Trade-offs in Environmental Sustainability between
Urban Densification and Sprawling in Amsterdam:
A quantitative assessment and spatial analysis
Thomas Hofman – University of Amsterdam
Key concepts: Urban densification, Sustainable development, Urban green spacing (UGS), sustainability goals (SG), ecosystem services (ES)
Abstract
The UN predicts an increase of 2.5 billion urban residents between 2018 and 2050. Urban planning and regulation aims to cope with this increase in residents by urban densification within the city boundaries. The argument supporting this planning direction is that urban densification is a more sustainable option as it would reduce travel time and protect the surrounding rural areas of the cities. Urban densification could however drastically affect the urban biodiversity, air quality and susceptibility to natural hazards. Amsterdam is considered a sustainable city and has an extensive variety in densification and urban planning throughout the city making it an interesting case study site. This proposal proposes to quantitatively analyse the sustainability of urban densification by performing a multiple criteria analysis using a select group of sustainability indicators. Analysis of trade-offs and synergies found could help urban planning in making increasingly sustainable urban environments.
Table of Contents
1. Introduction ... 5
1.1 Problem definition ... 5
1.2 Case study ... 5
1.3 Research questions and objectives: ... 6
2. Theoretical background ... 7
2.1 History of urban densification ... 7
2.2 Development of Amsterdam ... 8
2.3 Urban environmental sustainability ... 9
2.4 The indicators for a sustainable city ... 10
2.5 Synergies & Trade-offs ... 11
2.6 Energy usage and greenhouse gas emissions ... 13
2.7 Urban and non-urban ecology ... 13
2.8 Resource Utilization & pollution ... 13
2.9 Natural hazard protection ... 14
3 Method ... 14
3.1 Quantification of indicators ... 15
3.2 Data collection ... 15
3.3 Data Analysis ... 16
3.4 Spatial analysis... 17
4. Feasibility and planning... 17
1. Introduction
1.1 Problem definition
The united nations predicts 68% of the global population to live in urban areas by 2050, an increase of 13% since 2018 (United Nations, 2019). Like several other cities, Amsterdam aims to cope with this projected population growth by expanding within the boundaries of the city (Gemeente Amsterdam 2011), a practice called urban densification. This management direction has been dominant in Europe for the past 50 years (Broitman & Koomen, 2015; Westerink et al., 2013) and is part of the 2040 vision of Amsterdam (Gemeente Amsterdam, 2011), as it would allegedly stimulates nature conservation in rural areas, reduces travel time and reduces greenhouse gas emissions in urban areas. However, claiming such a compact city is more sustainable than the alternative, a sprawling city, is an oversimplification. Some issues may be regarded as more sustainable in the compact city whereas others are more sustainable in the sprawling city. Westerink et al. (2013) called this ‘a trade-off in sustainability’. For example, whist the net per capita emissions and pollutants might be lower in the densified city as a result of reduced travel time, accumulation of these pollutants may be higher as a result of reduced windspeeds and increased local emissions leaving more people exposed and affecting biodiversity and quality of life in these areas (Westerink et al., 2013).
The effects of densification on different aspects of environmental sustainability have been researched extensively. However, existing literature mainly focusses on the qualitative assessment of Sustainability in urban areas (Shen et al.,2011, Lynch et al., 2011, Marzuki et al., 2011 & Westerink et al., 2013), neglecting potential benefits of quantitative assessment. Quantitative data is for sustainability indication is available at in sufficient amounts (Gemeente Amsterdam, 2018) and multiple studies have assessed the dynamics of urbanization gradients (Frenkel and Ashkenazi, 2008, Irwin and Bockstael, 2007, Mubareka et al., 2011, Torrens and Alberti, 2000, Yu and Ng, 2007). The relation of sustainability to such a gradient however has not been studied yet. Such a quantitative analysis of an urban densification could reveal synergies and trade-offs between the different aspect of environmental sustainability such as the relations species richness and air pollution for different levels of densification. It also allows for analysis of sustainability indicators along a densification gradient to identify trends.
