THERMAL SCIENCE

International Scientific Journal

IDENTIFYING WEAK AREAS OF URBAN LAND USE CARBON METABOLISM IN HIGH-DENSITY CITY

ABSTRACT
For high-density cities, it is necessary for city managers to achieve precise regulation of carbon emissions and sequestration. For reference, taking Dongguan as example, this study proposed a complex framework to identify weak areas of urban land use carbon metabolism in high-density city. On the basic of defining the urban land use carbon metabolism units, LEAP, Markove-PLUS, and LANDIS model were applied to spatialize land use carbon emissions and carbon sequestration. Finally, the weak areas of urban land use carbon metabolism were clearly indicated through overlapping the spatial pattern of land use carbon emissions and sequestration. Accordingly, carbon emissions limit regions can be delimited, and its carbon emissions are recommended to be metabolize through connecting the limit regions to green spaces with various ecological corridors. The results will serve as a foundation to plan and control carbon emissions in high-density cities that are similar to Dongguan in international communities.
KEYWORDS
PAPER SUBMITTED: 2023-09-24
PAPER REVISED: 2023-12-08
PAPER ACCEPTED: 2024-01-17
PUBLISHED ONLINE: 2024-02-18
DOI REFERENCE: https://doi.org/10.2298/TSCI230924002L
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2024, VOLUME 28, ISSUE Issue 1, PAGES [791 - 809]
REFERENCES
  1. Grimm, N. B., et al., Global Change and the Ecology of Cities, Science, 319 (2008), 5864, pp. 756-760
  2. Zhang, Y., et al., Analyzing Spatial Patterns of Urban Carbon Metabolism: A Case Study in Beijing, China, Landscape and Urban Planning, 130 (2014), Oct., pp. 184-200
  3. Chen, Q., et al., Analysis of Urban Carbon Metabolism Characteristics Based on Provincial Input-Output Tables, Journal of Environmental Management, 265 (2020), 110561
  4. Baccini, P., Understanding Regional Metabolism for a Sustainable Development of Urban Systems, Environmental Science and Pollution Research, 3 (1996), 2, pp. 108-111
  5. Xia, L., et al., Analysis of the Ecological Relationships of Urban Carbon Metabolism Based on the Eight Nodes Spatial Network Model, Journal of Cleaner Production, 140 (2017), Part 3, pp. 1644-1651
  6. Cai, C., et al., Spatial-Temporal Characteristics of Carbon Emissions Corrected by Socio-Economic Driving Factors Under Land Use Changes in Sichuan Province, Southwestern China, Ecological Informatics, 77 (2023), 102164
  7. Cai, B., et al., A New Model for China's CO2 Emission Pathway Using the Top-Down and Bottom-Up Approaches, Chinese Journal of Population, Resources and Environment, 19 (2021), 4, pp. 291-294
  8. Unnewehr, J. F., et al., Open-Data Based Carbon Emission Intensity Signals for Electricity Generation in European Countries - Top Down vs. Bottom up Approach, Cleaner Energy Systems, 3 (2022), 4, 100018
  9. Yang, J., et al., Development of Bottom-Up Model to Estimate Dynamic Carbon Emission for City-Scale Buildings, Applied Energy, 331 (2023), 120410
  10. Zhang, C., Luo, H., Research on Carbon Emission Peak Prediction and Path of China's Public Buildings: Scenario Analysis Based on LEAP Model, Energy and Buildings, 289 (2023), 113053
  11. Zhang, Y., et al., Review of Spatial Analysis of Urban Carbon Metabolism, Ecological Modelling, 371 (2018), Mar., pp.18-24
  12. Xia, C., et al., Analyzing Spatial Patterns of Urban Carbon Metabolism and its Response to Change of Urban Size: A Case of the Yangtze River Delta, China, Ecological Indicators, 104 (2019), Sept., pp. 615-625
  13. Zhang, F., et al., Effects of Land Use and Land Cover Change on Carbon Sequestration and Adaptive Management in Shanghai, China, Physics and Chemistry of the Earth, 120 (2020), 102948
  14. Cui, X., et al., Examining Spatial Carbon Metabolism: Features, Future Simulation, and Land-Based Mitigation, Ecological Modelling, 438 (2020), 109325
  15. Chrysoulakis, N., et al., Sustainable Urban Metabolism as a Link Between Bio-Physical Sciences and Urban Planning: The BRIDGE Project, Landscape and Urban Planning, 112 (2013), Apr., pp. 100-117
  16. Jing, Z., et al., Ecosystem Services Assessment Based on Land Use Simulation: A Case Study in the Heihe River Basin, China, Ecological Indicators, 143 (2022), 109402
  17. Zhao, N. Z., et al., Forecasting China's GDP at the Pixel Level Using Nighttime Lights Time Series and Population Images, Giscience & Remote Sensing, 54 (2017), 3, pp. 407-425
  18. Chen, S. Q., Chen, B., Determining Carbon Metabolism in Urban Areas Though Network Environ Theory, Procedia Environmental Sciences, 13 (2012), Mar., pp. 2246-2255
  19. Xia, L., et al., Spatial Analysis of the Ecological Relationships of Urban Carbon Metabolism Based on an 18 Nodes Network Model, Journal of Cleaner Production, 170 (2018), Jan., pp. 61-69
  20. Wang, A., et al., Investigating Drivers Impacting Vegetation Carbon Sequestration Capacity on the Terrestrial Environment in 127 Chinese Cities, Environmental and Sustainability Indicators, 16 (2022), 100213

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence