Analyzing Direct Sunlight Duration for Urban Agriculture in Seoul's

energies

Article Exploring Outdoor Solar Potential in High-Density Living: Analyzing Direct Sunlight Duration for Urban Agriculture in Seoul’s Residential Complexes

Hyungkyoo Kim 1 ID , Kyung Sun Lee 2,*, Jae Seung Lee 1,* and Saewon Lee 2

1 Department of Urban Design and Planning, Hongik University, Seoul 04066, Korea; [email protected] 2 School of Architecture, Hongik University, Seoul 04066, Korea; [email protected] * Correspondence: [email protected] (K.S.L.); [email protected] (J.S.L.); Tel.: +82-10-3731-2170 (K.S.L.); +82-2-320-1667 (J.S.L.) Received: 2 July 2018; Accepted: 2 August 2018; Published: 6 August 2018

Abstract: Urban agriculture has become a favored activity in many cities around the world. This study explores how urban agriculture’s potential can be maximized in Seoul, South Korea, a city characterized by high-density residential complexes. It selects six existing residential complexes with representative site typologies and diverse density levels. The study’s aim is to assess the impact of various typology and density settings on percentages of ground-level surface with direct sunlight above certain thresholds during warmer seasons when crops can grow. DIVA-for-Rhino is used for simulation. The ﬁndings suggest that parallel typologies and lower density levels offer the best performance, while other combinations show mixed results. This study could beneﬁt citizens and policymakers to facilitate urban agriculture practices around the world by suggesting feasible solutions for high-density residential developments.

Keywords: urban agriculture; high-density residential complexes; direct solar access; DIVA-for- Rhino; Seoul

1. Introduction Urban agriculture, including urban farming and urban gardening, is proliferating around the world. Defined as the practice of cultivating and producing food within an urban area and marketing it to consumers within that area [1], urban agriculture is increasingly gaining support from cities’ citizens, community activists, planners, and policymakers for a number of reasons. It is considered an alternative food supply system that dramatically shortens food chains in urban areas [2,3]. It is also widely believed to positively impact urban society [4–7]. Empirical research from many corners of the world has identified the broad range of benefits of urban agriculture. First, it promotes sustainable food security for urban dwellers [8,9] by improving food availability and accessibility [10]. Scholars suggest that it significantly helps the urban poor who may lack the means to secure food [11]. Second, urban agriculture helps build communities that are economically and environmentally sound and socially just [12]. Community engagement and problem solving in urban agriculture facilitate the development of strong civic virtues [13,14]. Some scholars suggest that it plays a critical role in establishing strong connections within the community by embracing under-represented groups, such as immigrants and the disadvantaged [15,16]. Third, urban agriculture contributes to public health [17]. Researchers argue that it has a positive, significant association with a higher nutritional status of school-age children [18], particularly low-income youth [19]. Dixon et al. [20] suggest that urban agriculture contributes to citizens’ mental

Energies 2018, 11, 2030; doi:10.3390/en11082030 www.mdpi.com/journal/energies Energies 2018, 11, 2030 2 of 15 health by dispelling the stress of food anxiety. Dennis and James [21] identify domestic gardens as having a more significant health impact in cities than regular green spaces. Lastly, scholars have shown interest in urban farming’s role as a mitigation–adaptation approach to climate change amidst a growing world population [22]. Research shows that it may reduce greenhouse gas emissions, especially in the transportation sector [23], and may be a more efficient use of land in urban areas than typical urban development [24]. Due to all these benefits, and perhaps many more yet to be identified, cities around the world are concentrating on securing land for agricultural use. A common implementation strategy is to make use of vacant or underutilized land. City planners gather geographical information system (GIS) or aerial imagery to identify vacant lots, open spaces, and underutilized parts of their jurisdiction to build an inventory of where farming can occur that serves the local community [6,25,26]. While these efforts are most relevant for cities with sprawling low-density development patterns where potential land could be easily found, higher density cities, in which vacant or underused land is scarce, need other approaches to finding spaces to practice urban agriculture. A popular solution for high-density urban settings is the creation of rooftop gardens; in other words, growing crops on top of buildings. These spaces successfully secure the preferred thermal conditions for urban gardening as they enjoy direct access to abundant sunlight with little interference. Rooftop gardens have been the focus of extensive research. Astee and Kishnani [27], Orsini et al. [28], and Specht et al. [29] argue that utilizing rooftop spaces may help increase domestic vegetable production that meets a substantial portion of local demand. Whittinghill and Rowe [30] suggest that rooftop gardens have the potential to alleviate the challenge of finding spaces for urban farming in today’s cities. Vertical farming is another important endeavor to facilitate urban agriculture. Despommier [31] argues that vertical farms require little or no land because they are easily established in abandoned buildings or on deserted lots and provide freshly grown and harvested food. Specht et al. [32] describe zero-acreage farming, or ZFarming, which kicked off in the early 2010s in Germany, and its acceptance by the local community. However, despite their innovative aspects and wide implementation by many local communities and governments, these solutions still have several problems. Critics suggest that rooftop gardens may annoy occupants on the top floors of high-rise residential buildings due to possible noise or smells and that frequent entrance and exit of urban farmers may cause privacy and access issues [30]. Some documents doubt the ability of such spaces to overcome economic and technical barriers [33]. A key weakness of vertical farming, for example, is the difficulty of ensuring the right environmental conditions to grow crops. Research indicates the challenge of supplying adequate space, light, carbon dioxide, and water in buildings earmarked for vertical farming, as opposed to natural conditions in which these are available freely [34]. Another study mentions the technical infancy of the concept and reports that vertical farming produces a carbon footprint that is much higher than conventional practice [35]. As an alternative, in this study we explore the potential for utilizing ground-level open spaces in high-density residential complexes, where there are usually abundant open spaces between tall residential buildings. These spaces may provide better access and raise fewer concerns about privacy or other environmental issues as they are usually a communal asset shared by all residents. Existing open spaces already accommodate various amenities and can be redesigned to include space for urban agriculture without much additional effort. A key challenge of promoting ground-level space for farming practice in high-density living is securing access to direct sunlight, which is critical for the successful growth of crops. Bulky and tall buildings tend to hinder sunlight from reaching the ground surface. Therefore, the task is to explore out how these high-density residential environments can be designed to secure the minimum hours of direct sunlight needed for crops to grow so that they can be harvested and consumed by neighborhood members. Energies 2018, 11, 2030 3 of 15

