What is a baseline?
Judging the effectiveness of any action or policy addressing climate change relies upon the concept of a “baseline”. But are you sure you know what a baseline is? We probably all share an understanding of the crude concept, as illustrated in the dictionary definition:
baseline (noun): an initial set of critical observations or data used for comparison or a control
More simply put, a baseline is a reference state or the values against which we measure change.
You will recognize that the term “baseline” is ubiquitous in the domains of greenhouse gas (GHG) mitigation policy and emissions reductions crediting. Unfortunately, though, the word is often thrown out too casually by the non-expert, as if a baseline is an easily referenced single object. However, GHG professionals properly understand that baselines have multiple components that must be distinguished to avoid confusion and mistakes. Clarity in distinguishing these components is critical when you advise policymakers and other decision-makers on the emission reduction impacts of their actions.
In this brief post, I will focus on core baseline concepts and then provide some further reading links at the end if you want to learn more.
But before we discuss any of these baseline component distinctions, you must understand the concept of an “intervention.” A baseline is defined by the absence of a recognized intervention—a general term for the policy, decision, investment, incentive, or other act intended to influence activities that produce GHG emissions and whose impact is being assessed. For example, a policy intervention could be a government tax on petro-guzzling cars, where the baseline would be described as a time series of outcomes in the absence of this tax. Similarly, in the context of GHG emission offset crediting, an intervention should be recognized as the economic incentive created by the offset market (i.e., the price signal to a producer), or stated more plainly, the revenue project developers expect to earn from selling offset credits issued to them.
Now with that essential concept addressed, the first distinction we must make is between a baseline scenario and baseline emissions. The baseline scenario is a description of the situation and outcome most likely to occur, again, under conditions where the recognized intervention is absent while holding all (or most) other factors constant. A properly elaborated baseline scenario will specify these “other factors” along with the outcome.
This description is colloquially referred to as the “business as usual” scenario. In the context of emission offset projects, it is this determination of the baseline scenario outcome that establishes whether a proposed project is additional. In short, if a GHG emission reduction or removal enhancement project proposal is the same as its baseline scenario outcome, then it is not additional. Determining the baseline scenario involves considering various alternative scenarios that could be the baseline. You then determine which of these scenarios is the most likely by predicting behavior and outcomes in the absence of the intervention.
The second distinction is the metric used to quantify the outcome described in the baseline scenario. For us, this outcome metric is typically a time series of GHG emissions or removals (e.g., tonnes of CO2-equivalent emissions each year), referred to as baseline emissions. For a given baseline scenario, you may be able to choose from a variety of methodologies to estimate baseline emissions, and thereby quantify your prediction. The complexity, cost, and quality (i.e., uncertainty) of this estimation will depend on the requirements of the user of these results.
The third distinction is whether this quantified prediction is performed ex ante or ex post. More specifically, an ex ante baseline estimate is a forecast done before the event (i.e., action taken in response to the intervention) in question occurs. While an ex post baseline estimate is defined as the counterfactual (i.e., ahistorical) prediction of past performance after (or during) the event in question. For both, a change (reduction or increase) in GHG emissions is calculated as the difference between the baseline and intervention (e.g., project or policy) emission estimates across the time series of the intervention.
There are also differences in how these ex ante and ex post calculations are conducted. For ex ante, estimates of both baseline and intervention emissions are a prediction of future performance under the baseline and intervention scenarios, respectively. In contrast, for ex post estimates only the baseline emissions are a prediction because actual data can be collected for the intervention scenario. As such, ex post baseline emissions are a counterfactual.[1]
Figure 1. Understanding the components of a “baseline”
For the purpose of crediting an emission reduction or removal enhancement offset project, it is the ex post quantification (i.e., actual performance) that is used by crediting programs. An ex ante quantification is really only used at the beginning of a project and in the registration phase (e.g., in a project design document) to inform what the parties involved should expect from the project (e.g., how many credits it is likely to be issued that it can then sell).
There is much more to discuss within this topic of baselines. For example:
- What are the assumptions regarding baseline scenarios that you must make to properly determine the additionality of proposed projects?
- Should a baseline scenario be assumed or baseline emissions be quantified in a conservative manner to avoid over-crediting, or does this cause more problems?
- Given that ex post baseline emission estimates are generally quantified over years of a project’s implementation period, should you dynamically revise the assumptions made ex ante to address factors that did not unfold as predicted when the proposed project’s additionality was determined? (This choice is typically referred to as selecting between a static and dynamic baseline).
If you are interested in an advanced learning expedition into deeper theoretical questions like the ones above, you can start with these two posts:
How do you explain additionality?
Additionality is assessed against a counterfactual. True or False?
For an explanation of how to operationalize baselines in crediting programs, I recommend these notes my colleagues Derik Broekhoff and Michael Lazarus:
The Nuts and Bolts of Baseline Setting: Why, What and How?
Options and Guidance for the Development of Baselines
This post covers the basics of baselines for GHG professionals. As professionals, we should strive to educate others and reduce common misunderstandings and oversimplifications of these core GHG mitigation concepts.
[1] It is also possible to do the opposite, where the intervention was not implemented, and the baseline is what actually took place. In such cases, one could quantify the ex post outcome that an intervention would have achieved if it had been implemented. In this case it is the intervention scenario that is the ahistorical counterfactual.
“Quantifying GHG Reductions from Projects” image: The Greenhouse Gas Protocol: Guidelines for Quantifying GHG Reductions from Grid-Connected Electricity Projects, World Resources Institute (WRI) and World Business Council for Sustainable Development (WBSCD), 2007.
Thanks – great explanation of baselines. I’ll add this to the reading list for my course on consequential/mitigation outcome accounting.
Thanks a lot. Being and energy efficiency consultant, this helps me to understand the concept better. Good Job
Thanks very much for another very informative article!
Thanks very much for this clarifying article.
While working on my thesis research project, trying to quantify the emissions reduction of some renewable energy and energy efficiency projects in Africa, I actually faced some difficulties while figuring out the baseline scenarios. Same for determining the additionality of these projects. The CDM Methodology Booklet-UNFCCC was of great help in the process, as it provides guidelines and also default values or assumptions in cases of lack of data.
Can any year be chosen as your baseline year or is there some criteria which dictates which year your baseline must be?
Nathan,
I would point you to the papers at linked at the bottom of this blog as well as these two papers:
http://ghginstitute.org/wp-content/uploads/2015/04/AdditionalityPaper_Part-1ver3FINAL.pdf
http://ghginstitute.org/wp-content/uploads/2015/04/AdditionalityPaper_Part-2ver3FINAL.pdf
A historical baseline is just one way of establishing a baseline value. When you choose a past year (or average of past years) as your baseline you are functionally assuming that the future, in the absence of your intervention, will stay unchanged relative to the past year you choose. That may be a perfectly good baseline scenario assumption for some situations. But, it can also be a horrible assumption in others.
Hope that helps.
Michael