sustainability

Article TOPOI RESOURCES: Quantification and Assessment of Global Warming Potential and Land-Uptake of Residential Buildings in Settlement Types along the Urban–Rural Gradient—Opportunities for Sustainable Development

Ann-Kristin Mühlbach 1,* , Olaf Mumm 2,* , Ryan Zeringue 2, Oskars Redbergs 2 , Elisabeth Endres 1 and Vanessa Miriam Carlow 2

1 TU Braunschweig, Institute for Building Services and Energy Design (IGS), Mühlenpfordtstr. 23, 38106 Braunschweig, Germany; [email protected] 2 TU Braunschweig, Institute for Sustainable Urbanism (ISU), Pockelsstr. 3, 38106 Braunschweig, Germany; [email protected] (R.Z.); [email protected] (O.R.); [email protected] (V.M.C.) * Correspondence: [email protected] (A.-K.M.); [email protected] (O.M.); Tel.: +49-531-391-3524 (A.-K.M.); +49-531-391-3537 (O.M.)



 Abstract: The METAPOLIS as the polycentric network of urban–rural settlement is undergoing Citation: Mühlbach, A.-K.; constant transformation and urbanization processes. In particular, the associated imbalance of the Mumm, O.; Zeringue, R.; Redbergs, shrinkage and growth of different settlement types in relative geographical proximity causes negative O.; Endres, E.; Carlow, V.M. TOPOI effects, such as urban sprawl and the divergence of urban–rural lifestyles with their related resource, RESOURCES: Quantification and land and energy consumption. Implicitly related to these developments, national and global sustain- Assessment of Global Warming able development goals for the building sector lead to the question of how a region can be assessed Potential and Land-Uptake of without detailed research and surveys to identify critical areas with high potential for sustainable Residential Buildings in Settlement development. In this study, the TOPOI method is used. It classifies settlement units and their intercon- Types along the Urban–Rural nections along the urban–rural gradient, in order to quantify and assess the land-uptake and global Gradient—Opportunities for Sustainable Development. warming potential driven by residential developments. Applying standard planning parameters in Sustainability 2021, 13, 4099. combination with key data from a comprehensive life cycle assessment of the residential building https://doi.org/10.3390/su13084099 stock, a detailed understanding of different settlement types and their associated resource and energy consumption is achieved. Academic Editor:

Oriol Pons-Valladares Keywords: life cycle assessment; LCA; settlement types; CO2 emissions; GWP; land-uptake; density; urban–rural development; TOPOI; sustainability Received: 26 February 2021 Accepted: 29 March 2021 Published: 7 April 2021 1. Introduction Publisher’s Note: MDPI stays neutral Urban–rural systems can be described as interconnected settlement units linked by with regard to jurisdictional claims in networks and flows of people, goods, information and services [1,2]. Usually, they are published maps and institutional affil- iations. characterised by high dynamics in transformation processes, which have an impact on individual settlement units as well as on the general settlement patterns [3–5]. There are various approaches offering insights into urban–rural relations [4] such as Netzstadt [1], Zwischenstadt [6], or Metacity [7] and newly Metapolis [8]. However, a prevailing lack of sufficiently integrated urban–rural planning strategies [9] often results in increased land Copyright: © 2021 by the authors. consumption for housing and infrastructure and higher resources and energy demands [10]. Licensee MDPI, Basel, Switzerland. Significant interrelations between these phenomena and impacts are observed [11–13]. This article is an open access article This article focuses on the energy and resource consumption of residential develop- distributed under the terms and conditions of the Creative Commons ments along the rural–urban gradient. In particular, phenomena of simultaneous settlement Attribution (CC BY) license (https:// growth, including urban sprawl and shrinkage, including vacancies and associated diverg- creativecommons.org/licenses/by/ ing lifestyles, lead to increased land consumption, diverging demand for resources, greater 4.0/). land requirements for housing and high energy consumption. Land-uptake by urban and

Sustainability 2021, 13, 4099. https://doi.org/10.3390/su13084099 https://www.mdpi.com/journal/sustainability Sustainability 2021, 13, 4099 2 of 33

other artificial land development in the European Union (EU) amounted to 539 km2/year for the years 2012 to 2018 [14]. The main driver of the increasing land-uptake is the expansion of “industrial and commercial sites”, including structures under construction. This accounts for a share of nearly 60% (approx. 1860 km2)[15]. The “urban diffuse residential expansion” is already the second strongest driver of the increasing land consumption with a share of 12% (390 km2) in total. In Germany, the land covered by settlements and transport infrastructure expanded by around 28% from 40,305 to 51,489 km2 between 1992 and 2019 [16]. The 4-year average land-uptake for 2015 to 2018 was 56 hectares per day (204.4 km2/year) [17]. The increase in land-uptake, both proportionately and in relation to the number of inhabitants, becomes smaller with increasing settlement density [18,19]. Besides the demand on the finite resource of land, the building sector represents a significant share of the use of abiotic material and energy for construction and operation. Residential buildings make up to 75% of the EU’s building stock, wholesale, retail and office buildings 7%, and industrial buildings 1% [20]. In the EU, approx. 65% (Germany: approx. 73%) of residential buildings were built before 1980 [20,21]. Within the building sector in Germany, residential buildings have a share of 87.5% of the total building stock and are responsible for 64% of the total end-energy consumption of all buildings [22]. The goal of the German federal government to reach a “nearly climate-neutral” build- ing stock by 2050 leads the focus of future sustainability measures to the buildings sec- tor [23]. In 2019, the building sector was responsible for around 14% of the total greenhouse gas (GHG) emissions in Germany. In a holistic view of the GHG emissions caused by buildings, the emissions for the provision of electricity and heat previously assigned to the energy sector, as well as the emissions assigned to the industrial sector for the production of building materials and buildings services, must also be considered. Using this framework, buildings account for approximately 40% of total GHG emissions in Germany [24]. This difference of 25% shows that the cross-sectoral consideration in terms of the entire life cycle of a building is necessary and has a decisive impact on the achievement of climate protection targets. Within the last years, the impact of the energy used for the construction of a building is increasing, due to new energy saving regulations to reduce the energy demands in the operational phase (heating and electricity). The operation of a building still has a larger impact with around 60%; nevertheless, 40% of energy for the construction of a new building is not negligible [25]. As this study is focusing on the existing residential building stock, the share of so-called embodied energy—energy used for raw material min- ing, storage, transport, construction of building material and disposal—is lower than for a new building. Nevertheless, the calculation of embodied energy shows how much energy has already been used in the past and that demolition with a new substitute building may be a questionable solution for sustainable future developments. In order to address the challenges of sustainable development, an evaluation of the building stock, its energy and resource consumption in relation to different settlement types and their specific characteristics is required. Studies on building types as sustainable solutions often focus on the individual building but not on settlement units to take a holistic point of view on a regional level. The method presented in this paper draws on a combination of available data on different scales, to describe the existing building stock without the need of detailed surveys. This approach offers to analyse a whole region by using settlement units and building classifications. The evaluation will focus on the building’s footprint in terms of the global warming potential (GWP) on the one hand and land-uptake on the other. The objectives of this study are as follows: (a) to develop a multi-scale analysis method for quantifying and assessing the land-uptake and CO2 emissions of the residential build- ing stock at both the regional and settlement unit level; (b) to identify the interrelations between resource and energy consumption and density (population and buildings) for re- spective settlement types; (c) to identify a derivation of possible approaches for sustainable development in urban–rural regions. Sustainability 2021, 13, 4099 3 of 33

2. The Urban–Rural Settlement System of Lower Saxony Sustainability 2021, 13, x FOR PEER REVIEW 4 of 33 In this research, two regions in Lower Saxony (Germany) are examined: (A) Vechta- Diepholz-Verden, and (B) the larger Braunschweig region (see Figure1). Simultaneous shrinkage and growth in close geographical proximity are characteristic for the recent urbanand current fringe developmentor urban cores. of Describing Lower Saxony, the centrality with often degree unsustainable of the settlements, effects, the such pre- as fixesa high exo sealing, disseminated of land, periurban and dense and commuting node are used patterns (see Tables [2,10]. A2 The and study A3) region[2,31]. The Vechta- au- thorsDiepholz-Verden expanded the is TOPOI characterised method by to a largeinclude number energy of and evenly resource-related distributed, predominantly parameters to identifyprosperous unsustainable medium-sized and more and smallsustainable towns development in a rural setting. patterns The of larger different Braunschweig settlement typesregion, along meanwhile, the urban–rural includes gradient. both prospering cities and shrinking municipalities [3].

Figure 1. TOPOI classification classification of of the settlement units units in in the two study regions: ( (AA)) Vechta-Diepholz-Verden, Vechta-Diepholz-Verden, ( B) larger Braunschweig region [2]. Braunschweig region [2].

3. MaterialsTo this day, the German spatial planning system is based on the theory of central placesIn [this26,27 study,]. It distinguishes data-based methods between largeof geospatial cities with analysis above are 100,000 applied, inhabitants, utilizing medium- ArcGIS Prosized 2.5.1 towns [34] betweenusing geospatial 20,000 and data 100,000 from ALKI inhabitants,S (Authoritative small towns Real withEstate up Cadastre to 20,000 Infor- and mationvillages System) with less [35], than the 5000 Digital inhabitants Landscape [26,28 Model]. Settlement (Basis typesDLM; are DLM250) classified of as the city Authori- (county tativefree), urbanTopographic-Cartographic county, rural county with Informat densificationion System tendencies (ATKIS) or as sparsely [36,37], populated and Open- rural StreetMapcounty [29 ].[38] In Loweras well Saxony, as life cycle only 20%assessment of the population data of residential live in large buildings cities (>100,000 (see Sections Inh.) 3.1and and 9% 3.2) in medium-sized and statistical towns.data from With the 71%, 2011 the German majority Census of the ([32]; population see Section lives 3.3.) in. small towns (<50,000 Inh.), suburban, and rural areas [30]. The two study regions exemplarily 3.1.reflect Life the Cycle diverse Assessment spatial Da developmentta—Residential patterns Building inLower Stock Saxony [30,31]. TheIn Germany, authors developed around 87.5% a method of all buildingsto quantify are the residential energy demand buildings, and which resources shows of thethe entire significance residential of the building development stock on of sustainablea regional scale scenarios with a for resolution residential at the buildings settlement [22]. unitTherefore, level. The this building study focuses stock onwas the classified residential by buildingbuilding stock.types and Of all constr residentialuction year buildings clas- sesin Germany, based on 78.1%a study are by single- the Institute or two-family for Housing houses. and In LowerEnvironment Saxony, (IWU) the share [39]. of Similar single- and two-family houses is even higher with 85.1%, and within the study regions their data are not available for other types of buildings, such as industrial or office buildings. share is 88.1%. The distribution of the building age of existing buildings within the study Taking this classification into consideration (see Tables 1 and 2), for each combination a regions correlates to the distribution in Lower Saxony and the whole of Germany: the life cycle assessment was conducted. The embodied energy calculation includes the life cycle phase “production” with all environmental impacts of the production of building material, as well as the “maintenance” of buildings during operation, as for example the repair or replacement of damaged building components [40]. The assessment is based on typical structures and building materials for each building type and age [39], using the Sustainability 2021, 13, 4099 4 of 33

majority of the residential buildings were built before 1976 with 62.8% in the study regions (66.6% Lower Saxony, 68.3% Germany). The share of buildings with a higher energy saving standard (built after 1995, see Section 5.3) in the study regions is 19.6%, while it is only 13.9% in Lower Saxony and 12.1% in Germany [32]. This shows that comparatively more buildings have been built in the study regions in recent years than the national average. The following investigations will show the distribution of building types and construction year classes in detail in order to analyse possibilities of a more sustainable future development of the two study regions. Understanding urbanisation patterns and their relationships along the urban–rural gra- dient towards a new approach to sustainable development [2,33] requires a comprehensive approach to the analysis and planning of urban regions, encompassing cities and their sur- rounding areas with multi-faceted interactions, interdependencies, spatial linkages and a fluid transition between urban and rural lifestyles [11,12]. The study utilises the analytical frame- work of the TOPOI method [2], which describes the urban–rural settlement system in greater detail than the standard planning approach allows. The latter is based on population sizes within administrative boundaries [28] while the TOPOI method enhances the understanding of the built environment by clustering and describing settlement units along similar charac- teristics of their physical form, function and connectivity based on building footprints (see Tables A1 and A2) [2]. A settlement unit is understood as a cohesively built group of buildings. This enables a high resolution and comprehensive analysis and interpretation of changes in the physical environment in order to derive recommendations for sustainable transformation. Applying the TOPOI method, 13 settlement types have been identified for the two study regions (see Figure1). The TOPOI types include isolated and dispersed units, at the urban fringe or urban cores. Describing the centrality degree of the settlements, the prefixes exo, disseminated, periurban and node are used (see Tables A2 and A3)[2,31]. The authors expanded the TOPOI method to include energy and resource-related parameters to identify unsustainable and more sustainable development patterns of different settlement types along the urban–rural gradient.

3. Materials In this study, data-based methods of geospatial analysis are applied, utilizing ArcGIS Pro 2.5.1 [34] using geospatial data from ALKIS (Authoritative Real Estate Cadastre Informa- tion System) [35], the Digital Landscape Model (Basis DLM; DLM250) of the Authoritative Topographic-Cartographic Information System (ATKIS) [36,37], and OpenStreetMap [38] as well as life cycle assessment data of residential buildings (see Sections 3.1 and 3.2) and statistical data from the 2011 German Census ([32]; see Section 3.3).

3.1. Life Cycle Assessment Data—Residential Building Stock The authors developed a method to quantify the energy demand and resources of the entire residential building stock on a regional scale with a resolution at the settlement unit level. The building stock was classified by building types and construction year classes based on a study by the Institute for Housing and Environment (IWU) [39]. Similar data are not available for other types of buildings, such as industrial or office buildings. Taking this classification into consideration (see Tables1 and2), for each combination a life cycle assessment was conducted. The embodied energy calculation includes the life cycle phase “production” with all environmental impacts of the production of building material, as well as the “maintenance” of buildings during operation, as for example the repair or replacement of damaged building components [40]. The assessment is based on typical structures and building materials for each building type and age [39], using the Ökobaudat (standardized database for ecological evaluations of buildings [41]) and the tool eLCA of the Federal Institute for Research on Buildings, Urban Affairs and Spatial Development [42] for the life cycle assessments. Energy consumption is calculated by average benchmark data for residential buildings, considering the specific building type and age, as well as the energy supply distribution [39,43]. For the building stock, a renovation rate of 1% is Sustainability 2021, 13, 4099 5 of 33

considered [22], starting in 1995 after the third thermal insulation ordinance. There is a certain percentage of buildings which are protected for heritage purposes and therefore are excluded from the renovation calculations: this applies to 5% (based on the net floor area) of buildings built between 1949 and 1994, 10% (single-family houses (SFHs)) and 20% (multi-family houses (MFHs)) of buildings built before 1948, based on an estimation of the Institute for Applied Ecology [44]. An assumption for already renovated old buildings (until 1978) of 17.7% for single-family and terraced houses and 21% for multi-family houses and apartment block was included in the calculation, based on Hoier and Erhorn [45]. The embodied energy for building services is not included in the calculations, due to data availability.

