Low-carbon city development based on land use planning
Climate change has become a pressing global environmental issue, with urban areas contributing to a significant portion (71-76%) of greenhouse gas (GHG) emissions. While traditional strategies for reducing emissions focus on industry optimization and clean technologies, the spatial distribution and structure of cities also have a profound impact on carbon emissions. Changing land use patterns can lead to variations in building types and layouts, influencing carbon emissions. Therefore, spatial planning plays a crucial role in realizing a low-carbon city.
Objectives:
The research aims to develop an integrated framework for low-carbon city land use strategies, encompassing four key procedures: carbon emission estimation, spatial analysis, spatial optimization, and carbon emission prediction. The framework strives to provide essential insights for achieving a more sustainable urban future.
Methodology:
Carbon emission estimation is achieved through a comprehensive GIS-based assessment model, incorporating open data to visualize the city-scale spatial distribution of carbon emissions. Sectors like buildings, transportation, vegetation, and residence are assessed to estimate carbon emissions. Feature selection methods are applied to identify significant attributes of building and vegetation, such as land use composition and greenspace coverage. Classification techniques like cluster analysis help differentiate land use patterns, showing variations in carbon emissions among different categories. Spatial optimization is structured using multi-objective optimization principles, integrating objectives like carbon emission, population, and spatial indicators, considering various constraints. An evolutionary algorithm seeks efficient solutions for sustainable land use planning under different scenarios. To understand the carbon emission performance of specific land use plans in realistic situations, a prediction model utilizing support vector regression and Monte Carlo simulation is developed.
Findings and Outcomes:
The research's estimation results provide essential datasets for analyzing the impact of spatial attributes on carbon emissions, allowing for the identification of factors significantly affecting emissions. The classification of land use patterns showcases the differences in carbon emissions among various categories, supporting the optimization of land use layout for sustainable urban planning. The integrated spatial planning framework is applied in Eindhoven, the Netherlands, to aid decision-making in local urban development. It offers a potential tool for deriving effective and efficient land use planning strategies, addressing the challenges of population growth while promoting environmental protection.
Conclusion
This research contributes to the development of low-carbon city land use strategies through an integrated and effective framework. By considering carbon emission estimation, spatial analysis, optimization, and prediction, the study offers valuable support for sustainable urban planning, enabling policymakers to make informed decisions towards a more environmentally conscious and resilient urban future.
Project information
- Category Energy transition
- Author Genzhe Wang
- Project date 2016 - 2021