Every day, getting control of global warming becomes ever more urgent. We know the causes and we know the cures. We know that the challenge of fighting climate change is a considerable one, but what can we do as investors to hold companies to account? At a corporate level, it’s easy, or fairly easy, to announce goals that extend to five, ten, 20 years into the future. Not many company directors will be around in 2050 to answer for the commitments they made in 2023. If we’re to hold companies accountable, we need to measure adherence to those commitments over shorter, more practical timespans. 

The usual way to measure a company’s compliance with its commitments is to draw a line between its emissions at its reference year and its stated goal. This is better than nothing, but not much. Life is rarely linear and the emissions for which a sizeable company is responsible are difficult to assess even in the present, never mind 20 or 30 years into the future. A perfect answer is almost certainly a wrong answer. Neither should we confuse commitment with action. Taking a company’s promises on faith is not the same as measuring its actual progress towards fulfilling those promises. 

Investors will always factor in a company’s own revenue projections, but few will accept them blindly. Just as we want evidence of likely future earnings, and realistic forecasts, we should want evidence that a company’s actual behaviours are putting it on a trajectory to meet its targets. We might also bear in mind that emission targets typically go much further into the future than earnings forecasts. Increasingly, if we are to make effective investment decisions, we need robust data on the former as much as on the latter. 

This is the tool that we have sought to create, with J O Hambro, Horizon, a model that acknowledges the frailty of human prescience while grappling with the totality of the challenge. But if the complexity is daunting, conceptually it is simple. We look at a company’s emission targets and compare them to its historic and current behaviours. From this we can draw out the probable path of its trajectory toward its goals as stated either to the Carbon Disclosure Project (CDP) or the Science Based Targets Initiative. Acknowledging the significant uncertainty of projecting over such long periods, we generate a range of probable outcomes built from tens of thousands of possible pathways.

We start with framing the data we wish to examine. The model looks at the Scope 1 and 2 carbon-dioxide emissions of publicly listed companies worldwide, which account for around US$100 trillion of economic value¹. Scope 3 emissions, covering those generated by the use of a company’s products, are beyond the direct control of the company and need to be approached in a different manner. 

The next step is real-world data. We start with data from CDP, which we complement with data from other providers and internal research as needed and where appropriate. These data are, though, by definition historic. Companies are most likely to disclose aggregate emissions data through or alongside their annual reports, with the result that it can be 12 months old by the time it’s published. We prefer this to agency data, where we have no control over processing or methodologies, because, even if it is raw, it is accurate and from the source. 

But it is only a start. For the model to function at full power, we need data that is more up to date and more comprehensive. As this data can’t be obtained directly, we use correlations between the historical emissions and financial data relating to a company’s operations. Financial data is typically reported quarterly rather than annually, offering a reasonably good proxy of a company’s current state, and there is clearly a direct parallel between operations and Scope 1 and 2 emissions. 

In this context, behaviour refers not only to actual disclosures but also to modifications of existing data. History is only tentative. Restatements, changes in methodology or to the reporting period or to reasons for change in reported outcomes can all have a significant impact on the probably forward trajectory. Historic data is not necessarily settled. We further need to accept the principle of inertia. Real change tends to come slowly, piece by piece, incrementally. The model needs to be alert to changes that are sudden and dramatic and to look for explanations. 

Companies don’t operate in a vacuum. In the context of J O Hambro Horizon, we don’t attempt to measure commercial or economic pressures, the impact of monetary policy or the likely adoption of a company’s products. But what we can do is take account of legal and regulatory pressures and industry-wide commitments. Net Zero Tracker provides data on targets set by countries, regions and cities, while Leadership Group for Industry Transition offers data on sector-level targets, such as those for Green Steel or Green Cement. We think of these externalities as the ‘gravity’ affecting the emission reduction trajectories that companies are likely to achieve. Those operating in highly regulated markets, and/or in high-profile industries, are likely to hold more closely to their commitments, even where these are evidently challenging. 

The last step on the analytical process is to compare a company’s range of probable outcomes with data tracking broader emission forecasts. We do this using scenario analysis from the Network for Greening the Financial System (NFGS), a non-governmental organisation which supports the work of central banks, including the Federal Reserve, the European Central Bank, the Bank of Japan and the Bank of England. The extent to which an individual company’s probable emission trajectory aligns to the forecasts of the NFGS gives us an added dimension to assess the practical reality of it meeting its targets. If the bulk of a company’s probable trajectories fall above the NFGS scenario, we need to admit that its chances of success are less than if they sit below. 

Given the homogeneity and the quality of the data, and our ability to process it in a uniform manner for each company, it can be aggregated in almost any way that might be required. It can be used as a good proxy for the trajectory of the whole of global corporate emissions. It can be cut up by sector, by market capitalization, by country of domicile. It can just as easily be used to show the transition path for a given portfolio, or it can be used to optimise a portfolio to align with a preferred trajectory. Any and each of these different cuts of the data can be accessed in moments. 

As awareness of impending climate catastrophe becomes both more widespread and more urgent, companies will increasingly be forced to engage with a rapidly changing business environment, from radical shifts in consumer behaviours to social activism to political and regulatory impositions, from new technologies to new, high-growth markets. In this already evolving transition economy, a company’s ability to reduce its emissions, and to show it is adhering to its commitments, will have a critical impact on its market value. 

For investors, we believe that J O Hambro Horizon will become the key tool to monitor and understand that impact. 
 

Source:

[1] https://data.worldbank.org/indicator/CM.MKT.LCAP.GD.ZS