Table 1. Building typology used for the classification of the housing stock, based on [39].

Building Types Description Single-Family Houses (SFHs) detached, 1–2 apartments Terraced Houses (THs) semi-detached or terraced, 1–2 apartments Multi-Family Houses (MFHs) 3–12 apartments Apartment Blocks (ABs) 13 or more apartments

Table 2. Construction year classes [39].

Construction Year Classes A–F Construction Year Classes G–L A before 1859 G 1979–1983 B 1860–1918 H 1984–1994 C 1919–1948 I 1995–2001 D 1949–1957 J 2002–2009 E 1958–1968 K 2010–2015 F 1969–1978 L after 2016

3.2. Life Cycle Assessment Data–Streets In addition to the building stock, the embodied energy of streets within the settle- ment units is quantified. The classification of OpenStreetMap (OSM)[46] has been used to identify the road types (see TableA5). For each type, a basic layer structure and size has been defined in accordance with the guidelines of the German Road and Transportation Research Association (FGSV) [47–50]. This was used for the calculation of embodied en- ergy and maintenance for one meter length of each road type, by conducting a life cycle assessment with eLCA [42] and data from Ökobaudat [41]. These results can be combined with the total length of each road type in both study regions, which is available from OSM data [38].

3.3. German Census 2011 The spatial information about the existing building stock is based on the census of 2011 in Germany [32]. It includes information on existing buildings, for example sizes, number of rooms, building ages or types. A customized data evaluation by the state office for statistics [51] offered spatial data, that was based on the same building types and construction year classes as the life cycle assessments. The data are georeferenced in a grid according to the INSPIRE specification (2007/2/EG) with a resolution (grid size) of 1 km [52]. Using these data to quantify existing buildings is an approximation, as the census data sometimes are omitted due to privacy protection. This results in limited information; for example, for areas with few houses, the overall distribution of buildings is representative though, as the evaluation on a square kilometre grid is very high spatial resolution. Sustainability 2021, 13, x FOR PEER REVIEW 6 of 33

Sustainability 2021, 13, 4099 6 of 33

for example, for areas with few houses, the overall distribution of buildings is representa- tive though, as the evaluation on a square kilometre grid is very high spatial resolution. 3.4. Settlement Types The study applies3.4. Settlement the TOPOI Types [ 2] settlement types as an analytical framework to show patterns of energy andThe resourcestudy applies usage. the TOPOI The data [2] settlement set TOPOI—Urban types as an analytical Rural Settlement framework to show Types—Version 1.0patterns [53], isof a energy geospatial and classificationresource usage. of Th urbane data and set rural TOPOI—Urban settlement typesRural inSettlement Lower Saxony, asTypes—Version described in Section 1.0 [53],2 .is a geospatial classification of urban and rural settlement types in Lower Saxony, as described in Section 2. 4. Method 4. Method The analysis is carried out in four steps, described in the following. In step 1 and 2, the key data are generatedThe analysis which is iscarried superposed out in four to thesteps, TOPOI described settlement in the following. types in In step step 3 1 and 2, (see Figure2). Inthe step key 4, data the latterare generated are analysed which withis superposed a view to to resources the TOPOI and settlement energy types in a in step 3 (see Figure 2). In step 4, the latter are analysed with a view to resources and energy in a three-level evaluation framework (see Figure3). three-level evaluation framework (see Figure 3).

FigureFigure 2. Overview 2. Overview of methodology: of methodology: key key data data andand superpositionsuperposition with with TOPOI TOPOI settlement settlement types. types.

4.1. Temporal–Spatial Dynamics of Growth 4.1. Temporal–Spatial Dynamics of Growth In a first step, a dataset based on the 2011 German Census data [32] was built to show In a first step,the growth a dataset of the based residential on the area2011 (residential German building Census dataand open [32] spaces was built area as to per defi- show the growthnition of the in residential [54]) in hectare area in (residential the different buildingbuilding periods and open defined spaces in TABULA area as per (see Section definition in [54])3.1, in [39]). hectare A residential in the different area is a buildingstructurally periods shaped defined area including in TABULA the associated (see open Section 3.1,[39]).space A residential (courtyard area area, is house a structurally garden), which shaped is used area exclusively including or the predominantly associated for res- open space (courtyardidentialarea, purposes house [18,54]. garden), The whichcalculation is used of land-uptake exclusively is orbased predominantly on the growth of the for residential purposesnumber of [18 residential,54]. The buildings calculation [32] of in land-uptake the respective is time based period on themultiplied growth by of the aver- the number of residentialage “residential buildings building [32 and] in open the respective space area” time per building period multiplied(909 m2) in Lower by the Saxony in average “residential2015 building [55]. These and numbers open space are visualized area” per using building the census (909 m grid2) in with Lower the Saxonyaim to show the in 2015 [55]. Thesetemporal–spatial numbers are visualizeddynamics of using growth. the census grid with the aim to show the temporal–spatial dynamics of growth. 4.2. Calculation of Global Warming Potential (GWP) 4.2. Calculation of GlobalThe Warming investigation Potential uses the (GWP) global warming potential (GWP) to compare different as- pects of sustainability. As described before, life cycle analyses are used as the database to The investigation uses the global warming potential (GWP) to compare different evaluate the GWP. Within this research framework, only the energy for the construction aspects of sustainability.phase, use As phase described and maintenance before, life cycleare considered, analyses areexcluding used as disposal the database or reuse, to because evaluate the GWP.theWithin actual status this researchof the building framework, stock should only be the represented. energy for In the combination construction with identi- phase, use phasefied and settlement maintenance types, are the considered, embodied excludingenergy in existing disposal residential or reuse, buildings because theand the en- actual status of theergy building consumption stock during should operation be represented. can be rated In combination as properties with of each identified TOPOI type. In settlement types,order the embodiedto compare energythe results, in existingthey are residentialcalculated per buildings year. Regarding and the the energy embodied en- consumption duringergy, operationthe calculations can beare rated based ason properties a lifecycle of 50 each years, TOPOI which type. means In that order the energy to compare the results,already theyused arefor the calculated construction per year.of the Regardingbuilding is divided the embodied by 50 years energy, for the the whole life calculations arecycle. based Furthermore, on a lifecycle the ofembodied 50 years, energy which in all means streets thatand roads the energy is considered. already To rate the used for the constructionGWP, all calculations of the building results isare divided expressed by in 50 CO years2 equivalents, for the whole based on life GEMIS cycle. 4.95 [56] Furthermore, the embodied energy in all streets and roads is considered. To rate the GWP, all calculations results are expressed in CO2 equivalents, based on GEMIS 4.95 [56] for the energy supply and Ökobaudat 2016 [41] for construction and maintenance for existing residential buildings. Sustainability 2021, 13, x FOR PEER REVIEW 7 of 33

Sustainability 2021, 13, 4099 for the energy supply and Ökobaudat 2016 [41] for construction and maintenance7 of 33 for exist- ing residential buildings.

4.3. Superposition of the Generated Key Data with the TOPOI Settlement Units 4.3. Superposition of the Generated Key Data with the TOPOI Settlement Units In order to perform in-depth analysis for different settlement units along the urban– In order to perform in-depth analysis for different settlement units along the urban– rural gradient, the results from Sections 4.1 and 4.2 were superposed with the TOPOI set- rural gradient, the results from Sections 4.1 and 4.2 were superposed with the TOPOI tlement units. TOPOI, by their formative process, do not conform to grid systems. When settlement units. TOPOI, by their formative process, do not conform to grid systems. the data describing the building stock and the TOPOI borders do not perfectly align, situ- When the data describing the building stock and the TOPOI borders do not perfectly ations occur where multiple TOPOI are within a 1km grid cell. Therefore, the existing data align, situations occur where multiple TOPOI are within a 1 km grid cell. Therefore, the on the building stock (2011 German Census [32]) has been transferred from the 1km grid existing data on the building stock (2011 German Census [32]) has been transferred from (see Section 3.3) to the TOPOI. First, the TOPOI were split into smaller parts using the the 1 km grid (see Section 3.3) to the TOPOI. First, the TOPOI were split into smaller parts given 1-kilometre grid as a cutting mask. In the next step, procedures for two possible using the given 1-kilometre grid as a cutting mask. In the next step, procedures for two scenarios are applied: (1) when a grid cell contains parts of only one TOPOS (single set- possible scenarios are applied: (1) when a grid cell contains parts of only one TOPOS (single tlement unit of a specific settlement type) the data values can simply be transferred from settlement unit of a specific settlement type) the data values can simply be transferred grid cell to the respective TOPOS piece at a 1:1 ratio; (2) when a grid cell contains parts of from grid cell to the respective TOPOS piece at a 1:1 ratio; (2) when a grid cell contains multiple TOPOI (more than one settlement unit of a specific or different settlement types) parts of multiple TOPOI (more than one settlement unit of a specific or different settlement within the same grid cell, a procedure to distribute the data values proportionally is con- types) within the same grid cell, a procedure to distribute the data values proportionally ducted. For this purpose, the total area of building footprints [35] that are located within is conducted. Forresidential this purpose, areas according the total areato ATKIS of building [36,37] footprintsis calculated [35 for] thateach are TOPOI located piece in that grid within residentialcell. areas Next, according for each togrid ATKIS cell, the [36 ,sum37] is of calculated residential for building each TOPOI footprints piece within in the cell is that grid cell. Next,calculated for each and gridthe proportional cell, the sum share of residential for each piece building of TOPOI footprints located within within the grid cell the cell is calculatedis determined. and the proportionalBased on this shareshare, forthe eachgrid ce piecell data of are TOPOI distributed located to within the respective piece the grid cell is determined.of the TOPOI. Based In situations on this share,where thea part grid of cella TOPOS data areconstitutes distributed 90% toor thegreater than the respective piecetotal of the area TOPOI. of the residential In situations building where footprints a part of aof TOPOS a grid cell, constitutes that TOPOS 90% is or given 100% of greater than thethe total data area of of that the grid residential cell. After building the data footprints values ofhave a grid been cell, distributed that TOPOS to all is pieces of the given 100% of theTOPOI, data ofthey that are grid re-joined cell. After and theoverall data growth values haveper time been period distributed and the to sum all of the LCA pieces of the TOPOI,data is they calculated. are re-joined and overall growth per time period and the sum of the LCA data is calculated. 4.4. Evaluation Framework 4.4. Evaluation Framework The evaluation framework of this study is divided into three levels of detail. The first The evaluation framework of this study is divided into three levels of detail. The first level (Level A) focusses on the spatio-temporal growth, i.e., the correlation of specific level (Level A) focusses on the spatio-temporal growth, i.e., the correlation of specific growth growth patterns and land-uptake of all 13 settlement types within the study regions. Level patterns and land-uptake of all 13 settlement types within the study regions. Level B focuses B focuses on the quantification of energy consumption and embodied energy in different on the quantification of energy consumption and embodied energy in different settlement settlement typologies, calculated in CO2-equivalents. The goal is to quantify the environ- typologies, calculatedmental in impact CO2-equivalents. of specific settlement The goal is types to quantify in order the to environmental be able to compare impact them and to of specific settlementidentify types the most in order influential to be able TOPOI to compare type with themin the and study to identify regions the in terms most of the GWP. influential TOPOIIn Level type C, within the “best the case” study TOPOI regions type in with terms th ofe highest the GWP. negative In Level impact C, will the be compared “best case” TOPOIwith type the withmost theinfluential highest TOPOI, negative by impact using willa de betailed compared analysis with of input the most parameters of the influential TOPOI,building by using stock a detailedto derive analysis statements of input for future parameters sustainable of the developments building stock within to the study derive statementsregions. for future sustainable developments within the study regions.

Figure 3. EvaluationFigure framework. 3. Evaluation framework. GWP: global warming potential. Sustainability 2021, 13, x FOR PEER REVIEW 8 of 33

Sustainability 2021, 13, 4099 8 of 33 5. Results 5.1. Results—Level A 5. Results 5.1. Results—LevelLevel A of Athe evaluation framework analyses the temporal–spatial dynamics of growthLevel and A ofquantifies the evaluation the land framework up-take. analyses The superposition the temporal–spatial of the dynamics data onto of the TOPOI settlementgrowth and units quantifies as described the land up-take. in Section The 4.3 superposition allows for of a thehigher data spatially onto the TOPOI resolved perspec- tivesettlement of the units data as and described understanding in Section 4.3 of allows the urban for a higher development. spatially resolved perspective of theThe data settlement and understanding area growth of the urbanis visualised development. in Figure 4, which shows in which time pe- The settlement area growth is visualised in Figure4, which shows in which time riodperiod the the highest highest growth growth took took place place per 11km km grid cell.cell. Figure5 5(see (see also also Table Table A4 )A4) describes thedescribes average the averageland-uptake land-uptake calculation calculation based based on onresidential residential buildings buildings according according to to building agebuilding classes. age The classes. spatial The spatialdistribution distribution of the of ex thepansion expansion of ofthe the settlement settlement area area forfor each build- ingeach age building class ageseparately class separately are shown are shown in Figures in Figures A2 A2 to– A12.A12.