1.2 Case study
The Netherlands has a relatively high population density compared to other European countries. Adequate urban development and management therefore is required to safeguard the natural environment. Preservation of natural open spaces has been the dominant urban management direction for the past 50 years (Broitman & Koomen, 2015). Due to its extensive history of implementing urban densification strategies Amsterdam in an interesting study site with an abundance of variation in densification across neighbourhoods due to environmental and cultural regulations (Broitman &
Koomen, 2015). Sustainable urban management in the Netherlands is considered successful however there are some incoherencies in management.
Whilst Amsterdam set out to expand within the boundaries of the city thus increasing the population density, the neighbourhood ‘IJburg’, the latest urban expansion in Amsterdam to date, has a population density very close to the city average population density (Gemeente Amsterdam, 2018). This average includes the remote industrial sites at the border of the city. If the population density in IJburg would have been higher, the current development of ‘Strandeiland’ (figure 1), which is the expansion of the IJburg neighbourhood, would not have been required to sustain urban growth. The development of such urban areas comes at the cost of natural areas such as the IJsselmeer right next to the neighbourhood.
Figure 1: Expansion of the newly built neighbourhood of Ijburg, source: Gemeente amsterdam (2020)
In 2015, Amsterdam published the ‘Agenda Groen’ in which they issued the aim to increase urban green spaces to promote quality of life and ecology within the city. Between 2003 and 2016 a decrease in urban green spaces of 3.07 square kilometres occurred, 11% of the total urban green spaces (Giezen et al., 2018). This is in conflict with the Green Agenda programme which, among other targets, aimed to increase the green spaces as preventative measure against flooding. The municipality of Amsterdam reports an increase of green roofs as a measure to deal with the loss in green spaces, (Giezen et al., 2018) however argues the compensation is inadequate as the green spaces lost are 76 greater than the increase in green roofs. The quantification sustainability for city densification could help identify areas of conflict in relation to urban densification such as reduced protection against flooding and provide adequate alternatives.
1.3 Research question and objectives:
The aim of this research is to provide insights in sustainability trade-offs between urban sprawling and urban densification in Amsterdam. Ultimately to answer the question: What is the effect of urban densification on the environmental sustainability of the city of Amsterdam? To answer this question the following objectives are set:
1. To identify quantitative qualifiers for urban environmental sustainability in the city of Amsterdam
2. To provide a quantitative analysis on the sustainability indicators to:
a. Identify potential relations between urban densification and individual sustainability indicators
b. Identify potential synergies between individual sustainability indicators
c. Assess if urban densification is indeed more sustainable based on sustainability indicators
3. To provide a spatial sustainability map of Amsterdam
This proposal expects to find a positive quantitative effect of urban densification on sustainability. This proposal however expects some sustainability indicators will not be positively affected by densification. Subsequently this proposal expects to identify synergies and trade-offs between sustainability indicators. This trade-offs and synergies can potentially be used in future policy making and urban planning to stimulate sustainable urban environmental development on a global level. On a more regional level, this proposal expects to provide a sustainability map of the municipality of Amsterdam which could potentially provide insights for urban planning in Amsterdam.
2
.
Theoretical background
2.1 History of urban densification
Urban densification itself has been interpreted in multiple ways in literature: The amount of residents per area, The amount of housing units per area and the amount of build land per area. Broitman & Koomen (2015) for example concluded urban densification throughout the Netherlands increased based on an increased number of housing units per area. If such units do house less residents per unit this would not necessarily result in an increase in population per area, and if these housings are created by splitting existing units neither would the build area. Urban densification is part of the spatial structure of the city.
This spatial structure of cities is historically described from a monocentric point of view where land rents and population density are high at the center of the city and decrease monotonically with increased distance from the center (Broitman & Koomen, 2015). These densification gradients should theoretically flatten with increased higher incomes and decreased transportation costs. Most literature regarding residential expansion in cities is linked to planning and land-use change, and is mainly focused on the velocity at which low density urban sprawl occurs (Frenkel and Ashkenazi, 2008, Irwin and Bockstael, 2007, Mubareka et al., 2011, Torrens and Alberti, 2000, Yu and Ng, 2007). Some
literature focusses on the land-change dynamics between urban and rural areas, closely related to urban expansion (Bell Ka and Irwin, 2002 & Irwin and Bockstael, 2004).
