Quantifying the Well-Being Benefits of

Urban Green Space

Submitted to the school of Environmental Sciences at the University of East Anglia for the

degree of Doctor of Philosophy.

On 12th September 2014

By Barnaby Andrews.

This copy of the thesis has been supplied on condition that anyone who consults it is understood to recognise that its copyright rests with the author and that use of any information derived there from must be in accordance with current UK Copyright Law. In addition, any quotation or extract must include full attribution.

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Abstract

Rapid urbanisation compounded by underlying population growth has placed increasing

pressures upon green space areas within cities. Anecdotal evidence suggests that such areas

are major sources of wellbeing yet the complex nature of the services provided by such areas

and the non-market, unpriced characteristics of the benefits they yield raise concerns that

they are inadequately incorporated within decision making and planning systems. This thesis

seeks to address the problem of quantifying the well-being benefits of urban green space

through the extension of two complementary strands of research. The first seeks to contribute

to the incorporation of urban green space benefits within conventional decision making

systems. Within this strand of the research the authors report two studies designed to address

various challenges associated with the estimation of economic values for the non-market

benefits generated by urban green space. The first of these studies contributes to the

literature on the estimation and transferral of valuation functions across locations to allocate

available resources at an inter-city, national level. The second valuation study operates at an

intra-city level through an experimental study the dimensions of which are designed to reveal

optimal locations in the presence of potential local dis-amenities (a potentiality which is

confirmed through the application of advanced statistical analysis techniques). The second

strand of research addresses the complexities of relationships between urban green space and

individual well-being. Here recent methodological advances in the field of applied social-

psychology are extended to yield a richer picture of the diverse impact of both direct

experience and passive viewing of green space upon wellbeing. An experiment is designed to

permit enhanced controls for the potential correlation between environment and activity in

determining experiential perceptions of well-being effects. A common theme of all

applications is the explicit incorporation of spatial complexity and variation in the environment

within each study and across the various methodologies employed. From a practical

perspective it is argued that these results provide inputs to both the decision making and

planning fields. More fundamentally, the work presented within this thesis represents a useful

methodological contribution to both the applied economic valuation and social-psychology

research literatures.

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Contents

List of Figures. 8

List of Tables 10

List of Abbreviations. 15

Acknowledgments. 16

1. Introduction

1.1. Well-being and the Influence of the Urban Environment 17

1.2. Evolutionary Theories for the Benefits of Nature 18

1.3. Evidence for the Benefits of Urban Green Space. 19

1.4. The UKs Urban Green Spaces 21

1.5. Two Perspectives on Quantifying the Benefits of Urban Green Space.. 23

1.6. Thesis Aims and Research Questions.. 26

1.7. The Case Studies. 26

2. Perspectives on Measuring Well-Being

2.1. Introduction .. 28

2.2. The Development of Environmental Valuation Research. 28

2.3. The Development of Research into the Measurement of Subjective Well-

Being... 32

2.4. Well-Being in UK Politics 35

2.5. The Compatibility of Economic and Psychological Perspectives to Quantifying Urban

Green Space Benefits.. 36

3. Valuing Great Britains Urban Green Space: A GIS Based Benefits Transfer Study of the

Value of Urban Green Space

3.1. Introduction...... 40

3.2. Background...............................................................................................................

40

3.3. Methods...... 44

3.3.1. Meta-Analysis...... 45

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3.3.2. Developing a Spatially Sensitive Marginal Value Function....... 45

3.3.3. The NEA Scenarios........ 46

3.3.4. The Case Study Cities......... 47

3.4. Methods Implementation....... 48

3.4.1. A Typology of Urban Green Space...... 48

3.4.2. Meta-Analysis Implementation........49

3.4.3. Marginal Value Functions..... 52

3.4.4. Function Variable Measurements......... 55

3.4.4.1. Define Study Areas and City Variables for the Five Case Study cities... 55

3.4.4.2. Define FRS Layer and Calculate Size and Distance........ 55

3.4.4.3. Define City Edge Green Space......... 56

3.4.4.4. Define Informal Green Space....... 56

3.5. NEA Scenario Data....... 58

3.6. The Five Case Study Cities......... 60

3.7. Analysis & Results........... 60

3.7.1. Applying the Scenarios to the Case Study Cities........ 60

3.7.2. Extrapolating and Aggregating Benefits.......... 61

3.7.3. Distributional Weights.......... 68

3.8. Conclusions.......... 69

4. Good Parks - Bad Parks: The Influence of Location on WTP and Preference Motives for

Urban Parks

4.1. Introduction............................................................................................................ 72