FigureFigure 4. Spatio-temporal 4. Spatio-temporal growth: growth: VisualisationVisualisation of theof the expansion expansion of the of settlement the settlement area based area on residentialbased on buildingsresidential buildings accordingaccording to building to building age age classes. classes. The cellcell of of the the grid grid with with a resolution a resolution (grid size) (grid of size) 1 km isof coloured 1 km is according coloured to according the to the buildingbuilding period period in inwhich which the the main growthgrowth took took place. place.

exo-industrial zones Due to to data data availability, availability, no growth no growth is mapped is mapped for the so-called for the so-called exo-industrial, for zones, example. The availability and integration of additional building types would impact the forresults example. significantly. The availability and integration of additional building types would impact the resultsThe main significantly. growth of residential buildings and the resulting land-uptake between 1919 and 2011The main has taken growth place of in residential the TOPOI typesbuildingsnode cityand(n the = 1;resulting 1629.7 ha, land-uptake 4.65% of total between 1919 andgrowth), 2011node has townstaken(n place = 7; 4190.7 in the ha, TOPOI 11.95% types of total node growth), city periurban(n = 1; 1629.7 towns (nha, = 244.65%; of total growth),6089.9 ha, node 17.37% towns of total(n = 7; growth), 4190.7periurban ha, 11.95% village of total(n = 42;growth), 4935.3 periurban ha, 14.80% towns of total (n = 24; 6089.9 growth) and most clearly the exo villages (n = 524; 12,667.6 ha, 36.13% of total growth). ha,After 17.37% steady of growth total growth), in the first periurban half of the village 20th century, (n = 42; the 4935.3 main growth ha, 14.80% in the ofnode total city growth) and most(Braunschweig) clearly the took exo place villages after the (n second= 524; World 12,667.6 War withha, 36.13% 22.6 ha annually of total between growth). 1949 After steady growthand 1978, in with the its first peak half between of the 1949 20th and 1957century, (26.6 ha/a).the main growth in the node city (Braun- schweig) took place after the second World War with 22.6 ha annually between 1949 and 1978, with its peak between 1949 and 1957 (26.6 ha/a). Likewise, a large increase in area growth occurs in node towns (peak: 1958 to 1968 with 95.6 ha/a), periurban towns (Peak: 1958 to 1968 with 117.8 ha/a), periurban villages (peak: 1979 to 1983 with 94.0 ha/a) and exo villages (peak: 1969 to 1978 with 235.2 ha/a) in the post- war period. After comparably lower growth in the period between 1984 and 1994, there was a strong increase after German reunification in the period between 1995 and 2001. Sustainability 2021, 13, 4099 9 of 33

Likewise, a large increase in area growth occurs in node towns (peak: 1958 to 1968 with 95.6 ha/a), periurban towns (Peak: 1958 to 1968 with 117.8 ha/a), periurban villages (peak: 1979 Sustainability 2021, 13, x FOR PEER REVIEW 9 of 33 to 1983 with 94.0 ha/a) and exo villages (peak: 1969 to 1978 with 235.2 ha/a) in the post-war period. After comparably lower growth in the period between 1984 and 1994, there was a strong increase after German reunification in the period between 1995 and 2001. The exoexo villagevillage,, with with an an increase increase of 282.6of 282.6 ha/a, ha/a, stands stands out out in particular. in particular. This This TOPOI TOPOI type istype characterised is characterised by both by its both isolated its isolated location location and high and functional high functional density. It density. is predominantly It is pre- locateddominantly in study located region in study (B), the region larger (B), Braunschweig the larger Braunschweig region. The considerablyregion. The considerably higher land- 2 2 uptakehigher land-uptake for this TOPOI for this type TOPOI (1919–2011: type (1919–2011: 111.9 km 111.9compared km2 compared to 13.9–55.9 to 13.9–55.9 km for km the2 aforementionedfor the aforementioned types) is types) related is onrelated the one on handthe one to thehand number to the ofnumber settlements of settlements of this type of (nthis = type 524) (n as = can 524) be as seen can in be the seen numbers in the numbers for the median for the growthmedian acrossgrowth the across time the periods time 1919–2011:periods 1919–2011:exo villages exo1.4 villages ha compared 1.4 ha tocompared 10.6/23.6 to ha 10.6/23.6 for the periurban ha for villagesthe periurban/periurban vil- townslages/periurbanand 59.5/154.8 towns and ha for 59.5/154.8 the node ha towns for /thenode node city towns. /node city.

FigureFigure 5.5. Spatio-temporal growth: Total land-uptakeland-uptake per TOPOI typetype inin hectares basedbased on residential buildingsbuildings accordingaccording toto buildingbuilding ageage classesclasses andand associatedassociated areas.areas.

In thethe furtherfurther analysisanalysis considering considering additional additional data data on on population population and and building building density, den- itsity, is revealedit is revealed that therethat there are fundamental are fundamenta differencesl differences with regard with regard to the relationshipto the relationship of the surfaceof the surface area in area relation in relation to population to population size and size density and density as well as aswell the as building the building density density (see Figure(see Figure6; Table 6; 3Table for definitions 3 for definitions of the indicators).of the indicators). For example, For example, that the thatexo the satellite exo satellite towns havetowns thehave highest the highest residential residential building building density de (RBD)nsity (RBD) and population and population density density (PD) while (PD) residentialwhile residential land-uptake land-uptake (RLU) is(RLU) very low.is very In contrast,low. In contrast, it can be seenit can for be the seenexo for village the, forexo example,village, for that example, we have that a we relatively have a highrelatively RBD high combined RBD combined with a very with high a very total high RLU total and aRLU low and PD. a Noticeable low PD. Noticeable is the low medianis the low RLU. median Within RLU. the setWithin of TOPOI the set types, of TOPOI population types, densitypopulation in exo density villages inranks exo villages 7th, yet ranks with 7th, a high yet density with a ofhigh residential density buildings.of residential The build- same isings. visible The same for the is periurbanvisible for village the periurban(8th) and villageperiurban (8th) and town periurban(9th) as town well (9 asth) the as wellnode as town the (10th).node town This (10th). allows This conclusions allows conclusions to be drawn to be that drawn these that are these low-density are low-density settlement settlement types suchtypes as such single-family as single-family and terraced and terraced houses. houses. For the Fornode the citynode, the city densities, the densities for population for popu- andlation residential and residential buildings buildings both align both at align a high at level.a high The level.exo The satellite exo townsatellitehas town the highesthas the populationhighest population and housing and housing density, density, while land while consumption land consumption is very low. is very low.

Sustainability 2021, 13, x FOR PEER REVIEW 10 of 33

Table 3. Definition of indicators for the residence-focused analysis.

INDICATOR Min. Max. Population Density (PD) 0.00 83.32 PD is the number of inhabitants per hectare. The sum of the total population per 1 inhabitants/ha inhabitants /ha ha cell within one settlement unit is divided by the area (A) of the settlement unit. Residential Building Density (RBD) 0.00404 0.12021 RBD is the number of buildings in the areas defined residential [54] in each settle- buildings/ha buildings/ha ment unit divided by the TOPOI area A (ha). Residential land-uptake (RLU) RLU is the total area of residential land-uptake. A residential area is a structurally shaped area including the associated open space, which is used exclusively or pre- 0.186 599.860 dominantly for residential purposes [18,54]. RLU results from the count of erected ha ha residentialSustainability buildings2021, 13[32], 4099 multiplied by the average “residential building and open 10 of 33 space area” per building (909 m2) in Lower Saxony [55].

Figure 6.Figure Radar 6. chartRadar showing chart showing residential residential building building density density (RBD), (RBD), population population density density (PD), residential(PD), residential land-uptake land-uptake (RLU) (RLU) (total and(total median and median across across all building all building age age classes; classes; see see TableTable A4 A4). The). The data data shown shown in this in graph this havegrap beenh have normalized been normalized to a to a scale of 0–100scale of for 0–100 legibility for legibility and andcomparison comparison.. The The plot plot enablesenables the the identification identification of possible of possible correlations correlations between RBD, between PD RBD, PD and RLU.and RLU.

Table 3. Definition of indicators for the residence-focused analysis. 5.2. Results—Level B INDICATOR Min. Max. Results on Level B include the evaluation of the GWP for different settlement types Population Density (PD) PD is the number of inhabitantsby creating per hectare. box The plots. sum of The the total box plot method0.00 is very suitable for a large83.32 number of numeric population per 1 ha cell withindata, one as settlement is divides unit isall divided data byin the quartilesinhabitants/ha and hereby displays theinhabitants information /ha with five num- area (A) of the settlement unit. Residential Building Densitybers: (RBD) minimum, 1st quartile, median, 3rd quartile and maximum. The initial box plot eval- 0.00404 0.12021 RBD is the number of buildingsuation in the for areas all defined 13 TOPOI residential had [54 to] be reduced to 10 TOPOI: as only residential buildings are buildings/ha buildings/ha in each settlement unit divided by the TOPOI area A (ha). Residential land-uptake (RLU) RLU is the total area of residential land-uptake. A residential area is a structurally shaped area including the associated open space, 0.186 599.860 which is used exclusively or predominantly for residential ha ha purposes [18,54]. RLU results from the count of erected residential buildings [32] multiplied by the average “residential building and open space area” per building (909 m2) in Lower Saxony [55]. Sustainability 2021, 13, 4099 11 of 33

5.2. Results—Level B Results on Level B include the evaluation of the GWP for different settlement types by creating box plots. The box plot method is very suitable for a large number of numeric data, as is divides all data in quartiles and hereby displays the information with five Sustainability 2021, 13, x FOR PEERnumbers: REVIEW minimum, 1st quartile, median, 3rd quartile and maximum. The initial11 of 33 box plot

evaluation for all 13 TOPOI had to be reduced to 10 TOPOI: as only residential buildings are considered, the exo industrial zone had to be eliminated. Furthermore, two TOPOI (disseminatedconsidered, the hamlet exo industrialand disseminated zone had to living be eliminated. agri hamlet Furthermore,) had very two differing TOPOI values (dissem- and the interquartileinated hamlet ranges and disseminated (IQR) were living too agri high hamlet to see) had significant very differing results. values and the inter- quartileAs the ranges data (IQR) of the were census too high in combination to see significant with results. life cycle data include approximations and inaccuracies,As the data of the the outliers census havein combination been removed with life to focuscycle data on the include most approximations frequently occurring data.and inaccuracies, To compare the the outliers GWP have of embodied been remo energyved to focus with on the the GWP most duringfrequently operation, occur- the resultsring data. are expressedTo compare per theyear GWP and of embodied person. Theenergy division with the by GWP person during (inhabitants) operation, the offers the results are expressed per year and person. The division by person (inhabitants) offers the comparison of all settlement types, as the population density is essential to rate the GWP comparison of all settlement types, as the population density is essential to rate the GWP in terms of sustainability. Figure7 shows the results for the GWP per year and person, in terms of sustainability. Figure 7 shows the results for the GWP per year and person, divideddivided into into operational operational and and embodied embodied emissions. emissions.

FigureFigure 7. Average 7. Average global global warming warming potential potential (GWP) (GWP) perper TOPOI type: type: embodied embodied and and operational operational emissions emissions in residential in residential buildings, embodied emissions in streets (t CO2 eq/a*person). buildings, embodied emissions in streets (t CO2 eq/a*person). The distribution of GWP for the operation of a building reaches an average higher levelThe than distribution the embodied of GWPenergy. for The the median operation of different of a building settlement reaches types for an emissions average higher level than the embodied energy. The median of different settlement types for emissions during operation ranges from 2.57 t CO2 eq/a*p (exo satellite town) to 3.97 t CO2 eq/a*p during(periurban operation town). The ranges difference from 2.57of 1.4 t tCO CO22 eq/a*p is (veryexosatellite meaningful, town taking) to 3.97 the t overall CO2 eq/a*p (periurbangoal of the town German). The government difference into of 1.4 account t CO 2toeq/a*p reduce the is very greenhouse meaningful, gas emissions taking until the overall goal2050 of by the 80% German to 95% government compared to into1990 account[57]. Starting to reduce by around the greenhouse 12.5 t CO2 eq/a*p gas emissions in 1990, until 2050the bygoal 80% for to2050 95% would compared lead to less to 1990 than [157 t ].CO Starting2 eq/a*p byincluding around mobility, 12.5 t CO nutrition2 eq/a*p and in 1990, theall goal general for 2050consumptions would lead of a toperson less than[58,59]. 1 t CO2 eq/a*p including mobility, nutrition and all generalThe distribution consumptions of CO of2 aequivalent person [58 emissions,59]. for the construction and maintenance of buildings is, compared to the emissions during the operation of a building, quite small The distribution of CO2 equivalent emissions for the construction and maintenance of buildingswith fewer is, compareddeviations. toThe the median emissions of embodied during the emissions operation per of year a building, and person quite ranges small with from 0.29 t CO2 eq/a*p (exo satellite town) to 0.39 t CO2 eq/a*p (periurban town). The devia- fewer deviations. The median of embodied emissions per year and person ranges from tion of 0.1 t CO2 eq/a*p has a small impact. This shows that the median embodied energy per person in buildings does not vary a lot with different settlement types. Sustainability 2021, 13, 4099 12 of 33

Sustainability 2021, 13, x FOR PEER REVIEW 12 of 33

0.29 t CO2 eq/a*p (exo satellite town) to 0.39 t CO2 eq/a*p (periurban town). The deviation of 0.1 t COThe2 eq/a*pembodied has energy a small in streets impact. can This also showsbe displayed that theas GWP median per person embodied and year. energy per personThe evaluation in buildings in a doesbox plot not diagram vary a lotshows with th differente rising impact settlement of GWP types. in streets with in- creasingThe embodied rural location energy of the in streetssettlement can type. also beThe displayed agri village as as GWP the permost person rural TOPOS and year. The evaluationresults in ina median a box plot of 0.22 diagram t CO2 showseq/a*p, thewhile rising the impactnode city of has GWP a median in streets of 0.05 with t CO increasing2 ruraleq/a*p. location Nevertheless, of the settlement in comparison type. to the The emagriissions village of buildings,as the most the ruralimpact TOPOS of the em- results in bodied energy in streets is rather small. a median of 0.22 t CO2 eq/a*p, while the node city has a median of 0.05 t CO2 eq/a*p. Nevertheless,By rating in the comparison values of embodied to the emissions emissions ofin buildings,terms of sustainability, the impact it of has the to embodied be energykept in in mind streets that is the rather expression small. of values per year is a way to make embodied emissions comparable to, in this case, operational emissions that occur every year. Actually, the By rating the values of embodied emissions in terms of sustainability, it has to be building stock and streets are already built and the energy has been used years ago. kept in mind that the expression of values per year is a way to make embodied emissions The evaluations show that the settlement type exo satellite town has the smallest envi- comparableronmental impact, to, in thiscompared case, to operational the other TOPOI emissions types. thatTherefore, occur the every next year.research Actually, level the buildingwill, on stockthe one and hand, streets focus are on already exo satellite built towns and and the their energy attributes has been andused properties, years fol- ago. lowingThe the evaluations question, what show can that be learned the settlement from this settlement type exo type satellite and adapted town has for the other smallest environmentalor future settlements. impact, Taking compared a closer to look the otherat the TOPOIimpact of types. all residential Therefore, buildings the next and research levelstreets will, in onthe thestudy one regions hand, will focus help on toexo identify satellite settlement towns and types their with attributes a large impact. and properties, followingFigure 8 shows the question, the total GWP what for can buildings be learned and streets from thisfor the settlement status of 2011. type and adapted for other orExo future villages settlements. have by far the Taking largest a environmenta closer look atl impact. the impact Around ofall 40% residential of all residen- buildings andtial streets buildings in theare built study inregions exo villages will, with help more to identify than 30% settlement of all residents types within with the a large study impact. Figureregions.8 shows The analysis the total per GWP person for also buildings showed and that streets exo villages for the are status one of of the 2011. settlement types with a higher GWP (Figure 7).