In urban planning literature, densification is often opted as a way to create urban sustainability by protecting the rural areas from urban sprawl. Daneshpour and Shakibamanesh (2011) compared sustainability in densified cities and urban sprawling and concluded that although sustainability could be part of the solution a compact city in not automatically sustainable. Moreover, since the 1990s the proposed compact city model has been challenged on three levels: whether it can deliver its benefits towards sustainability, Whether it could actually be implemented in the urban environment, and if it is acceptable to the local population to implement such drastic changes (Daneshpour & Shakibamanesh, 2011). Quantitative research based on adequately assessed parameters could provide insights on the state of these models.
2.2 Development of Amsterdam
In the past decade land used by cities throughout Europe increased. Resident increase is the main driver although cities that do not experience any population growth are expanding too (Broitman & Koomen, 2015). Cities appear to regain attractiveness due to the amenities they offer.
The Netherlands is one of the most densely populated countries in Europe although the major cities are relatively small with the capital of Amsterdam still just below 1 million residents. The polycentric nature of the Dutch urban development puts high pressure on open spaces. This resulted in the authorities on urban planning to implement the governing concept of accommodating anticipated growth whilst preserving rural and open areas for the past 50 years (Broitman & Koomen, 2015). Recent policies mainly focus on steering residential development towards large-scale urban development zones. Nature conservation poses another restriction on urban development in the Netherlands, the European policy for nature restoration and conservation prohibits member states to build in Natura 2000 areas. At the national level, National Ecological Network (NEN) acts as a network and corridor system to preserve nature.
Recent policies and urban planning in Amsterdam regarding densification and sustainability are presented in the Green Agenda (Gemeente Amsterdam, 2015). Amsterdam is growing faster than anticipated with rapid densification as a result. This densification admittedly affects urban green spaces (UGS). To overcome this lack in UGS the municipality aims to improve the attractiveness and functionality of the current green spaces.
In the sturcuurvisie 2040 report (Gemeente Amsterdam, 2011), a structural vision for Amsterdam in 2040, the municipality of Amsterdam states that urban densification and increased land use are important tools to create an economically strong and sustainable city. Development of high buildings in the city centre is limited as this is part of the UNESCO world heritage. As such, the cultural value of the inner city pushes densely populated areas out of the city. Figure 3 gives an overview of the
projected building sites for 2040. Subsequently ‘special’ neighbourhoods with cultural value have a restriction to build above 30m to limit the impact of such new buildings on the architectural heritage.
With regards to transportation, a key aspect of sustainable development, the municipality envisions an improved public transport network within the city, altering current roads. Bicycle and public transport are the main choices for short distance travel to date (Gemeente Amsterdam, 2011), however public transport experiences shortcomings in the middle travel distances (10km – 30km). Trains in combination with bicycles are more frequently used for longer distances (>30km) as alternative to cars.
The variety in densification due to world heritage neighbourhoods, the cultural ‘special neighbourhoods’ and the new neighbourhoods at the border of the city make Amsterdam an interesting case study area. Indicators are required to assess the differences in sustainability within those neighbourhoods.
Figure 2: Planned development of high buildings in 2040: purple areas are destined for high buildings whereas red areas are prohibited and green areas are open green spaces (Gemeente Amsterdam, 2011)
2.3 Urban environmental sustainability
Addressing effects on urban environmental sustainability requires a measurable definition of what environmental sustainability is. This definition is frequently debated and according to the US Federal Trade Commission in 2010 no clear understanding of the term existed among experts (Morelli, 2011),
it was therefore argued the term cannot be defined or tested. However, the need for environmental sustainability is clear as current and future generations rely on ecosystem services for their existence. Morelli (2011) described sustainability as “meeting the resource and services needs of current and future generations without compromising the health of the ecosystems that provide them” (p. 6), by which he intends to operationalize the concept, increasing clarity in valuing ecological services. In 2015 the United Nations (UN) listed the 17 Sustainable Development Goals (SDGs) to be achieved by 2030, in which they proposed certain indicators for urban sustainable development. The next section will discuss the indicators proposed by the UN and other literature regarding urban sustainable development.
