The Use of the UK Times Energy Systems Model

The Use of the UK Times Energy Systems Model

As a result of the increased industrial revolution over the past few years, climate change has become a threat. The levels of greenhouse gases in the air have significantly increased making the earth warmer. The UK is committed to reducing its emissions to net-zero by the end of 2050 (IEA, 2019). However, energy and climate departments in the country require strong evidence on carbon emissions to implement consistent decarbonisation strategies. The article by Fais among other authors on the ‘impact of technology uncertainty on future low-carbon pathways in the UK’ uses the UK Times Energy System model (UKTM) to provide insights into the uncertainty of the decarbonisation strategies.

Main features of the Modelling Exercise

            The authors of the article use UKTM to present a substantial and quantitative analysis of greenhouse gases emissions in the UK. The modeling exercise analyzes various scenarios to acquire important information that can be used by energy and climate policymakers to implement efficient strategies to minimize carbon emissions. According to Fais et al. (2016), most policymakers depend on information published in policy reports to make well-informed decisions. The primary aim of the article is to develop a policy report that can be used by UK policymakers to implement long-term strategies for greenhouse gases emission reduction. Also, the article outlines various methods to improve energy efficiency. In addition, the article outlines major technology approaches that can be used to improve energy efficiency.

According to Fais et al. (2016), a lot of research has been conducted in climate analysis in terms of uncertainty analysis. However, the article intends to add to existing uncertainty analysis by using UKTM and other methods. Also, the article identifies which low-carbon technologies can be used as alternatives to existing energy resources to facilitate emissions reduction in the UK. To accomplish these goals, the authors of the article limits the modeling exercise to manageable scenarios to enable them to interpret variability. The process allows them to analyze the influence of the technology uncertainty to reduce carbon emissions in each scenario. The article also explores various metrics to provide concrete results that can be relevant in strategizing plans for future low-carbon pathways.

To acquire concise results of the effect of technology ambiguity on carbon emissions reduction in the UK, the modeling exercise uses several carbon-reducing technology dimensions for the study. The first dimension is nuclear energy. Fais et al. (2016), outline that even if nuclear energy power technology has helped the UK generate electricity over the last few years, its future is surrounded by several uncertainties. These uncertainties relate to increased nuclear power costs and public approval. In most advanced countries, nuclear power provisions are declining. Given their initial installation times, nuclear plants in advanced economies are shutting down, with 25% of the remaining nuclear volumes projected to be depleted by 2025 ((IEA, 2019). It is very costly to create a new nuclear plant as compared to other energy options such as renewable energy sources. Hence, nuclear energy as an option to reduce carbon emissions in the UK is not a viable option since it runs the risk of future decline.

The second technology considered in the article is bioenergy. According to Fais et al. (2016), bioenergy provides multiple usage options and more importantly offers manageable energy output. As a result, bioenergy in the future can be significant in the reduction of carbon emissions. Bioenergy in conjunction with carbon capture and storage (CCS) provides an alternative technology solution with the potential of zero-emissions (Hanssen et al., 2020). However, ambiguities arise regarding bioenergy sustainability and its impact on biodiversity and food production. Based on any scenario outlined in the article, bioenergy resources are carbon-neutral and do not pollute the environment. Hence, the UK in its decarbonisation plans for the future should consider bioenergy.

Key Findings

            According to the article, the UK can only meet its carbon reduction targets if all or at least some of the considered technologies perform as expected. Based on the analyzed scenarios, it is almost impossible for the UK to achieve its climate goals. A major key finding is that even if all other technologies fail, bioenergy has the potential minimize carbon emissions. However, for bioenergy to be a feasible option, the UK needs to consider its biomass resources when implementing decarbonisation plans.


            Some parts of the article meet the guidelines illustrated in section 3 of Decarolis et al., 2017 whereas others don’t. According to DeCarolis et al. (2017), energy system modeling exercises need to consider various possible scenarios due to the existence of many ambiguities about the future. Hence, the analysis should be focused on conducting scenario analysis and providing insights. The analysis conducted by the authors of the article meets the guidelines in quantifying uncertainty. The authors have conducted a quantitative analysis of multiple scenarios that may impact the success of various technologies in mitigating carbon emissions in the future. For instance, for each technology, Fais and the other authors develop a consistent narrative that includes both the sensitivity variant and the central case. The results from these scenarios are then translated into the UKTM input assumptions. Moreover, the article offers some insights on the effect of the technologies on decarbonisation plans.

However, they are some parts that do not follow the guidelines set in section 3. According to DeCarolis et al. (2017), before starting an energy system model exercise, the modelers need to frame research questions to guide the subject matter. There is no part of the article that illustrates any research questions used by the authors to map their analysis. Even if the analysis outlines the goals of the article, it does not consider the broader issues of study design. A clear set of research questions enables authors to conduct a thorough analysis to match the needs of the target audience. Therefore, the analysis does not meet the best practice guidelines since the authors have omitted research questions.


The authors can improve their analysis by including good research questions in the study design. Good research questions are vital as they guide the study. These questions allow the researcher to exactly pinpoint the aim of the study and provide them with a clear purpose (Bryman, 2016). Well formulated research questions provide authors with clarity of the research process and the issues being studied. In addition, good research questions can help the authors to effectively plan their research. In doing so, they can foresee any potential challenges, saving them time and effort.

Also, it is critical for the analysis to be as simple as possible. The reason is that other people who are not familiar with the topic may be interested in the results of the analysis. The primary purpose of a study is to inform and gather evidence that can be used to develop knowledge in a particular area (Loeb et al., 2017). Hence, by making the analysis simple, the authors can reach more people. However, if the analysis is complex more than expected, the target audience will tend to avoid it, which means it will not fulfill its intended purpose. Understanding the analysis is essential since it builds on the knowledge and at the same time, provides readers with an opportunity to support facts and disapprove lies. Therefore, the authors can improve the analysis by making it easy to comprehend.

Lastly, the authors can improve their analysis by making it as transparent as possible. Results from a model-based analysis can be achieved without major challenges. Hence, the analytical data should be accessible to all interested parties. Data transparency in research allows readers to review data used in the formulation of empirical research facts and recommendations (Moravcsik, 2019). The transparency provides third parties with the chance to appreciate sources used and assess how they relate to the study topic. Transparency provides the authors with a platform to interpret the study results to third parties. In doing so, they build trust with the target audience and the analysis can have an impact on them and future studies. Hence, transparency will help the authors improve their analysis.
















Bryman, A. (2016). Social research methods. Oxford university press.

DeCarolis, J., Daly, H., Dodds, P., Keppo, I., Li, F., McDowall, W., … & Zeyringer, M. (2017). Formalizing best practice for energy system optimization modelling. Applied energy194, 184-198.

Fais, B., Keppo, I., Zeyringer, M., Usher, W., & Daly, H. (2016). Impact of technology uncertainty on future low-carbon pathways in the UK. Energy Strategy Reviews13, 154-168.

Hanssen, S. V., Daioglou, V., Steinmann, Z. J. N., Doelman, J. C., Van Vuuren, D. P., & Huijbregts, M. A. J. (2020). The climate change mitigation potential of bioenergy with carbon capture and storage. Nature Climate Change10(11), 1023-1029.

IEA. (2019). Nuclear power in a clean energy system. IEA.

Loeb, S., Dynarski, S., McFarland, D., Morris, P., Reardon, S., & Reber, S. (2017). Descriptive analysis in education: A Guide for Researchers.

Moravcsik, A. (2019). Transparency in qualitative research. SAGE Publications Limited. DOI:


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