Assessing climate impacts of planning policies—An estimation for the urban region of Leipzig (Germany)

Environmental Impact Assessment Review 31 (2011) 97–111

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Assessing climate impacts of planning policies—An estimation for the urban region of Leipzig (Germany) Nina Schwarz ⁎, Annette Bauer, Dagmar Haase Helmholtz Centre for Environmental Research—UFZ, Department of Computational Landscape Ecology, Permoserstrasse 15, 04318 Leipzig, Germany

a r t i c l e

i n f o

Article history: Received 30 October 2009 Received in revised form 2 February 2010 Accepted 5 February 2010 Available online 19 March 2010 Keywords: Climate regulation Urban heat island Spatial planning Leipzig Urban planning

a b s t r a c t Local climate regulation by urban green areas is an important urban ecosystem service, as it reduces the extent of the urban heat island and therefore enhances quality of life. Local and regional planning policies can control land use changes in an urban region, which in turn alter local climate regulation. Thus, this paper describes a method for estimating the impacts of current land uses as well as local and regional planning policies on local climate regulation, using evapotranspiration and land surface emissivity as indicators. This method can be used by practitioners to evaluate their policies. An application of this method is demonstrated for the case study Leipzig (Germany). Results for six selected planning policies in Leipzig indicate their distinct impacts on climate regulation and especially the role of their spatial extent. The proposed method was found to easily produce a qualitative assessment of impacts of planning policies on climate regulation. © 2010 Elsevier Inc. All rights reserved.

1. Introduction Local climate regulation is an important urban ecosystem service (Bolund and Hunhammar, 1999; Pauleit et al., 2005; Tratalos et al., 2007; Whitford et al., 2001): Urban green areas like parks, urban forests, lawns and gardens as well as water surfaces of rivers, lakes and ponds provide fresh and cool air for the urban environment and population. Accordingly, these urban ecosystems deliver a valuable service for the inhabitants of a city by reducing the urban heat island effect (Bolund and Hunhammar, 1999). Evaluating and steering urban structures is therefore necessary to maintain urban quality of life and to adapt urban regions to climate change, e.g. by mitigating the impact of future heat waves like the European one in 2003 (Robine et al., 2008). Spatial planning at the local and regional scale aims at steering land use changes in an urban region by assigning new areas for residential and commercial development, protecting nature conservation areas or restoring brownfields (Hein, 1995). These land use changes have intended or unintended effects on local climate regulation, e.g. by creating or reducing urban green areas, water surfaces and/or corridors of cold air. Therefore, an assessment of spatial planning policies with regard to effects on local climate regulation is useful to further integrate them into spatial planning. In the European Union, the Strategic Environmental Assessment (SEA) was established with Directive 2001/42/EC. The SEA aims at integrating an environmental assessment and monitoring into the

⁎ Corresponding author. Tel.: + 49 341 235 1970. E-mail addresses: [email protected] (N. Schwarz), [email protected] (A. Bauer), [email protected] (D. Haase). 0195-9255/$ – see front matter © 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.eiar.2010.02.002

development of plans and programmes. From 2004 onwards, SEA should be implemented by all European Union member states. SEA is mandatory for all plans and programmes that are required by law and are prepared and/or adopted by national, regional or local authorities. In Article 5 (1) f) of the Directive, “climatic factors” are mentioned as mandatory contents of the environmental report which has to be compiled during the SEA. Climatic factors are mostly referred to as emission of greenhouse gases, influencing global climate (European Commission, 2009a,b for SEA and Environmental Impact Assessment in Europe). However, local climate regulation encompasses climate effects on the local and regional scale, where inhabitants are directly influenced. Set against this background, the aim of this paper is to develop and test a method for estimating the impacts of current land use and spatial planning policies on local climate regulation in an urban region. This method should be easily comprehensible and appropriate for local practitioners to use and adapt to their own region. Land surface emissivity and evapotranspiration have been selected as indicators for local climate regulation. The urban region of Leipzig (Germany) was chosen as case study region as it is expected to face higher temperatures and less humidity in the future, and data availability for the region is very promising for testing such a new method. Accordingly, impacts of current land use and possible future land uses as intended by local and regional planning policies on local climate regulation in Leipzig are estimated. The paper is organised as follows: Section 2 gives an overview of the case study region and the exemplary planning policies for the urban region. In Section 3, data and methods for converting these policies into maps (Section 3.1) and estimating climate regulation (Section 3.2) are shown. In Section 4, results are presented and discussed. Section 5 gives conclusions.

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2. The case study region 2.1. Biophysical conditions and land use The case study region Leipzig is situated in Central Germany. It covers the city of Leipzig and their adjacent municipalities which form major parts of the planning region of Western Saxony (1033 km2). With a total population of 620 000 and a population density of 600 persons per km2, the urban region of Leipzig belongs to the most important urban agglomerations in Germany. The city of Leipzig (515 000 inhabitants) is the central settlement and major city of the region. Belonging until 1990 to the former GDR, the case study region was facing highly dynamic developments after 1990: People moved to the western part of Germany to find new jobs and therefore induced a strong out-migration. Furthermore, inhabitants of the city moved from the core city to the peri-urban municipalities around the city, leading to a sprawling suburbanisation and sealing of open land. Due to low fertility and out-migration, most of the municipalities of the Leipzig region have lost inhabitants since 1990. A beginning resurgence of the inner city started in 1999, which led to a positive net-migration in the last few years (Stadt Leipzig, 2007). The Leipzig region is situated in a fertile loess landscape with a long agricultural history. Opencast lignite mining and chemical industries took over the primary role in regional economy in the 1920s until the fall of the GDR (Haase and Nuissl, 2007). Today, most of the former mines are under recultivation and will become lakes of up to 10 km2 total area. Leipzig holds one of the largest and diverse floodplain forests in Central

