A Deep Graph Learning-Enhanced Assessment Method for Industry-Sustainability Coupling Degree in Smart Cities
文献类型：期刊论文
| 作者 | Bian, Hengran; Liu, Yi1 |
| 刊名 | SUSTAINABILITY
 |
| 出版日期 | 2023 |
| 卷号 | 15期号:2页码:1226 |
| 关键词 | deep graph learning intelligent assessment smart cities graph neural network |
| DOI | 10.3390/su15021226 |
| 文献子类 | Article |
| 英文摘要 | The construction of smart cities has been a common long-term goal around the world. In addition to fundamental infrastructures, it also remains important to assess healthy development status of cities with use of intelligent algorithms. Currently, machine learning has gradually been the prevalent technical means to develop digital assessment methods. However, the whole social system can be regarded as a kind of graph-level complex network, in which node entities and their internal relations are involved. To deal with this challenge, this paper takes graph-level feature into consideration, and proposes a deep graph learning-enhanced assessment method for industry-sustainability coupling degree in smart cities. Specifically, an improved graph neural network model is developed to output the industry space aggregation consequence, and a multi-variant regression model is utilized to output the sustainability status level consequence. Taking the Guangdong-Hong Kong-Macau Greater Bay Area (GBA) as an example, simulative experiments are carried out on the real-world data collected from realistic society. The obtained results can well prove that the proposed method is able to effectively assess the industry-sustainability coupling degree in smart cities. |
| WOS关键词 | ENERGY EFFICIENCY ; AGGLOMERATION |
| WOS研究方向 | Science & Technology - Other Topics ; Environmental Sciences & Ecology |
| WOS记录号 | WOS:000927117900001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/200784]  |
| 专题 | 区域可持续发展分析与模拟院重点实验室_外文论文 |
| 作者单位 | 1.Guangdong Acad Sci, Inst Strategy Res Guangdong Hong Kong Macao Greate, Hong Kong 510070, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Bian, Hengran,Liu, Yi. A Deep Graph Learning-Enhanced Assessment Method for Industry-Sustainability Coupling Degree in Smart Cities[J]. SUSTAINABILITY,2023,15(2):1226. |
| APA | Bian, Hengran,&Liu, Yi.(2023).A Deep Graph Learning-Enhanced Assessment Method for Industry-Sustainability Coupling Degree in Smart Cities.SUSTAINABILITY,15(2),1226. |
| MLA | Bian, Hengran,et al."A Deep Graph Learning-Enhanced Assessment Method for Industry-Sustainability Coupling Degree in Smart Cities".SUSTAINABILITY 15.2(2023):1226. |
入库方式： OAI收割
来源：地理科学与资源研究所
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