2.4 The indicators for a sustainable city
To assess what the effect of city densification is on the sustainability of a city, a framework or model is required to assess the sustainability of a city in general. To assess the environmental impact a city has on its environment, an extensive scope is required as cities are part of a larger infrastructure in which they are embedded and cannot be evaluated without this broader infrastructure (Ramaswami et al., 2012). To determine qualifiers for a sustainable city therefore requires knowledge on the surrounding ecosystem, the energy supply and ecology within the city. Especially in the case of city densification, the sustainable benefits of efficient infrastructure become apparent. Alberti (1996) stated that clear linkage patterns are required between urban patterns and the natural resource base to establish environmental impact. Such frameworks and linkages are based on indicators which act as performance measures of a system (Hiremath et al., 2013). The purpose of these indicators is to show how well a system is performing, what measures should be taken to address certain problems, and to reduce the amount of information required to understand the system.
Ramaswami et al. (2012) assembled an interdisciplinary, transboundary framework for qualifying sustainable development beyond city boundaries, socio-ecological infrastructure systems (SEIS) which take into account the energy and food supply chains the and the ecological effects of these supply chains. However, since this case-study sets out to focus on neighborhoods of Amsterdam, differences in complex systems are less likely to have a huge impact. Neighborhoods are unlikely to differ in waste water treatment plants, energy supply and food supply. The importance of this complex systems predominantly lies in the magnitude at which neighborhoods require products from those complex systems.
Indicators for sustainability have been assessed extensively (Hiremath et al., 2013). In table 1 an overview of indicators addressed in current literature is provided. For this study, sustainable indicators are required that are susceptible to variation in densification. Westerink et al. (2013) provided a such a list of indicators based on densification in their article. Most of the literature on indicators for urban sustainability is qualitative however some efforts have been made to quantify the indicators. Shen et al. (2011) produced a list of qualitative indicators for urban sustainability. The next section will elaborate on how such indicators relate and react to one another. To adequately assess the qualitative
indicators a more in depth analysis on some of the parameters based on the feasibility and the occurrence in current literature is provided in the subsequent sections.
The preliminary indicators selected and elaborated in this proposal are based on the indicator assessment by Westerink et al. (2013) as this research incorporates the effect of densification. A more select subset is made as some qualitative indicators proposed in the research are hard to quantify. In the next sections, the effect of energy usage and greenhouse gas emissions, the difference in environmental quality, the consumption of natural resources, and the mechanisms to prepare and implement environmental plans will be evaluated. This set of indicator requires more research in the final paper.
Table 1: Overview of indicators of environmental sustainability in current literature
2.5 Synergies & Trade-offs
Multiple synergies and trade-offs in sustainability are recognised by the IPCC and the SDGs (IPCC, 2018). There are however different ways to interpret the definition of synergies. While some literature relates to synergies as the correlation between two parameters, others refer to the added effect by two correlating parameters, which is larger than the sum of its parts (Luukkanen et al., 2012).
The first interpretation is important in determining correlation between urban densification and the indicators as this indicates a potential relation between densification and the sustainability indicator.
Factors Shen et al. (2011) Lynch et al. (2011) Marzuki et al. (2011) Westerink et al. (2013) Geographically balanced x - x x Freshwater availability x - x - Wastewater treatment x - x - Air/Atmosphere quality x - x x Noise pollution x - x x
Sustainable land use x x x x
Waste generation and
management x x x -
Effective transportation systems x x - x
Mechanisms to prepare and
implement environmental plans x x x x
Biodiversity x x - x
Given that such correlation exists advanced analysis can analyse the combined effects of two or more indicators and assess if certain synergies or trade-offs are present in relation to each other.
Luukkanen et al. (2012) proposed a new way to quantitatively assess synergies and trade-offs between different policies and development trends, building on the Advanced Sustainability Analysis (ASA) approach developed the European framework programmes FP6 and FP7. They describe synergy by the effect of cxy in equation 1, where x and y are the parameters determining the output z and a, b & d are the coefficients determining the single effects of the parameters on the output.