Europe which is rarely flooded, due to embankment and regulation measures. These floodplains belong to the most frequented recreation areas for the core city inhabitants and is a significant cooling and humidity-providing landscape element (Haase, 2003) (Fig. 1). Leipzig has a continental climate with an average annual temperature of 9 °C and an average annual precipitation of 550 mm. Warm summers are characterised by an average temperature of 17 °C and average precipitation of approx. 180 mm. For Central Germany, IPCC (2007) and Hattermann et al. (2006) expect an increase of the annual mean temperature of 1.5 °C until 2050, a decrease of the mean summer precipitation (−10 mm) and an increase of the winter precipitation of ∼50 mm. Furthermore, an increase of spring and summer floods is assumed, due to rising precipitation and extreme rainfall events respectively. These trends are predicted regardless precipitation shortage in combination with a long-term water deficit for the coming 100 years (DKKV, 2004). 2.2. Planning policies 2.2.1. Climate regulation through planning Climate impacts of land use changes are currently considered in spatial planning in the Leipzig region only to a limited extent. This is evident in spatial planning aims and principles, besides and in addition to SEA. “Climate” is one of a range of protected commodities of the Saxon spatial development plan, the overarching planning framework for Leipzig (Freistaat, 2003a, p. 34). Resultant measures to protect the regional climate include the protection of continuous open spaces,

Fig. 1. Land use map for the case study region of Leipzig. (Data source: Corine Land Cover 2000 provided by the EEA; Layout: D. Haase).

N. Schwarz et al. / Environmental Impact Assessment Review 31 (2011) 97–111 Table 1 Selected policies. Broad aim

Selected policies

Objectives

• To convert and restore former open-pit mining areas • To create an ecologically stable area and safe grounds • To enable diverse land use options (Freistaat, 2004, p. 17) Urban Renewal • To revitalise urban brownfields (Stadt [Stadtentwicklungsplan Leipzig, 1999, p.78–79; Stadt Leipzig, STEP] 2009, Map B1.2) Development Municipal Development • To regulate new development Plans [Bebauungspläne] Economic Development • To support economic development and [Wirtschaftsförderung] investment settlement in the region (Stadt Leipzig, 2009) Nature Green Corridors • To maintain ecologically valuable areas protection [Regionale Grünzüge] • To enable recreational uses (Regionaler Planungsverband, 2008a, p. 75) Green Ring • To restore and protect the cultivated, [Grüner Ring] man-made landscape • To develop recreational uses (Grüner Ring, 2009) Brownfield Mining Redevelopment conversion [Braunkohlepläne]

brownfield redevelopment and an increase of civic greens, but remain relatively fuzzy in terms of specific actions and spatial extent (Freistaat, 2003a, pp. 34–36). The Saxon landscape programme briefly addresses the beneficial effects of unsealed space, green corridors and larger woodlands on regional climate (Freistaat, 2003b, pp. 15–17). According to the regional plan of Western Saxony, the main negative impacts of

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spatial planning on the climate emanate from land conversion, surface sealing, barrier effects, emission of pollutants and impairments of the water balance (Regionaler Planungsverband, 2008b, p. U-7). Such concerns about effects of land use change on climate translate regionally and locally into a series of climate-related spatial planning objectives (Regionaler Planungsverband, 2007, p. 15): reforestation, preservation and extension of the green infrastructure in and around Leipzig and the protection of soils, air and water bodies (Regionaler Planungsverband, 2008b, p. U-12; Stadt Leipzig, 2008, p. 30). Land use and spatial development plans thus offer starting points for enhancing local climate regulation. 2.2.2. Relevant policies This paper introduces a method to estimate climate impacts of diverse existing spatial planning policies. To this end, we found 60 local (= municipal), and regional policies for the case study region. Therefore, a selection of policies was necessary before testing the method proposed in this paper. In order to provide for a broad range of examples, such policies were selected which convert existent land uses (“brownfield conversion”), develop land for housing or industry/ commerce (“development”) or protect or develop green areas (“nature protection”). A further criterion for policy selection was spatial explicitness, i.e. a discernible cartographic or digital reference. This excluded spatial planning policies above the regional level, as these—while providing a normative and procedural framework for municipalities and regions—do not refer to precisely delineated areas in a comprehensive fashion. Table 1 illustrates the broad scope of the six spatial planning policies selected for this study. Figs. 2 and 3 show their spatial range.

Fig. 2. Summarised spatial extent of policies.

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Fig. 3. Spatial extent of individual policies.

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Fig. 4. Policies and levels of the planning system.

The selected policies are devised by lower level state administrations: the local and regional levels, respectively (Fig. 4). This reflects the fact that the national government's role is mainly that of a provider of planning guidelines and a co-financer of spatial development programmes (BVBS, 2009; Eggers and Schüttlohr, 2005; Jakubowski and Melzer, 2002). Some of the selected policies are set out in the planning and building code as mandatory tasks (formal planning), others are informal and optional (Fig. 4). 3. Data and methods for estimating local climate regulation The aim of this paper is to develop and test a method for estimating local climate regulation which can be used by local practitioners to assess the possible impacts of their planning policies. This has implications for the expected results of such a method and the applicability. In terms of the results, a qualitative assessment (improvement or worsening of the situation) is needed, which is based upon comprehensible, but scientifically sound methods. Regarding applicability, the method should be easy to use for local practitioners. Thus, the method should (1) make use of publicly available rather than expensive data and (2) be applicable with basic GIS and programming skills. The method proposed here consists of two steps: First, a set of maps on current and future land uses was derived. For this purpose, local and regional spatial planning policies were assembled and converted into maps of land use (Section 3.1). Second, methods to estimate two indicators for local climate regulation out of land use maps were established: land surface emissivity (Section 3.2.1) and

evapotranspiration (Section 3.2.2). Linking land use maps and these indicators led to maps of present and future local climate regulation for the case study area (Fig. 5). 3.1. Converting policies into maps In order to calculate their (potential) impacts on climate regulation, the policies discussed in Section 2.2.2 were converted into spatially explicit land use data sets. These build upon the current land use in the case study region (see Section 3.3). Based on these land use classes, a set of land use transition rules was developed (Table 2) which translate policy goals for a specific land unit/area into ‘new’ land use classes to simulate future land use change. When deriving the transition rules, planning policies served for • either identifying future land use patterns intended by the policy: for example, the development plan always means that covered areas will be partly built-up, • or providing a certain probability how or in which direction land use might change to achieve a policy goal: e.g. the Green Ring initiative is a nature-oriented voluntary association. Its projects can be understood as renaturation and green space enhancement projects. Therefore, the areas under planning of the Green Ring are converted into green urban areas. All transition rules assume—the hypothetical—complete land conversion according to the respective policy. However, it is our primary aim to present a method to estimate (un-)intended impacts Table 2 Transition rules for translating planning policies into land use changes. Policy