This study focuses on Seoul, South Korea, a city where high-density living is universal and Energies 2018, 11, x FOR PEER REVIEW 3 of 15 where urban agriculture is gaining interest from its citizens. We analyze the potential for urban groundagriculture level in so thisthat high-densitycrops can be grown city with in residential a specific focuscomplexes on the with duration varying of typologies direct sunlight and density on the levels.ground Our level findings so that may crops support can be grownurban inand residential architectural complexes design with practice varying in high-density typologies and cities density that seeklevels. to promote Our findings urban may agriculture support and urban enjoy and its architectural diverse benefits. design practice in high-density cities that seek to promote urban agriculture and enjoy its diverse benefits. 2. Urban Agriculture in Seoul, South Korea 2. Urban Agriculture in Seoul, South Korea Seoul, the capital city of South Korea, is one of the world’s most dense cities. As of 2015, the city Seoul, the capital city of South Korea, is one of the world’s most dense cities. As of 2015, housed slightly over 9.9 million residents in 605.2 squares kilometers of land. Its population density, the city housed slightly over 9.9 million residents in 605.2 squares kilometers of land. Its population 16,365 persons per square kilometer, is considerably lower than that of Mumbai and Kolkata, where density, 16,365 persons per square kilometer, is considerably lower than that of Mumbai and Kolkata, more than 20,000 people reside per square kilometer. However, it is about the same as the population where more than 20,000 people reside per square kilometer. However, it is about the same as the density of Tokyo, 1.5 times greater than that of New York City, and twice as large as that of Singapore, population density of Tokyo, 1.5 times greater than that of New York City, and twice as large as that of three cities renowned for their exceptionally high-density living conditions. As a result, high-density Singapore, three cities renowned for their exceptionally high-density living conditions. As a result, residential complexes with tall residential buildings are pervasive and accommodate about 56 high-density residential complexes with tall residential buildings are pervasive and accommodate percent of total households. about 56 percent of total households. Seoul has seen a rapid expansion of space for urban farming, which quintupled from 29 hectares Seoul has seen a rapid expansion of space for urban farming, which quintupled from 29 hectares in 2011 to 162 hectares in 2016 [36], as shown in Figure 1. Of this space, 53 percent is located in the in 2011 to 162 hectares in 2016 [36], as shown in Figure1. Of this space, 53 percent is located in the city’s city’s greenbelt that runs along its outskirts where vacant land is available and usually cultivated greenbelt that runs along its outskirts where vacant land is available and usually cultivated only on only on weekends, while a much smaller percentage is found on rooftops, in schools, in parks, or in weekends, while a much smaller percentage is found on rooftops, in schools, in parks, or in small vacant small vacant spaces in the city [37]. The Seoul Metropolitan Government announced the Seoul Urban spaces in the city [37]. The Seoul Metropolitan Government announced the Seoul Urban Agriculture Agriculture Visions, the first in 2012 and the second in 2015. The second aims to secure up to 420 Visions, the first in 2012 and the second in 2015. The second aims to secure up to 420 hectares of space hectares of space in the city for urban agriculture by 2018 with significantly improved access to in the city for urban agriculture by 2018 with significantly improved access to promote community promote community capital and citizen satisfaction [38]. capital and citizen satisfaction [38].

Area (ha) 500 420 400

300

200 162 170 141 108 118 100 84 29

0 2011 2012 2013 2014 2015 2016 2017* 2018** Year

Figure 1. Changes in urban agriculture area in Seoul between 2011 and 2018. Notes: * Revised estimate; Figure 1. Changes in urban agriculture area in Seoul between 2011 and 2018. Notes: * Revised ** Target set in Seoul Urban Agriculture Vision II [38]. estimate; ** Target set in Seoul Urban Agriculture Vision II [38].

However,However, the the future future of of urban urban agriculture agriculture in in Seoul Seoul is is not not all all bright. bright. Figure Figure 1 shows that the actual expansionexpansion of of farming farming spaces spaces in in the the city city is is slowing slowing down down in in the the very very recent recent years years compared compared to to a a major major expansionexpansion in in the the early early 2010s; 2010s; therefore, therefore, it it is is very very un unlikelylikely that that the the 2018 2018 target target will will be be met. met. Local Local critics critics suggestsuggest that that the the strategies strategies adopted adopted in in the the two two vi visionssions do do not not incorporate incorporate approaches approaches that that exceed exceed currentcurrent practices practices to to secure secure more more la landnd in in the the city city and and that that finding ﬁnding a additionaldditional space space to to grow grow crops crops is is extremelyextremely difficult difﬁcult because because land land is is a a highly highly scarce scarce resource resource in Seoul [39,40]. [39,40]. Instead, Instead, they they call call for for utilizing ground-level open spaces in high-density residential complexes as a plausible solution to meet this target [41,42].

Energies 2018, 11, 2030 4 of 15 utilizing ground-level open spaces in high-density residential complexes as a plausible solution to meet this target [41,42].

3. Methods To explore the impact of different typologies and density levels of high-density residential complexes in Seoul on the duration of direct sunlight on ground-level open spaces, we followed a three-step process, as described below.

3.1. Crops and Their Growing Conditions Our ﬁrst step was to establish guidelines for analyzing the duration of direct sunlight on ground-level open spaces in high-density residential settings. We reviewed websites run by local governmental agencies, including the Rural Development Administration and the Seoul Agricultural Technology Center, as well as the related literature [43] that provides technical guidelines on urban agriculture in the country. From these, we identiﬁed twelve crops most favored by local urban farmers, as shown in Table1. The table also presents two thermal conditions that are of interest in this study, minimum direct sunlight duration and temperature range for each crop. It was evident that three or six hours of direct sunlight are critical for most of the crops and that ten degrees Celsius is the lowest temperature limit for growth. Thus, we set up three and six hours as thresholds for minimum direct sunlight in the analysis. We also chose a nine-month period from March to November as another guideline, during which the temperature rarely falls below ten degrees Celsius.

Table 1. Twelve most common crops in South Korea and their growing conditions.

Min. Direct Annual Annual Sunlight Temperature Cultivation Crop Production Production Duration Aange (◦C) Area (ha) (kg) per 10 ha (ton) Required (hours) Root Carrot 3 18~21 21,485 5051 1,085,223 vegetables Cherry Tomato N/A 10~30 44,661 4345 1,940,733 Fruit Cucumber 6 25~28 vegetables Eggplant 6 22~30 Korean Zucchini N/A 23~25 Seasoning Green Onion 3 15~20 96,584 2379 2,297,756 vegetables Lettuce 3 15~25 Leaf Spinach 6 15~20 vegetables Napa Cabbage 8 15~20 Korean Perilla 6 20~30 45,474 114 52,024 Persimmon 6 10~15 25,060 1411 353,655 Potato 6 10~23 22,000 2526 555,670 Sources: Seoul Agricultural Technology Center (http://agro.seoul.go.kr/); Nongsaro (http://www.nongsaro.go. kr/); Korean Statistical Information Service (http://kosis.kr/search/search.do?query=%EC%B1%84%EC%86%8C% EC%83%9D%EC%82%B0%EB%9F%89).

