Organizational Governance

How to Calculate Fair Company Carbon Targets for the Energy Sector

Fair carbon-reduction targets and the assessment of a company’s performance relative to targets are an incredibly powerful way to assess how well a company is performing. It also sends a clear risk message: The greater the difference between a company’s fair target and its actual performance, the greater its value at risk.

Brand Provided | By Erik Thomsen and Rob Lindner | Published 1 year ago | About a 13 minute read

Image: David Mark

In our last installment, we saw that in order to break the carbon logjam, it is critical to convert high-level planetary and sector goals into company-specific targets that fairly reflect real company and grid constraints (e.g., fuel use and grid location); and that, if all companies meet their target, we can achieve our global goal for reducing carbon emissions. In this installment, we describe a method for calculating fair company targets based on existing global, sector, company and facility-level information.

There are three broad classes of information needed to compute company-specific fair targets:

Larger-scale (e.g., global, regional and sector) information about emissions, trends and targets.
Relevant company and facility information (e.g., output, emissions, fuel-tech used).
Relevant performance information (e.g., best-in-class, fuel-specific emissions performance).

Combining these three kinds of information makes it possible to calculate fair, company-specific targets that reflect production, company and facility-specific constraints (grid constraints appear when it comes to pricing, which we will focus on in the next installment). They can be used by companies to infer forward-looking risk assessments; and they can be meaningfully aggregated and analyzed by policy-oriented agencies at a sector, regional or other coordinating level to monitor and nudge aggregate performance.

1. Global, regional and sector information

Science can tell us about likely weather pattern differences as global temperatures rise; it can even tell us how climate change is likely to impact biodiversity, food production or employment. However, science cannot say whether one temperature change is or isn’t correct; or should or should not be a societal target. The 1.5°C target for global atmospheric warming is best thought of as a consensus desire shared by numerous national and international expert committees.

In contrast, given a global target and given a sector’s requirements for achieving its portion of that target, market actions (e.g., fuel-technology changes) that can be taken are the product of science. Some actions will, and some will not, induce the required behavior changes. The Science-Based Targets initiative (SBTi) has produced sector-specific emissions goals, which if met, would keep global temperature change to under 1.5°C (SBTi, 2020). Each sector’s plan-to-achieve goal consists of a series of stepped-up carbon efficiencies-of-production and efficiencies-of-distribution, finishing with zero, near-zero or even negative net CO2 emissions by 2050. In Figure 1, we can see the GHGs emitted per sector, with Energy contributing 27 percent of global emissions.

How much sector behavior change is required to solve the problem? The IPCC and SBTi agree that successful limitation to 1.5°C warming will require all sectors to reduce emissions rapidly. Figure 2 shows the annual amount of CO2 that can be emitted per sector on a decade-by-decade basis between now and 2050 in order for us to meet our planetary 1.5°C target.

Though not discussed here, emissions allowances factor in sequestration efforts; so that at a planetary/societal level, we can reach net-zero emissions. To understand just how dramatically emissions need to drop in order to meet sector goals, consider Figure 3 below — which shows sector emissions targets as the percentage reduced from current emissions levels.

Note how fast the power-producing sector needs to improve its carbon efficiency of production. Figure 4 shows the increasingly lower aggregate carbon intensity (in units of CO2e emissions per unit of energy produced) that the sector needs to achieve in order to meet its 1.5 or 2°C goals.

Though the 1.5°C path ambitiously requires three-fourths of its efficiencies to be realized in the next ten years, the 2°C path (while still assuming that the energy sector reduce its carbon intensity by 50 percent over the next ten years) seems to also require something of a ‘Hail Mary’ in that the energy sector must become a significant net absorber of emissions by 2050.

2. Company and facility information

Given a sector target for the carbon intensity of production (regardless of its optimism), how should a company determine a fair target for itself? Should the company’s target simply equal the sector target? After all, if every energy-producing company met the evolving sector target; the sector, by definition, would meet its target. It depends on the goal we are trying to achieve by imputing a target.