Land use transition rules

Mining Redevelopment Urban Renewal Municipal Development Plans

Mineral extraction sites → water (512) Restructuring areas → green urban areas (141) Any land usea → W (residential): discontinuous urban fabric (112), M (mixed land use) → discontinuous urban fabric (112), GE, GI, SO: (commerce) industrial and commercial units (121), Green: green urban areas (141) Any land usea → industrial and commercial units (121) Any land usea → green urban areas (141) Any land usea → green urban areas (141)

Economic Development Green Corridors Green Ring Fig. 5. Method for estimating local climate regulation impacts of policies.

Numbers in brackets refer to the CLC code. a Except for water areas, which remain constant.

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of planning policies on climate regulation, so this assumption can be made. The GIS-procedure to create the land use transition maps has been applied as follows. All planning policy documents listed in Table 2 were transferred into GIS-maps using the existing land use map and administrative municipality data. After the identification of both transition rules and spatial units of change, the current land use map was intersected with the planning policy map. According to the transition rules, new land use classes were assigned to all units covered by the respective policy map. Thus, six hypothetical future land use maps have been created. Each represents the spatial realisation of one planning policy exclusively. Based on these maps, local climate regulation in terms of surface emissivity and evapotranspiration was modelled (Fig. 6).

3.2. Estimating local climate regulation The most obvious indicator for local climate regulation is a map of air temperature patterns for the urban region, indicating the differences in air temperatures between urban core and urban periphery or rural areas, respectively (Tratalos et al., 2007; Whitford et al., 2001). But air temperatures cannot be estimated easily in a spatially explicit way. Small-scale measurements for single buildings or small structures in cities exist (e.g. Offerle et al., 2007). However, relationships between specific land use classes and air temperatures are hard to derive (Cheng et al., 2008), requiring rather data-intensive simulation models for even simple calculations. This is far from being relevant for the daily planning practice. Therefore, indicators serving as proxies for air temperature are needed. In the few studies that quantified influences of land use changes on local climate, surface temperatures were chosen as indicator (Gill et al., 2007; Pauleit et al., 2005; Tratalos et al., 2007; Whitford et al., 2001). Therefore, land surface thermal emissions were chosen as one indicator. They indicate the total amount of energy emitted by a surface. Their relationship to air temperatures over a given land use is not linear, because latent heat fluxes or horizontal heat fluxes also play a role for the actual air temperature. Evapotranspiration was selected as the second indicator. Evapotranspiration was selected because it has a linear relationship with

latent heat: QE = evapotranspiration · Lv, with Lv = latent heat of vaporisation. The higher evapotranspiration is, the more energy is used for transferring water to the gas phase—and the less energy is available in form of sensible heat which is related to actual air temperatures. Furthermore, evapotranspiration is not only relevant for air temperatures and human comfort, but also for the urban water balance (Haase and Nuissl, 2007). 3.2.1. Land surface thermal emissions With respect to land surface thermal emissions, three possible methodological approaches are distinguished: (1) To establish linkages between empirical values of land surface thermal emissions for given land use classes out of the literature (lookup tables, Chen et al., 2006; Gluch et al., 2006; Kottmeier et al., 2007). (2) To use a local climate model for land use dependent maps of surface emissions. (3) To analyse thermal data for the case study region to derive a case study specific lookup table. The first approach is the simplest one, but does not take into account regional conditions. The second approach is very data-intensive and time consuming, as such a model needs to be calibrated and validated for the case study. Therefore, the third approach was used to provide a method which can easily be adapted for other regions. The following index value was created for each land use i to show differences in thermal emissions between land uses (see Section 3.3 for details on the data): emission Index½i =

  emission½i = emission ½urbanGreen 100 − 100

⋅

ð1Þ

Table 3 summarises means and standard deviations of thermal emissions for all land cover classes as well as the derived index values using Eq. (1). 3.2.2. Evapotranspiration Evapotranspiration is the sum of (1) evaporation of water from the earth's surface and (2) transpiration of vegetation. As evapotranspiration needs energy in form of sensible heat for vaporising water, areas with high evapotranspiration rates are cooler than surrounding areas. Several methods of estimating (hydrological equations, water

Fig. 6. Method for converting land use management/planning policies into maps.

N. Schwarz et al. / Environmental Impact Assessment Review 31 (2011) 97–111 Table 3 Thermal emissivity of land cover and respective index. CLC

Emission mean

Emission (SD)

Index [%]

Continuous urban fabric (111) Discontinuous urban fabric (112) Industrial or commercial units (121) Road and rail networks and associated land (122) Airports (124) Mineral extraction sites (131) Dump sites (132) Construction sites (133) Green urban areas (141) Sport and leisure facilities (142) Non-irrigated arable land (211) Fruit trees and berry plantations (222) Pastures (231) Complex cultivation patterns (242) Land principally occupied by agriculture, with significant areas of natural vegetation (243) Broad-leaved forest (311) Coniferous forest (312) Mixed forest (313) Natural grasslands (321) Moors and heathland (322) Transitional woodland-shrub (324) Sparsely vegetated areas (333) Inland marshes (411) Water bodies (512)

143.2 139.4 141.5 145.1 139.9 137.0 139.0 134.8 134.3 138.4 138.9 141.4 135.4 136.6 135.7

