Transcript Environment, Land-use and Well-being Finbarr Brereton, J Peter Clinch and Harut Shahumyan UCD Dublin.

Environment, Land-use and Well-being
Finbarr Brereton, J Peter Clinch and Harut Shahumyan
UCD Dublin
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HAPPINESS
(Happiness, Political Institutions, Natural
Environment and Space)
A Comparative Analysis of the influence of environmental
conditions, environmental regimes and political context on
subjective well-being
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Outline
• Motivation
– Background
• Environment and Well-being
– The Economics of Happiness
– Environment and Happiness
• Methodology
– European Social Survey and CORINE land cover
– Geographical Information Systems
• Results and conclusions
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Motivation
•
Advance the understanding of the influence of land use and
environment on welfare and the implications for policy
•
Quality of life and land use are mutually inter-related (MEA,
2005)
•
In large central cities, the literature shows that
environmental and urban conditions operate jointly to reduce
welfare (e.g. MacKerron and Mourato, 2009)
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Background
•
Urbanization, expressed as the proportion of people living in
urban places shows a value around 80% in most European
countries. There is concern about the impacts of current
patterns of urban development on environmental conditions
and quality of life within urban-regions (Antrop, 2004).
•
European Environment Agency (EEA) studies showed a
significant increase of built surface at the expense of open
space (EEA 2002; 2006)
•
Green space is under pressure from various urban
development processes such as densification and urban
regeneration programmes (Pauleit et al. 2003).
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Environment and Well-being
• Environment is found to influence well-being
– Increases recovery rates among hospital patients with
natural views (Ulrich, 1984)
– Noise is found to affect health (Haralabidis et al., 2008)
– Air Pollution: lead, particulate matter (Cifuentes et al.,
2001)
• Long tradition in the Hedonic literature (Rosen, 1974,
Roback, 1982 and Bloomquist et al., 1988).
– Constructing quality of life indices as the weighted
averages of amenities in a particular area, usually a city
region
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The Economics of Happiness
• Happiness (categorical) data have been used to
understand the determinants of Subjective Well
Being (SWB)
– Socio-demographic characteristics
– Socioeconomic and macroeconomic characteristics
(Clark and Oswald, EJ 1994; Easterlin, JEBO 1995; Oswald,
EJ 1997; Di Tella et al., AER 2001; Stutzer and Frey, EJ 2001;
Blanchflower and Oswald, JoPE 2004; Stutzer, JEBO 2004;
Clark et al., 2008 JoEL)…………..
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The Economics of Happiness
• LS functions, some examples (from Alesina et al. JoPE
2001 and from van Praag and Baarsma, EJ 2005)
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Environment and Happiness
•
A more recent literature on the economics of happiness has
begun examining the effect of environmental amenities on
human welfare directly
– Frey and Stutzer, 2004
– Van Praag and Baarsma, 2005
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–
–
–
•
Welsch, 2002; 2006
Rehdanz and Maddison, 2005
Ferrer-i-Carbonell and Gowdy 2007
Luechinger 2009 etc…
Using subjective well-being data to construct quality-of-life
indices as an alternative to the conventional indices using
weights derived from hedonic regressions,
– Moro et al. 2008
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Environment and Happiness
• Data constraints
– Previous studies were hindered by a lack of adequately
disaggregrated data (Welsch, 2006; Rehdanz and
Maddison, 2002), where data constraints at the local and
regional levels restricted analysis to aggregated data at
the national level, or to focusing on a particular localised
area where richer data was available.
– Only a few papers to date have been able to capture the
effects of environmental factors at the geographical level
(local or regional) at which individuals actually
experience them
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Environment and Happiness
• Luenchinger 2009
– Air Quality in Germany
– Valuing Flood disasters
• MacKerron and Mourato 2009
– Air quality in London city centre
• Brereton et al. 2008
– Local amenities in Ireland
These papers use Geographical Information
Systems (GIS)
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Methodology
• Data
– LS scores, individual, social and economic data
provided by Rounds 1-3 of the European Social Survey
– Land use data provided by CORINE land use dataset
• Question types
– “How satisfied are you with your life as a whole these
days?”
– “Taken all together, how would you say things are
these days - would you say that you are very happy,
pretty happy, or not too happy?”
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European Social Survey
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15
10
5
0
Percent
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25
Life Satisfaction in Europe
0
2
6
4
How satisfied with life as a whole
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10
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Life Satisfaction in Europe
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CORINE Land cover
•
Pan-European land cover database carried out within
each European member state;
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–
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Vector spatial dataset
Land cover digitized based on the interpretation of medium
resolution satellite imagery and assigned a land use class
based on a standardized land cover nomenclature defined by
the EEA.
The minimum area mapped in the dataset is 25 hectares.