If the goal of setting a target is to induce behavior change in the direction of the target, it makes no sense to apply the sector target to all energy-producing companies. All companies that already exceed the target would have no motivation to further improve. Furthermore, all coal-based facilities would see that they could never meet sector targets and so would likely find ways to resist the whole targeting process. We need to stop using fossil fuels; and the sooner, the better. However, international energy sector projections (e.g., from the WEF and IEA) show energy demand increasing and coal being used for at least the next 30-40 years. This is because we cannot produce enough renewable energy fast enough and renewables are not (in the absence of sufficient storage capacity) yet able to provide reliably continuous energy. The projections include less coal and lower-carbon-intensity coal for sure, but some coal for the foreseeable future. What’s needed are company-specific targets useful for the company’s own portfolio management, as well as for company analysts that provide meaningful targets that reflect how companies produce energy today, as that is the only realistic starting point for making improvements.

To better understand why fair company targets need to reflect current fuel-technologies, let’s begin by looking at historical company emissions, how they are trending, and how they compare to sector targets. Figure 5 shows the emissions intensity and trends for a selection of the largest US power companies (who reported emissions to CDP). It illustrates the wide range of emissions intensity in the US market, not all on track to meet sector targets. It also allows us to estimate the gap between company performance projections and sector target ideals.

Extrapolating company emissions-intensity trends, as we did above, generates projections (the straight lines) that can be compared with SBTi’s 1.5 and 2°C targets. Though it might be tempting to compare company trends with SBTi’s increasingly efficient targets, the single number for company carbon efficiency covers what can be wide differences that exist between the various facilities operated by any one company. This can be seen in Figures 6-9, which drill into one company — WEC Energy Group — and show absolute output, emissions and emissions-intensity data by facility and grouped by fuel type. Consider first the power generated per facility, as shown in Figure 6.

The light red reflects coal-fired plants that have been retired. The darker red reflects coal plants still in operation; and the blue reflects gas-powered plants. The amount of energy WEC produces by other means is not significant. In 2020, WEC produced about 46 percent of its energy from coal and 42 percent from gas. In contrast, as shown in Figure 7, about 70 percent of its carbon emissions came from the coal-fired plants. Generation of WEC’s wind, hydro and solar power do not produce significant carbon emissions; hence, their emissions lines are lying on the X axis.

We can also see that WEC achieved efficiency gains by retiring some of its (probably least efficient) coal plants (e.g., Pleasant Prairie and Presque Island) and replacing them with gas-powered plants (e.g., AJ Mihm and FE Kuester) that are far more efficient; (but not efficient enough to meet medium-term SBTi goals). Combining facility output from Figure 6 and facility emissions from Figure 7, we can calculate the carbon intensity of emissions (the units for the SBTi sector target) as shown in Figure 8 below.

Although some of the individual facilities show a large amount of year-to-year variation, the aggregate (the black, dotted lines) shows the gradual improvement for WEC as a whole. Aggregate trends in the carbon intensity of emissions become easier to see when we group facilities by their fuel type — used as shown in Figure 9. We can see that WEC vastly improved the efficiency of its gas-powered facilities between 2000 and 2010. Coal plant efficiencies improved; but since 2009, there has been a huge difference between the carbon efficiency of WEC’s coal and gas-powered plants. This again is why targets need to reflect facility-based realities such as fuel type.

Though companies publish and make pledges around carbon emissions at the all-company level, facilities are where we find all the action. Facilities are the assets on the books that have specific structures (e.g., based on coal, gas, wind or sun). It costs time and money to upgrade, decommission and/or start up new facilities. There are limits to the carbon efficiency of production based on the fuel type of the facility. Some changes can be retrofitted into existing facilities (improving the efficiency of a coal-fired plant or sometimes switching coal to gas). Others — e.g., switching from coal or gas to solar or wind — require a shutting down of the existing facility and the creation of a new facility, most likely in a different location. At the end of the day, for the purpose of aligning global goals with company-specific targets, companies need to be represented as collections of (at least partially) owned geo-located facilities.

3. Relative performance information

Now let’s introduce best-in-class (BIC) carbon-intensity figures for coal and gas that make up the majority of WEC’s energy portfolio and the vast majority of carbon emissions from the energy sector more broadly. The BIC intensity for coal today is about 0.67 metric tons of CO2 equivalent per MWh of produced energy. The BIC for gas is about 0.35. For any individual facility, absent additional context, its target (if remaining operational above a minimum level of output relative to capacity) should be to achieve BIC.

