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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it