Sectoral carbon emission prediction and spatial modeling framework: A local climate zone-based case study of the Guangdong-Hong Kong-Macao Greater Bay Area
文献类型：期刊论文
| 作者 | Wang, Renfeng1; Ren, Chao1; Liao, Cuiping2; Huang, Ying2; Liu, Zhen2; Cai, Meng3 |
| 刊名 | SUSTAINABLE CITIES AND SOCIETY
 |
| 出版日期 | 2024-11-01 |
| 卷号 | 114页码:15 |
| 关键词 | Carbon emissions Spatial model Local climate zone The Greater Bay Area (GBA) Carbon emissions reduction |
| ISSN号 | 2210-6707 |
| DOI | 10.1016/j.scs.2024.105756 |
| 通讯作者 | Ren, Chao(renchao@hku.hk) |
| 英文摘要 | Understanding the spatio-temporal pattern of carbon emission (CE) is prerequisite for formulating carbon reduction policies. Previous studies emphasized quantitative analysis of CE inventory while ignoring sectoral spatial distribution. This study fills this gap by developing a framework for coupling the CE quantitative prediction model with the sectoral CE spatial model based on the Long-range Energy Alternatives Planning (LEAP) model, spatial proxy data and local climate zone (LCZ). The framework's sectoral CE results reveal a great varied landscape within the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), one of the leading bay areas in the world with rapid urbanization and emphasis on low-carbon development, under four carbon reduction scenarios. By 2060, the CN3 scenario that considers both energy-supply and consumption sides, predicts a drastic emission cut to 35.76 million tons, just 10 % of the business as usual (BAU) scenario's forecast, mainly from transportation (29.45 million tons) and industry (9.34 million tons) sectors. Besides, compared with the common CE spatial products, the spatial simulation results of sectoral CE in our framework present detailed spatial differences at the jurisdictional level. The findings are conducive for governments to formulate accurate CE reduction and optimization strategies of the cities towards to the 2060 carbon neutrality. |
| WOS关键词 | CO2 EMISSIONS ; SPATIOTEMPORAL VARIATIONS ; BIG DATA ; CHINA ; LEVEL ; CITY ; CHALLENGES ; MITIGATION ; DATABASE ; PEAK |
| 资助项目 | HK-RGC CRF |
| WOS研究方向 | Construction & Building Technology ; Science & Technology - Other Topics ; Energy & Fuels |
| 语种 | 英语 |
| WOS记录号 | WOS:001301082500001 |
| 出版者 | ELSEVIER |
| 资助机构 | HK-RGC CRF |
| 源URL | [http://ir.giec.ac.cn/handle/344007/42714]  |
| 专题 | 中国科学院广州能源研究所 |
| 通讯作者 | Ren, Chao |
| 作者单位 | 1.Univ Hong Kong, Fac Architecture, Dept Architecture, Div Landscape Architecture, Hong Kong, Peoples R China 2.Chinese Acad Sci, Guangzhou Inst Energy Conservat, Guangzhou 510640, Peoples R China 3.Wuhan Univ, Sch Urban Design, Wuhan 430072, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Renfeng,Ren, Chao,Liao, Cuiping,et al. Sectoral carbon emission prediction and spatial modeling framework: A local climate zone-based case study of the Guangdong-Hong Kong-Macao Greater Bay Area[J]. SUSTAINABLE CITIES AND SOCIETY,2024,114:15. |
| APA | Wang, Renfeng,Ren, Chao,Liao, Cuiping,Huang, Ying,Liu, Zhen,&Cai, Meng.(2024).Sectoral carbon emission prediction and spatial modeling framework: A local climate zone-based case study of the Guangdong-Hong Kong-Macao Greater Bay Area.SUSTAINABLE CITIES AND SOCIETY,114,15. |
| MLA | Wang, Renfeng,et al."Sectoral carbon emission prediction and spatial modeling framework: A local climate zone-based case study of the Guangdong-Hong Kong-Macao Greater Bay Area".SUSTAINABLE CITIES AND SOCIETY 114(2024):15. |
入库方式： OAI收割
来源：广州能源研究所
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