3.2. Study Sites High-rise residential complexes are the most popular residential option in South Korea. The Enforcement Decree of the nation’s Building Act ﬁrst adopted in 1976 mandates that certain distances are kept between high-rise residential buildings that face each other so as to protect the privacy of each dwelling unit and provide access to abundant direct sunlight. This resulted in not only a fair amount of ground-level open space in the complexes but also various site typologies. Instead of using generic models of high-density residential complexes, we used existing sites for analysis so as to produce practical implications that may beneﬁt design and development practice. Energies 2018, 11, 2030 5 of 15

We focused on the three most common typologies of high-density residential complexes in Seoul, which are parallel, grid, and tower, and selected two existing complexes for each of them, resulting in six study sites in total. Table2 describes the six selected study sites, their typologies, and various physical characteristics, including size and density, and provides satellite and aerial images for each. StudyEnergiesEnergiesEnergiesEnergiesEnergiesEnergiesEnergies sites20182018 2018 2018 2018 ,2018, 201811,201811, ,11 ,11,A x,11,x , ,x11,x FOR FOR11x 11, FOR FOR andx,FOR ,x, FORxx FOR PEER PEERFOR FOR PEERPEER PEER PEER BPEER PEER PEER REVIEWREVIEW REVIEWREVIEW areREVIEW REVIEW REVIEW REVIEWREVIEW typical residential complexes built in the 1970s with55 5 ofof5 of 5 of15155 of1555 I-shapedof 15 of of 15 15 1515 buildings arranged in parallel. The buildings in A generally run east–west, while those in B run StudyStudyStudyStudyStudyStudy sites sites sites sites sites sitesA A A and A and A andA and and Band B Bare Bare Bare Bare typicalare aretypical typical typical typical typical residential residential residential residential residential residential comple comple comple comple comple complexesxesxesxes xesbuilt xesbuilt built built built built in in in thein thein thein the 1970sthe the1970s 1970s 1970s 1970s 1970s with with with with with with I-shaped I-shaped I-shaped I-shaped I-shaped I-shaped buildings buildings buildings buildings buildings buildings southeast–northwest.arrangedarrangedarrangedarrangedarrangedarranged in in in parallel.in parallel.ininparallel. parallel. parallel.parallel. Study The The The The The Thebuildings sitesbuildings buildings buildings buildingsbuildings C and in in in Ain DAin in A generally A representgenerallyA generallyA generally generallygenerally run run run I-shapedrun run east–west,run east–west, east–west, east–west, east–west,east–west, residential while while while while whilewhile those those those those thosethose buildings in in in Bin Bin inB run B run Brun Brun run southeast–run southeast– arrangedsoutheast– southeast– southeast–southeast– in grid form. Thesenorthwest.northwest.northwest.northwest.northwest.northwest. typologies, Study Study Study Study Study Study sites sites sites sites whichsites sites C C Cand Cand Cand C and and Dand wereD Drepresent Drepresent D representD represent represent represent popular I-shaped I-shaped I-shaped I-shaped I-shaped I-shaped in the re re residentialsidential residential lateresidential residentialsidentialsidentialsidential 1990s buildingsbuildings buildings buildings buildings buildings buildings andbuildings thearrangedarranged arranged arranged arranged arranged earlyarrangedarranged inin in 2000s,in gridgridin gridin gridinin grid grid gridgridform.form. form. form. contain form. form. form.form. TheseThese These These These These TheseThese multiple enclosedtypologies,typologies,typologies,typologies,typologies,typologies, outdoortypologies,typologies,typologies, which which whichwhich spaces.which which whichwhichwhich were were werewerewere were Study werewerewere popular popular popularpopularpopular popular popularpopularpopular sites in in ininEin inthe the intheandtheininthe the thelate latethethe latelatelate F late late consistlate1990s late 1990s 1990s1990s1990s 1990s 1990s1990s1990s and and andandand of and and andandthe Y-shaped the thethethe the theearly earlythethe earlyearlyearly early earlyearlyearly 2000s, 2000s, 2000s,2000s, towers,2000s, 2000s, 2000s,2000s,2000s, contain contain containcontaincontain contain containwhichcontaincontain multiple multiple multiplemultiplemultiple multiplehave multiplemultiplemultiple enclosed become enclosed enclosedenclosedenclosed enclosed enclosedenclosedenclosed popular since theoutdooroutdooroutdooroutdoor 2000soutdooroutdoor spaces. spaces. becausespaces. spaces. spaces.spaces. Study Study Study Study StudyofStudy sites their sites sites sites sites sitesE E outstanding Eand Eand Eand Eand and andF F Fconsist Fconsist Fconsist Fconsist consistconsist capabilityof of of Y-of Y- ofofY- shapedshapedY- shaped Y-shapedY-shapedshapedshapedshapedto towers,towers, towers, towers, accommodatetowers, towers, towers,towers, whichwhich which which which which whichwhich havehave have have have have high-densityhavehave becomebecome become becomebecome become become becomebecome popularpopular popular popularpopular popular popular popularpopular residences. sincesince sincesince since since since sincesince thethethethethethe 2000sthe2000s the2000sthe2000s 2000s 2000s 2000s 2000s2000s becausebecause becausebecause because because because becausebecause ofof ofof of theirtheirof theirtheirof theirofof their their theirtheir outstandingoutstanding outstandingoutstanding outstanding outstanding outstanding outstandingoutstanding capabicapabi capabicapabi capabi capabi capabi capabicapabilitylitylitylitylitylity litytoto litylitytoto to accommodateaccommodateto accommodateaccommodateto accommodatetoto accommodate accommodate accommodateaccommodate high-densityhigh-density high-densityhigh-density high-density high-density high-density high-densityhigh-density residences.residences. residences.residences. residences. residences. residences. residences.residences. Table 2. Characteristics of study sites A to F. TableTableTableTableTableTable 2. 2. 2.CharacteristicsCharacteristics 2.CharacteristicsCharacteristics 2.Characteristics 2.Characteristics Characteristics CharacteristicsCharacteristics ofof ofof of study study of study study of study of ofstudy study studystudy sitessites sitessites sites sites sites sitesAsitesA AA Atoto Atoto A to F. AF.A to F. F. to F. to to F. F. F. F.

