Multi-Scale Mapping of Energy Consumption Carbon Emission Spatiotemporal Characteristics: A Case Study of the Yangtze River Delta Region
Climate issues significantly impact people’s lives, prompting governments worldwide to implement energy-saving and emission-reducing measures. However, many areas lack carbon emission data at the lower administrative divisions. Additionally, the inconsistency in the standards, scope, and accuracy of...
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MDPI AG
2025-01-01
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| author | Kangjuan Lv Qiming Wang Xunpeng Shi Li Huang Yatian Liu |
| author_facet | Kangjuan Lv Qiming Wang Xunpeng Shi Li Huang Yatian Liu |
| author_sort | Kangjuan Lv |
| collection | DOAJ |
| description | Climate issues significantly impact people’s lives, prompting governments worldwide to implement energy-saving and emission-reducing measures. However, many areas lack carbon emission data at the lower administrative divisions. Additionally, the inconsistency in the standards, scope, and accuracy of carbon dioxide emission statistics across different regions makes mapping carbon dioxide spatial patterns complex. Nighttime light (NTL) data combined with land use data enable the detailed spatial and temporal disaggregation of carbon emission data at a finer administrative level, facilitating scientifically informed policy formulation by the government. Differentiating carbon emission data by sector will help us further identify the carbon emission efficiency in different sectors and help environmental regulators implement the most cost-effective emission-reduction strategy. This study uses integrated remote-sensing data to estimate carbon emissions from fossil fuels (CEFs). Experimental results indicate (1) that the regional CEF can be calculated by combining NTL and Landuse data and has a good fit; (2) the high-intensity CEF area is mainly concentrated in Shanghai and its surrounding areas, showing a concentric circle structure; (3) there are obvious differences in the spatial distribution characteristics of carbon emissions among different departments; (4) hot spot analysis reveals a three-tiered distribution in the Yangtze River Delta, increasing from the west to the east with distinct spatial characteristics. |
| format | Article |
| id | doaj-art-fe28c5e2b7e6493384f52a2a3bfd797e |
| institution | Kabale University |
| issn | 2073-445X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj-art-fe28c5e2b7e6493384f52a2a3bfd797e2025-01-24T13:37:52ZengMDPI AGLand2073-445X2025-01-011419510.3390/land14010095Multi-Scale Mapping of Energy Consumption Carbon Emission Spatiotemporal Characteristics: A Case Study of the Yangtze River Delta RegionKangjuan Lv0Qiming Wang1Xunpeng Shi2Li Huang3Yatian Liu4SHU-UTS SILC Business School, Shanghai University, Shanghai 201900, ChinaSchool of Economics, Shanghai University, Shanghai 201900, ChinaAustralia-China Relations Institute, University of Technology Sydney, Sydney, NSW 2007, AustraliaSHU-UTS SILC Business School, Shanghai University, Shanghai 201900, ChinaSchool of Economics and Management, Beijing Jiaotong University, Beijing 100080, ChinaClimate issues significantly impact people’s lives, prompting governments worldwide to implement energy-saving and emission-reducing measures. However, many areas lack carbon emission data at the lower administrative divisions. Additionally, the inconsistency in the standards, scope, and accuracy of carbon dioxide emission statistics across different regions makes mapping carbon dioxide spatial patterns complex. Nighttime light (NTL) data combined with land use data enable the detailed spatial and temporal disaggregation of carbon emission data at a finer administrative level, facilitating scientifically informed policy formulation by the government. Differentiating carbon emission data by sector will help us further identify the carbon emission efficiency in different sectors and help environmental regulators implement the most cost-effective emission-reduction strategy. This study uses integrated remote-sensing data to estimate carbon emissions from fossil fuels (CEFs). Experimental results indicate (1) that the regional CEF can be calculated by combining NTL and Landuse data and has a good fit; (2) the high-intensity CEF area is mainly concentrated in Shanghai and its surrounding areas, showing a concentric circle structure; (3) there are obvious differences in the spatial distribution characteristics of carbon emissions among different departments; (4) hot spot analysis reveals a three-tiered distribution in the Yangtze River Delta, increasing from the west to the east with distinct spatial characteristics.https://www.mdpi.com/2073-445X/14/1/95energy consumptioncarbon emissionsnighttime lightspatiotemporal changesYangtze River Delta |
| spellingShingle | Kangjuan Lv Qiming Wang Xunpeng Shi Li Huang Yatian Liu Multi-Scale Mapping of Energy Consumption Carbon Emission Spatiotemporal Characteristics: A Case Study of the Yangtze River Delta Region Land energy consumption carbon emissions nighttime light spatiotemporal changes Yangtze River Delta |
| title | Multi-Scale Mapping of Energy Consumption Carbon Emission Spatiotemporal Characteristics: A Case Study of the Yangtze River Delta Region |
| title_full | Multi-Scale Mapping of Energy Consumption Carbon Emission Spatiotemporal Characteristics: A Case Study of the Yangtze River Delta Region |
| title_fullStr | Multi-Scale Mapping of Energy Consumption Carbon Emission Spatiotemporal Characteristics: A Case Study of the Yangtze River Delta Region |
| title_full_unstemmed | Multi-Scale Mapping of Energy Consumption Carbon Emission Spatiotemporal Characteristics: A Case Study of the Yangtze River Delta Region |
| title_short | Multi-Scale Mapping of Energy Consumption Carbon Emission Spatiotemporal Characteristics: A Case Study of the Yangtze River Delta Region |
| title_sort | multi scale mapping of energy consumption carbon emission spatiotemporal characteristics a case study of the yangtze river delta region |
| topic | energy consumption carbon emissions nighttime light spatiotemporal changes Yangtze River Delta |
| url | https://www.mdpi.com/2073-445X/14/1/95 |
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