3.6 3.4 4.9 4.8 3.0 4.5 3.2 2.5 3.0 4.0 3.9 4.9 3.0 3.5 3.2

7 4 5 8 4 2 3 0 0 3 3 5 1 2 1

134.0 137.4 132.8 135.0 137.0 136.0 139.1 140.4 131.3

3.1 4.0 2.5 3.2 3.2 3.1 3.4 2.6 4.2

0 2 −1 1 2 1 4 5 −2

Note: Thermal band 6.1 (low gain) was used. Image courtesy of the U.S. Geological Survey. SD: standard deviation. The CLC classes 123, 212, 213, 221, 223, 241, 244, 323, 331, 332, 334, 335, 412, 421–423, 511, 521–523 are not included as they are not represented in the case study region.

balance) or measuring (eddy covariance) evapotranspiration are available (e.g. the water balance including evapotranspiration by Glugla and Fürtig, 1997; Interlandi and Crockett, 2003; Wessolek, 1988; the long-term urban water balance by Haase, 2009). According to the pragmatic requirements for this study, the f-value for evapotranspiration potential of a land use class (assuming enough

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precipitation) was chosen as an indicator. The f-value is an approximation for the evapotranspiration potential of a land use class and therefore for emitting latent heat rather than sensible heat. f ½i = max evapotranspiration ½i = ET0

ð2Þ

with: max evapotranspiration [i] = maximum evapotranspiration for land use type i; ETO = reference evapotranspiration of grass, 12 cm high, depending on local climate. To estimate f-values for land uses in the case study region, equations based upon empirical estimations and taking into account local specifications like soil types and local climate conditions were used. For water surfaces, the equations provided in DVWK (1996) were used; all other f-values were calculated using the equations in ATV (2002). Appendix A Table 1 gives the equations and assumptions used to compute f-values for all land cover classes. The f-values for these land use classes were transferred to the (aggregated) land use classes for this study using the assumptions stated in Appendix A Table 2. 3.3. Data According to the aim of this study, publicly available rather than expensive data were preferred for this approach. The current land use in the case study region is represented by the Corine Land Cover (CLC) data set for the year 2000 (issued by the European Environment Agency, EEA). It is freely available after registration. The finest resolution available by the EEA is 100 × 100 m. Table 3 gives an overview of the land use classes that are distinguished in CLC. For land surface thermal emissions, freely available remote sensing data from Landsat 7 ETM + satellite (band 6, spatial resolution of 60 × 60 m) was used to create case study specific indices for all land use classes in form of a lookup table. The satellite scene was collected on 20 August 2002 at approximately 10:30 am. The indices were created without correcting for in-scene variability or atmospheric

Table 4 Land use according to CLC and changes if policies were fully implemented [km²]. CLC

Continuous urban fabric (111) Discontinuous urban fabric (112) Industrial or commercial units (121) Road and rail networks and associated land (122) Airports (124) Mineral extraction sites (131) Dump sites (132) Construction sites (133) Green urban areas (141) Sport and leisure facilities (142) Non-irrigated arable land (211) Fruit trees and berry plantations (222) Pastures (231) Complex cultivation patterns (242) Land principally occupied by agriculture, with significant areas of natural vegetation (243) Broad-leaved forest (311) Coniferous forest (312) Mixed forest (313) Natural grasslands (321) Moors and heathland (322) Transitional woodland-shrub (324) Sparsely vegetated areas (333) Inland marshes (411) Water bodies (512)

2000

Changes due to implemented policy compared to 2000 Mining Redevelopment

Urban Renewal

Municipal Development Plans

Economic Development

Green Corridors

Green Ring

6.6 160.7 37.4 2.9 11.8 25.1 1.2 0.8 26.8 10.1 609.0 1.6 24.7 5.4 13.7

0.0 0.0 0.0 0.0 0.0 − 16.5 0.0 0.0 0.0 0.0 − 0.4 0.0 0.0 0.0 0.0

− 1.4 − 4.8 − 0.2 0.0 0.0 0.0 0.0 0.0 6.5 − 0.2 0.0 0.0 0.0 0.0 0.0

− 1.1 4.1 41.6 − 0.5 − 9.5 − 0.6 0.0 − 0.7 25.2 − 0.7 − 44.7 − 0.1 − 1.9 − 0.6 − 0.1

− 0.7 − 5.9 18.1 − 0.5 0.0 0.0 0.0 0.0 − 0.1 − 0.1 − 10.7 0.0 0.0 − 0.1 0.0

0.0 − 7.5 − 0.4 0.0 − 0.3 − 0.7 0.0 − 0.8 227.0 − 2.7 − 148.5 0.0 − 10.9 − 2.7 − 8.0

− 0.4 − 19.1 − 1.3 − 0.1 0.0 − 18.5 0.0 0.0 173.0 − 2.1 − 80.1 0.0 − 4.6 − 0.9 − 8.7

26.6 0.1 21.4 4.9 0.9 13.5 12.8 0.6 14.4

0.0 0.0 0.0 0.0 0.0 − 9.0 − 7.2 0.0 33.0

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

− 1.5 0.0 − 0.2 − 0.3 0.0 − 2.8 − 5.5 0.0 0.0

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

− 20.5 0.0 − 15.9 − 1.9 0.0 − 5.0 − 0.8 − 0.4 0.0

− 8.2 0.0 − 0.9 − 4.3 0.0 − 11.7 − 12.0 0.0 0.0

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Fig. 7. Climate regulation in the case study region based on CLC 2000 for evapotranspiration (left) and emissivity (right).