StudyStudyStudyStudyStudyStudy S S iteiteSite iteSite S iteS ite iteite ABCDEFAA A A A A BB B B B B CC C C C C DD D D DD EE E E E E F F F F F F

Study Site

SiteSiteSiteSite typologySite Sitetypology SiteSitetypology typology typology typology typologytypology Parallel Parallel Parallel Parallel Parallel Parallel Parallel Parallel Parallel Parallel Parallel Parallel Parallel Parallel Parallel Parallel Grid Grid Grid Grid Grid Grid Grid Grid Grid Grid Grid Grid Grid Grid Grid Grid Tower Tower Tower Tower Tower Tower Tower Tower Tower Tower Tower Tower Tower Tower Tower NumberNumberNumberNumberNumberNumber of of of of of of Number of 387838783878 3878 610961096109 6109 425842584258 4258 567856785678 5678 341034103410 3410 686468646864 6864 38783878387838783878 3878 6109 61096109610961096109 6109 42584258 4258425842584258 4258 567856785678 567856785678 5678 3410341034103410 34103410 3410 68646864686468646864 68646864 unitsunitsunitsunitsunitsunitsunits NumberNumberNumberNumberNumberNumber of of of of of of Number of 82828282 82 82 8282 71717171 71 71 7171 56565656 56 56 5656 61616161 61 61 6161 44444444 44 44 4444 66666666 66 66 6666 buildingsbuildingsbuildingsbuildingsbuildingsbuildings 82 71 56 61 44 66 buildings MaximumMaximumMaximumMaximumMaximumMaximum Maximumnumbernumbernumbernumbernumbernumber of of of of of of 15151515 15 15 1515 16161616 16 16 1616 23232323 23 23 2323 34343434 34 34 3434 29292929 29 29 2929 36363636 36 36 3636 number of 15 16 23 34 29 36 storiesstoriesstoriesstoriesstoriesstoriesstoriesstoriesstories stories GrossGrossGrossGrossGrossGross site site site site site site 416,126416,126416,126416,126416,126416,126416,126416,126 474,848474,848474,848474,848474,848474,848474,848474,848 223,762223,762223,762223,762223,762223,762223,762223,762 258,577258,577258,577258,577258,577258,577258,577258,577 240,939240,939240,939240,939240,939240,939240,939240,939 282,551282,551282,551282,551282,551282,551282,551282,551 Gross site area22222 2 222 416,126 474,848 223,762 258,577 240,939 282,551 areaareaareaareaareaarea (marea(marea (m (m (m) )(m ))(m(m ) ) ) )) 416,126 474,848 223,762 258,577 240,939 282,551 (m2) BuildingBuildingBuildingBuildingBuildingBuilding Buildingcoveragecoveragecoveragecoveragecoveragecoveragecoveragecoverage 0.1750.1750.1750.1750.1750.1750.175 0.175 0.1650.1650.1650.1650.1650.1650.165 0.165 0.1940.1940.1940.1940.1940.1940.194 0.194 0.1310.1310.1310.1310.1310.1310.131 0.131 0.1230.1230.1230.1230.1230.1230.123 0.123 0.1280.1280.1280.1280.1280.1280.128 0.128 coverageratioratioratioratioratioratioratio ratioratio(BCR)(BCR) (BCR)(BCR) ratio(BCR) (BCR) (BCR) (BCR)(BCR) 0.175 0.165 0.194 0.131 0.123 0.128 Floor(BCR)FloorFloorFloorFloorFloor area area area area area area 2.1452.1452.1452.1452.1452.1452.145 2.145 1.9871.9871.9871.9871.9871.9871.987 1.987 3.1713.1713.1713.1713.1713.1713.171 3.171 3.0503.0503.0503.0503.0503.0503.050 3.050 3.2353.2353.2353.2353.2353.2353.235 3.235 3.7803.7803.7803.7803.7803.7803.780 3.780 Floorratioratioratioratioratioratioratio ratioarearatio(FAR)(FAR) (FAR)(FAR) (FAR) (FAR) (FAR) (FAR)(FAR) 2.145 1.987 3.171 3.050 3.235 3.780 ratioBuildingBuildingBuilding (FAR)BuildingBuildingBuilding Buildingcoveragecoveragecoveragecoveragecoveragecoveragecoveragecoverage 0.1750.1750.1750.1750.1750.1750.175 0.175 0.1650.1650.1650.1650.1650.1650.165 0.165 0.1940.1940.1940.1940.1940.1940.194 0.194 0.1310.1310.1310.1310.1310.1310.131 0.131 0.1230.1230.1230.1230.1230.1230.123 0.123 0.1280.1280.1280.1280.1280.1280.128 0.128 coverageratioratioratioratioratioratioratio ratioratio(BCR)(BCR) (BCR)(BCR) ratio(BCR) (BCR) (BCR) (BCR)(BCR) 0.175 0.165 0.194 0.131 0.123 0.128 Floor(BCR)FloorFloorFloorFloorFloor area area area area area area 2.1452.1452.1452.1452.1452.1452.145 2.145 1.9871.9871.9871.9871.9871.9871.987 1.987 3.1713.1713.1713.1713.1713.1713.171 3.171 3.0503.0503.0503.0503.0503.0503.050 3.050 3.2353.2353.2353.2353.2353.2353.235 3.235 3.7803.7803.7803.7803.7803.7803.780 3.780 Floorratioratioratioratioratioratioratio ratioarearatio(FAR)(FAR) (FAR)(FAR) (FAR) (FAR) (FAR) (FAR)(FAR) 2.145 1.987 3.171 3.050 3.235 3.780 ratio (FAR)Notes:Notes:Notes:Notes:Notes:Notes: North North North North North North is is isshown isshown isshown isshown shown shown upward upward upward upward upward upward in in inthe inthe in thein the satellitethe satellitethe satellite satellite satellite satellite images images images images images images presentedpresented presentedpresented presented presented presented presentedpresented inin inin in the the in the the in the in in the firstfirstthe firstfirstthe thefirst first first row. row.first first row.row. row. row. row. row.Aerialrow.Aerial AerialAerial Aerial Aerial Aerial AerialAerial imageimage imageimage image image image imageimage source:source: source:source: source: source: source: source:source: Notes: North©© ©Naver ©Naver ©Naver ©Naver Naver isNaver shownMaps Maps Maps Maps Maps Maps (http://map.naver.com). upward(http://map.naver.com). (http://map.naver.com). (http://map.naver.com). (http://map.naver.com). (http://map.naver.com). in the satellite images presented in the ﬁrst row. Aerial image source: © Naver Maps (http://map.naver.com). FigureFigureFigureFigureFigureFigure 2 2 illustrates2 illustrates2 illustrates2 2illustrates illustrates illustrates the the the the locationthe thelocation location location location location of of of theof theof theof the sixthe thesix six six study six studysix study study study study si si tes.sites. tes.tes.si tes.si sites. tes.TheyThey tes. tes.TheyThey They They They TheyThey areare areare are are allareall areallareall all alllocatedlocated alllocated located allalllocated located located locatedlocated southsouth southsouth south south south southsouth ofof ofof of thetheof thetheof theofof the HantheHan theHantheHan Han Han Han Han HanRiver,River, River,River, River, River, River, River,River, whichwhichwhichwhichwhichwhich cuts cuts cuts cuts cuts cuts through through through through throughthrough Seoul Seoul Seoul Seoul SeoulSeoul from from from from fromfrom east east east east east east to to to to west.to west.to west. west. west.west. 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This area has experienced extensive residential development since the 1970s and therefore is a relatively newer part of the city designed to accommodate the soaring population.

