Abstract
This dissertation explores the dependency and relation between land use, temperature, and energy demand in urban systems. There are three main parts of this research: the relation between temperature and land use; the relation between energy demand and land use; and the dynamic relation between urban land use, temperature, and energy demand.
In Chapter 3 of this dissertation, a systematic review of urban development modeling, which concerns sustainability, microclimate, and energy, is conducted. The city is facing a rapid transition over the last two decades due to urbanization, immigration, and climate change. To countermeasure with future problems and build a sustainable city, urban development models are broadly discussed. This chapter discusses the three major types of sustainable urban development models for environmental impact assessment: 1) sustainable development model, and 2) urban microclimate model, and 3) urban energy demand model. This chapter reviews these urban models and their research methods in real urban areas. Based on the state-of-art in academic research, the research achievements on three major types of sustainable urban development models are mentioned, and future perspectives are provided.
For the land use and urban climate relation, Chapter 4 studies land surface temperature dynamics from a spatial and temporal scale perspective in the city of Eindhoven. Chapter 4 investigates the dependency between land use and summertime land surface temperature (LST) to explore whether optimizing land use planning can mitigate LST, and consequently adapt to the climate change forecast in Eindhoven, the Netherlands. Summertime data between 2000 and 2010 were plotted in a rasterized map of 30-meter cells, using a geographical information system (GIS) and cellular automata model. Subsequently, a set of spatial regression analysis was performed on the impact on LST of the central cells from a collection of circular von Neumann neighborhood-cell rings with five types of land use as neighborhood factors. The analysis indicates that the neighboring built-up area and openly abandoned space increase the LST of the central cell, whereas the neighboring artificial green space, natural green space, and water decrease the central cell’s LST. The impervious surface material has a heating effect on LST, whereas tree canopy has a cooling effect. The surrounding land-use effects on the central cells’ LST decreases after 450m. The LST of a built-up area or natural green area is more sensitive to the neighboring impacts than the water body, artificial green area, and open abandoned space. Implications of this research are spatial planning opportunities for reducing Surface Urban Heat Island (SUHI) effects through allocating different land-use types within certain neighboring rings.
To investigate the relation between energy demand and land use, Chapter 5 analyzes urban morphology indicators for urban energy modeling in a spatial context. The reduction of energy consumption in the urban environment has attracted numerous empirical studies in recent decades. However, limited studies have been conducted to investigate the impact of the urban layout on urban energy demand. To analyze the spatial correlation between urban layout and energy demand, Eindhoven is used as a case study in this paper. The urban layout is described by urban morphology indicators (UMIs), which are calculated by Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), building height, plot area and volume of the built environment. The energy demand model includes annual gas and electricity demand. Ordinary least squares regression (OLS) and geographically weighted regression (GWR) models are applied for the regression of energy demand to the UMIs. For the spatial regression, land use is clustered into five types: urban areas (U), open space (O), artificial green areas (G), natural green areas (V), and water body (W). The results revealed that energy demand and UMIs have a significant geographical relation. Among the UMIs, NDVI, building height, and plot area together constitute the most influential indicators for urban energy demand. Finally, the possible usage of the results in sustainable urban design and planning are discussed.
The relation between energy consumption, land use, and climate change is broadly discussed. However, there are comparatively little studies that study the effect of climate change and, more specifically, temperature change on energy consumption on an urban scale. Chapter 6 aims to discover the cause-effect relation between land use (L), energy (E), and temperature (T) in the urban system. Furthermore, various indicators combinations for land use (L) and temperature (T) are applied to optimize the urban energy model performance. Besides, the impact from the neighboring land use within the distance 450m is also included as a land-use indicator. The result of Chapter 6 can be applied to sustainable urban planning, targeting energy reduction, and climate adaptation.
This dissertation includes empirical spatial and temporal modeling for land use, temperature, and energy demand on an urban scale. Based on an extensive data set, different indicators for describing the urban environment have been analyzed to evaluate their significance and impact on the interaction between these three aspects. This dissertation contributes to the existing body of literature on climate change by providing insight on relevant urban land use indicators for urban system modeling, whereas most studies focus either on local/ building level or the regional/ national level. Additionally, this dissertation investigates the relation between energy consumption and climate change in an urban context, which is hardly covered by scientific literature until now.
The results can be applied to cities with similar climate conditions like Eindhoven, the Netherlands. For example, existing neighborhoods suffering from urban heat island effect can be adapted to mitigate these effects, not only focusing on the neighborhood itself but also its surroundings. Newly developed areas can take advantage of the findings when planning an energy and climate sustainable land use mix and urban morphology.
