Evaluating the Net Effect of the ISDE Subsidy Scheme in the Netherlands – Comparison of Evaluation Methods to Estimate Additionality
Bibliographic record
Abstract
The national government encourages Dutch households and businesses to use less natural gas and more sustainable heat. The Sustainable Energy Investment Subsidy (Dutch: Investeringssubsidie Duurzame Energie or ISDE), which has been in place since 2016, provides a subsidy for the purchase of solar boilers, heat pumps, biomass boilers and pellet stoves. The scheme is meant for both private individuals and business users. An important element in the evaluation of the ISDE was the ‘additionality’ of the scheme. This was evaluated by the Dutch organization for applied natural scientific research (TNO) in 2018 and by SEO Economic Research (SEO) in 2019. In this paper the evaluation methods are described, compared and critically reviewed in order to identify uncertainties and limitations of the methods and possible improvements. The methods lead to different results for the additionality of the ISDE scheme per type of device and this paper looks into the reasons for this. Additional evaluation studies’ results depend on many factors elaborated on in this paper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.050 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".