A comparative analysis of policies and strategies supporting district heating expansion and decarbonisation in Denmark, Sweden, the Netherlands and the United Kingdom – Lessons for slow adopters of district heating
Bibliographic record
Abstract
This paper undertakes a comprehensive comparative analysis of policy challenges and opportunities for the deployment of low-carbon DH. Through literature review and complementary qualitative analysis of interviews with key institutional stakeholders in the heating sector (n= 20) of Denmark, Sweden, the Netherlands, and the UK, the paper draws some important lessons on preconditions for successful roll-out of DH. We find that more governments must create appropriate conditions, provide more support, and speed up actions to enhance the role of DH in heat decarbonisation, to educate, encourage the adoption, and involve citizens, politicians, and other key stakeholders in the heat transition to DH. Amid the current energy price crisis, slow adopters must act fast to develop low-carbon DH networks to ensure the supply of secure, sustainable, and affordable heating sources. They would have to create appropriate conditions to reduce fossil-fuel path dependence, lock-out fossil fuel-based infrastructure and lock-in the diffusion and adoption of low-carbon DH. • More government support and actions are crucial to enhance the role of DH. • The greater the potential for DH expansion, the greater the path-dependent barriers. • Amid the energy price crisis, laggard countries must act fast to develop DH. • DH will attract consumers more if it is cheaper than their current heating sources. • A DH regulator is crucial for the development of functioning markets and DH systems.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".