FigureFigure 8. Annual 8. Annual global global warming warming potential potential (GWP)(GWP) per TOPOI TOPOI type: type: embodied embodied and and operational operational emissions emissions in residential in residential buildings and embodied emissions in streets (t CO2 eq/a). buildings and embodied emissions in streets (t CO2 eq/a). 5.3. Results—Level C Exo villages have by far the largest environmental impact. Around 40% of all residential The results of the analysis in Level B form the basis for Level C, in which the best- buildings are built in exo villages, with more than 30% of all residents within the study case settlement exo satellite town will be analysed in detail and compared with the exo vil- regions. The analysis per person also showed that exo villages are one of the settlement lages. The latter have an average size of 42 ha with a total building density of 13.1 build- typesings/ha, with population a higher GWPdensity (Figure of 14.07 inhabitants/ha). and an open space ratio at 86.8%. They 5.3. Results—Level C The results of the analysis in Level B form the basis for Level C, in which the best-case settlement exo satellite town will be analysed in detail and compared with the exo villages. The Sustainability 2021, 13, 4099 13 of 33 Sustainability 2021, 13, x FOR PEER REVIEW 13 of 33

latter have an average size of 42 ha with a total building density of 13.1 buildings/ha, popula- aretion characterised density of 14.0 by inhabitants/ha their highg number and an open of fu spacenctions, ratio including at 86.8%. Theya small are percentage characterised of ag- riculturalby their high buildings, number while of functions, the retail including ratio is a smallvery percentagelow. The settlement of agricultural units buildings, around exo villageswhile the are retail few ratio and isthe very connectivity low. The settlement to other unitssettlement around unitsexovillages is low.are Exo few satellite and the towns haveconnectivity the highest to other population settlement density units is of low. all TOExoPOI satellite types towns withhave 48.2 the inhabitants/ha highest population (median) atdensity an average of all TOPOIsize of types81 ha withwith 48.2 a total inhabitants/ha building density (median) of 10.9 at an buildings/ha average size and of 81 an ha open spacewith a ratio total buildingat 82.8%. density They ofcan 10.9 be buildings/haconsidered the and suburban an open space developments ratio at 82.8%. to the They periurban can townsbe considered due to their the suburban proximity developments and transport to the linksperiurban to the townssame.due However, to their proximityexo satellite and towns exo satellite towns stilltransport have linksa high to functional the same. However, diversity (see also Tablesstill A2 have and aA3). high functional diversity (see also Tables A2 and A3). Exo villages have the highest environmental impact in terms of total GWP (embodied Exo villages have the highest environmental impact in terms of total GWP (embodied energy + operational energy + streets) in the study regions (Figure 8), while exo satellite energy + operational energy + streets) in the study regions (Figure8), while exo satellite townstowns havehave the the lowest lowest GWP GWP per per person person (Figure (Figure 7).7). The The research research aims aims at at identifying identifying signif- icantsignificant input inputparameters parameters which which lead to lead the to lower the lower GWP GWP per person per person for exo for satelliteexo satellite towns in comparisontowns in comparison to exo villages to exo. As villages the calculations. As the calculations are based are on based construction on construction year classes year and buildingclasses and typologies, building typologies,the distribution the distribution for both is for evaluated. both is evaluated. Due to the Due results to the on results Level B (seeon Level Figure B (see7) the Figure following7) the followinganalysis will analysis focus will on focusthe operational on the operational emissions, emissions, since this is thesince biggest this is impact the biggest compared impact to compared embodied to energy embodied in buildings energy in and buildings streets. and streets. The residentialresidential buildingbuilding stock stock of of the the study stud regionsy regions is dominatedis dominated by single-familyby single-family houses (SFHs) (67%)(67%) and and terraced terraced houses houses (THs) (THs) (21%). (21%). The The share share of multi-familyof multi-family houses houses (MFHs) is 12% 12% and and apartment apartment blocks blocks (ABs) (ABs) contribute contribute only 0.4%. The distribution distribution of dif- ferentdifferent building building typologies typologies within within a a settlemen settlementt typetype is is essential essential for for the the GWP, GWP, as building as building dimension and volume in combination with number of apartments correlate directly. For dimension and volume in combination with number of apartments correlate directly.2 For example, a single-family house built in the 1960s has around 20% more CO2 eq/m a example, a single-family house built in the 1960s has around 20% more CO2 eq/m2a com- compared to a multi-family house in the same construction year class. Figure9 shows the pared to a multi-family house in the same construction year class. Figure 9 shows the dis- distribution of all residential buildings and the corresponding GWP (t CO2 eq/a). tribution of all residential buildings and the corresponding GWP (t CO2 eq/a).

Figure 9. Distribution of building typestypes andand correspondingcorresponding distribution distribution of of total total annual annual GWP GWP for for thethe operational phasephase in inexo exo satellite satellite towns townsand andexo exo villages. villages.The The distribution distribution of building of building types types differs dif- ferssignificantly significantly in both in settlementboth settlement types. Thetypes. distribution The distribution of GWP of for GWP the operation for the operation shows the shows influence the influenceof building of types building on the types GWP. on the GWP.

There are significant differences of both settlement types in terms of building typol- ogies. SFHs, THs and MFHs have almost equal shares within exo satellite towns, while SFHs dominate exo villages with 76%. There are no ABs and very few multi-family houses in exo Sustainability 2021, 13, 4099 14 of 33

There are significant differences of both settlement types in terms of building typolo- gies. SFHs, THs and MFHs have almost equal shares within exo satellite towns, while SFHs dominate exo villages with 76%. There are no ABs and very few multi-family houses in exo villages. The distribution of GWP in exo villages correlates almost directly with the building count, while the MFHs (44%) have the biggest environmental impact in terms of GWP in exo satellite towns, followed by ABs (36%). This shows that the relation between building typology and GWP is different in both settlement types, which leads to the assumption that the construction year, as another input parameter of the calculation, varies as well between both settlement types. In the following paragraph, the building stock will be analysed regarding construction year classes, using the division in five groups. The construction year classes are distributed to buildings built pre-war (until 1948) and post-war (1949–1978) on the one hand and assigned to different regulations concerning thermal insulation and energy saving ordi- nance on the other hand. The first thermal insulation ordinance (WSVO) was adopted in 1977 with restrictions on maximum values for heat transmission coefficients for exterior building components [60]. Six years later in 1984, this regulation was revised with more strict regulations for heat transmission coefficients. From today’s point of view, the change of 1.75 W/m2K (in 1977) to 1.5 W/m2K (in 1984) for exterior walls and 3.4 W/m2K to 3.1 W/m2K for windows is no significant difference [61], putting it in relation to today’s requested values of 0.28 W/m2K for exterior walls and 1.3 W/m2K for windows (EnEV 2009) [62]. In 1995, the updated WSVO introduced a new method to compare heat losses by transmission and ventilation with heat gains from internal loads and solar radiation. Using this method, the annual heating requirement for a whole building was calculated, and not only heat losses through exterior components were considered [63]. As this method is a more comprehensive approach and it was the base for today’s energy saving regulation, 1995 is considered as the “real” beginning of energy efficient buildings, besides the thermal insulation ordinance a heating appliance ordinance (HeizAnlV) that existed with the first ver- sion in 1978 [64]. In 2002, the first energy saving ordinance (EnEV) was introduced, which combined both the WSVO and HeizAnlV, and apart from the annual heating requirements, also calculated the primary energy consumption [65]. The EnEV was revised four times until 2011 (research framework) [62]. The classification of the 11 construction year classes into five groups is shown in Table4 and is chosen to evaluate the existing building stock in terms of energy consumption levels.

Table 4. Construction year classes [39] and building classification for evaluation. WSVO: thermal insulation ordinance; EnEV: energy saving ordinance.

Construction Year Classes Evaluation Classification A before 1859 B 1860–1918 Pre WWII buildings C 1919–1948 D 1949–1957 E 1958–1968 Post WWII buildings F 1969–1978 G 1979–1983 1st WSVO 1977 and 2nd WSVO 1984 H 1984–1994 (thermal insulation ordinance) I 1995–2001 3rd WSVO 1995 (thermal insulation ordinance) J 2002–2009 EnEV 2002, 2004, 2007, 2009 (energy saving K 2010–2011 ordinance)

Within the study regions, more than 60% of all residential buildings were built before the first thermal insulation ordinance and 80% before the revised version in 1995. The distribution of building ages in exo satellite towns and exo villages are shown in Figure10. Sustainability 2021, 13, 4099 15 of 33 Sustainability 2021, 13, x FOR PEER REVIEW 15 of 33

FigureFigure 10. 10. DistributionDistribution of ofconstruction construction year year classes classes (building (building share) share) in exo in satelliteexo satellite towns towns and andexo exo villagesvillages withwith building building types types distribution distribution for for majo majorityrity of of construction construction year year classes. classes.

TheThe majority majority of of residentialresidential buildingsbuildings in inexo exo satellite satellite towns townsare post-warare post-war buildings buildings (65%), (65%),built built between between 1949 1949 and 1978.and 1978. Only Only 32% have32% have been been built built after after the first the thermalfirst thermal insulation insu- lationordinance, ordinance, which which cause cause 20% of20% GWP of GWP during du operation.ring operation. The environmental The environmental impact impact of 65% ofpost-war 65% post-war buildings buildings is more is significantmore significant and responsible and responsible for 78% for of 78% CO2 ofemissions CO2 emissions during duringoperation. operation. The distribution The distribution of building of buildi typesng within types the within dominating the dominating construction construction year class yearcorrelates class correlates to the distribution to the distribution of building of typesbuilding in the types whole in the TOPOI whole type. TOPOI There type. are There only a arefew only less a SFHsfew less and SFHs more and MFHs more built MFHs post-war. built post-war. TheThe share share of of pre-war pre-war buildings buildings in in exoexo villages villages isis higher higher (21%) (21%) compared compared to to exoexo satellite satellite townstowns, though, though in ingeneral, general, more more buildings buildings were were built built after afterthe first the thermal first thermal insulation insulation ordi- ordinance (40%). This share of “new buildings” produces 33% of all CO emissions during nance (40%). This share of “new buildings” produces 33% of all CO2 2emissions during operation,operation, while while post-war post-war buildings buildings are are responsible responsible for for 40% 40% GWP GWP and and pre-war pre-war buildings, buildings, forfor 27% 27% GWP. GWP. The The distribution distribution of of building building ty typespes in in pre- pre- and and post-war post-war classes classes correlates correlates withwith the the distribution distribution withinwithin thethe whole whole settlement settlement type, type, despite despite a slight a slight deviation deviation to a higher to a share of SFHs and THs and less MFHs. higher share of SFHs and THs and less MFHs. The analysis of construction year classes shows that both settlement types are domi- The analysis of construction year classes shows that both settlement types are domi- nated by old buildings. Comparing the environmental impact in terms of GWP reveals that nated by old buildings. Comparing the environmental impact in terms of GWP reveals the distribution of building types within a construction year class has a significant impact that the distribution of building types within a construction year class has a significant on CO2 emissions. impact on CO2 emissions. 6. Discussion 6. Discussion The development of strategies for the sustainable retrofitting of existing settlement unitsThe and development their building of stock, strategies as well for as the the sustainable sustainable developmentretrofitting of of existing new areas, settlement requires unitsa comprehensive and their building understanding stock, as well of the as interrelationshipsthe sustainable development between resource of new and areas, energy re- quiresdemands a comprehensive together with understanding the form and structure of the interrelationships of settlement units. between The building-type-based resource and en- ergyanalysis demands built ontogether the census with datathe allowsform and a spatially structure explicit of settlement analysis acrossunits. scalesThe building- based on type-basedthe TOPOI analysis types. Hence, built on a number the census of conclusions data allows can a bespatially drawn: explicit analysis across scales Particularbased on the TOPOI TOPOI types, types. especially Hence, a the numberexo villages of conclusions, are largely can responsible be drawn: for high resourceParticular and energyTOPOI consumption types, especially in both the study exo villages regions., are These largely are characterisedresponsible for by ahigh low resourcepopulation and andenergy building consumption density. Thisin both can stud be attributedy regions. toThese the developments are characterised of residential by a low populationextensions and dominated building by density. detached This and can terraced be attributed houses. to It the is strikingdevelopments that this of overallresidential high extensions dominated by detached and terraced houses. It is striking that this overall high Sustainability 2021, 13, 4099 16 of 33

negative effect is the result of rather small developments of each individual settlement unit throughout the years. Due to the lack of a region-wide integrated evaluation approach in urban land use planning, the extent of the negative impacts does not become apparent at first. For sustainable development, such a comprehensive perspective in approval processes is indispensable from the authors’ point of view. The method presented here offers the possibility to do so. A notable observation in the course of the study is the exo satellite town, which stands out positively in terms of both land consumption in relation to population density and GWP. Exo satellite towns are large-scale, master-planned high-density urban extensions developed as mass housing estates in the 1960s and 1970s. The results of the detailed analysis of exo satellite towns and exo villages (see Section 5.3) lead to the conclusion that the biggest differences between both are the population density, distribution of building types and the share of pre-war buildings. A redensification of existing neighbourhoods in exo villages would theoretically lead to a reduction in the GWP per person. In this context, it is important to mention the challenge of redensification strategies in existing neighborhoods with mostly single-family houses—also in terms of embracing existing struc- tures and their embodied energy. However, the need for denser development patterns for a sustainable growth of new settlement areas is becoming evident through this analysis. For the existing building stock, a potential approach would be the extensive renovation on up-to-date building standards (EnEV), taking residential buildings built before 1995 into account. However, the following shows that the renovation approach cannot deliver the necessary reductions for the most significant settlement type within the study regions. A scenario with 100% renovated SFHs and THs by 2050 (in consideration of the non- renovatable building stock due to heritage purposes), which would be a renovation rate of 2.4% per year and a continuing renovation rate of 1% for MFHs leads approximately to a reduction of around −0.6 t CO2 eq./pers*a (3.3 t CO2 eq./pers*a) for exo villages. A renovation approach including 100% renovated residential buildings (all building types) in exo villages would result in a reduction of around −0.7 t CO2 eq/pers*a (3.2 t CO2 eq./pers*a). This result reflects the evaluation of Section 5.3, that SFHs are the main driver within exo villages. Taking into consideration that only SFHs will be renovated, a reduction of −1.0 CO2 eq/pers*a (2.9 t CO2 eq./pers*a) is possible by using a higher renovation standard than the regulation (EnEV) demands. Besides the higher reduction in energy consumption, the use of material would increase, and therefore the embodied energy. It also needs to be mentioned that no costs or social aspects are considered in this study. The overall governmental goals for 2050 request total reductions in greenhouse gas emissions of 95% [57], which leads to around 200 kg CO2 eq/pers*a for buildings. To achieve significant impacts to reduce the GWP, the stated reductions would need to go along with a change in the energy supply. Taking 2050 scenarios for energy supply into account, the share of renewable energies will dominate the system, which will reduce the GWP per person in residential buildings significantly. However, a study on energy supply scenarios is not part of this article. The introduced method to identify main drivers in terms of land-uptake and global warming potential on the level of the region and settlement units offers evaluations that are based on classifications and assumptions. Using the method, an extrapolation for the whole residential building stock in Germany is possible, provided that appropriate data are available. Working on a large scale, the results are representative and general statements are possible. Nevertheless, there are some restrictions that have to be kept in mind. The data about the existing building stock are based on the German Census 2011, which is missing ten years to today’s status of the building stock. As the analysis has shown, new buildings have a small share within the study regions and the rate of new constructions of residential buildings in Lower Saxony from 2011 to 2019 was an average of 0.6% per year [21]. For this reason, the missing numbers of the last ten years may not have a major impact on the overall result. For a conclusive assessment of this impact, the study needs to be carried out again with data from the new German Census planned for 2022. Sustainability 2021, 13, 4099 17 of 33

Due to the limitations of the available dataset, the analysis of the temporal–spatial dynamics of growth as well as GWP is based on residential buildings only and thereby does not include, for example, industrial buildings and linked land-uptake. The study does not address the potential consequences of land-uptake, for example, in terms of ecosystems. Furthermore, the calculations on residential buildings are based on generalised types and classifications. The distribution of buildings across multiple settlement units within each 1 km2 grid cell is carried out proportionally, possibly leading to some bias when distributing the data by area. As the building data used are accumulated on the 1 km grid, there is no way to more precisely pinpoint the buildings’ exact location. The calculations of energy consumption during operation are based on benchmarks, combined with statistic data for energy supply shares in Lower Saxony, which also is a simplification. The life cycle assessments to evaluate the embodied energy are based on the Ökobaudat 2016, which includes data on how a building would be built today. The way of transport or construction processes in the actual construction year class, for example in 1860, might have been different. The building constructions are based on assumptions and classification and represent an approximation. Life cycle assessments result in a number of environmental indicators, e.g., GWP, primary energy consumption, ozone depletion potential or acidification potential. This study evaluates only the GWP,as CO2 emissions often are the focus of political statements or regulations. Nevertheless, other environmental indicator assessments would be necessary for a holistic life cycle analysis. The embodied energy has been excluded in the detailed evaluations of this study. The chosen method to express the GWP per year and person is a valid way to compare different settlement types by including population density. The expression of embodied energy per year and person for existing buildings is questionable though, as the buildings have already been built and the energy used for construction cannot be changed. Therefore, the consideration of embodied energy is more significant for new buildings or regarding the question of demolition versus renovation. As new buildings are built more energy efficient, the significance of embodied energy is increasing. A building built in 2015 has a share of 40% embodied energy only for the construction of the building. In 2009, which represents almost the newest buildings within this study, the share of embodied energy was only 30% [25]. This trend shows that the impact of embodied energy is increasing and needs to be included in holistic approaches for new buildings.