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Fig. 8. Relationship between f-evapotranspiration and emissivity. See Appendix C for an interpretation.

influence. The raw data on radiance from band 6.1 (low gain) was used directly. 4. Results and discussion 4.1. Current land use and policies in Leipzig Table 4 lists the land uses for the year 2000 in the case study region as well as the changes in land uses assuming that one of the selected six policies was fully implemented. Green Corridors and the Green Ring clearly show the highest amount of urban green which would be created according to these policies (227 and 173 km², respectively). Surprisingly, the full implementation of all Municipal Development Plans also would almost double the amount of urban green areas within the region (adding another 25 km² to the currently 27 km²). Mining Redevelopment would lead to additional 33 km² of water surfaces in the region, given that all mineral extraction sites are being flooded. Municipal Development Plans as well as Economic Development policies would significantly increase sealed surfaces in the region, by converting land uses such as arable land or urban fabric to industrial and commercial uses as well as urban fabric. Urban Renewal would convert small parts of (mostly) urban fabric into urban green, but the total area of land use change is comparably small (26 km²). Policies aiming at brownfield conversion would create either more urban green (Urban Renewal) or more water surfaces (Mining Redevelopment), while policies emphasising development (Municipal Development Plans and Economic Development) would in sum increase sealed surfaces. However, Municipal Development Plans also include enlarging urban green as already mentioned above. Finally, nature protection policies aim at enlarging urban green areas by reducing arable land, urban fabric or industrial or commercial uses. 4.2. Current status of climate regulation in Leipzig For CLC data of 2000, a mean f-evapotranspiration value of 1.05 and a mean emissivity index of 3.0 were computed for the case study area (see maps in Fig. 7). Slightly different pictures for the case study area in terms of climate regulation were found, depending on the chosen indicator.

The overall pattern of the two indicators clearly shows that water surfaces as well as forested areas lead to lower emissivity and higher evapotranspiration, both hinting at low air temperatures. On the contrary, highly sealed surfaces like the dense city centre, industrial and commercial areas or main roads have high emissivities and low evapotranspiration, indicating higher air temperatures in these areas. The differences in the two assessments are due to the different indicator values for the land use classes. Fig. 8 depicts the relationship between emissivity and f-evapotranspiration for all land use classes. The scatter plot for the two indicators in Fig. 8 shows that there is a general linear relationship between the two indicators (r =−.6). The correlation is negative, so a high emissivity index is correctly paired with a low evapotranspiration value and vice versa. 4.3. Changes in climate regulation related to planning policies In order to analyse the impacts of different planning policies, the resulting land use changes were assessed. An aggregated view on the two climate regulation indicators is given in Table 5. The summarised results for brownfield conversion policies show that Mining Redevelopment influences both indicators in the direction of lower temperatures when compared with CLC in 2000, whereas Urban Renewal does not show changes in the two indicators, although urban green areas would be created. This is due to the rather small extent and the high patchiness of the Urban Renewal policy within the entire case study area, which does not suffice to significantly influence the mean values of the two indicators. For development policies, emissivities are increased both times, whereas evapotranspiration is Table 5 Means of climate regulation indicators for the year 2000 and planning policies. Land use

f-evapotranspiration

Emissivity index

CLC 2000 Mining Redevelopment Urban Renewal Municipal Development Plans Economic Development Green Corridors Green Ring

1.05 1.07 1.05 1.04 1.05 1.05 1.06

3.0 2.9 3.0 3.1 3.1 2.5 2.6

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only decreased by the Municipal Development Plans, not by the Economic Development. The latter is again due to the rather small spatial extent of the policy which does not considerably influence the mean of the evapotranspiration indicator. Finally, both nature protection policies strongly decrease emissivities, whereas again evapotranspiration is only influenced by one policy. These aggregate findings show that (1) the spatial extent of land use changes matters, and (2) the emissivity index seems to be more sensitive than f-evapotranspiration, because the latter did not respond to small-scale land use changes. To derive a geographical representation including both indicators, the results of the two indicators were merged, using land use of the year 2000 as baseline. The resulting categories indicate: In which areas do both indicators point a) in the same direction, b) in different ones or c) remain unchanged after policy implementation? In Table 6, the total areas belonging to each category are given, and Fig. 9 gives the corresponding maps. The policies Mining Redevelopment, Urban Renewal, Municipal Development Plans, Green Corridors and Green Ring show locally decreasing temperatures for both indicators: The Green Ring encompasses the largest area (54 km²) where both indicators change towards lower temperatures, followed by Mining Redevelopment (33 km²) and Municipal Development Plans (14 km²). The latter arrange for green areas because they are required for very large construction projects according to the German federal building code. Furthermore, large areas can be identified where the f-evapotranspiration values do not change, but emissivity does: Green Corridors (177 km²), Green Ring (110 km²) and Municipal development (15 km²). No significant areas were identified where f-evapotranspiration values increase, but emissivity remains constant. On the contrary, several policies show locally increasing temperatures according to both indicators: Municipal Development Plans (49 km²), Economic Development (17 km²), but also Green Corridors (16 km²). The former two policies convert agricultural and green areas into sealed surfaces, whereas the results for the Green Corridors refer to areas where formerly mixed forests (CLC 313) are converted into urban green due to the transition rules applied. Moreover, Municipal Development Plans encompass 9 km² of land where f-evapotranspiration values do not change, but emissivity increases. No significant areas were identified where f-evapotranspiration values decrease, but emissivity does not change. Finally, no significant areas were identified where the changes in indicators are contradictory. These spatially explicit findings provide a more detailed view on changes in local climate due to the policies analysed. They also indicate that f-evapotranspiration values are less sensitive to land use changes induced by these policies than the emissivity index. 4.4. Conflicts between policies The six selected policies cannot be implemented simultaneously, as the analysis revealed spatial overlaps between the policies. In some cases these overlaps indicated conflicting land use changes which is

very typical for urban regions. In order to identify these areas, conflict maps were created using the GIS “intersection” mode (Table 7). Some of the spatial overlaps are unproblematic because e.g. the Green Ring is part of the Green Corridors, and therefore the overlap of 94 km² is intended. At first sight, the overlap between Green Ring and Mining Redevelopment (27 km²) shows a conflict because the former aims at expanding urban green areas, but the latter at expanding water surfaces. But this conflict is merely due to rather strict conversion rules which were set up for this study. In fact, not the whole area of a former mineral extraction site will be converted into a lake, but rather the main part in the middle of the area, while a buffer of the new lake could actually become green area. Nevertheless, some of the spatial overlaps indicate strong conflicts, e.g. between Economic Development and other policies: While Economic Development would enlarge industrial and commercial areas, other policies aim at enlarging the urban fabric (Municipal Development Plans) or urban green areas (Municipal Development Plans, Urban Renewal, Green Ring, Green Corridors). Further conflicts exist between Municipal Development Plans and Economic Development in the sense that Municipal Development Plans include green areas which are mandatory around large industrial or commercial areas. These green areas are not mentioned separately in the corresponding areas in the Economic Development policy, although they are included implicitly. To summarise, some of the conflicts either hint at (1) too coarse conversion rules when transforming the policies into land use changes (e.g. mining Redevelopment versus Green Ring), or at (2) different levels of detail in the policies (e.g. Municipal Development Plans versus Economic Development). (3) Finally, there are “real” conflicts between policies (e.g. Economic Development versus Green Ring) because these policies are created by different actors in the region and reflect their different aims.

