D6.1c - Global Climate Governance for the Decarbonisation of the Buildings Sector
Bibliographic record
Abstract
Emissions from the buildings sector account for 21% of global GHG emissions. This paper aims to analyse the potential of global climate governance to promote the decarbonisation of this sector. The paper proceeds in four steps. First, the paper summarise existing knowledge on which barriers are impeding decarbonisation of the buildings sector as well as opportunities that may be leveraged. Second, the paper discusses how global governance may help with overcoming these barriers and mobilising potentials (“governance potential”). Third, the paper maps out the existing landscape of international institutions that are active in the buildings sector and discusses to what extent these institutions have already been able to exploit the governance potential identified in the preceding step. This discussion results in an identification of governance gaps and unexploited potential. Finally, the paper discusses options for filling the identified gaps and mobilising unexploited potential. Global governance and cooperation in the buildings sector are generally difficult given its mostly localised supply chains, lack of exposure to international trade, and highly differentiated needs in relation to geography and climate. The paper nonetheless identifies a number of potential avenues for global climate governance, but this potential has been exploited only to a limited extent. The sector was not even mentioned in recent outcomes of institutions such as the G7 or the Major Economies Forum. While the challenge of providing climate-friendly cooling is governed with clear targets, rules, and transparency mechanisms under the Kigali Amendment to the Montreal Protocol, regarding the buildings sector as a whole, there is no central institution, no strong government-backed signal on the need to decarbonise, and also is little rule-setting. The potential to provide transparency and accountability of countries’ actions also has been exploited only to a very low extent. Regarding means of implementation, while substantial resources seem to be provided, there is a lack of data on actual needs. IPCC and IEA consider that investments need to grow by a factor of 3-4 by 2030 to get onto a Paris-compatible trajectory. Several already existing institutions could in theory help to close the governance gaps identified but in practice, all have limitations, such as the diverging interests among the parties to the UNFCCC and the Paris Agreement and the need to achieve consensus. The best way forward may therefore be a coalition of ambitious countries and other others, such as a “Breakthrough” in the buildings sector, that draws on the strengths of existing institutions. To add value to the existing institutional landscape, such a “Breakthrough” should include an ambitious global target or roadmap as well as ambitious individual targets and pledges to increase means of implementation for developing countries. The GlobalABC and the IEA could track implementation, as the IEA is already doing case with the existing Glasgow Breakthroughs. Successive COP presidencies could use the annual COP sessions as a platform and occasion to demand demonstration of clear progress. In addition, if country members included their Breakthrough pledges in their NDCs, they would thereby be subject to the transparency mechanisms of the Paris Agreement. However, the success of such as “Breakthrough” is far from assured given that so far several calls for building decarbonisation commitments by governments gained only a handful of signatories. A fallback option would be to strengthen the GlobalABC in terms of its membership and administrative capacity.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.033 | 0.009 |
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".