(1) 𝑧 = 𝑎𝑥 + 𝑏𝑦 + 𝑐𝑥𝑦 + 𝑑
Luukkanen et al (2012) assume a time-invariant system, however time could easily be substituted by an urban densification gradient given there is a correlation between indicators and urban densification. This could for example indicate the positive effect of reduction in pollution and biodiversity increase have on each other (synergy) for different levels of urban densification. This synergy (s) is expressed by dividing the coefficient of x by the coefficient of y (equation 2), in the case of a time-invariant system this coefficient is expressed by the change in x over time however in the case of urban densification this could be replaced by the change in x over population density. This does however require a null-point or benchmark level from which to start. This starting null-point would ideally be a value for which the environmental impact is null so that increase and decrease predict negative or positive effects on the environment.
(2) 𝑠 = ∆𝑥 ∆𝑦
Figure 4 shows the effect when synergy is projected over delta x and delta y where the middle is the starting point which is similar to the benchmark level of urban densification.
2.6 Energy usage and greenhouse gas emissions
Urban densification has multiple advantages when it comes to energy usage and greenhouse gas (GHG) emissions. First, due to the increased proximity of amnesties travel distance shortens which reduces GHG emission (Broitman & Koomen, 2015; Clark, 2013). Next, densification requires highly developed infrastructure which improves walkability and public transport use (Clark, 2013). The municipality of Amsterdam provides walkability and public transport maps which could be used as indicators. Subsequently, Clark (2013) found negative correlations between energy consumption and urban densification throughout the united states. Whilst the effect of reduced travel time and car use if often mentioned in the favour of the dense city opposed to the sprawling city, Ramaswami et al. (2012) argue energy usage and greenhouse gas (GHG) emissions are currently not well assessed in urban sustainability frameworks. They argue the effects of energy usage and its resulting GHG emission by remote powerplants is often not taken into consideration. Indeed densified urban areas could in theory reduce the amount of solar energy produced as a result of reduced rooftop surface per household. The municipality of Amsterdam plotted all current solar panels including their potential energy supply at their website.
2.7 Urban and non-urban ecology
Pickett et al., (2001) described two definitions of urban ecology. In the scientific field of ecology ‘urban ecology’ is defined as the organisms in and around the city, whereas in urban planning ‘urban ecology’ is focussed on designing in such a way environmental impact is reduced. Urban environments are significantly different from rural areas and farmlands. First, temperatures are up to 5-10 degrees Celsius warmer in urban areas with increased temperatures with increase in artificial human made surface ,this difference in temperature interferes with the growing seasons resulting in earlier flowering time and delayed leaf drop (Pickett et al., 2001). Next, due to increased temperature ozone levels are higher around the city and suburban areas potentially resulting in reduced crop yield by 5-10% in these areas. Subsequently, precipitation is enhanced up to 5-10% in and around the city due to a higher concentration of particulate condensation nuclei in urban atmospheres. Finally, hydrology is drastically altered in urban areas with evapotranspiration decreasing from 40% to 25% and surface runoff increasing from 10% to 30% (Pickett et al., 2001). These factors and a more hazardous environment due to highways could result in lower biodiversity. The municipality of Amsterdam provides maps of endangered species on its website. UGS can also indicate biodiversity and ecology within the city, UGS% can be assessed using GIS analysis of Amsterdam.
2.8 Resource Utilization & pollution
Food and product supply chains put high pressure on the environment as they require transport which emits carbon dioxide. With increased urban densification pressure on these supply chains increases due
to failure of local production. A potential way of measuring supply chains is by looking at the amount of shops selling local products, such a map is available at the website of the municipality of Amsterdam.
Supply chains, local traffic and increased household in densified areas could also increase local concentrations of pollutants such as PM10 substances which could affect species richness and increase mortality and morbidity rates (Tiwary et al., 2009). Emission of such pollutants is measured at multiple locations throughout Amsterdam and data is available at www.luchtmeetnet.nl with data provided by the national institute for public health (RIVM).
2.9
Natural hazard protection
Urban densification can result in increased build up land which highly alters waterflow dynamics as build up land reduces the water infiltration capacity and thus result in increased run-off in urban areas. This makes urban areas more prone to flooding if no sufficient drainage systems are in place (Kaźmierczak & Cavan., 2011). In their research in flood prone areas in the UK Kaźmierczak & Cavan (2011) found a relation between high and low density residential areas and flood risk. They concluded this relation was a result of private gardens. The percentage of unpaved green spaces could provide an risk indication for environmental hazards, which could be derived from high resolution maps of Amsterdam discussed in the method section.
