The climate ambition of North Macedonia to lower greenhouse gas emissions by 51% (or 80% net emissions) in 2030 compared to 1990 is commendable. This goal stems from the analytical work of local experts from the Macedonian Academy of Sciences and Arts, who have years of experience of applying local data in established mathematical models in order to discern what climate goals are most appropriate for our country and what measures can best help us reach them. The modeling process entails years of complex data collection, analysis and assumptions creation regarding our socioeconomic context especially in the area of energy. However, unfortunately, up until recently this modeling process was happening behind closed doors.
Modeling transparency is important because the mathematical models used to aid decision-making in energy governance or climate change governance can be used as political weaponry. This is because models are more sophisticated than mere statistics, which makes for a difficult quality assurance process, yet they are still used to support policy making. In the context of modeling, transparency is defined as more than just sharing of input data or a description of the modeling methodology. Beyond data sharing, modeling transparency also refers to model comprehensibility as well as the affordability of modeling software as these factors are critical for enabling results replication or model quality assurance tests.
North Macedonia is not the only country faces with modeling transparency problems. The EU uses a framework of models centered around PRIMES, the United States use NEMS-EPSA, Norway uses SNOW; and they all face similar issues as modeling is done exclusively by a limited number of experts. One of the main barriers to modeling transparency is the fact that sharing data, methods and related know-how or completely opening the modeling process of external stakeholders is extremely time-consuming both for modeling experts and outside parties interested in engaging in the modeling process. Unfortunately, time for this is neither allocated nor paid according to any existing institutional processes. Next, another barrier to modeling transparency is the fear that it might induce fair or unfair criticism of the models and their results. Specifically, small mistakes found within the models and their data may unjustifiably discredit model results or model use by an inexperienced modeler may similarly create unjustifiable criticisms. Finally, the model might contain sensitive data that sourcing parties might prefer to keep private or data that is protected according to official ethics standards. These reasons for deferring or altogether avoiding modeling transparency is quite understandable. However, continuous lack of modeling transparency may foster lack of trust in the models, which is a shame since models are very useful tools for aiding decision-making.
The benefits of opening up the process of data collection, modeling and model results interpretation are plentiful. To begin with, model quality would increase because models would include information from a wider set of diverse stakeholders and it would be easier to spot any modeling inconsistencies since more people would oversee the modeling process. If model users, i.e., policy makers, can more easily understand the models, then the likelihood that model recommendations are implemented would increase. Even more so, if policy makers are directly included in the modeling process, then that would increase the sense of ownership over modeling results further increasing the probability of their implementation. Lastly, the act of opening up the modeling process can popularize the model, which would subsequently increase the use of model results in related sectors thus enabling inter-sectoral cooperation.
We have been working on increasing modeling transparency for climate change mitigation through the project “Strengthening national capacities to meet the transparency requirements of the Paris Agreement (CBIT)”. For example, the team from the Macedonian Academy of Arts and Sciences held two trainings on energy models. Further, some of the data used for analyzing climate change mitigation policies are available through the Open Government Partnership.