| 引用本文: | 付博，翟家宁，许耀天，赵冰玉，谢 潇，薛冰.2023.基于POI 大数据的城乡能耗空间量化表达及实证：以山东省潍坊市为例[J].地球环境学报,14(6):774-785 |
| FU Bo, ZHAI Jianing, XU Yaotian, ZHAO Bingyu, XIE Xiao, XUE Bing.2023.Mapping urban-rural energy consumption pattern based on the POI data: a case study of Weifang City, Shandong Province[J].Journal of Earth Environment,14(6):774-785 |
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| 摘要: |
| 城乡能源消费格局是应对气候变化的重要议题，也是低碳治理的关键对象。市域尺度城乡能耗空间定量化研究对区域可持续发展及规划决策具有重要支撑作用。基于山东省潍坊市行业部门综合能源消耗量，结合兴趣点（point of interest，POI）大数据和土地利用等多源空间数据，统筹考虑企业数量、人口密度、车流量及耕地面积等关键因素，量化分析工业、生活、交通及农业部门的能耗格网空间分布。研究发现：工业、交通及生活用能的空间分布具有相似性；能源总消费量在各区域间具有差异性，呈现不同的热点分布特征，以团簇热点式分布为主；核心城区的能耗需求强烈，高强度用能区覆盖率显著高于其他地区。通过分析市域尺度上的能源消费格网格局特征，探索公里格网尺度下融合多源数据的空间可视化比较分析方法，可为区域低碳规划、国土空间规划以及可持续发展提供理论支持。 |
| 关键词: 能源消费 POI 公里格网 碳排放 潍坊市 |
| DOI：10.7515/JEE222071 |
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| 基金项目:国家自然科学基金项目（41971166）；山东省人才计划（tsqn202103159）；潍坊市科技发展计划（2021ZJ1132） |
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| Mapping urban-rural energy consumption pattern based on the POI data: a case study of Weifang City, Shandong Province |
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FU Bo, ZHAI Jianing, XU Yaotian, ZHAO Bingyu, XIE Xiao, XUE Bing
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1. Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
2. Key Lab for Environmental Computation and Sustainability, Liaoning Province, Shenyang 110016, China
3. University of Chinese Academy of Sciences, Beijing 100049, China
4. Weifang Academy of Modern Agriculture and Ecological Environment, Weifang 261071, China
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| Abstract: |
| Background, aim, and scope The urban and rural energy consumption pattern is an important issue to deal with climate change, and it is also the key object of low carbon governance. The spatial quantification of urban and rural energy consumption at the municipal-scale plays a crucial role in supporting regional sustainable development and planning decisions. Weifang City of Shandong Province is a national-level low-carbon pilot city in China. The carbon emissions generated by its energy consumption have become the focus of attention and an urgent scientific problem for academic fields. Materials and methods Based on point of interest (POI) data and multi-source spatial data, the spatial distribution of energy consumption grids in industrial, residential, transportation and agricultural sectors is quantitatively analyzed, considering key factors such as the number of enterprises, population density, traffic flow and cultivated area. Results Energy consumption in Weifang is distributed across 16283 grids, covering 98.1% of the total area. The distribution and proportion of grids for industrial, residential, transportation, and agricultural energy are 1683 (10.1%), 1927 (11.6%), 8841 (53.3%), and 16120 (97.1%), respectively, based on composite statistics from a functional perspective. At the kilometer grid-scale, total energy consumption hotspots are concentrated in six districts at a 99% confidence level. The overall hotspot areas are spread based on the core built-up areas of the districts and counties, showing a concentrated and continuous distribution. Discussion Low carbon growth is the main direction of urban and rural development. There is an urgent need for a spatially refined expression of energy consumption in hotspots and critical areas for urban and rural low-carbon governance. However, traditional methods suffer from problems such as missing data and inaccurate spatial information. This study integrates multi-source data to reveal the spatial pattern of energy consumption in grid. However, the attribute information and spatial correction of POI data need further research. Meanwhile, the refinement of different section and the verification of traffic flow need more research. Actively responding to the call of green development of China’s urban and rural construction, more studies will be conducted on different scale city and long-term comparison in the future. Conclusions The spatial pattern of industrial, transportation and living energy is similar. The total amount of energy consumption among regions is different, showing a clustered hot spot distribution. The demand for energy consumption in core urban areas is strong, and the coverage rate of high-intensity energy-consuming areas is significantly higher than other regions. Recommendations and perspectives In the future, it will be necessary to enhance the expansion of data attribute information and utilize more precise spatial expression analysis techniques to quantify the spatial pattern of energy consumption. Thus, we can more accurately reveal the spatial coupling between human activities and energy consumption. |
| Key words: energy consumption POI 1 km grid carbon emission Weifang City |