3 Method
Quantification and mapping of sustainability within urban areas has been done before by Derkzen et al. (2015), who focussed on urban green spacing in Rotterdam. Applying similar methods to Derkzen. et al. (2015), high resolution imagery and a geographical information system (GIS) will be used to map spatial patterns and relationships of the different ecosystem services such as water retention capacity and reducing the urban heat-island effect in Amsterdam. The main objective of this research is to assess the different sustainability qualifiers for Amsterdam and to determine how such qualifiers can be quantified. Qualifiers will be based on existing qualitative literature on sustainability trade-offs between urban densification and sprawling and the current goals to increase sustainability set by the municipality of Amsterdam. Information will be derived from data sources such as the European Environmental Agency (EEA), who provide data on air quality throughout the years for multiple locations throughout Amsterdam and for a variety of environmental pollutants. Sustainability will be compared to a densification gradient which will provide certain synergies and trade-offs between densification and sustainability indicators.
3.1 Quantification of indicators
Preliminary aspects of sustainability have been proposed in the theoretical framework. These aspects require quantifiable indicators to project sustainability per neighbourhood and per level of densification e.g. to perform regression analysis between indicator and densification and spatial analysis per indicator and neighbourhood. The indicators proposed in this proposal are: Species richness (n), UGS (%), energy consumption in Watt per person or household (W/p), Travel type (% per type), Consumption of local products (%), Renewable energy produced (W/m2), Air pollution (ug/ NOx), elements at risk per natural
hazard (€). The final research will elaborate on these parameters and they are subjective to change. The units used depend on availability, accuracy & frequency of data in the study area. Next section will elaborate on the collection of data. Certain benchmark values can be determined for which these values are considered acceptable which is important in comparing different types of data with one another. Benchmark values will be decided by using literature, local urban planning reports and global institutions such as the IPCC.
3.2 Data collection
To compare different types of densification and sprawling, distinct types of urban planning are required. Giezen et al. (2018) researched the effects of conflicting policies regarding densification and urban green spacing in Amsterdam using spatial analysis to assess the implications of these policies by comparing density in 2003 with density in 2016. Unlike Giezen et al. (2018), this research aims to compare different neighbourhoods in Amsterdam instead of temporal differences. This allows for a more robust way of comparing emissions and species richness as temporal effects can be neglected. One of the ways to project the difference in urban densification is by population density per postal code. The municipality of Amsterdam provides such data (Gemeente Amsterdam, 2018).
Each qualifier for sustainability requires its own data set. To determine the amount of UGS per neighbourhood high resolution satellite imagery can be used (Giezen et al., 2018; Derkzen et al., 2015). Such data is available at Copernicus who provide a freely accessible urban map of all large European cities. This data can be analysed in ARCGIS to determine the amount of green space per neighbourhood, after which field observations can determine the accuracy of the UGS analysis.
Species richness data is available at the website of the municipality of Amsterdam: https://maps.amsterdam.nl/. Which provides data on the amount of endangered species per neighbourhood among other aspects of biodiversity such as ecological passages which could be used to determine biodiversity. The municipality of Amsterdam provides information on the air quality for a number of neighbourhoods, A map of all solar panels including their effectiveness (Watts produced), The intensely used public transport, cycling and car routes, and hydrology maps among other information at their website (Gemeente Amsterdam, 2018). This data is freely accessible. The incorporation of the data requires quantitative analysis.
3.3 Data Analysis
Collection of the indicator data will result in an excel sheet with data per location. These locations possess a densification level e.g. number of people per square meter. First, regression analysis will establish whether synergies or trade-offs exist between densification and sustainability. This regression analysis will be done by performing a Pearson correlation test in R-Studio. In case of positive regression synergies exist between the sustainability indicator and densification, in case of negative correlation a trade-off exists between the indicator and densification, in the case of no correlation the sustainability indicator is not affected by densification. Especially the cases in which a trade-off is observed are interesting as this goes against the premise that densified cities are more sustainable.
