Deep decarbonization pathways in the building sector: China’s NDC and the Paris agreement
Bibliographic record
Abstract
Abstract China’s economic growth has been largely relying on the consumption of coal. The country has realized that its economic development has to be free from dependence on fossil fuels. On 30 June 2015, China submitted its ‘Nationally Determined Contribution (NDC)’ in preparation for the Conference of Parties 21 (COP21). One of the important actions in China’s NDC is to lower carbon dioxide (CO2) emissions per unit of GDP by 60% to 65% from 2005 levels by 2030. This study examines the efforts from China’s building sector (i.e. urban residential, rural residential and service) in achieving the CO2 reduction target stated in China’s NDC. Furthermore, this study also explores the post-NDC era and looks into the opportunities towards deep decarbonization in the building sector by mid-century for contributing to the Paris agreement. The study covers 31 provincial regions of mainland China anddisparities of climate and socioeconomic indicators across regions are fully considered. We use a bottom-up cost optimization model called AIM/Enduse to evaluate the CO2 reduction potential brought by efficient technologies in China’s building sector. Five scenarios are designed to illustrate the emission pathways through 2050. The results show that, when energy constraint and emission target is introduced in mitigation scenarios, new generation biomass contribute a lot to emission reduction. Reduction potential in the nearly zero emission scenario is mainly from the urban residential sector, and to achieve deep decarbonization by 2050, it is important to bring a significant reduction of per-capita energy consumption in addition to ci improvement both in urban and rural households. Co-benefit analysis suggests that air pollutants can also be significantly reduced by deep decarbonization policies.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".