Energy Modelling for Policy Support (EMoPS) is more than simply an analytical activity — it affects and involves communities. Including those involved in real time in the EMoPS proces, and those outside of the EMoPS process, who may be affected by its outcomes at some later time.
Communities in focus include those involved in the analytical and policy formulation work itself, subsequent (energy dependent) service users, those locally and internationally who are impacted by the uptake of energy system development, as well as project developers and financiers. Energy is not an end in and of itself, but a means to supply — in the context of financial and non-monetary costs — energy services. Energy demands are thus driven by other sectors and determine wider production possibilities.
With its policy impact EMoPS, which can influence energy system configuration and service costs — influences key aspects of societal development, and its constituencies. For these reasons, EMoPS should be held accountable through adherence to good governance as well as rigorous scientific practice.
The significance of integrated modelling
EMoPS is in part an analytical activity that, via mathematical abstractions programmed into computer models, projects internally consistent scenarios of energy system development. As an academic activity, it requires scientific rigour. Its analytical components include links with scientific bodies of knowledge and analysts often interact with specialists from other domains.
EMoPS scenarios (one of several modelling artifacts 1,2 noted in the supplementary online material (SOM)) typically provide directly, or with additional analysis, explicit quantitative pictures of: technology and infrastructure configurations; energy security and trade levels; direct and secondary market implications and investment provisions; emissions trajectories; revenue and public subsidy requirements; and life-style implications. Scenarios enable a ‘thought-experiment space’ that allows one to run through various futures that may be driven by new policies and evaluate the effect of sets of policies. One common practice is to develop aspirational end-points for those scenarios and then model to investigate normative scenarios of how one might get there — a technique known as backcasting.3
Aspects of scenarios have relevance to large sets of communities. They can also be translated into a ‘language’ that is understood by each community — though they are often domain specific. Scenarios can also be loaded with tacit judgments that are not necessarily made explicit.4
No existing guidelines govern the identification of the relevant communities, the identification of the scenario elements and aspirations of importance to those communities, their active translation and appropriate interface of that community with an EMoPS process. Neither are the costs nor benefits of potential guidelines articulated, should they indeed be adopted. This despite widespread, often tacit agreement of the value of scenarios for those communities.5
The communities affected
Aside from those that will later be potentially affected, all specific communities associated with the EMoPS ecosystem of activities and scenarios produced need support for the health of the activity. These may include: the energy modelling team and broader energy modelling community; energy policy analysts that bridge the policy process with EMoPS; a (formal or informal) coordination group that enables strategic intelligence and manages information flows with key external communities; a coordination group of a broader participation process; and, the funders that support the EMoPS process. The latter might arise from dedicated government budgets or external support agencies.
From EMoPS scenarios, the implications and ‘underpinning goals’ of different futures are articulated, and their implications for affected communities sketched (Table 1). The communities that are represented (or can be represented in those sketches) include various policy organs; direct energy industry suppliers and indirect (energy) service users; civil society and international actors. Here, internationals are at least divided into business, development, environment, and energy(‑system) trading partners. Consider for example, multinational energy companies, the UNFCCC, countries with electricity and gas interconnectors, respectively as examples.
Table 1: Affected communities across domains (to the right), ending in those that control infrastructure spend, and actors (descending)
Four critical observations arise
- Opaque analysis: even when deeply domain specific, despite a growth of efforts to combat this, the modelling process often remains a ‘black box’.6 That means that it is often impossible to develop sensible knowledge management within a national modelling team. When active knowledge management is missing due to opacity, it is not feasible to create an ecosystem with institutions and research centres that could increase national competence and capacity. That results in continuous wasted effort. In developing country contexts, wasted resources and negative consequences are reported.7
- Community cooperation: the level of involvement of socio-economic actors is necessarily constrained as resource, time, and other factors create impractical overheads. The benefits of competent cooperation and co-creation are well known, including the potential for increased accountability 8 and ownership.9 But various levels of cooperation with the modelling team are possible. A stylised representation is given by in figure 1.10 Yet many EMoPS activities are without appropriately defined analytical and organisational workflows. They have no explicit interface for community participation. As such even ‘light’ cooperation can be difficult.
- Downstream matters: the impact of the resulting energy policy from EMoPS is not trivial. And the continuum from EMoPS to implementation is often broken. This can range from the translation of energy model results into energy policy to energy policy that is incoherent and contradictory to policy in other areas. A common example of the former is the implementation of non-market mechanisms to proxy the output of a market model, creating inaccuracies. A common example of the latter is the dependence on indirect fossil fuel taxes for national development revenue, while efforts are made to reduce fossil fuel consumption for reasons of GHG mitigation.
- Appropriate answerability: accountability for related national development financing (as it relates to EMoPS and its output) is lacking. Money might be directly wasted 7 as there may be no simple knowledge management process in the analytical team. As its insights might not be scrutinizable, the resulting (much larger indirect) investments and the broader development they drive are at risk as a result. (That is not to say that an ‘exemplary’ EMoPS will produce scenarios that perfectly ‘predict’ future needs. Although it might be built on the best available information, it is still imperfect knowledge.12 However, if its outputs and process cannot be scrutinized and built upon, it is not possible to know if due diligence was applied.)