Energies 2018, 11, 2030 6 of 15 Energies 2018, 11, x FOR PEER REVIEW 6 of 15

Figure 2. Location of study sites A to F. Figure 2. Location of study sites A to F. For analysis, in addition to looking at the six study sites that represent three different typologies, we diversifiedFor analysis, their in additiondensity levels. to looking We atkept the thei sixr study building sites footprints that represent the same, three differentmeaning typologies, that their webuilding diversified coverage their ratios density (BCRs) levels. were We constant, kept their bu buildingt modified footprints the building the same, heights meaning to set their that floor their buildingarea ratios coverage (FARs) ratiosat 1.5, (BCRs) 2.0, 2.5, were 3.0, constant,3.5, and 4.0 but by modified adding theor subtracting building heights floors to evenly set their throughout floor area ratiosthe complexes. (FARs) at Because 1.5, 2.0, we 2.5, were 3.0, 3.5, dealing and 4.0with by real-world adding or subtractingsites, it was floorsimpossible evenly tothroughout precisely meet the complexes.the desired FAR Because values. we wereHence, dealing we modified with real-world the number sites, of stories it was impossibleof the buildings to precisely to be as meet close the to desiredeach FAR FAR value values. as possible. Hence, weTable modified 3 shows the the number projected of storiesFARs for of theeach buildings study site. to beFor as each close of tothe each six FARstudy value sites, as there possible. are six Table cases3 showsbased on the FAR projected variatio FARsns, resulting for each study in a total site. of For 36 each cases of in the the six study. study sites, there are six cases based on FAR variations, resulting in a total of 36 cases in the study. Table 3. Current and modified FAR values of study sites A to F and their variations for analysis. Table 3. Current and modified FAR values of study sites A to F and their variations for analysis. Current FAR and Variations Study Site Current Current1.5 FAR2.0 and Variations2.5 3.0 3.5 4.0 Study Site A Current 2.145 1.5 1.480 2.0 1.978 2.5 2.477 3.02.976 3.53.475 3.974 4.0 AB 2.145 1.987 1.480 1.521 1.978 1.987 2.477 2.454 2.976 3.076 3.4753.542 4.008 3.974 BC 1.987 3.171 1.521 1.533 1.987 2.079 2.454 2.443 3.076 2.989 3.5423.535 4.084 4.008 CD 3.171 3.050 1.533 1.461 2.079 1.950 2.443 2.561 2.989 3.050 3.5353.538 4.027 4.084 DE 3.050 3.235 1.461 1.489 1.950 1.954 2.561 2.537 3.050 3.002 3.5383.468 4.050 4.027 E 3.235 1.489 1.954 2.537 3.002 3.468 4.050 F 3.780 1.499 1.979 2.580 3.060 3.540 4.019 F 3.780 1.499 1.979 2.580 3.060 3.540 4.019

3.3. Solar Environment Analysis 3.3. Solar Environment Analysis Computer simulation is commonly used to analyze solar environments. Researchers who Computer simulation is commonly used to analyze solar environments. Researchers who explore explore design strategies to secure sunlight access or enhance thermal comfort in high-density design strategies to secure sunlight access or enhance thermal comfort in high-density residential residential environments use various software tools, such as Ecotect [44–46], Sanalyst [47], and environments use various software tools, such as Ecotect [44–46], Sanalyst [47], and LadyBug [48]. LadyBug [48]. DIVA-for-Rhino is used by many researchers to simulate solar environments [49–53]. DIVA-for-Rhino is used by many researchers to simulate solar environments [49–53]. It is a daylighting It is a daylighting and energy-modeling plugin for Rhinoceros 3D, a nonuniform rational basis spline and energy-modeling plugin for Rhinoceros 3D, a nonuniform rational basis spline modeling software, modeling software, and carries out parametric performance evaluations of individual buildings and and carries out parametric performance evaluations of individual buildings and urban landscapes [54]. urban landscapes [54]. Using DIVA-for-Rhino, we followed a procedure to parametrically analyze the duration of direct Using DIVA-for-Rhino, we followed a procedure to parametrically analyze the duration of direct sunlight on the ground-level open space of the 36 study cases from the six selected sites at six density sunlight on the ground-level open space of the 36 study cases from the six selected sites at six density levels, as outlined below: levels, as outlined below: (1) ConstructingConstructing dwg dwg files ﬁles (drawing (drawing file ﬁle genera generatedted by Autodesk AutoCAD) of the 36 study cases usingusing local local GIS GIS information, information, satellite satellite imagery, imagery, and database information on local real estate.estate. (2) Importing the dwg files into Rhinoceros 3D and creation of three dimensional models that precisely represent the size and height of the residential buildings. (3) Entering the latitude and longitude coordinates of Seoul (37°34′N 126°58′E) and time (07:00 to