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CORINE Land cover
GRID
CLC
LABEL1
LABEL2
LABEL3
1
111
Artificial surfaces
Urban fabric
Continuous urban fabric
2
112
Artificial surfaces
Urban fabric
Discontinuous urban fabric
3
121
Artificial surfaces
Industrial, commercial and transport units
Industrial or commercial units
4
122
Artificial surfaces
Industrial, commercial and transport units
Road and rail networks
5
123
Artificial surfaces
Industrial, commercial and transport units
Port areas
6
124
Artificial surfaces
Industrial, commercial and transport units
Airports
7
131
Artificial surfaces
Mine, dump and construction sites
Mineral extraction sites
8
132
Artificial surfaces
Mine, dump and construction sites
Dump sites
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133
Artificial surfaces
Mine, dump and construction sites
Construction sites
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141
Artificial surfaces
Artificial, non-agricultural vegetated areas
Green urban areas
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142
Artificial surfaces
Artificial, non-agricultural vegetated areas
Sport and leisure facilities
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211
Agricultural areas
Arable land
Non-irrigated arable land
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212
Agricultural areas
Arable land
Permanently irrigated land
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213
Agricultural areas
Arable land
Rice fields
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221
Agricultural areas
Permanent crops
Vineyards
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222
Agricultural areas
Permanent crops
Fruit trees and berry plantations
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223
Agricultural areas
Permanent crops
Olive groves
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231
Agricultural areas
Pastures
Pastures
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241
Agricultural areas
Heterogeneous agricultural areas
Annual crops
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242
Agricultural areas
Heterogeneous agricultural areas
Complex cultivation patterns
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243
Agricultural areas
Heterogeneous agricultural areas
Significant areas of natural vegetation
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244
Agricultural areas
Heterogeneous agricultural areas
Agro-forestry areas
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CORINE Land cover
GRID
CLC
LABEL1
LABEL2
LABEL3
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311
Forest and semi natural areas
Forests
Broad-leaved forest
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312
Forest and semi natural areas
Forests
Coniferous forest
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313
Forest and semi natural areas
Forests
Mixed forest
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321
Forest and semi natural areas
Scrub/herbaceous vegetation associations
Natural grasslands
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322
Forest and semi natural areas
Scrub/herbaceous vegetation associations
Moors and heathland
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323
Forest and semi natural areas
Scrub/herbaceous vegetation associations
Sclerophyllous vegetation
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324
Forest and semi natural areas
Scrub/herbaceous vegetation associations
Transitional woodland-shrub
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331
Forest and semi natural areas
Open spaces with little or no vegetation
Beaches, dunes, sands
31
332
Forest and semi natural areas
Open spaces with little or no vegetation
Bare rocks
32
333
Forest and semi natural areas
Open spaces with little or no vegetation
Sparsely vegetated areas
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334
Forest and semi natural areas
Open spaces with little or no vegetation
Burnt areas
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335
Forest and semi natural areas
Open spaces with little or no vegetation
Glaciers and perpetual snow
35
411
Wetlands
Inland wetlands
Inland marshes
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412
Wetlands
Inland wetlands
Peat bogs
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421
Wetlands
Maritime wetlands
Salt marshes
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422
Wetlands
Maritime wetlands
Salines
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423
Wetlands
Maritime wetlands
Intertidal flats
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511
Water bodies
Inland waters
Water courses
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512
Water bodies
Inland waters
Water bodies
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521
Water bodies
Marine waters
Coastal lagoons
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522
Water bodies
Marine waters
Estuaries
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523
Water bodies
Marine waters
Sea and ocean
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CORINE Land cover - 2000
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CORINE Land cover - 2006
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CORINE Land cover
•
For the purposes of this study, the original 44 land use
categories of the CORINE nomenclature were recategorised for the regression analysis into the
following classes:
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–
–
–
–
–
–
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Residential
Commercial and Industrial
Mines and Dumps
Green Urban Spaces
Agricultural Land
Forestry
Natural Areas
Water bodies
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Geographic Information Systems (GIS)
• GIS is a powerful computing tool that allows the visual
representation of spatially referenced data.
– Represents data as countable numbers of points, lines and
polygons in two-dimensional space (Goodchild and Haining,
2004)
– Link various datasets using spatial identifiers (Bond and Devine,
1991).
– It represents a base for spatial data analysis and provides a
range of techniques for analysis and visualisation of spatial data.
– It provides tools for integrating, querying and analysing a wide
variety of data types, such as scientific and cultural data, satellite
imagery and aerial photography, as well as data collected by
individuals, into projects, with geographic locations providing the
integral link between all the data.
• GIS is widely-used as a planning and analysis tool and
provides a powerful set of tools for spatial analysis and
modelling.
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Geographic Information Systems (GIS)
• GIS requirements
– Collection, assimilation and pre-processing of
spatial datasets
• GIS data layers
– Land use; Administrative boundaries
• Constructed variables at NUTS level
• European Nomenclature of Territorial Units for
Statistics (NUTS) is a geocode standard for
referencing administrative divisions of countries
for statistical purposes developed by the
European Union.
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Preliminary Results
Variable
Model 1
Model 2
Model 3
Agriculture
-0.0021***
-0.003***
Water bodies
0.0005***
0.0004***
Green Urban
0.001*
-0.002**
Location
City suburb
0.3576***
Large city
Town
0.2116***
Village
0.3565***
Countryside
0.8244***
Land uses
Significance Level: *** = 1%, ** = 5%, * = 10%
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Conclusions
• Land use influences well-being
– Well-being is lower in more urbanised, central city locations and
increases as the typology becomes more rural
• Green areas
+ sig.
• Agricultural areas - sig.
• Water bodies
+ sig.
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Caveats
• Minimum mapping area in the CORINE land cover
database is 25 hectares
• Low sample size in some NUTS regions
• Ongoing Work
–
–
–
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Aggregate the NUTS based on local association
More disaggregated land use classes
Country models
Multi-level modelling
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Thank you
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