Since 1993, the International Energy Agency (IEA) has provided medium- to long-term energy projections using the World Energy Model (WEM). The model is a large-scale simulation model designed to replicate how energy markets function and is the principal tool used to generate detailed sector-by-sector and region-by-region projections for the World Energy Outlook (WEO) scenarios. Updated every year and developed over many years, the model consists of three main modules: final energy consumption (covering residential, services, agriculture, industry, transport and non-energy use); energy transformation including power generation and heat, refinery and other transformation; and energy supply. Outputs from the model include energy flows by fuel, investment needs and costs, CO2 emissions and end-user pricing.

The model is a leading source of strategic insight on the future of energy and energy-related emissions, providing detailed scenarios that map out the consequences of different energy policy and investment choices. The IEA has become one of the most important inputs into government decision-making about energy, and its annual WEO report has a significant effect on the political and economic decisions of administrations and stakeholders regarding both conventional and renewable energy. Specifically, the WEM is used by all OECD member nations as well as many non-member countries to inform energy and climate policies, and it has a broad role in promoting alternate energy sources, including renewable energy, rational energy policies, and multinational cooperation in energy technology. In fact, WEM helps policy-makers in assess the cost of each policy option related to energy, both in terms of necessary capital investments and the impact on economic growth, as well as of the overall environmental impact and climate-change adaptation costs. A core application of the WEM is also on the Paris Climate Agreement, as well as to the Sustainable Development Goals. Other policy areas where it has been used include implement energy strategies for sustainable development, including diversified energy sources using cleaner technologies, increasing the share of renewable sources to meet climate objectives, diversifying energy supplies, strengthening the EU Emissions Trading Scheme, reducing energy consumption through improved energy efficiency, promoting carbon capture and storage, and improving integration of energy efficiency and environment into energy policies.

There are several challenges in the application of WEM which are recognised in the literature. A common argument (inter al. Mohn 2017) against the methodology and models of the WEM is that the flexibility of economic behaviour is effectively contained, and that the relations of the modelling system are not sufficiently responsive to shifts and shocks in technology, preferences, policies and prices. Critics also argue that the IEA’s World Energy Outlook, which uses the WEM, is largely a product of historical trends and developments, which lead to a status quo bias in favour of fossil fuels. Mohn also says that “any sort of feedback effects from energy policies, technological change and energy back on economic activity (growth) is neglected in the main scenarios. This is clearly a shortcoming of the modelling approach,” he says. There is also an underestimation of the power of new technologies. Hoekstra et al. (2017) argue that the WEM and other models “underestimate the potential of technologies that diverge from the status quo.” The paper focuses on WEM’s photovoltaic predictions in the World Energy Outlook, saying “stagnation of the solar industry is predicted over and over again.” “This disconnection from reality could be due to, for example, sponsor requirements or mental biases like confirmation bias, status quo bias, or system justification bias, but the way the model works could also be a factor,” the authors conclude. They argue that “most of the energy transition management model requirements that we deduce from the literature are implemented partially or not at all. The result is a model that is unable to envision and leverage the exponential developments in solar energy”’. By the same token, Mohn sees `general suspicion that IEA’s methodology and modelling strategy puts too little emphasis on the flexibility in economic behaviour.’ Finally, some researchers argue for a lack of transparency. Richard G. Newell, Stuart Iler and Daniel Raimi also urge greater transparency, but with a broader argument – to improve the comparability of the projections produced by different organizations. “Outlooks vary in a number of important methodological aspects, and comparing between outlooks is not straightforward,” they say in a 2018 paper. “Without a way to clearly compare one outlook to the next, decision-makers may not understand the range of possibilities envisioned by different short-, medium- and long-term projections, or the assumptions that underpin those projections.” On the other hand, the IEA defends itself with the argument that the WEO does not make forecasts, but provides policy-dependent projections. As declared by the IEA Executive Director Birol “Some colleagues and friends in the renewables industry have at times criticised the projections of future renewables energy supply in our main scenario as too conservative. But they rest squarely on the foundation of officially declared policy intentions.’ Further, the WEO in 2017 introduced the Sustainable Development Scenario, which is focused on climate issues. In this regard, consultancy Menlo Energy Economics praised the 2018 edition of the WEO for expanding the focus beyond oil and other fossil fuels, and including the growing role of electricity as the fuel of choice among end-users. Finally, there has also been an improvement in terms of transparency. In fact, in the latest edition of the WEO, the IEA says: `We have made all the key policy assumptions available for all scenarios, along with all the underlying assumptions on population, economic growth and energy resources (which are held constant across the scenarios) and information on prices and technology costs (which vary by scenario depending on the market and policy context).’

References

• Hoekstra, A; Steinbuch, M; Verbong, Geert. 2017. Creating Agent-Based Energy Transition Management Models That Can Uncover Profitable Pathways to Climate Change Mitigation. Complexity 2017, 1-23.
• Mohn, Klaus. (2017). Undressing the emperor: A review of IEA's WEO.
• Newell, R; Iler, S; and Raimi, D. 2018. Global Energy Outlooks Comparison Methods: 2018 Update. Resources for the Future.