Author Contributions: Conceptualization, A.-K.M., E.E., O.M., O.R., R.Z. and V.M.C.; methodology, A.-K.M., O.M., R.Z. and V.M.C.; investigation, A.-K.M., O.M. and R.Z.; writing—original draft preparation, A.-K.M. and O.M.; writing—review and editing, E.E. and V.M.C.; visualization, A.-K.M. and R.Z.; supervision, E.E. and V.M.C.; funding acquisition, V.M.C. and O.M. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the Ministry of Science and Culture of Lower Saxony and by the Volkswagen Foundation under Grant ZN3121. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The TOPOI–Urban Rural Settlement Types–Version 1.0 is openly avail- able in the Digital Library Institutional Repository of TU Braunschweig at doi:10.24355/dbbs.084- 202003180800-0. The data on global warming potential and land-uptake presented in this study is available on request from the corresponding author. These data are not publicly available due to privacy reasons of the special evaluation of base data on building stock obtained from the State Office for Statistics Lower Saxony. Acknowledgments: We acknowledge support by the German Research Foundation and the Open Access Publication Funds of Technische Universität Braunschweig. Conflicts of Interest: The authors declare no conflict of interest. Sustainability 2021, 13, 4099 18 of 33

Appendix A

Table A1. Range of eleven indicators of form, function, and spatial linkages used in the affinity propagation clustering for classifying settlement types according to the TOPOI method [2].

FORM Min. Max. Area (A) 0.84 ha 3662.06 ha The is the area of a settlement unit in ha. Compactness (C) √ The compactness of a settlement unit is calculated: C = 2 πA⁄P*100(%); 14% 99% C = compactness, A = area, P = perimeter [66]. Building Density (BD) 0.82 32.36 BD is the number of buildings in each settlement unit divided by the area A (ha). buildings/ha buildings/ha Open Space Ratio (OSR) OSR describes the amount of space that is not occupied by built structures within a 45.74% 99.74% certain area. OSR is calculated by applying the formula: OSR = A–BA; A = area, BA = Built up area. FUNCTION Functional Richness (FR) FR describes the presence of different functions in a settlement unit ranging from 1 to 8 available functions here (residential area; retail and services; public facilities; 0 8 industrial and commercial area; agricultural facilities; supply facilities; disposal facilities; parks, sport and recreation facilities) Population Density (PD) PD is the number of inhabitants per hectare. The data source gives the absolute 0.00 83.32 number of inhabitants per 1 ha cell (yz). The sum of the total population per cell inhabitants/ha inhabitants /ha within one settlement unit is divided by the area (A). Retail and Services Ratio (RSR) 0.00% 92.85% RSR is the percentage of the area A with retail and services per unit. Agricultural Facilities Ratio (AFR) 0.00% 89.88% AFR is the percentage of the area A with agricultural facilities within each unit. SPATIAL LINKAGES Settlement Density (SD) The density of settlement units assesses the number of units within a 3 km radius, 1 71 using the Euclidean distance between centroids. Public Transport Connectivity (PTC) PTC is the number of settlement units directly linked to each other by public transport. 0 76 It can be calculated as PTC = ΣL (L1 + L2 + Ln) where L is the number of unique settlement units reached by each public transit line that goes through a TOPOI. Proximity to Regional Train Station (PRTS) The proximity to (operating) regional train stations calculates the shortest distance 0.00269 km 28.11 km along the street network between a regional train station and the centroid of each settlement unit in km [37,67–69]. Sustainability 2021, 13, 4099 19 of 33

Table A2. The TOPOI for the two study regions in Lower Saxony (Germany) and the median per indicator. The order of the TOPOI in the table develops along the population density, with the exception of “exo satellite town”, which is subordinated to Node City and Periurban Town [2].

Indicators Form Function Linkages TOPOI Unit Count Building Population Retail and Agricultural Public Proximity to Open Space Functional Settlement Area (ha) Compactness (%) Density Density (in- Services Ratio Facilities Transport Regional Train Ratio (%) Richness Density (buildings/ha) habitans/ha) (%) Ratio (%) Connectivity Station (m) Node city 1 3662 14% 13.9 80% 8 43.5 8.9% 0.3% 4 68 2669 Node town 7 1153 22% 15.5 81.8% 8 22.5 5.8% 1.6% 18 31 1649 Periurban 24 526 29% 14.4 82.8% 8 21.4 5.8% 1.0% 23 23 1407 town Exo satellite 9 81 61% 10.9 82.9% 7 48.2 2.3% 0.1% 13 3 3920 town Periurban 42 224 38% 15.2 84.5% 8 19.7 4.8% 2.3% 13 21 1665 village Small periurban 37 53 60% 15.1 86.6% 7 17.2 1.7% 5.0% 13 18 3654 village Exo village 524 42 63% 13.1 86.8% 7 14.0 1.4% 7.3% 10 5 6559 Small exo 73 14 76% 11.3 88.6% 4 11.0 0.0% 13.4% 11 6 6172 village Disseminated 160 27 53% 8 90.2% 6 7.2 2.1% 8.6% 44 10 8851 village Agri village 35 20 58% 7.7 89.6% 5 5.2 1.4% 14.2% 12 23 8440 Disseminated 1071 4 81% 4.5 91.4% 3 2.3 0.0% 0.0% 34 0 7017 hamlet Disseminated living agri 4283 3 89% 4.7 92.6% 2 2.3 0.0% 23.9% 38 0 8098 hamlet Exo industrial 35 18 69% 1.9 68.6% 3 0.0 0.0% 0.0% 15 0 3779 zone Sustainability 2021, 13, 4099 20 of 33 Sustainability 2021, 13, x FOR PEER REVIEW 20 of 33

SustainabilitySustainability 2021 2021, ,13 13, ,x x FOR FOR PEER PEER REVIEW REVIEW 2020 of of 33 33

Table A3. Description of the thirteen TOPOI identified in the two study regions. The given TOPOI exemplars result from Table A3. Description of the thirteen TOPOI identified in the two study regions. The given TOPOI exemplars result from affinityTableTable propagation A3.A3. DescriptionDescription clustering ofof thethe thirteen thirteen[2]. TOPOITOPOI idenidentifiedtified inin thethe twotwo studystudy regions.regions. ThThee givengiven TOPOITOPOI exemplarsexemplars resultresult fromfrom affinity propagation clustering [2]. affinityaffinity propagationpropagation clusteringclustering [2].[2]. TOPOI Description Exemplary TOPOI Map TOPOITOPOITOPOI DescriptionDescription Description Exemplary Exemplary Exemplary TOPOITOPOI TOPOI MapMap Map

Node city (n = 1) InNode Nodethe two citycity study ((nn == 1) 1)regions, only one node city was identified (Braunschweig).InIn thethe twotwo studystudy Due regions,regions, to its onlyphysicalonly oneone nodeform,node city citycomprising waswas identifiedidentified large (Braunschweig).Node city (n = 1)Due to its physical form, comprising large un-built(Braunschweig).In the areas, two studysuch Due regions,as parks,to its only physical this one TOPOS node form, city is comprising waslarge identified but the large un-built areas, such as parks, this TOPOS is large but the leastun-built (Braunschweig).compact areas, agglomeration. such Due as toparks, its physical The this node TOPOS form, city comprising is TOPOS large but has large the the un-built least compact agglomeration. The node city TOPOS has the highestleastareas, compact count such of asagglomeration. public parks, thistransport TOPOS The connections: is node large city but theTOPOS no least othercompact has the highestagglomeration. count of public The node transport city TOPOS connections: has the highest no other count of public TOPOIhighest are count so well of public connected transport to other connections: settlement no units, other even TOPOItransport are so connections: well connected no other to TOPOI other aresettlement so well connected units, even to other thoughTOPOIsettlement its are dimension so units, well evenconnected increases though to itsthe other dimension distance settlement increasesto the units, closest the distanceeven re- to though its dimension increases the distance to the closest re- gionalthoughthe train closest its stationdimension regional and train increases decreases station the andthe distance decreasesamount to theof the settlement amount closest of re- gionalsettlement train station units in and its surroundings decreases the (3 kmamount radius). of Thesettlement retail ratio is unitsgional in itstrain surroundings station and decreases(3 km radius). the amount The retail of settlement ratio is unitsunitshighest inin itsits in surroundingssurroundings comparison to (3(3 other kmkm TOPOI.radius).radius). In TheThe the retailtworetail study ratioratio regions, isis the highestnode in citycomparison shows a high to othe diversityr TOPOI. of functions In the and two large study number re- of highesthighest inin comparisoncomparison toto otheotherr TOPOI.TOPOI. InIn thethe twotwo studystudy re-re- gions,buildings, the node and city the shows population a high density diversity (43 inhabitants/ha) of functions and is the gions,gions, thethe nodenode citycity showsshows aa highhigh diversitydiversity ofof functionsfunctions andand large secondnumber highest of buildings, of all TOPOI. and the population density (43 largelarge numbernumber ofof buildings,buildings, andand thethe populationpopulation densitydensity (43(43 inhabitants/ha) is the second highest of all TOPOI. inhabitants/ha)inhabitants/ha) isis thethe secondsecond highesthighest ofof allall TOPOI.TOPOI.

Node town (n = 7) NodeNode towntown ((nn == 7)7) NodeNodeNodeNode towns townstowns town are areare ( nthe =thethe 7)second secondsecond least leastleast compact compactcompact TOPOI. TOPOI.TOPOI. They TheyThey fea- fea-fea- turetureture aNode high aa highhigh towns diversity diversitydiversity are the of of secondof functions. functions.functions. least compactTheir TheirTheir public public TOPOI.public transport transporttransport They feature con- con-con- a high nectivitynectivitydiversity and and ofpopulation functions.population Theirdensity density public are are transport around around connectivityhalf half of of that that andof of a a nectivitypopulation and densitypopulation are around density half are of around that of a half node of city. that The of a nodenode city. city. The The settlement settlement density density in in a a3 3km km radius radius is is much much nodesettlement city. The density settlement in a 3 kmdensity radius in is a much 3 km higher radius than is much in node cities. higherhigherhigherThe than thandistancethan in in innode node tonode a cities. regional cities.cities. The The trainThe distance distancedistance station, measuredto toto a a aregional regionalregional from train trainthetrain centroid, station,station,station,is less measured measuredmeasured than 2 km, from fromfrom which the thethe means centroid, centroid,centroid, that theseis isis less lessless settlement than thanthan 2 22 km, km, unitskm, which whichwhich are strategically well located with respect to public transport. meansmeansmeans that thatthat these thesethese settlement settlementsettlement units unitsunits are areare strategically strategicallystrategically well wellwell lo- lo-lo- catedIn comparisonwith respect to ato node public city, transport. these settlement In comparison units have to a lowera catedcatedretail with with ratio. respect respect to to public public transport. transport. In In comparison comparison toto aa nodenodenode city, city,city, these thesethese settlement settlementsettlement units unitsunits have havehave a a alower lowerlower retail retailretail ratio. ratio.ratio.

PeriurbanPeriurbanPeriurban town towntown (n (( n=n =24)= 24)24) PeriurbanPeriurbanPeriurban towns townstowns are areare well wellwell connected connectedconnected to toto node nodenode cities citiescities and andand node nodenode towns.towns.towns.Periurban Since SinceSince they towntheythey are are (aren relatively= relativelyrelatively 24) small, small,small, they theythey feature featurefeature relatively relativelyrelatively shortshortshort Periurbandistances distancesdistances towns to to toregional regionalregional are well train connected traintrain stations, stations,stations, to node which whichwhich cities are areare and comfort- comfort-comfort- node towns. Since they are relatively small, they feature relatively short distances ablyablyably reachable reachablereachable on onon foot footfoot or oror by byby bike bikebike from fromfrom within withinwithin the thethe given givengiven set- set-set- tlementto regional unit. trainPeriurban stations, towns which are are comparatively comfortably reachable smaller on foot or tlementtlementby bikeunit. unit. from Periurban Periurban within the towns towns given are settlementare comparatively comparatively unit. Periurban smallersmaller towns are than node towns but have not significantly different charac- thanthan nodecomparatively node towns towns but smallerbut have have than not not node significantly significantly towns but different havedifferent not significantly charac-charac- teristics with respect to functional richness and retail ratio. teristicsteristicsdifferent with with characteristicsrespect respect to to functional functional with respect richness richness to functional and and retail retail richness ratio.ratio. and retail Theyratio. are They slightly are slightly smaller smaller in other in other indicators, indicators, such such as aspopula- population TheyThey are are slightly slightly smaller smaller in in other other indicators, indicators, such such asas popula-popula- tiondensity, density, building building density density and slightlyand slightly bigger bigger in terms in of terms open spaceof tiontion density, density, building building density density and and slightly slightly bigger bigger inin termsterms ofof openratio. space Additionally, ratio. Additionally, in comparison in tocomparison node towns, to periurban node towns openopen havespace space fewer ratio. ratio. connections Additionally, Additionally, to other in in settlement comparison comparison units to to via nodenode public transport. towns, periurban towns have fewer connections to other set- towns,towns, periurban periurban towns towns have have fewer fewer connections connections toto otherother set-set- tlement units via public transport. tlementtlement units units via via public public transport. transport. Sustainability 2021, 13, x FOR PEER REVIEW 21 of 33 Sustainability 20212021,, 1313,, xx FORFOR PEERPEER REVIEWREVIEW 2121 ofof 3333 Sustainability 2021, 13, x FOR PEER REVIEW 21 of 33