Let’s drill down on WEC’s facilities, looking at each facility’s actual energy production and emissions, and compare each facility’s actual carbon intensity (emissions/production) with its BIC as shown in Figure 10 below. The relative, actual GHG intensity index allows us to compare how efficient the facility is relative to other facilities using the same fuel type; and how relatively efficient the facility is compared to other facility’s whether or not using the same fuel type.

WEC’s facilities range from a high of 1 for its Fox Energy Center gas-powered facility, to a low of 0.29 also for a gas-powered plant. Its coal-powered plants range from 0.73 to 0.66; not a huge range, but all significantly worse than BIC.

Carbon intensity is a big part, but not the only part, of CO2 emissions — output is equally important. After all, it is energy output x carbon intensity that equals emissions; and it is emissions that are warming up the planet. Figure 11, below, extends Figure 10 by adding columns for energy output, emissions, the facility’s percentage of total company output, and the emissions the facility could be producing if it were operating at BIC levels.

Adding output data begins to paint a more nuanced picture of WEC’s operations. For example, the Germantown gas plant — whose last-place relative efficiency is 0.29 — only produces 0.03 percent of WEC’s total output. Meanwhile, its super-efficient Fox Energy Center gas plant generates over 13 percent of total company output. We can sum the emissions from each facility to calculate WEC’s total emissions; but its aggregate efficiency needs to reflect each facility’s relative output with the following formula (where n is facility ID)

Aggregate company carbon intensity of production =

sum((facility output/WEC output) * (facility carbon intensity))n

Applied to WEC, this yields an aggregate intensity of 0.61 (Figure 11).

The next step in building toward a fair target for WEC is to take the facility output weightings and multiply them by the BIC carbon intensity instead of facility actuals, as shown in the formula below (where n is facility ID)

Aggregate BIC target for company carbon intensity of production

= sum((facility output/WEC output) * (BIC carbon intensity))n

That number is shown in Figure 12 below as the fair target, given the current fuel mix.

If WEC were operating at BIC across all facilities, its weighted-average carbon intensity would be 0.45. Its relative performance (fair target / aggregate actual performance) is thus 0.45/0.61 = 0.74.

Fair company targets and the assessment of a company’s performance relative to targets are an incredibly powerful way to assess how well a company is performing relative to BIC. It also sends a clear risk message: The greater the difference between a company’s fair target and its actual performance, the greater its value at risk. Look again at Figure 12: The righthand column calculates the amount of carbon emitted by each facility in excess of what it would have emitted were it to be operating at a BIC level. They emitted almost 20 million tons of carbon; but they could have emitted only 15 million tons, were they operating at target. Whether through carbon pricing, restricted access to capital, lowered stock price, lost brand equity or regulatory penalties, those 5 million tons are a real risk to WEC’s financial performance. The more that risk is made public, the greater the likelihood that WEC will try to reduce it.

The fair targets described in this installment reflect a static fuel mix. However, we know that over time, fossil fuels need to be phased out and zero- or near-zero-emission renewables will need to be phased in; because we cannot possibly meet our planetary goal of keeping warming to 1.5°C using the same percentage of fossil fuels in our total energy mix as we do today. Therefore, in addition to BIC per fuel type targets, there also need to be fuel-type-mix targets; so that in the aggregate, the Energy sector can meet its goals. But unlike fuel-type-specific goals, fuel-type-mix goals do not apply to individual facilities — but rather, to companies and regions. And relevant factors for setting fuel-type mix targets include where the facility is in its expected life span and where the facility is, geo-spatially. These considerations will be added to the mix in our next installment — as we examine practical market mechanisms to nudge companies towards meeting their fair, fuel-type-specific and fuel-type-mix targets.

Previous articles in this series:

Erik Thomsen

Erik is a world authority in the field of multidimensional information systems, an internationally recognized expert in the application of logic to information systems, and a well-known innovator in ontologically grounded approaches to AI and socio-economic-environmental (or ESG) applications. As CTO of Blender Logic, he designed a semantic controller that improves the performance of machine learning applications that was selected as an exemplar of advanced AI algorithms by the DoD's Joint Center for AI (JAIC). Recently, Blender Logic teamed with sciGaia, a Montana-based provider of sustainability services, in order to produce a new generation of wellbeing-based planning systems. He is the author of "OLAP Solutions: Building Multidimensional Information Systems," which is used around the world in both graduate computer science and MBA programs, and of publications for both scholarly and IT journals.