigate Climate Change?” by Vitali Kalesnik and his co-authors Marco Wilkens and Jonas Zink of the University of Augsburg. For further detail on the research methodology described in this article, please refer to their paper.