Sustainability 2021, 13, 4099 21 of 33

ExoSustainability satellite 2021 towns, 13, x FOR(n == PEER 9)9) REVIEW 21 of 33 Exo satellite towns (n = 9) Exo satellite towns have the highest population density (me- Exo satellite towns have the highest population densityTable (me- A3. Cont. dian of 48 inhabitants/ha) They have a very low connectivity dianby public of 48 transportinhabitants/ha) and their They distance have a tovery a regional low connectivity train sta- bExoy public satellite transport towns and(n = their9)TOPOI distance Description to a regional train sta- Exemplary TOPOI Map btiony public is with transport more than and 4 theirkm relatively distance high.to a regional Due to theirtrain sta- tiontionExoExo issatelliteis withwith satellite moremore towns towns thanthan have (n 44= kmkmthe 9) relativelyrelativelyhighest population high.high. DueDue density toto theirtheir (me- proximitytion is with and more transport than 4 kmconnectivity relatively to high. periurban Due to towns,their proximitydianExo of satellite48 andinhabitants/ha) townstransport have connectivity the They highest have population ato very periurban low density connectivity towns, (median of 48 theyproximity can be and considered transport as connectivity their suburban to periurban developments, towns, pre- theytheyby inhabitants/ha)public cancan bebe transport consideredconsidered They and have asas their theithei a verydistancerr suburbansuburban low connectivityto a developments,developments, regional by train public pre-sta-pre- they can be considered as their suburban developments, pre- dominantlytransport andcharacterized their distance by to ho ausing. regional However, train station exo is satellite with more tionthan is with 4 km more relatively than high. 4 km Due relatively to their proximityhigh. Due and to their transport townsdominantly still have characterized a high functional by housing. diversity. However, exo satellite townsproximityconnectivity still andhave totransport a periurban high functional connectivity towns, they diversity. canto periurban be considered towns, as their Periurbantowns still villagehave a high(n = 42)functional diversity. Periurbantheysuburban can be village considered developments, (n == 42)42) as predominantlytheir suburban characterized developments, by housing. pre- PeriurbanThe periurban village villages (n = 42)have a relatively high ratio of retail ThedominantlyHowever, periurban characterized exo villages satellite have towns by a stillho reusing.lativelylatively have aHowever, high highhigh functional ratioratio exo ofof retailretailsatellite diversity. andThe periurbana high functional villages richness. have a re Thelatively accessibility high ratio to ofa regional retail andtownsPeriurban a high still havefunctional village a high (n richness.= functional 42) The diversity. accessibility to a regional trainand a station high functional is—in comparison richness. Theto the accessibility (larger) size to of a regionalother traintrainPeriurbanThe stationstation periurban village is—inis—in villages (comparisoncomparisonn = 42) have a relatively toto thethe (larger)(larger) high ratio sizesize of of retailof otherother and a high settlementtrainfunctional station units—a is—in richness. comparison very The high accessibility (median to the to (larger) a1.7 regional km). size This train of means,other station is—in settlementsettlementThe periurban units—aunits—a villages veryvery have highhigh a (median(medianrelatively 1.71.7 high km).km). ratio ThisThis of means,means, retail fromsettlementcomparison within units—a a toperiurban the (larger)very village,high size (median of regional other settlement 1.7 train km). stations This units—a means, are very high fromfromand a withinwithin high functional aa periurbanperiurban richness. village,village, The regionalregional accessibility traintrain stationsstations to a regional areare comfortablyfrom(median within 1.7 areachable km).periurban This on means, village, foot fromand regional withinby bike. atrain periurbanIn general, stations village, theare comfortablytrainregional station train reachableis—in stations comparison on are foot comfortably and to the by (larger)bike. reachable In sizegeneral, on footof other and the by bike. connectivitycomfortably reachableby public transporton foot and is almostby bike. as In high general, as that the of connectivitysettlementIn general, units—a by the public connectivity very transport high by (median public is almost transport 1.7 as km). high is This almost as thatmeans, as highof as periurbanconnectivitythat of periurban towns. by public Amongst towns. transport Amongst all ot heris allalmost village other as village types, high types, asthe that peri- the of periurbanfrom within towns. a periurban Amongst village, all ot herregional village train types, stations the peri- are urbanperiurbanperiurban villages towns. villages are Amongst the are least the least compact.all ot compact.her village types, the peri- urbancomfortably villages reachable are the least on foot compact. and by bike. In general, the urban villages are the least compact. connectivity by public transport is almost as high as that of periurban towns. Amongst all other village types, the peri- Smallurban periurbanvillages are village the least (n compact.== 37)37) Small periurban village (n = 37) SmallSmall periurban periurban villages village have (n = 37)on average a medium number SmallSmall periurban periurban villages villages have have on averageaverage a a medium medium number number of of connections by public transport and also diversified func- tions.of connectionsconnections These settlement byby public public transportunits transport have and theand also highest also diversified diversified average functions. build- func- These tions.tions.Smallsettlement TheseThese periurban settlementsettlement units village have the unitsunits (n highest = ha ha37)ve average the highest building average density. build- The ingtions. density. These Thesettlement presence units of retailhave andthe highest agricultural average buildings build- ingingSmallpresence density.density. periurbanof TheThe retail presencepresencevillages and agricultural have ofof retailretail on average buildings andand agriculturalagricultural a in medium particular buildingsbuildings number in small ining particularpercentages density. The in contributes small presence percentages to of their retail village contributesand status.agricultural to their buildings village ininof particularparticularconnections inin bysmallsmall public percentagespercentages transport contributescontributes and also diversified toto theirtheir villagevillage func- status.in particular in small percentages contributes to their village status.status.tions. These settlement units have the highest average build- status. ing density. The presence of retail and agricultural buildings in particular in small percentages contributes to their village status. Exo village (n == 524)524) AnExo exo village village (n =is 524) generally located more than 6 km from a An Exoexo villagevillage ( nis= generally 524) located more than 6 km from a An exo village is generally located more than 6 km from a regionalregionalAn exo traintrain village station.station. is generally TheyThey locatedhavehave aa morehighhigh thannumbernumber 6 km ofof from functions,functions, a regional includingregionaltrain station. train a small station. They percentage have They a high have of number agricultural a high of number functions, buildings, of including functions, while a small includingincludingExo village aa smallsmall(n = 524) percentagepercentage ofof agriculturalagricultural buildings,buildings, whilewhile theincludingpercentage retail ratio a small of is agricultural very percentage low. buildings,The of settlement agricultural while theunits buildings, retail around ratio iswhile exo very low. thetheAn retailexoretail village ratioratio isisis veryverygenerally low.low. TheThelocated settlementsettlement more than unitsunits 6 kmaroundaround from exoexo a villagestheThe retail settlement are ratio few is and unitsvery the aroundlow. connectivity The exo settlement villages to areother units few settlement and around the connectivity exo villagesregionalto other are train settlement few station. and the units They connectivity is low.have Exo a high villages to othernumber are settlement mostly of functions, found in the unitsvillages is low.are few Exo and villages the connectivity are mostly found to other in settlementthe larger unitsincludinglarger is low. Braunschweig a small Exo villages percentage region. are mostly Inof theagricultural region found Vechta-Diepholz-Verden, in buildings, the larger while Braunschweigunitsthey is appearlow. Exo closeregion. villages to periurban In theare regionmostly towns. Vechta-Diepholz- found in the larger Braunschweigthe retail ratio region.is very low.In the The region settlement Vechta-Diepholz- units around exo Verden,Braunschweig they appear region. close In the to regionperiurban Vechta-Diepholz- towns. Verden,villages arethey few appear and theclose connectivity to periurban to towns.other settlement Verden, they appear close to periurban towns. units is low. Exo villages are mostly found in the larger Braunschweig region. In the region Vechta-Diepholz- Small exo village (count 73) SmallVerden, exo they village appear (count close 73) to periurban towns. SmallSmall exo exo village village (count (count 73) 73) Small exo villages are very isolated with a large distance to a stationSmallSmall exo and exo villages limited villages are access are very very to isolated isolated public withtransport.with a a large large They distance distance are to in ato av- station a stationstationand limitedandand limitedlimited access accessaccess to public toto transport.publicpublic transport.transport. They are TheyThey in average areare inin 14 av-av- ha big, eragestation 14 and ha limitedbig, have access no retail to public function, transport. and a Theygenerally are in low av- erageSmallhave 14 exo no ha retailvillage big, function,have (count no andretail 73) a generallyfunction, low and functional a generally richness. low functionalerageThe 14 share ha richness. big, of agricultural have The no shareretail buildings function,of agricultural is comparatively and a buildings generally high withis low 13% functionalfunctionalSmall exo villagesrichness.richness. are TheThe very shareshare isolated ofof agriculturalagricultural with a large buildingsbuildings distance isis to a comparativelyfunctionalon average. richness. high Thewith share 13% onof agriculturalaverage. buildings is comparativelystation and limited high accesswith 13% to public on average. transport. They are in av- comparatively high with 13% on average. erage 14 ha big, have no retail function, and a generally low functional richness. The share of agricultural buildings is comparatively high with 13% on average.

Sustainability 2021, 13, 4099 22 of 33

SustainabilitySustainability 20212021,,, 1313,,, xxx FORFORFOR PEERPEERPEER REVIEWREVIEWREVIEW 2222 ofof 3333

Table A3. Cont.

TOPOI Description Exemplary TOPOI Map

DisseminatedDisseminated villagevillage ((nn === 160)160)160) Most of the disseminated villages are part of a dense fine- MostMostDisseminated ofof thethe disseminateddisseminated village (n =villagesvillages 160) areare partpart ofof aa densedense fine-fine- meshedmeshedMost network ofnetwork the disseminated (median:(median: villages4444 unitsunits are withinwithin part of 33 a km)km) dense ofof fine-meshedvillagesvillages andandnetwork characterizedcharacterized (median: byby 44 aa units largelarge within remoteness.remoteness. 3 km) of TheThe villages connectivityconnectivity and characterized isis lowlowby andand a large thethe distance remoteness.distance toto Thethethe connectivityrailwayrailway stationstation is low isisand thethe thehighesthighest distance to the comparedrailway stationto all other is the TOPOI highest comparedwith almost to all 9 km. other The TOPOI compar- with comparedcomparedalmost 9 toto km. allall The otherother comparatively TOPOITOPOI withwith high almostalmost proportion 99 km.km. of TheThe farm compar-compar- buildings of atively high proportion of farm buildings of almost 9% of ativelyativelyalmost highhigh 9% proportionproportion of the surface ofof area farmfarm indicates buildingsbuildingsbuildings its agricultural ofofof almostalmostalmost 9%9%9% character. ofofof thethe surfacesurface areaarea indicatesindicates itsits agriculturalagricultural character.character.

AgriAgri villagevillage ((nn === 35)35)35) AgriAgriAgri villagesvillages village areare (n characterized=characterized 35) byby aa comparativelycomparatively highhigh pro-pro- portionportionAgri villages(14.2%(14.2% of areof thethe characterized totaltotal area)area) by ofof a agriculturalagricultural comparatively facilities.facilities. high proportion (14.2% of the total area) of agricultural facilities. They are located TheyThey areare locatedlocated ratherrather farfar fromfrom otherother settlementsettlement unitsunits andand railwayrather stations far from but other have settlement good connections units and railway to the stations bus-based but have railwayrailwaygood stationsstations connections butbut to havehave the bus-based goodgood connectionsconnectionsconnections public transport tototo thethethe network. bus-basedbus-basedbus-based publicpublic transporttransport network.network.

DisseminatedDisseminated hamlethamlet ((nn === 1071)1071)1071) ThisDisseminated TOPOS consists hamlet of (an large= 1071) group of units. Access is only ThisThisThis TOPOSTOPOS TOPOS consistsconsists consists ofof of aa a largelarge group groupgroup of ofof units. units.units. Access AccessAccess is only isis onlyonly possible possible via individual mobility. They have a low number of possiblepossiblevia individual viavia individualindividual mobility. mobility.mobility. They have TheyThey a low havehave number aa lowlow of functionsnumbernumber of andof no functionsfunctionsretail. However, andand nono retail.retail. disseminated However,However, hamlets disseminateddisseminated have a big numberhamletshamlets of other havehavesettlements aa bigbig numbernumber around. ofof ototherher settlementssettlements around.around.

DisseminatedDisseminated livingliving agriagri hamlethamlet ((nn === 4283)4283)4283) DisseminatedDisseminatedDisseminated livingliving living agriagri agri hamletshamlets hamlet form (formn = 4283) thethe biggestbiggest TOPOITOPOI groupgroupDisseminated andand areare mostlymostly living agri foundfound hamlets inin thethe form regionregion the biggest ofof Vechta-Di-Vechta-Di- TOPOI group and are mostly found in the region of Vechta-Diepholz-Verden. They epholz-Verden.epholz-Verden. TheyThey featurefeature mainlymainly twotwo functions:functions: agricul-agricul- turefeature and living, mainly and two they functions: are finely agriculture dispersed and living, within and a dense they are tureturefinely andand dispersedliving,living, andand within theythey a areare dense finelyfinely network disperseddispersed of hamlets withinwithin (median: aa densedense 38units networknetworkwithin ofof 3 km). hamletshamlets There (median:(median: are no public 3838 unitsunits transport withinwithin options 33 km).km). available. ThereThere areare nono publicpublic transporttransport optionsoptions available.available.

Sustainability 2021, 13, 4099 23 of 33

Table A4. Spatio-temporal growth: land-uptake in hectares based on residential buildings according to building age classes and associated areas without transport infrastructure per TOPOI type (TOTAL = total growth per building age class; averages: 1st quartile, median, 3rd quartile).