In Chapter 3 of this dissertation, a systematic review of urban development modeling, which concerns sustainability, microclimate, and energy, is conducted. The city is facing a rapid transition over the last two decades due to urbanization, immigration, and climate change. To countermeasure with future problems and build a sustainable city, urban development models are broadly discussed. This chapter discusses the three major types of sustainable urban development models for environmental impact assessment: 1) sustainable development model, and 2) urban microclimate model, and 3) urban energy demand model. This chapter reviews these urban models and their research methods in real urban areas. Based on the state-of-art in academic research, the research achievements on three major types of sustainable urban development models are mentioned, and future perspectives are provided.
For the land use and urban climate relation, Chapter 4 studies land surface temperature dynamics from a spatial and temporal scale perspective in the city of Eindhoven. Chapter 4 investigates the dependency between land use and summertime land surface temperature (LST) to explore whether optimizing land use planning can mitigate LST, and consequently adapt to the climate change forecast in Eindhoven, the Netherlands. Summertime data between 2000 and 2010 were plotted in a rasterized map of 30-meter cells, using a geographical information system (GIS) and cellular automata model. Subsequently, a set of spatial regression analysis was performed on the impact on LST of the central cells from a collection of circular von Neumann neighborhood-cell rings with five types of land use as neighborhood factors. The analysis indicates that the neighboring built-up area and openly abandoned space increase the LST of the central cell, whereas the neighboring artificial green space, natural green space, and water decrease the central cell’s LST. The impervious surface material has a heating effect on LST, whereas tree canopy has a cooling effect. The surrounding land-use effects on the central cells’ LST decreases after 450m. The LST of a built-up area or natural green area is more sensitive to the neighboring impacts than the water body, artificial green area, and open abandoned space. Implications of this research are spatial planning opportunities for reducing Surface Urban Heat Island (SUHI) effects through allocating different land-use types within certain neighboring rings.
To investigate the relation between energy demand and land use, Chapter 5 analyzes urban morphology indicators for urban energy modeling in a spatial context. The reduction of energy consumption in the urban environment has attracted numerous empirical studies in recent decades. However, limited studies have been conducted to investigate the impact of the urban layout on urban energy demand. To analyze the spatial correlation between urban layout and energy demand, Eindhoven is used as a case study in this paper. The urban layout is described by urban morphology indicators (UMIs), which are calculated by Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), building height, plot area and volume of the built environment. The energy demand model includes annual gas and electricity demand. Ordinary least squares regression (OLS) and geographically weighted regression (GWR) models are applied for the regression of energy demand to the UMIs. For the spatial regression, land use is clustered into five types: urban areas (U), open space (O), artificial green areas (G), natural green areas (V), and water body (W). The results revealed that energy demand and UMIs have a significant geographical relation. Among the UMIs, NDVI, building height, and plot area together constitute the most influential indicators for urban energy demand. Finally, the possible usage of the results in sustainable urban design and planning are discussed.
The relation between energy consumption, land use, and climate change is broadly discussed. However, there are comparatively little studies that study the effect of climate change and, more specifically, temperature change on energy consumption on an urban scale. Chapter 6 aims to discover the cause-effect relation between land use (L), energy (E), and temperature (T) in the urban system. Furthermore, various indicators combinations for land use (L) and temperature (T) are applied to optimize the urban energy model performance. Besides, the impact from the neighboring land use within the distance 450m is also included as a land-use indicator. The result of Chapter 6 can be applied to sustainable urban planning, targeting energy reduction, and climate adaptation.
This dissertation includes empirical spatial and temporal modeling for land use, temperature, and energy demand on an urban scale. Based on an extensive data set, different indicators for describing the urban environment have been analyzed to evaluate their significance and impact on the interaction between these three aspects. This dissertation contributes to the existing body of literature on climate change by providing insight on relevant urban land use indicators for urban system modeling, whereas most studies focus either on local/ building level or the regional/ national level. Additionally, this dissertation investigates the relation between energy consumption and climate change in an urban context, which is hardly covered by scientific literature until now.
The results can be applied to cities with similar climate conditions like Eindhoven, the Netherlands. For example, existing neighborhoods suffering from urban heat island effect can be adapted to mitigate these effects, not only focusing on the neighborhood itself but also its surroundings. Newly developed areas can take advantage of the findings when planning an energy and climate sustainable land use mix and urban morphology.
| Original language | English |
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| Qualification | Doctor of Philosophy |
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| Supervisors/Advisors |
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| Award date | 29 Jun 2020 |
| Place of Publication | Eindhoven |
| Publisher | |
| Print ISBNs | 978-90-386-5064-7 |
| Publication status | Published - 29 Jun 2020 |