To assess whether the found data indicators are sustainable or not, a benchmark level for each indicator is required which will be obtained from current literature and environmental policies. The indicator are weighted against this benchmark level which creates a marginal value. This marginal value is projected in a polar chart for each neighbourhood which results in a combined sustainability projection for each neighbourhood per indicator. An example of such a polar chart in given in figure 5.
Figure 4: Example polar chart reflecting sustainability indicators per neighbourhood (red line) and the neighbourhood average (grey bar).
Subsequently, the indicators will be combined to test the hypothesis of more sustainability with increased densification. A way to combine overall sustainability is by creating a composite sustainability index (CSI). Such an index was used by Lior & Kim (2018) to project sustainability of water desalination. In their scenario the composite weighted the relative importance for each indicator. Relative weights are however controversial in this proposal as a clear definition of urban environmental sustainability does not exist and literature on the topic varies in which indicators they use. The weights given to certain indicators based on literature or polls would be highly debatable. Another way to test the hypothesis is by an assuming sustainability equally for all indicators, the regression between all indicators and the urban densification could in that case depict the sustainability of urban densification. This requires a multiple regression analysis which can be performed in R-studio.
The advanced analysis of synergies in sustainability indicators will be performed according to the modified protocol of Luukkanen et al. (2012), decribed in more detail in the theoretical framework.
3.4 Spatial analysis
Spatial analysis and mapping of sustainability in Amsterdam requires an definition of sustainability. For reasons discussed in the data analysis section it is highly controversial to weight indicators and selecting one over the other. In the spatial analysis this proposal therefor proposes to not include weights in for different indicators. This proposal does however propose to use the relative effects of each indicator by dividing them by an normalized benchmark value, a level of urban densification for which that specific indicator is acceptable, derived from literature. This benchmark value will be normalized (e.g. benchmark value equals 1), in which case normalized (divided by benchmark value) observation values >1 are considered (more) sustainable and <1 are considered less sustainable. Each site now has its specific normalized observations which could be added and divided by the amount of observations where again values >1 are considered sustainable locations and <1 are considered unsustainable locations. These locations can be mapped for Amsterdam using ArcGIS. If sufficient time is available different benchmark values could be projected. This allows us to compare different interpretations of sustainability e.g. benchmark values for sustainability in Amsterdam can be compared with the value derived from the IPCC.
4. Feasibility and planning
The output and planning of the research depends on the data available. Although Amsterdam is a highly monitored area with data on plenty of data on sustainable indicators, a situation might occur in which data for an indicator is not available for most of the neighbourhoods. To deal with such scenarios a significant amount of the research focusses on assessing of the right indicators. The ideal way of tackling problems in data availability is by finding substitute indicator which does have data for all areas. However, if the problem keeps emerging aggregation of plots with similar population density could solve the problem. This does however reduce the sample size, in which case alternative statistic methods might be required. Another issue that most likely will present itself is noise in correlations, older neighbourhoods could for example be less densely populated, but also have less isolated buildings than new ones which makes them less energy efficient. This effect could just be the result of the building constructions. Finally, some analysis is based on high resolution imagery, a map presenting such imagery is available although the analysis has not yet been conducted. If the resolution appears to be insufficient alternative maps or data input are required to analyse (for example) urban green spaces. The research takes place over a 5 month period starting at 17-02-2020 and finishing at 17-07-2020. The planning is projected in a GANTT chart (figure 6). The research takes into account that
delays could occur, for which a 7 day period is planned to catch up. Alternative additions to the research are proposed in the case of no delays.
Figure 5: GANTT Chart indication of the schedule of the project starting at 17-02-2020 and finishing at 17-07-2020
38 20 5 6 20 5 5 5 11 8 5 26 10 7 5 5 5 6 5 0 20 40 60 80 100 120 140 160
Sustainability Indicator assessment Data sources for the indicators
Location selection Assess optimal benchmark values
Dataset assemblage Write introduction Write method Statistical correlations Synergies & trade-offs Write statistical conclusions Data transformation Create maps Identify incoherencies with statistics Different benchmark values/ delay in research
write results write conclusion
write discussion Prepare presentation Finalize report
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