Building Age Class

1860– 1919– 1949– 1958– 1969– 1979– 1984– 1995– 2002– 2010– 1918 1948 1957 1968 1978 1983 1994 2001 2009 2011 TOPOI Count Land-Uptake Residential Buildings 1 (ha) Node City1 TOTAL 236.8 448.5 212.9 236.5 205.1 59.1 79.9 54.7 90.8 5.4 TOTAL 241.0 272.4 408.6 955.8 774.4 317.3 497.3 420.4 266.3 37.2 1st Quartile 15.3 25.9 39.2 83.7 87.2 40.1 51.0 45.9 37.5 2.4 Node Town 7 Median 33.8 37.2 45.7 120.6 109.2 43.8 68.9 63.3 42.9 4.3 3rd Quartile 43.1 46.8 86.2 173.4 139.0 52.3 76.6 76.0 45.1 8.7 TOTAL 495.4 795.6 665.9 1.178.2 1.013.9 371.5 627.9 580.3 332.1 29.2 Periurban 1st Quartile 5.5 12.9 15.2 33.8 29.2 11.1 20.4 48.3 8.5 0.3 24 Town Median 13.3 25.3 22.5 47.1 39.2 15.7 24.6 23.3 13.5 0.8 3rd Quartile 25.0 45.7 34.2 62.5 55.6 18.7 33.7 34.1 18.3 2.0 TOTAL 362.6 355.5 468.9 818.3 770.5 376.0 658.7 660.1 425.8 38.8 Periurban 1st Quartile 4.4 4.1 5.5 12.3 12.9 5.4 9.9 1.1 5.6 0.1 42 Village Median 1.0 7.5 9.0 15.8 16.5 8.5 13.3 15.5 9.1 0.5 3rd Quartile 10.2 11.4 12.7 25.1 23.5 11.4 20.5 19.6 12.5 1.1 TOTAL 8.0 8.0 14.4 132.7 69.7 16.8 31.7 21.8 9.2 1.9 Exo Satellite 1st Quartile 0.1 0.0 0.5 0.5 0.6 0.2 0.2 0.0 0.0 0.0 9 Town Median 0.2 0.0 1.3 3.8 4.2 0.4 0.8 0.7 0.2 0.0 3rd Quartile 0.3 0.4 2.5 23.8 16.1 1.3 2.9 1.2 0.7 0.1 TOTAL 104.6 85.5 80.0 152.6 164.1 61.3 151.4 180.1 88.5 5.1 Small 1st Quartile 0.1 0.8 0.9 1.7 1.5 0.6 1.6 2.0 0.7 0.0 Periurban 37 Village Median 2.3 1.5 1.6 3.2 3.8 1.4 3.2 4.2 1.8 0.0 3rd Quartile 3.4 2.5 3.4 6.5 5.0 2.2 5.4 7.3 4.0 0.3 TOTAL 1479.7 974.2 984.8 2024.7 2117.0 915.5 1366.1 1695.8 1049.0 60.4 1st Quartile 0.8 0.5 0.5 1.1 1.0 0.4 0.5 0.7 0.3 0.0 Exo Village 524 Median 2.1 1.0 1.1 2.5 2.4 1.0 1.5 1.8 1.0 0.0 3rd Quartile 3.8 2.0 2.4 5.0 5.0 2.2 0.3 4.4 2.4 0.0 TOTAL 60.3 20.5 24.5 42.7 45.6 23.1 40.7 47.4 27.4 1.6 Small Exo 1st Quartile 0.3 0.0 0.0 0.2 0.1 0.0 0.2 0.2 0.0 0.0 73 Village Median 0.6 0.3 0.3 0.4 0.4 0.2 0.4 0.4 0.3 0.0 3rd Quartile 1.2 0.4 0.5 0.9 0.7 0.4 0.7 0.7 0.4 0.0 TOTAL 142.9 134.8 127.4 239.1 281.9 127.9 252.2 314.6 187.3 13.6 Disseminated 1st Quartile 0.3 0.3 0.1 0.3 0.2 0.0 0.1 0.2 0.0 0.0 160 Village Median 0.5 0.5 0.4 0.7 0.8 0.3 0.6 0.7 0.5 0.0 3rd Quartile 1.2 1.1 0.9 2.1 2.0 0.9 1.8 2.4 1.5 0.0 TOTAL 31.4 20.4 16.9 24.4 28.2 11.6 24.1 27.0 11.4 0.8 1st Quartile 0.3 0.1 0.0 0.2 0.2 0.0 0.1 0.0 0.0 0.0 Agri Village 35 Median 0.6 0.5 0.3 0.3 0.3 0.2 0.3 0.3 0.0 0.0 3rd Quartile 1.2 0.9 0.5 0.8 0.7 0.5 0.7 0.7 0.3 0.0 TOTAL 87.5 83.9 57.4 109.8 83.7 29.7 55.9 59.5 42.4 1.8 Disseminated 1st Quartile 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.071 Hamlet Median 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3rd Quartile 0.2 0.2 0.1 0.2 0.1 0.0 0.1 0.1 0.0 0.0 Sustainability 2021, 13, 4099 24 of 33

Table A4. Cont.

Building Age Class

1860– 1919– 1949– 1958– 1969– 1979– 1984– 1995– 2002– 2010– 1918 1948 1957 1968 1978 1983 1994 2001 2009 2011 TOPOI Count Land-Uptake Residential Buildings 1 (ha) TOTAL 198.5 182.8 126.6 200.4 153.0 57.4 104.0 103.0 59.8 2.4 Disseminated 1st Quartile 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Living Agri 4.283 Hamlet Median 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3rd Quartile 0.1 0.1 0.1 0.1 0.1 0.0 0.0 0.0 0.0 0.0 TOTAL 0.9 0.9 1.2 1.5 0.8 0.3 0.0 0.6 0.1 0.0 Sustainability 2021Exo , 13, x FOR PEER1st REVIEW Quartile 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 25 of 33 Industrial 35 Zone Median 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3rd Quartile 0.0 0.1 0.1 0.1 0.1 0.0 0.1 0.1 0.0 0.0

FigureFigure A1. Spatio-temporal A1. Spatio-temporal growth: growth: annual annual land-uptake land-uptake per per TOPOI TOPOI typetype inin hectares hectares based based on on residential residential buildings buildings ac- cordingaccording to building to building age classes age classes and andassociat associateded areas areas without without transport infrastructure. infrastructure.

Figure A2. Spatio-temporal growth: visualisation of the expansion of the settlement area based on residential buildings according to building age classes. Developments in the time period before 1859 are shown with a resolution (grid size) of 1 km. Sustainability 2021, 13, x FOR PEER REVIEW 25 of 33

Sustainability 2021, 13, 4099 25 of 33 Figure A1. Spatio-temporal growth: annual land-uptake per TOPOI type in hectares based on residential buildings ac- cording to building age classes and associated areas without transport infrastructure.

FigureFigureSustainability A2. A2.Spatio-temporal Spatio-temporal 2021, 13, x FOR PEER growth: growth: REVIEW visualisation visualisation of the of expansionthe expansion of the of settlement the settlement area based area on based residential on residential buildings buildings26 of 33

accordingaccording to to building building age age classes. classes. Developments Developments in the time in the period time before period 1859 before are shown 1859 withare shown a resolution with (grid a resolution size) of 1 km.(grid size) of 1 km.

Figure A3. Spatio-temporal growth: visualisation of the expansion of the settlement area based on residential buildings Figure A3. Spatio-temporal growth: visualisation of the expansion of the settlement area based on residential buildings according to building age classes. Developments in the time period between 1860–1918 are shown with a resolution (grid accordingsize) to buildingof 1 km. age classes. Developments in the time period between 1860–1918 are shown with a resolution (grid size) of 1 km.

Figure A4. Spatio-temporal growth: visualisation of the expansion of the settlement area based on residential buildings according to building age classes. Developments in the time period between 1919–1948 are shown with a resolution (grid size) of 1 km. Sustainability 2021, 13, x FOR PEER REVIEW 26 of 33

Sustainability 2021Figure, 13, 4099A3. Spatio-temporal growth: visualisation of the expansion of the settlement area based on residential buildings26 of 33 according to building age classes. Developments in the time period between 1860–1918 are shown with a resolution (grid size) of 1 km.

Figure A4.FigureSpatio-temporal A4. Spatio-temporal growth: growth: visualisation visualisation of the of expansion the expansion of the of settlementthe settlement area area based based on residentialon residential buildings buildings according to building age classes. Developments in the time period between 1919–1948 are shown with a resolution (grid accordingSustainability to building 2021, 13, x age FOR classes. PEER REVIEW Developments in the time period between 1919–1948 are shown with a resolution (grid27 of 33 size) of 1 km. size) of 1 km.

Figure A5. Spatio-temporal growth: visualisation of the expansion of the settlement area based on residential buildings Figure A5. Spatio-temporal growth: visualisation of the expansion of the settlement area based on residential buildings according to building age classes. Developments in the time period between 1949–1957 are shown with a resolution (grid accordingsize) to of building 1 km. age classes. Developments in the time period between 1949–1957 are shown with a resolution (grid size) of 1 km.

Figure A6. Spatio-temporal growth: visualisation of the expansion of the settlement area based on residential buildings according to building age classes. Developments in the time period between 1958–1968 are shown with a resolution (grid size) of 1 km. Sustainability 2021, 13, x FOR PEER REVIEW 27 of 33

Sustainability 2021Figure, 13, 4099A5. Spatio-temporal growth: visualisation of the expansion of the settlement area based on residential buildings27 of 33 according to building age classes. Developments in the time period between 1949–1957 are shown with a resolution (grid size) of 1 km.

Figure A6. Spatio-temporal growth: visualisation of the expansion of the settlement area based on residential buildings Figure A6. Spatio-temporal growth: visualisation of the expansion of the settlement area based on residential buildings according to building age classes. Developments in the time period between 1958–1968 are shown with a resolution (grid accordingSustainability to building 2021, 13, x age FOR classes. PEER REVIEW Developments in the time period between 1958–1968 are shown with a resolution (grid28 of 33 size) of 1 km. size) of 1 km.

Figure A7.FigureSpatio-temporal A7. Spatio-temporal growth: growth: visualisation visualisation of the of expansion the expansion of the of settlementthe settlement area area based based on on residential residential buildings buildings according to building age classes. Developments in the time period between 1969–1978 are shown with a resolution (grid according to building age classes. Developments in the time period between 1969–1978 are shown with a resolution (grid size) of 1 km. size) of 1 km.

Figure A8. Spatio-temporal growth: visualisation of the expansion of the settlement area based on residential buildings according to building age classes. Developments in the time period between 1979–1983 are shown with a resolution (grid size) of 1 km. Sustainability 2021, 13, x FOR PEER REVIEW 28 of 33

Sustainability 2021Figure, 13, A7. 4099 Spatio-temporal growth: visualisation of the expansion of the settlement area based on residential buildings28 of 33 according to building age classes. Developments in the time period between 1969–1978 are shown with a resolution (grid size) of 1 km.

Figure A8. Spatio-temporal growth: visualisation of the expansion of the settlement area based on residential buildings Figure A8. Spatio-temporal growth: visualisation of the expansion of the settlement area based on residential buildings according to building age classes. Developments in the time period between 1979–1983 are shown with a resolution (grid Sustainability 2021, 13, x FOR PEER REVIEW 29 of 33 according size) to of building 1 km. age classes. Developments in the time period between 1979–1983 are shown with a resolution (grid size) of 1 km.

Figure A9. Spatio-temporal growth: visualisation of the expansion of the settlement area based on residential buildings Figure A9. Spatio-temporal growth: visualisation of the expansion of the settlement area based on residential buildings according to building age classes. Developments in the time period between 1984–1994 are shown with a resolution (grid accordingsize) to of building 1 km. age classes. Developments in the time period between 1984–1994 are shown with a resolution (grid size) of 1 km.

Figure A10. Spatio-temporal growth: visualisation of the expansion of the settlement area based on residential buildings according to building age classes. Developments in the time period between 1995–2001 are shown with a resolution (grid size) of 1 km. Sustainability 2021, 13, x FOR PEER REVIEW 29 of 33

Sustainability 2021Figure, 13, 4099A9. Spatio-temporal growth: visualisation of the expansion of the settlement area based on residential buildings29 of 33 according to building age classes. Developments in the time period between 1984–1994 are shown with a resolution (grid size) of 1 km.

Figure A10. Spatio-temporal growth: visualisation of the expansion of the settlement area based on residential buildings Figure A10. Spatio-temporal growth: visualisation of the expansion of the settlement area based on residential buildings according to building age classes. Developments in the time period between 1995–2001 are shown with a resolution (grid Sustainability 2021, 13, x FOR PEER REVIEW 30 of 33 according size) to of building 1 km. age classes. Developments in the time period between 1995–2001 are shown with a resolution (grid size) of 1 km.

Figure A11. Spatio-temporal growth: visualisation of the expansion of the settlement area based on residential buildings Figure A11. Spatio-temporal growth: visualisation of the expansion of the settlement area based on residential buildings according to building age classes. Developments in the time period between 2002–2009 are shown with a resolution (grid according to building age classes. Developments in the time period between 2002–2009 are shown with a resolution (grid size) of 1 km. size) of 1 km.

Figure A12. Spatio-temporal growth: visualisation of the expansion of the settlement area based on residential buildings according to building age classes. Developments in the time period between 2010–2011 are shown with a resolution (grid size) of 1 km. Sustainability 2021, 13, x FOR PEER REVIEW 30 of 33

SustainabilityFigure2021, 13A11., 4099 Spatio-temporal growth: visualisation of the expansion of the settlement area based on residential buildings30 of 33 according to building age classes. Developments in the time period between 2002–2009 are shown with a resolution (grid size) of 1 km.

FigureFigure A12. A12.Spatio-temporal Spatio-temporal growth: growth: visualisation visualisation of the of expansionthe expansion of the of settlementthe settlement area area based based on residential on residential buildings buildings according to building age classes. Developments in the time period between 2010–2011 are shown with a resolution (grid according to building age classes. Developments in the time period between 2010–2011 are shown with a resolution (grid size) of 1 km. size) of 1 km.

Table A5. Street types [46]—layer structure and sizes in accordance with [47–50].