Energies 2018, 11, 2030 7 of 15

Energies 2018, 11, x FOR PEER REVIEW 7 of 15 (2) Importing the dwg ﬁles into Rhinoceros 3D and creation of three dimensional models that precisely18:00 h) and represent date (March the size to and November) height of informat the residentialion into buildings. the Sun Position component of DIVA- (3) Enteringfor-Rhinothe and latitude generating and vectors longitude for shade coordinates analysis of by Seoul time. (37 ◦340N 126◦580E) and time (07:00 (4) to Entering 18:00 h)the and vectors date (Marchfrom the to Sun November) Position informationcomponent intointo thethe SunShadow Position component, component which of DIVA-for-Rhinocreates shades generated and generating by masses vectors based for on shade input analysis vectors, by of time. Grasshopper, a tool that operates (4) Enteringwithin DIVA-for-Rhino the vectors from to the build Sun generative Position component algorithms into and the analyze Shadow shades component, for each which study creates case shadesby time. generated by masses based on input vectors, of Grasshopper, a tool that operates within (5) DIVA-for-Rhino Subdividing the toground-level build generative space algorithmsof all the st andudy analyze cases into shades 12 m for × 12 each m studygrids; casebased by on time. the duration of direct sunlight per day each grid receives, cases were divided between those that (5) Subdividing the ground-level space of all the study cases into 12 m × 12 m grids; based on the experience at least three hours and those that receive at least six hours. duration of direct sunlight per day each grid receives, cases were divided between those that (6) Exporting the results to Microsoft Excel for analysis using gHowl, a Grasshopper addon that is experience at least three hours and those that receive at least six hours. created by Luis Fraguada [55]. gHowl helps hand over data from DIVA-for-Rhino to Microsoft (6) Exporting the results to Microsoft Excel for analysis using gHowl, a Grasshopper addon Excel. that is created by Luis Fraguada [55]. gHowl helps hand over data from DIVA-for-Rhino to MicrosoftFigure 3 is Excel. an example of one of the six study cases, each representing a different density level case for site E with gridded ground-level surfaces created for solar environmental analysis. Figure 4 is anFigure exemplary3 is an overlay example of the of one model of the for sixsite study E, showing cases, what each representinglies on the ground. a different We do density not consider level caseroads for and site parking E with griddedareas that ground-level exist on the surfaces actual ground created forin this solar study environmental because our analysis. goal is Figureto explore4 is anpotential, exemplary implying overlay that of the modeluse those for areas site E, may showing be rethought what lies from on the scratch. ground. Same We applies do not considerto all the roadsother andsites. parking areas that exist on the actual ground in this study because our goal is to explore potential, implying that the use those areas may be rethought from scratch. Same applies to all the other sites.

FAR = 1.5 (1.489) FAR = 2.0 (1.954)

FAR = 2.5 (2.537) FAR = 3.0 (3.002)

FAR = 3.5 (3.468) FAR = 4.0 (4.050)

FigureFigure 3. Models 3. Models for study for study site E site with E sixwith density six density levels levels for solar for environmentsolar environment analysis analysis using using DIVA-for-Rhino. DIVA- for-Rhino.

Energies 2018, 11, 2030 8 of 15 EnergiesEnergies 2018 2018, ,11 11, ,x x FOR FOR PEER PEER REVIEW REVIEW 88 of of 15 15

FigureFigureFigure 4. 4. ModelModel Model overlay overlayoverlay of ofof site sitesite E EE showing showingshowing actual actual roads roads and and parking parking areas. areas.

4.4. Results ResultsResults FigureFigure 55 5 isis is anan an exemplaryexemplary exemplary comparisoncomparison comparison ofof of thethe the amountamount amount ofof of sunlight-irradiatedsunlight-irradiated sunlight-irradiated areaarea area forfor for thethe the currentcurrent current conditioncondition of of study study site site E E with with the the sixsix testedtested FARs.FARsFARs. .It It presents presents how how ground-level ground-level areas areas that that receive receive directdirect sunlight sunlight maymay may changechange change over over over time time time depending depending depending on on on the the the FAR. FAR. FAR. Certainly, Certainly, Certainly, lower lower lower FARs FARs FARs provide provide provide more more more area areaforarea direct forfor directdirect sun access sunsun accessaccess and higher andand higherhigher ones less onesones but lessless are butbut also areare subject alsoalso subject tosubject site typologies. toto sitesite typologies.typologies. With these WithWith models, thesethese models,wemodels, simulated we we simulated simulated for each for FAR. for each each FAR. FAR.

FigureFigure 5. 5. Percentages Percentages of of ground-level ground-level surface surface with with direct direct sunlight sunlight for forfor three three or or more more hours hours ( left(left) )and and sixsix or oror more moremore hours hourshours ( right (right) )from from March March March to to to November November November for for for study study study site site site E E with Ewith with the the the current current current FAR FAR FAR (3.235) (3.235) (3.235) in in comparisonincomparison comparison with with with tested tested tested FARs FARs FARs (1.5 (1.5 (1.5,, ,2.0, 2.0, 2.0, 2.5, 2.5, 2.5, 3.0, 3.0, 3.0, 3.5, 3.5, 3.5, and and and 4.0). 4.0). 4.0).

4.1.4.1. FAR FARFAR = = 1.5 1.5 ForFor the the thresholdthreshold of of threethree oror moremore hours,hours, A,A, B,B, E,E, andand FF generallygenerally showedshowed aa similarsimilar trendtrend throughoutthroughout the the nine-monthnine-month period, period, as as FigureFigure6 6 6illustrates. ilillustrates.lustrates. Among AmongAmong these, these,these, A AA and andand B, B,B, the thethe parallel parallelparallel typologies,typologies, experienced experienced experienced the thethe most mostmost extensive extensiveextensive direct directdirect sunl sunlightsunlightight at atat ground groundground level levellevel from fromfrom mid-May mid-Maymid-May to toto late latelate July, July,July, peakingpeaking at atat over overover 90 9090 percent. percent. E E E and and and F, F, F, the the the tower tower tower ty ty typologies,pologies,pologies, showed showed showed the the the most most most extensive extensive extensive percentages percentages percentages in in springinspring spring and and and fall. fall. fall. C C Candand and D, D, D, the the the grid grid grid typologies, typologies, typologies, pr pr presentedesentedesented slightly slightly lower lower percentages percentages than thanthan the thethe other other four,four, falling falling below below 60 60 percent percent inin in earlyearly early MarchMarch March andand and 4040 40 percentpercent percent inin in latelate late November.November. November. ForFor the the six six or or more more hours hours threshold,threshold, ground-level ground-level open open spaces spaces in in A A had had the the highest highest percentage percentage valuesvalues for for the thethe longest longestlongest duration, duration,duration, with with a a peak peak exc exc exceedingeedingeeding 55 55 percent percent percent in in in summer. summer. summer. C, C, C, E, E, E, and and and F F Fshowed showed showed a a similarasimilar similar trend trend trend throughout throughout throughout the the the whole whole whole nine-month nine-month nine-month peri peri period.od.od. D D displayed displayed lower lower values values than thanthan most mostmost of ofof the the sites.sites. However, However, it it is is surprising surprising to to observe observe that that B, B, unlike unlike its its parallel-type parallel-type companion companion A, A, presented presented the the lowestlowest values values for for most most of of the the period, period, staying staying largely largely below below 25 25 percent. percent.