4.5. Methodological considerations The first step of the new method presented in this paper was to transfer planning policies into maps of land use changes, which were evaluated in the second step. Two questions arise out of the first step: (1) Is it necessary to use land use maps for the analysis? (2) Is CORINE land cover suitable for this procedure? (1) The alternative of directly converting the policies into land use maps would be to use simulation models like cellular automata, more comprehensive rule-based GIS-approaches or even agent-based models (Haase and Schwarz, 2009). Planning policies would be an input for these models, acting as zoning constraints on simulated land use changes for future scenarios. These models take into account various factors like economic and population development in order to simulate future land use changes, so that the impacts of a single policy cannot be measured. Because of this and the amount of data and effort needed, the more straightforward approach of directly transferring

Table 6 Impact of planning policies on climate regulation indicators compared to values of 2000: affected areas [km²]. Impact

f-evapotranspiration: f-evapotranspiration: f-evapotranspiration: f-evapotranspiration: f-evapotranspiration: f-evapotranspiration: f-evapotranspiration: f-evapotranspiration: No change

no change; emissivity: higher no change; emissivity: lower higher; emissivity: no change lower; emissivity: no change higher; emissivity: lower lower; emissivity: higher higher; emissivity: higher lower; emissivity: lower

Mining Redevelopment

Urban Renewal

Municipal Development Plans

Economic Development

Green Corridors

Green Ring

0.0 0.0 0.0 0.0 33.0 0.0 0.0 0.0 999.7

0.0 0.0 0.0 0.0 6.5 0.0 0.0 0.0 1026.2

8.9 14.7 0.0 0.1 13.9 48.5 0.0 0.1 946.6

0.0 1.1 0.0 0.0 0.0 17.0 0.0 0.0 1014.6

0.0 177.0 0.8 0.0 12.5 15.9 0.0 0.4 826.3

0.0 110.3 0.0 0.0 53.6 0.9 0.0 0.0 868.0

N. Schwarz et al. / Environmental Impact Assessment Review 31 (2011) 97–111

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Fig. 9. Impact of planning policies on climate regulation indicators: maps. (a) Green Ring. (b) Green Corridors, (c) Urban Renewal, (d) Mining Redevelopment, (e) Economic Development, and (f) Municipal Development.

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Table 7 Areal overlap between policies in the case study area [km²]. Policy

Mining Redevelopment

Urban Renewal

Municipal Development Plans

Economic Development

Green Corridors

Green Ring

Mining Redevelopment Urban Renewal Municipal Development Plans Economic Development Green Corridors Green Ring

[38.8] 0 8.3* 0 1.5* 27.1*

[26.4] 3.9* 0.8* 0.2 1.6

[123.6] 18.0* 6.7* 16.9*

[28.2] 0.1* 1.7*

[257.2] 93.9

[202.4]

Numbers in brackets indicate the total area of the policy within the study area. *Asterisks indicate potential conflicting land use changes.

policies into land use maps was chosen, bearing in mind that not all policies are implemented to 100% in reality. (2) CORINE land cover is available through the European Environment Agency for 1990 and 2000, with 2006 in preparation for 32 European countries. This makes the data set extremely attractive, as the data are available for a large area in a consistent manner and thus also for transferring the method of this study to any other European city region. The errors in classifying CLC are a disadvantage of the data set. EEA (2006) claims an accuracy of CORINE land cover of approximately 87% (with 22 out of 44 classes being validated), so that uncertainty remains. A further disadvantage of CLC is the spatial resolution of 100 m and a minimal mapping unit of 25 ha. Although this resolution is much too coarse for a detailed analysis of local climate in street canyons and the like, it seems appropriate for assessing climate effects of planning policies in a city region, because a) some of the policies themselves are rather conceptual and do not provide detailed spatial information, and b) the chosen indicators as well as their estimation procedures would not be suitable for finer spatial information. The second step of the assessment was to estimate impacts on local climate regulation using two indicators, land surface emissivity and f-evapotranspiration. Two additional questions have to be dealt with for this step: (3) Are both indicators necessary for estimating local climate regulation? (4) Are these two indicators estimated appropriately? (3) Two different indicators were chosen to approximate local climate regulation. These indicators mainly show similar results, but also some differences (see Fig. 8). Each indicator has its own difficulties in estimation (see below), and they both approximate the underlying indicator of air temperature, which cannot be estimated easily. Therefore, using two indicators rather than one provides a more stable picture. By differentiating the results for the two indicators and explicitly pointing out differences (see Fig. 9), the approach captures more of the potential uncertainties in estimating local climate regulation. Consequently, it is reasonable to use both indicators for the analysis. (4) The f-evapotranspiration value assumes that enough water is available for evapotranspiration, which might not be the case throughout the region and with climate change inducing less precipitation. But it provides the possibility to change local climate parameters like mean temperature at least for evapotranspiration by water surfaces. Land surface emissivity as estimated here provides a rather conservative estimate of surface emissivity differences between land use classes, because Landsat scenes are taken in the morning, whereas differences between land use classes due to cooling effects by evapotranspiration can be higher in the evening (e.g. Kottmeier et al., 2007, for Berlin). The lookup table created with a single Landsat scene showed reasonable results compared to literature values. The table was validated with another Landsat scene (taken on 13 September 1999) by a) producing a second lookup table with thermal emissivity per land use class and then b) correlating the two emissivity values per land use. This resulted in a correlation between thermal emissivities of land use classes of r = .7. This size of the correlation seems plausible, as phenology of plants is