Classification Width (m) Driving Direction 1 motorway 12.0 1 2 trunk 12.0 1 3 primary 4.25 1 4 secondary 4.25 1 5 tertiary 6.5 2 6 residential 6.5 2 7 service 4.0 2 8 living street 4.5 2 9 footway 1.5 1 10 path 1.5 1 11 pedestrian 4.5 2 12 cycleway 1.5 1

References 1. Baccini, P.; Oswald, F. Netzstadt—Transdisziplinäre Methoden zum Umbau urbaner Systeme; vdf Hochschulverlag an der ETH Zürich: Zürich, Switzerland, 1998. 2. Carlow, V.M.; Mumm, O.; Neumann, D.; Schneider, A.-K.; Schröder, B.; Sedrez, M.; Zeringue, R. TOPOI—A method for analysing settlement structures and their linkages in an urban rural fabric. Environ. Plan. B Urban Anal. City Sci. 2021. under review. 3. Carlow, V.M.; Institute for Sustainable Urbanism ISU (Eds.) Ruralism—The Future of Villages and Small Towns in an Urbanizing World; Jovis: Berlin, Germany, 2016. 4. Diener, R.; Herzog, J.; Meili, M.; Meuron, P.D.; Schmid, C. Die Schweiz, ein Städtebauliches Portrait; Birkhäuser: Basel, Switzerland, 2005. [CrossRef] 5. Koolhaas, R. Koolhaas in the country. Icon 2014. Available online: www.iconeye.com/architecture/features/rem-koolhaas-in- the-country (accessed on 15 January 2021). 6. Sieverts, T. Zwischenstadt: Zwischen Ort und Welt, Raum und Zeit, Stadt und Land; Vieweg: Braunschweig, Germany, 1997; p. 173. Sustainability 2021, 13, 4099 31 of 33

7. McGrath, B.; Pickett, S.T.A. The Metacity: A Conceptual Framework for Integrating Ecology and Urban Design. Challenges 2011, 2, 55–72. [CrossRef] 8. Ascher, F. Metapolis: Ou L’avenir des Villes; Odile Jacob: Paris, France, 1995. 9. Healey, P. Urban-Rural Relationships, Spatial Strategies and Territorial Development. Built Environ. 2002, 28, 331–339. 10. MSGG, Niedersächsisches Ministerium für Soziales Gesundheit und Gleichstellung. Handlungsorientierte Sozialberichterstattung Niedersachsen—Statistikteil; Niedersächsisches Ministerium für Soziales Gesundheit und Gleichstellung: Hanover, Germany, 2013; Available online: www.sozialberichterstattung-niedersachsen.de (accessed on 6 April 2021). 11. Akkoyunlu, S. The Potential of Rural–Urban Linkages for Sustainable Development and Trade. Int. J. Sustain. Dev. World Policy 2015, 4, 20–40. [CrossRef] 12. Andersson, K.; Eklund, E.; Lehtola, M.M.; Salmi, P. Beyond the Rural-Urban Divide: Cross-Continental Perspectives on the Differentiated Countryside and Its Regulation; Emerald Jai: Bingley, UK, 2009. 13. Tacoli, C. The Earthscan Reader in Urban-Rural Linkages; Earthscan: London, UK, 2006. 14. EEA, European Environment Agency. Land Take in Europe. Available online: https://www.eea.europa.eu/data-and-maps/ indicators/land-take-3/assessment (accessed on 15 January 2021). 15. EEA, European Environment Agency. Land Take and Net Land Take. Available online: https://www.eea.europa.eu/data-and- maps/dashboards/land-take-statistics#tab-based-on-data (accessed on 15 January 2021). 16. UBA, Federal Environment Agency. Siedlungs- und Verkehrsfläche. Available online: https://www.umweltbundesamt.de/ daten/flaeche-boden-land-oekosysteme/flaeche/siedlungs-verkehrsflaeche#anhaltender-flachenverbrauch-fur-siedlungs- und-verkehrszwecke- (accessed on 15 January 2021). 17. UBA, Federal Environment Agency. Indikator: Siedlungs- und Verkehrsfläche. Available online: https://www.umweltbundesamt. de/indikator-siedlungs-verkehrsflaeche#wie-wird-der-indikator-berechnet (accessed on 15 January 2021). 18. IOER, Leibniz Institute of Ecological Urban and Regional Development. Monitor der Siedlungs- und Freiraumentwicklung (IÖR-Monitor). Available online: https://www.ioer-monitor.de (accessed on 15 January 2021). 19. Hoymann, J.; Dosch, F.; Beckmann, G. Status quo und Projektion 2030—Trends der Siedlungsflaächenentwicklung; BBSR, Federal Institute for Research on Building, Urban Affairs and Spatial Development in the Federal Office for Building and Regional Planning (BBR), Eds.; BBSR: Bonn, Germany, 2012. 20. Herczeg, M.R.; McKinnon, D.; Milios, L.; Bakas, I.; Klaassens, E.; Svatikova, K.; Widerberg, O. Resource Efficiency in the Building Sector; ECORYS: Rotterdam, The Netherlands, 2014. 21. Landesamt für Statistik Niedersachsen LSN. LSN Online—Regionaldatenbank für Niedersachsen. Available online: https: //www1.nls.niedersachsen.de/statistik/default.asp (accessed on 15 January 2021). 22. DENA, German Energy Agency. dena-GEBÄUDEREPORT KOMPAKT 2019—Statistiken und Analysen zur Energieeffizienz im Gebäudebestand; DENA: Berlin, Germany, 2019. 23. BMU, Federal Ministry for the Environment, Nature Conservation and Nuclear Safety. Klimaschutzbericht 2019; German Bundestag: Berlin, Germany, 2020; pp. 69–81. 24. The Federal Government Germany. Deutsche Nachhaltigkeitsstrategie Weiterentwicklung 2021—Dialogfassung; Press and Information Agency of the Federal Government Germany: Berlin, Germany, 2020. 25. Braune, A.; Ruiz Durán, C. Life Cycle Assessments—A Guide on Using the LCA; Technical Report, German Sustainable Building Council; DGNB: Stuttgart, Germany, 2018. 26. BKG, Federal Agency for Cartography and Geodesy. Regionalstatistische Raumtypologie (RegioStaR). Available online: https: //www.bmvi.de/SharedDocs/DE/Artikel/G/regionalstatistische-raumtypologie.html (accessed on 15 February 2020). 27. Christaller, W. Die zentralen Orte in Süddeutschland: Eine ökonomisch-geographische Untersuchung über die Gesetzmäßigkeit der Verbreitung und Entwicklung der Siedlungen mit städtischen Funktionen; Gustav Fischer: Jena, Germany, 1933. 28. BBSR, Federal Institute for Research on Building, Urban Affairs and Spatial Developement. Laufende Stadtbeobachtung— Raumabgrenzungen. Available online: Bbsr.bund.de/BBSR/DE/Raumbeobachtung/Raumabgrenzungen/deutschland/ gemeinden/StadtGemeindetyp/StadtGemeindetyp_node.html (accessed on 21 March 2017). 29. BBSR, Federal Institute for Research on Building, Urban Affairs and Spatial Developement. Raumordnungsbericht 2017; BBSR: Bonn, Germany, 2018. 30. LSN, Landesamt für Statistik Niedersachsen. Bevölkerung am 31.12.2018 in Niedersachsen nach Einwohnergrößenklassen; Landesamt für Statistik Niedersachsen LSN: Hanover, Germany, 2019. 31. Carlow, V.M.; Mumm, O.; Neumann, D.; Schmidt, N.; Siefer, T. TOPOI MOBILITY: Accessibility and settlement types in the urban rural gradient of Lower Saxony—Opportunities for sustainable mobility. Urbanplan. Transp. Res. 2021. accepted for publication. 32. Destatis, Federal Statistical Office Germany. Zensus 2011; Destatis: Wiesbaden, Germany, 2015. 33. Suarez-Rubio, M.; Krenn, R. Quantitative analysis of urbanization gradients: A comparative case study of two European cities. J. Urban Ecol. 2018, 4, juy027. [CrossRef] 34. ESRI. ArcGIS Pro 2.5.1; ESRI: Redlands, CA, USA, 2020. 35. LGLN. Amtliches Liegenschaftskataster-Informationssystem (ALKIS). In Landesamt für Geoinformation und Landesvermessung Niedersachsen; LGLN: Hannover, Germany, 2016. 36. LGLN. Digitales Landschaftsmodell (Basis-DLM). In Landesamt für Geoinformation und Landesvermessung Niedersachsen Hannover; LGLN: Hannover, Germany, 2016. Sustainability 2021, 13, 4099 32 of 33

37. BKG. Digitales Landschaftsmodell (DLM 250); Bundesamt für Kartographie und Geodäsie: Frankfurt am Main, Germany, 2012. 38. Planet Dump [Data File from 27/11/2019]; OSM, OpenStreetMap Contributors (Ed.) GEOFABRIK: Karlsruhe, Germany, 2019. 39. Loga, T.; Stein, B.; Diefenbach, N.; Born, R. Deutsche Wohngebäudetypologie: Beispielhafte Maßnahmen zur Verbesserung der Energieef- fizienz von typischen Wohngebäuden, 2nd ed.; Institut Wohnen und Umwelt, Ed.; IWU: Darmstadt, Germany, 2015. 40. Mühlbach, A.-K.; Strohbach, M.W.; Wilken, T. A Spatially Explicit Life Cycle Assessment Tool for Residential Buildings in Lower Saxony: Development and Sample Application. In Progress in Life Cycle Assessment 2018; Teuteberg, F., Hempel, M., Schebek, L., Eds.; Springer International Publishing: Cham, Switzerland, 2019; pp. 103–113. [CrossRef] 41. BBSR, Federal Institute for Research on Building, Urban Affairs and Spatial Development in the Federal Office for Building and Regional Planning (BBR). ÖKOBAUDAT—Sustainable Construction Information Portal. Available online: https://www. oekobaudat.de (accessed on 16 February 2021). 42. Rössig, S. BBSR, Federal Institute for Research on Buildings, Urban Affairs and Spatial Development. eLCA. Available online: https://www.bauteileditor.de/ (accessed on 15 January 2021). 43. Cischinsky, H.; Diefenbach, N. Datenerhebung Wohngebäudebestand 2016: Datenerhebung zu den energetischen Merkmalen und Modernisierungsraten im deutschen und hessischen Wohngebäudebestand; IWU: Darmstadt, Germany, 2018. 44. Bürger, V.; Hesse, T.; Palzer, A.; Köhler, B.; Herkel, S.; Engelmann, P.; Quack, D. Klimaneutraler Gebäudebestand 2050: Energieef- fizienzpotenziale und die Auswirkungen des Klimawandels auf den Gebäudebestand; Umweltbundesamt: Freiburg, Germany, 2017; pp. 40–56. 45. Hoier, A.; Erhorn, H. Energetische Gebäudesanierung in Deutschland. Studie Teil I: Entwicklung und energetische Bewertung alternativer Sanierungsfahrpläne; Instituts für Wärme und Öltechnik e.V., Ed.; IBP Fraunhofer-Institut für Bauphysik: Stuttgart, Germany, 2013; pp. 30–31. 46. OpenStreetMap Wiki Contributors. OpenStreetMap—Map Features “Highway”. Available online: https://wiki.openstreetmap. org/wiki/Map_features#Highway (accessed on 16 February 2021). 47. FGSV, Road and Transportation Research Association. Richtlinien für die Anlage von Autobahnen: RAA, 2008 ed.; FGSV: Cologne, Germany, 2008; p. 119. 48. FGSV, Road and Transportation Research Association. Richtlinien für die Anlage von Landstraßen: RAL, 2012 ed.; FGSV: Cologne, Germany, 2012; p. 136. 49. FGSV, Road and Transportation Research Association. Richtlinien für die Anlage von Stadtstraßen: RASt 06; 2006, Revised Reprint 2012 ed.; FGSV: Cologne, Germany, 2012; p. 136. 50. FGSV, Road and Transportation Research Association. Richtlinien für die Standardisierung des Oberbaus von Verkehrsflächen: RStO 12, 2012 ed.; FGSV: Cologne, Germany, 2012; p. 52. 51. LSN, Landesamt für Statistik Niedersachsen. Building Stock per Building Type and Construction Year Class (Based on 2011 Census); Landesamt für Statistik Niedersachsen LSN: Hanover, Germany, 2019. 52. BKG, Federal Agency for Cartography and Geodesy. Geographische Gitter für Deutschland in UTM-Projektion (GeoGitter National); Federal Agency for Cartography and Geodesy: Frankfurt, Germany, 2019. 53. Carlow, V.M.; Mumm, O.; Neumann, D.; Sedrez, M.; Zeringue, R. TOPOI—Urban Rural Settlement Types—Version 1.0; Institute for Sustainable Urbanism: Braunschweig, Germany, 2020. [CrossRef] 54. AdV,Working Committee of the Surveying Authorities of the Laender of the Federal Republic of Germany. AdV-Nutzungsartenkatalog; AdV: Munich, Germany, 2011. 55. Destatis, Federal Statistical Office German. “Bodenfläche nach Art der tatsächlichen Nutzung—Gebäude- und Freifläche Wohnen” in Niedersachsen im Jahre 2015. Destatis: Wiesbaden, Germany, 2016. Available online: https://www.statistischebibliothek.de/ mir/receive/DEHeft_mods_00062202 (accessed on 6 April 2021). 56. IINAS, International Institute for Sustainability Analysis and Strategy. Globales Emissions-Modell Integrierter Systeme (GEMIS); 4.95; IINAS: Darmstadt, Germany, 2017. 57. BMWi, Federal Ministry for Economic Affairs and Energy Germany. Deutsche Klimaschutzpolitik. Available online: https: //www.bmwi.de/Redaktion/DE/Artikel/Industrie/klimaschutz-deutsche-klimaschutzpolitik.html (accessed on 1 June 2020). 58. Statista. CO2-Ausstoß je Einwohner in Deutschland Bis 2019 | Statista. Available online: https://de.statista.com/statistik/daten/ studie/153528/umfrage/co2-ausstoss-je-einwohner-in-deutschland-seit-1990/ (accessed on 1 June 2020). 59. UBA, Federal Environment Agency. Germany in 2050—A Greenhouse Gas-Neutral Country; UBA: Dessau-Rosßlau, Germany, 2014. 60. German Thermal Insulation Ordinance 1977 (“Verordnung über energiesparenden Wärmeschutz bei Gebäuden (Wärmeschutzverordnung—WärmeschutzV)“). Germany, 1977. Available online: https://www.bbsr-energieeinsparung.de/ EnEVPortal/EN/Archive/ThermalInsulation/1977/1977_node.html (accessed on 1 April 2021). 61. German Thermal Insulation Ordinance 1984 (“Verordnung über energiesparenden Wärmeschutz bei Gebäuden (Wärmeschutzverordnung—WärmeschutzV)“). Germany, 1984. Available online: https://www.bbsr-energieeinsparung.de/ EnEVPortal/EN/Archive/ThermalInsulation/1982_84/1982_84_node.html (accessed on 1 April 2021). 62. German Energy Saving Ordinance 2009 (“Energieeinsparverordnung (EnEV)“) Germany, 2009. Available online: https://www. bbsr-energieeinsparung.de/EnEVPortal/EN/Archive/EnEV/EnEV2009/2009_node.html (accessed on 1 April 2021). 63. German Thermal Insulation Ordinance 1995 (“Verordnung über energiesparenden Wärmeschutz bei Gebäuden (Wärmeschutzverordnung—WärmeschutzV)“) Germany, 1995. Available online: https://www.bbsr-energieeinsparung.de/ EnEVPortal/EN/Archive/ThermalInsulation/1995/1995_node.html (accessed on 1 April 2021). Sustainability 2021, 13, 4099 33 of 33

64. Heating Appliance Ordinance 1978 (“Verordnung über energiesparende Anforderungen an heizungstechnische Anlagen und Brauchwasseranlagen (Heizungsanlagen-Verordnung—HeizAnlV)“). Germany, 1978. Available online: https://www.bbsr- energieeinsparung.de/EnEVPortal/EN/Archive/HeatingAppliances/HeatingA1978/1978_node.html (accessed on 1 April 2021). 65. German Energy Saving Ordinance 2002 (“Energieeinsparverordnung (EnEV)“) Germany, 2002. Available online: https://www. bbsr-energieeinsparung.de/EnEVPortal/EN/Archive/EnEV/EnEV2002/2002_node.html (accessed on 1 April 2021). 66. Bogaert, J.; Rousseau, R.; Van Hecke, P.; Impens, I. Alternative area-perimeter ratios for measurement of 2D shape compactness of habitats. Appl. Math. Comput. 2000, 111, 71–85. [CrossRef] 67. DB, Deutsche Bahn AG. Streckennetz; Deutsche Bahn AG: Berlin, Germany, 2013. 68. DB, Deutsche Bahn AG. Stationsdaten; Deutsche Bahn AG: Berlin, Germany, 2016. 69. ESRI. ArcToolbox (Network Analyst Toolsets); ESRI: Redlands, CA, USA, 2018.