Energies 2018, 11, 2030 9 of 15 sites. However, it is surprising to observe that B, unlike its parallel-type companion A, presented the Energies 2018, 11, x FOR PEER REVIEW 9 of 15 lowestEnergies values2018, 11, forx FOR most PEER of REVIEW the period, staying largely below 25 percent. 9 of 15

100 100 100 A 100 A 90 A 90 A 90 B 90 B B B 80 80 80 C 80 C C C 70

70 (%) Percentage Percentage (%) Percentage D D 70

FigureFigure 6.6.6. PercentagesPercentagesPercentages of ofofground-level ground-levelground-level surface surfacesurface with withwith direct directdirect sunlight sunlightsunlight for forfor three threethree or or moreor moremore hours hourshours (left ((left)left and)) andand six orsixsix more oror moremore hours hourshours (right ((rightright) from)) fromfrom March MarchMarch to November toto NovemberNovember for studyforfor studystudy sites sitessites A to AA F toto when FF whenwhen FAR FARFAR is 1.5. isis 1.5.1.5.

4.2.4.2. FARFAR === 2.02.0 ForFor thethe threshold threshold of of threethree oror or moremore more hours,hours, hours, A,A, A, E,E, E, andand and FF F werewere were top-ranked,top-ranked, top-ranked, asas as shownshown shown inin inFigureFigure Figure 7.7. 7 AA. Ashowedshowed showed thethe the highesthighest highest percentages,percentages, percentages, whichwhich which reachedreached reached 9090 90 percentpercent percent betweenbetween between mid-Maymid-May mid-May andand and latelate July,July, andand EE hadhad thethe mostmost directdirect sunlightsunlight atat groundground levellevellevel ininin springspring andandand fall.fall.fall. B,B, C,C, andand DD were were bottom-ranked,bottom-ranked, withwith DD experiencingexperiencing thethethe leastleastleast directdirectdirect sunlight,sunlight,sunlight, peaking peakingpeaking below belowbelow 80 8080 percent percentpercent in inin summer. summer.summer. ForFor the the thresholdthreshold threshold ofof of sixsix six oror or moremore more hours,hours, hours, AA showedshowed A showed thethe highesthighest the highest percentagespercentages percentages forfor mostmost for ofof most thisthis period,period, of this period,nearingnearing nearing 5050 percentpercent 50 percent inin latelate JuneJune in late andand June earlyearly and July.July. early ItIt waswas July. followedfollowed It was followed byby C,C, E,E, by andand C, F,F, E, whichwhich and F, showedshowed which showed similarsimilar similarvaluesvalues valuesthroughoutthroughout throughout thethe wholewhole the whole periperiod.od. period. DD waswas D mostlymostly was mostly thethe secondsecond the second lowestlowest lowest withwith with percentagespercentages percentages belowbelow below 3030 30percentpercent percent atat atallall all times.times. times. Again,Again, Again, BB Bhadhad had thethe the lowestlowest lowest percperc percentagesentagesentages forfor for mostmost most ofof of thethe the nine-monthnine-month nine-month period, period, peaking peaking belowbelow 2020 percentpercent ininin summer.summer.summer.

100 100 100 A 100 A 90 A 90 A 90 B 90 B B B 80 80 80 C 80 C C C 70

70 (%) Percentage Percentage (%) Percentage D D 70

70 (%) Percentage Percentage (%) Percentage D D 60 E 60 E 60 E 60 E F F 50 F 50 F 50 50 40 40 40 40 30 30 30 30 20 20 20 20 10 10 10 10 0 0 1-Mar0 1-Apr 1-May 1-Jun 1-Jul 1-Aug 1-Sep 1-Oct 1-Nov Date 1-Mar0 1-Apr 1-May 1-Jun 1-Jul 1-Aug 1-Sep 1-Oct 1-Nov Date 1-Mar 1-Apr 1-May 1-Jun 1-Jul 1-Aug 1-Sep 1-Oct 1-Nov Date 1-Mar 1-Apr 1-May 1-Jun 1-Jul 1-Aug 1-Sep 1-Oct 1-Nov Date Figure 7. Percentages of ground-level surface with direct sunlight for three or more hours (left) and six FigureFigure 7.7. PercentagesPercentages ofof ground-levelground-level surfacesurface withwith directdirect sunlightsunlight forfor threethree oror moremore hourshours ((leftleft)) andand or more hours (right) from March to November for study sites A to F when FAR is 2.0. sixsix oror moremore hourshours ((rightright)) fromfrom MarchMarch toto NovemberNovember forfor studystudy sitessites AA toto FF whenwhen FARFAR isis 2.0.2.0. 4.3. FAR = 2.5 4.3.4.3. FARFAR == 2.52.5 For the threshold of three or more hours, A, E, and F were the top three sites, as indicated ForFor thethe thresholdthreshold ofof threethree oror moremore hours,hours, A,A, E,E, andand FF werewere thethe toptop threethree sites,sites, asas indicatedindicated inin in Figure8. A showed the highest percentages between mid-May and the end of July, peaking FigureFigure 8.8. AA showedshowed thethe highesthighest percentagespercentages betwbetweeneen mid-Maymid-May andand thethe endend ofof July,July, peakingpeaking withwith with percentages in the high 80s in late June. E had the highest values in spring and fall. B, C, and D percentagespercentages inin thethe highhigh 80s80s inin latelate June.June. EE hadhad thethe hihighestghest valuesvalues inin springspring andand fall.fall. B,B, C,C, andand DD werewere were the bottom three, with D consistently having the lowest percentage, peaking below 70 percent thethe bottombottom three,three, withwith DD consistentlyconsistently havinghaving thethe lowestlowest percentage,percentage, peakingpeaking belowbelow 7070 percentpercent inin in summer. One difference from the previous two FAR scenarios is that, in October and November, summer.summer. OneOne differencedifference fromfrom thethe previousprevious twotwo FARFAR scenariosscenarios isis that,that, inin OctoberOctober andand November,November, FF F had low percentages that neared those of C and D. hadhad lowlow percentagespercentages thatthat nearedneared thosethose ofof CC andand D.D. For the six or more hours threshold, A again maintained the highest percentages of direct sunlight ForFor thethe sixsix oror moremore hourshours threshold,threshold, AA againagain maintainedmaintained thethe highesthighest percentagespercentages ofof directdirect for most of the period, peaking just below 40 percent in late June and early July. The values for sunlightsunlight forfor mostmost ofof thethe period,period, peakingpeaking justjust belowbelow 4040 percentpercent inin latelate JuneJune andand earlyearly July.July. TheThe valuesvalues forfor C,C, E,E, andand FF werewere generallygenerally similarsimilar throughoutthroughout thethe wholewhole period.period. TheThe valuesvalues forfor DD werewere generallygenerally belowbelow 2020 percentpercent exceptexcept forfor aa veryvery shortshort periodperiod inin latelate June.June. Again,Again, BB showedshowed thethe lowestlowest percentages,percentages, whichwhich duringduring mostmost ofof thethe nine-monthnine-month periodperiod diddid notnot exceedexceed 1010 percent.percent.