more advanced four weeks later, and meteorological conditions in terms of temperature differed between these two days. To sum up, the method for estimating these two indicators seems appropriate given the prerequisites of ease of use, freely available data sources and comprehensiveness of the approach. Finally, the question of applicability needs to be tackled: (5) Is this methodology applicable to other case studies? (5) Both steps of the methodology (transfer of planning policies into maps of land use changes and evaluation of impacts) can in principle be applied to any other case study. For the first step of transferring planning policies into maps, both current land use maps and spatially explicit planning policies need to be available at appropriate scales. This refers on the one hand to the fit in terms of scales between land use maps and thermal data used and on the other hand to the scales of the land use changes in the planning policies. Very detailed land use changes should be represented in detailed land use maps and need to be assessed using fine-grained thermal data and vice versa. Planning policies that are not spatially explicit per se could be transferred into maps if local expert knowledge is used to “translate” policy aims like “expansion of urban forest by 5 km²” into maps. In such a case, different scenarios with different spatial locations of these land use changes should be compared in terms of their impacts. For the second step of evaluating the impacts of planning policies on local climate, the applicability of the two indicators, land surface thermal emission index and f-value for evapotranspiration, needs to be considered. While land surface thermal emissions are appropriate for all climatic surroundings, the f-value should be exchanged with another indicator in semi-arid or arid regions because it assumes that enough precipitation is available.

5. Conclusions The aim of this paper was to provide a method for estimating impacts of local and regional planning policies on local climate regulation as an important ecosystem service. The method should be applicable by local practitioners to derive qualitative, scientifically sound estimates. This paper presented a way to estimate impacts of land use changes on the two indicators land surface emissivity and evapotranspiration, using freely available data and basic GIS applications. This approach leads to spatially explicit maps which visualise the specific impacts of different policies on climate regulation. These maps are based upon easy to use procedures. It is possible to adapt the assessment to other case study regions by persons with basic GIS knowledge; handling simulation models is not required. The ease of use and the usage of publicly available data are the main advantages of this approach. It provides local and regional stakeholders with qualitative assessments of land use changes implied by their planning policies. This assessment lacks valid quantitative information which could be achieved by using more complex approaches. Therefore, there is a clear trade-off between the level of information and the ease of use.

N. Schwarz et al. / Environmental Impact Assessment Review 31 (2011) 97–111

The method was applied to the case study region Leipzig, Germany. The assessment does not provide surprising results, because green areas and water surfaces were proven to have cooling effects, while increasing sealed surfaces leads to increasing temperatures. It was shown that the exemplary policies for Leipzig have intended as well as unintended effects on local climate regulation. These effects are important for quality of life and human well-being, especially with respect to climate change and increasing temperatures. Moreover, the estimation shows the spatial patterns of local climate regulation emerging from policies. A side effect of using six different policies for the region of Leipzig was to realise that planning policies in this region do overlap spatially and—in some areas—show potential land use conflicts. This is related to the multitude of different actors in the urban region who have developed these policies, some of them within the statutory spatial planning framework, some as practitioners of a broader governance structure committed to the region and its development. Analysing spatial overlaps and differences in intended land use changes identified both conflicts and synergies between different policies and the practitioners involved. Such conflicts can be discussed using results of the method presented here. Based upon their own maps, practitioners can estimate the impacts of their policies and identify in which areas of the city region local climate regulation will be enhanced or worsened if a specific policy is implemented. Likewise, practitioners can realise

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which potential benefits of a specific policy will be lost if this policy is not implemented. This helps in setting priorities among policies and in contested decision making processes. By providing such estimates, the method can be useful for including local climate regulation into the Strategic Environmental Assessment and for evidence-based planning in general. Of course, other aspects need to be integrated into an overall integrated assessment of land use (change) impacts, where next to environmental also quality of life as well as economic aspects should be considered.

Acknowledgements This work is part of the PLUREL Integrated Project (Peri-urban Land Use RELationships) funded by the European Commission, Directorate-General for Research, under the 6th Framework Programme (project reference: 36921). The authors wish to thank the three anonymous reviewers for their very helpful comments on an earlier version of this paper, the organisers and participants of the “Climate and Planning” workshop at the European IALE Conference in Salzburg, 2009, as well as Wolfgang Loibl and Martin Volk for their comments, and our colleagues of the department of Computational Landscape Ecology at the Helmholtz Centre for Environmental Research, Leipzig, for helpful discussions and comments.

Appendix A Appendix Table 1 Equations for calculating evapotranspiration (f-value) in Leipzig (DVWK, 1996; ATV, 2002). Land use/cover

