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Compact meta-models to estimate the effects of energy efficiency policies and measures

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Abstract

Decision-makers want to be reliably advised on the implications of the decisions they make. Very sophisticated models, which decision-makers are often unfamiliar with, are typically used to provide such assessments for large and complex systems. However, even having access to these models, decision-makers can rarely handle them. A model is best known to its developers, who, therefore, need to be contracted to estimate the effects of the proposed policies. This takes time and money, yet leaves the credibility of the results questionable in countries with a limited culture of cooperation between decision-makers and a modeling community. One possible, yet partial, solution is to use an ensemble of models. Another option is to use a set of compact meta-models to address specific policies and measures; the parameters of such compact models can be assessed using other, large and complex, models. Decision-makers can run these simple compact models on their own to make policy dialogue more operational and to have more confidence in the results. This paper presents one such model, which consists of 95 compact sub-models designed to outline comprehensive energy efficiency programs, along with the results of its pilot application for an illustrative set of policies. This application has shown, that such models may serve as an effective tool for a prompt policy dialogue with all stakeholders in compiling the policy package to untap the most of the available energy efficiency potential to meet sector-specific or economy-wide goals in terms of energy savings or energy intensity reduction.

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Data availability

Data for the pilot model run are captured in the figures.

Notes

  1. There is abundant literature on meta-regression analysis. In order to improve the transparency and quality of the analysis, the Meta-Analysis of Economics Research Network was created along with reporting guidelines (Havránek et al., 2020), but it is mostly used for the calibration of key economic parameters, rather than for simulation of specific energy efficiency policies.

  2. If the input/outcome functions are estimated on some 20 test samples for each policy parameter, then 5000–10,000 model runs are required to test all of the 95 policies.

  3. These functions are widely used in models (Despres et al., 2018; EIA, 2020). IEA uses the same logic, yet a more complex set of functions (IEA, 2021). The empirical and theoretical foundations for the application of logit models can be found in Breitmoser (2021) and Cramer (2003).

  4. IEA acts in the same way. For the purpose of cost estimation, IEA consults a wide range of companies, experts, and think tanks and explores numerous sources (see IEA, 2021).

  5. Estimated by the authors based on Projected-Costs-of-Generating-Electricity-2020.pdf; residential_ee_grid_benefits_-_technical_appendix_07-14-2022.pdf.

  6. Energy Efficiency Trends & Policies | ODYSSEE-MURE. https://www.odyssee-mure.eu/.

  7. EU member states are actively using subsidies to reach their energy efficiency targets.

  8. For decades, efficiency tax credits and rebates have been widely used by utilities in Energy Efficiency Resource Standards implemented in many US states.

  9. Ecobonus 2022: detrazione 50 65 110% risparmio energetico: guida (studiomadera.it). Under this program, more than 122,000 applications were approved and €21 billion have been spent in tax deductions. Italy’s Superbonus 110% extended in 2022—idealista; Italy’s superbonus 110% scheme prompts surge of green home renovations | Italy | The Guardian;Microsoft Word—GREEN Home_GP Superbonus IT-KE_IWO (green-home.org).

  10. For a description see Bashmakov et al., 2022a.

  11. If incremental energy efficiency costs are ≤ 0, then \({dsub}_{i}^{t}=0\ and\ {{eint}_{i}^{BPT}=eint}_{i}^{BAT}{\text{in}} \left(15\right)\).  

  12. The best available technology is taken equal to the last value in the first decile on the typical output benchmarking curve. The best practical technology is the last value in the first output quartile.

  13. These are the actual values estimated for a real project (CENEf-XXI, 2018).

  14. At first glance, primary energy use reductions generated by carbon price (up to 3%) may seem small, but keeping in mind that in the EU, where price elasticity is higher, than in Russia, which has the legacy of a command economy, the carbon price (ranging between 40 and 50 €/tCO2) generated some 3–4% of emission reductions and even smaller energy use reductions (see Green, 2021; Tvinnereima & Mehling, 2018).

  15. This model is described in detail in Bashmakov et al. (2022a).

  16. The economic activity parameters for illustrative model run were specified based on the 4S scenario. See Bashmakov et al. (2022a).

  17. Promotion of EVs was not considered in this set of measures.

  18. Such savings are delivered only by measures which require only some modifications to the regulations in force, rather than new regulations.

  19. Economic activity parameters for the illustrative model run were specified based on the 4S scenario. See Bashmakov et al. (2022a).

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Authors and Affiliations

  1. Center for Energy Efficiency–XXI (CENEf-XXI), Novocheremushkinskaya St., 61, P.O. Box 28, 117418, Moscow, Russia

    Igor Bashmakov, Anna Myshak, Vladmir Bashmakov, Konstantin Borisov, Maxim Dzedzichek, Alexey Lunin, Oleg Lebedev & Tatiana Shishkina

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  1. Igor Bashmakov
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  2. Anna Myshak
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  3. Vladmir Bashmakov
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  4. Konstantin Borisov
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  6. Alexey Lunin
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  7. Oleg Lebedev
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  8. Tatiana Shishkina
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Contributions

Igor Bashmakov—development of the methodology and the model concept, integration or results, and manuscript writing. Anna Myshak—programming of the model. Vladimir Bashmakov, Konstantin Borisov, Maxim Dzedzichek, Alexey Lunin, and Oleg Lebedev—development of model blocks for different sectors. Tatiana Shishkina—manuscript writing and editing. All authors have read and approved the final manuscript.

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Correspondence to Igor Bashmakov.

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Bashmakov, I., Myshak, A., Bashmakov, V. et al. Compact meta-models to estimate the effects of energy efficiency policies and measures. Energy Efficiency 17, 45 (2024). https://doi.org/10.1007/s12053-024-10222-z

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