4.2. Background.......... 72

4.2.1. Determinants of WTP......... 73

4.2.2. Aims and Research Questions.......... 75

4.3. Methods......... 75

4.3.1. Park Choice and WTP Questions........ 76

4.3.2. Protest Bids....... 76

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4.3.3. Participant Characteristics...... 76

4.4. Results....... 77

4.4.1. The Sample...... 77

4.4.2. Environmental Attitudes....... 78

4.4.3. Park Choice Results....... 78

4.4.4. WTP Results....... 79

4.4.4.1. Marginal Effects......... 80

4.4.4.2. Testing for Preference Reversals...... 81

4.4.5. WTP Models....... 81

4.4.6. Evidence for the Localised Disamenity of City Centre Parks.......... 83

4.4.7. Aggregation....... 87

4.5. Discussion and Conclusions....... 88

5. Experienced Well-Being and Everyday Activities in the Urban Environment: The Influence

of Visual Exposure to Natural Features

5.1. Introduction...... 91

5.1.1. Study Aims and Hypothesis..... 93

5.2. Methods...... 95

5.2.1. The Day Reconstruction Method....... 95

5.2.2. Measuring Visual Exposure to Urban Features........ 96

5.2.2.1. Subjective Exposure Measures....... 96

5.2.2.2. Objective Exposure Measures..... 98

5.2.3. Controlling for Additional Person and Episode Level Confounders....... 102

5.3. Results.... 103

5.3.1. Descriptives..... 103

5.3.1.1. Person Level Descriptives.... 104

5.3.1.2. Episode Level Descriptives..... 102

5.3.1.2.1. Experienced Well-Being Descriptives ..... 106

5.3.1.2.2. Subjective Exposure Descriptives...... 108

5.3.1.2.3. Objective Exposure Descriptives........ 109

5.3.1.2.4. Relationship Between Subjective and Objective Exposure

Measures... 110

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5.3.2. Regression Analysis of the Determinants of Experienced Well-being .... 111

5.3.2.1. Baseline Variance Components Model...... 111

5.3.2.2. Accounting for the Effects of Everyday Activities....... 111

5.3.2.3. Accounting for Interactions with Other People ...... 113

5.3.2.4. Accounting for Time of Day Effects...... 116

5.3.2.5. Accounting for Person Level Covariates....... 119

5.3.2.6. Accounting for Subjective Exposure Effects on Experienced .........

Well-being... 124

5.3.2.6.1. Work Only Model.... 126

5.3.2.6.2. Transport Only Model...... 128

5.3.2.7. Accounting for Objective Exposure Effects on Experienced ........ .

Well-being... 129

5.4. Conclusions.... 130

6. Conclusions.... 133

6.1. Introduction... 133

6.2. Empirical Findings... 134

6.2.1. Chapter 3... 134

6.2.2. Chapter 4...... 135

6.2.3. Chapter 5...... 135

6.3. Methodological Implications... 136

6.4. Policy Implications.. 136

6.5. Limitations of the Studies and Future Directions.... 137

6.6. Final Conclusions.... 138

7. Appendixes..... 141

Appendix 3.1 Published version of the study reported in Chapter 3...... 141

Appendix 3.2: Data Used for Spatial Analysis...... 142

Appendix 4.1: CV Interview Wording and Flashcards.... 144

Appendix 4.2: Sample Descriptives..... 147

Appendix 4.3: GAC Scale Percentage Responses and Factor Analysis....... 149

Appendix 4.4: Percentages of Park Choice Reasons.......... 150

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Appendix 4.5: Tobit Models of WTP....... 151

Appendix 4.6: Interaction Models. 152

Appendix 4.7: Predicted WTP Descriptives . 153

Appendix 4.8: Spatial Data Used for Aggregation.. 156

Appendix 5.1: Gauss Kernal Smoothing Algorithm (Matlab Script)... 157

Appendix 5.2: GIS VBA Script to Create 2-D Isovist Fields.... 158

Appendix 5.3: Neuroticism and extraversion scale (taken from the Big Five Inventory,

John &