Computation of f

Assumptions for Leipzig

f for Leipzig

Sealed surface Area without vegetation

f = 0.8 1. AWC b=8.5% ➔ f = 0.8 2. AWC N8.5% ➔ f = 0.0186 * AWC + 0.6419 1. AWC b=11 ➔ f(12 cm) = 0.0125 AWC + 0.7108 2. AWC N11 ➔ f(12 cm) = 0.2866ln AWC + 0.1614 1. Loess-black earth ➔ f = 0.734ln AWC − 1.101 2. Other soils ➔ f = 0.221ln AWC + 0.431 1. AWC b=16 vol.% 1.1 Age of forest (age)b=90 years f = 0.84 + 0.25 * 10− 2age + 0.508 * 10− 3age² − 0.233 * 10− 4age³ + 0.422 * 10− 6age4 − 0.3494 * 10− 8age5 − 0.10946 * 10− 10age6 1.2 age N 90 years f = 1.038 + 0.49 * 10− 3age − 0.155 * 10− 5age² + 0.1686 * 10− 8age³ 2. AWC N16 vol.% 2.1 Age b=100 years ➔ f = 1.05 * f(for AWC b 16, age b90) 2.2 Age N100 ➔ f = 1.05 * f(AWC b 16, age N 90) 1. AWC b=16vol.% 1.1 Age of forest (age)b=130 years f = 0.8 + 0.2694 * 10− 1age − 0.63924 * 10− 3age² +0.8052 * 10− 5age³ − 0.5785 * 10− 7age4 + 0.223 * 10− 9 age5 − 0.356 * 10− 12age6 1.2 Age N130 years: f = 1.35 − 0.108 * 10− 2age + 0.178 * 10− 5age² 2. AWC N16 vol.% 2.1 Age b=130 years ➔ f = 1.03 * f(for AWC b 16, age b130) 2.2 Age N100 ➔ f = 1.03 * f(AWC b 16, age N 130) f = mean of (monthly mean evaporation for water / monthly mean evapotranspiration grass) Monthly mean evaporation for water = 0.41 * (saturation vapour pressure − vapour pressure) + 0.25 Saturation vapour pressure = 6,11 * EXP(17.62 * surface T / (243.12 + surface T)) Surface T = 0.98 * air T − 0.12 monthly mean evapotranspiration grass = ((global radiation + 93 * coast factor) * (air T + 22)) / (165 * (air T + 123)) * days of month / (1 + 0.00019 * altitude) Global radiation = extraterrestrial radiation * (0.19 + 0.55 * relative sunshine length) Extraterrestrial radiation = 2425 + SIN((0.0172 * mean date − 1.39)) + (4.3 + (latitude − 51) / 6) Coast factor = 1

– AWC = 18.2

0.8 1.0

AWC = 18.2

1.0

Other soils

1.1

AWC = 18.2 Age (when felling trees) = no differences in f for age 40 to 140 years)

1.1

AWC = 18.2 Age (when felling trees) = no differences in f for age 40 to 140 years)

1.3

Monthly mean values (1980–1999) for –Vapour pressure –Air temperature –Relative sunshine length (German weather service)

1.4

Pastures, grassland Arable land Broad-leaved forest

Coniferous forest

Water

Altitude = 140 m Latitude = 51°

Note that the land use/cover classes refer to the ones used in DVWK (1996) and ATV (2002). These were later transferred to CLC (see Appendix B Table 2). AWC: available water capacity, computed as a mean value of all soil types which are present in Leipzig according to soil map. Main soil types, % of occurrence in Leipzig, AWC: Slu, 30%, 21; Sl3, 26.5%, 18; Lu, 13%, 17. AWC values stem from DVWK (1996), using mean density (1.6 to 1.8).

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b) assumes full water supply and therefore neglects the positive effect of deeper tree roots, for example.

Appendix B Appendix Table 2 Lookup table to derive evapotranspiration values. CLC

f

Transfer algorithm

Continuous urban fabric (111)

0.8

Discontinuous urban fabric (112)

0.9

Industrial or commercial units (121) Road and rail networks and associated land (122) Airports (124) Mineral extraction sites (131) Dump sites (132) Construction sites (133) Green urban areas (141) Sport and leisure facilities (142)

0.8 0.8

95% sealed surface, 2.5% grass (f=1), 2.5% mixed forest 65% sealed, 35% non-sealed (= 17.5% grass+17.5% mixed forest) Sealed surface Sealed surface

Non-irrigated arable land (211) Fruit trees and berry plantations (222) Pastures (231) Complex cultivation patterns (242) Land principally occupied by agriculture, with significant areas of natural vegetation (243) Broad-leaved forest (311) Coniferous forest (312) Mixed forest (313) Natural grasslands (321) Moors and heathland (322) Transitional woodland-shrub (324) Sparsely vegetated areas (333) Inland marshes (411)

1.1 1.1 1.1 1.1 1.1

Water bodies (512)

1.4

0.8 1.0 1.0 1.0 1.1 1.0

1.1 1.3 1.2 1.1 1.1 1.1 1.0 1.4

Sealed surface No vegetation No vegetation No vegetation 20% grass + 80% mixed forest 20% sealed surface+40% grass+ 40% mixed forest 100% arable land 100% arable land 100% arable land 100% arable land 100% arable land

100% broad-leaved forest 100% coniferous forest 50% CLC 311 + 50% CLC 312 50% mixed forest + 50% grass 50% mixed forest + 50% grass 50% mixed forest + 50% grass No vegetation Water (as wetlands are defined as flooded oxbows of watercourses) Water

The CLC classes 123, 212, 213, 221, 223, 241, 244, 323, 331, 332, 334, 335, 412, 421–423, 511, 521–523 are not included as they are not represented in the case study region.

Appendix C. Relationship between f-evapotranspiration and emissivity (Fig. 8) Visual interpretation shows outliers like Inland marshes (CLC 411), Fruit trees and berry plantations (CLC 222) and Coniferous forest (CLC 312). According to CLC, these areas are extremely small (in sum 2.3 km²). Therefore, the differences between emissivity index and f-evapotranspiration value can be attributed to the small extent: The emissivity index is calculated from satellite data which were then matched with land cover, and, due to the small area, slight mismatches between raster emissivity data and vector land cover patches might produce inconsistencies. Furthermore, the four classes Continuous urban fabric, Industrial or commercial units, Road and rail networks and associated land, and Airports (CLC 111, 121, 122, and 124 respectively) have the same low f-value of 0.8, but different emissivities (although all are very high). This is due to the assumptions made for calculating the f-evapotranspiration values: It was assumed that the last three are sealed to 100%, and continuous urban fabric to 95%, leading to the same low f-evapotranspiration value of 0.8 (Appendix B Table 2), indicating that almost no evapotranspiration is present. But the analysis of emissivity data shows that the land surface emissivities are different because of the different surfaces: Roads and rail networks have higher emissivities than e.g. industrial or residential areas (same findings as for Berlin, Kottmeier et al., 2007). A similar issue is relevant for the two large classes of arable land (CLC 211) and urban green (141): The respective emissivity indices are quite different, while the f-evapotranspiration value is similar. This finding is related to the indicator f-evapotranspiration value itself, because a) it looks at the mean evapotranspiration over a whole year and therefore includes the growth phase of the crops and

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