Energies 2018, 11, 2030 10 of 15

C, E, and F were generally similar throughout the whole period. The values for D were generally below 20 percent except for a very short period in late June. Again, B showed the lowest percentages, whichEnergies during2018,, 11,, mostxx FORFOR ofPEERPEER the REVIEWREVIEW nine-month period did not exceed 10 percent. 10 of 15

100 100 A A 90 90 B B 80 80 C C 70

70 (%) Percentage Percentage (%) Percentage D 70 D

70 (%) Percentage Percentage (%) Percentage D D E E 60 E 60 E F F 50 F 50 50 40 40 40 30 30 30 20 20 20 10 10 10 0 0 0 Date 1-Mar 1-Apr 1-May 1-Jun 1-Jul 1-Aug 1-Sep 1-Oct 1-Nov Date 1-Mar 1-Apr 1-May 1-Jun 1-Jul 1-Aug 1-Sep 1-Oct 1-Nov Date 1-Mar 1-Apr 1-May 1-Jun 1-Jul 1-Aug 1-Sep 1-Oct 1-Nov Date

FigureFigure 8.8. PercentagesPercentages of ofofground-level ground-levelground-level surface surfacesurface with withwith direct direct sunlight sunlight for for three three or or more more hours hours (left (left) and) and six orsixmore or more hours hours (right (right) from) from March March to November to November for studyfor study sites sites A to A F to when F when FAR FAR is 2.5. is 2.5.

4.4.4.4. FAR == 3.0 ForFor thethe thresholdthreshold ofof threethree oror moremore hours,hours, AA outperformedoutperformed thethe other fivefive between mid-May andand thethe end of July, July, peaking at at percentages percentages in in the the mid-80 mid-80ss in in late late June, June, as as shown shown in in Figure Figure 9.9 However,. However, E Ehad had the the highest highest percentages percentages for for a amuch much a a longer longer peri periodod in in spring spring and and fall. fall. The The difference difference between EE andand thethe otherother fivefive becamebecame moremore significantsignificant inin MarchMarch andand NovemberNovember when all thethe othersothers presentedpresented similarsimilar values. A and E were followed by C and F, whichwhich presented a highly similar trend. B and D, thethe bottombottom two,two, showedshowed almostalmost identicalidentical percentagespercentages throughoutthroughout thethe nine-monthnine-month period.period. ForFor the threshold ofof sixsix or more hours,hours, A demonstrateddemonstrated the highest percentages of ground-level surfacesurface with direct direct sunlight sunlight for for most most of of the the nine-m nine-monthonth period, period, with with a peak a peak exceeding exceeding 30 30percent percent in insummer. summer. E showed E showed the thehighest highest values values during during short short periods periods in spring in spring and andafter after mid-September. mid-September. The Theperformance performance of C ofand C andF were F were mostly mostly very very similar. similar. DD andand D and BB hadhad B had thethe the lowestlowest lowest values,values, values, withwith with DD belowbelow D below 2020 20percent percent and and B below B below 10 10percent percent throughout throughout the the nine-month nine-month period. period.

100 100 A A 90 90 B B 80 80 80 C C 70

70 (%) Percentage Percentage (%) Percentage D 70 D

FigureFigure 9.9. PercentagesPercentages of ofofground-level ground-levelground-level surface surfacesurface with withwith direct direct sunlight sunlight for for three three or or more more hours hours (left (left) and) and six orsixmore or more hours hours (right (right) from) from March March to November to November for studyfor study sites sites A to A F to when F when FAR FAR is 3.0. is 3.0.

4.5.4.5. FAR == 3.5 ForFor thethe threethree or or more more hours hours threshold, threshold, A hadA had the th largeste largest percentages percentages of ground-level of ground-level surface surface with directwith direct sunlight sunlight in June in June and July,and July, peaking peaking at around at around 80 percent 80 percent in late in late June, June, as illustrated as illustrated in Figure in Figure 10. However,10. However, E ranked E ranked higher higher for a much for a longer much period longer and periodperiod outperformed andand outperformedoutperformed the other ﬁve thethe more otherother considerably fivefive moremore thanconsiderably in the previous than in scenarios, the previous particularly scenarios, in March particularly and November in March when and the November difference betweenwhen the E difference between E and the other five neared 20 percentage points. Again, C and F showed similar percentages, and B and D had the lowest percentages, failing to exceed 60 percent. For the six or more hours threshold, A displayed the highest values from April to August, exceeding 25 percent in summer. C, E, and F were rather constant throughout the whole period at mostly below 20 percent. D and B showed the lowest percentagespercentages forfor mostmost ofof thethe time,time, withwith DD peakingpeaking justjust belowbelow 1515 percentpercent andand BB belowbelow 1010 percent.percent.

Energies 2018, 11, 2030 11 of 15 and the other ﬁve neared 20 percentage points. Again, C and F showed similar percentages, and B and D had the lowest percentages, failing to exceed 60 percent. For the six or more hours threshold, A displayed the highest values from April to August, exceeding 25 percent in summer. C, E, and F were rather constant throughout the whole period at mostly below 20 percent. D and B showed the lowest percentages for most of the time, with D peaking justEnergies below 2018, 15, 11,, percent xx FORFOR PEERPEER and REVIEWREVIEW B below 10 percent. 11 of 15

100 100 A A 90 90 B B 80 80 C C 70

70 (%) Percentage Percentage (%) Percentage 70 D

70 D (%) Percentage Percentage (%) Percentage D D

60 E 60 E F F 50 50

40 40

30 30

20 20

10 10

0 0 1-Mar 1-Apr 1-May 1-Jun 1-Jul 1-Aug 1-Sep 1-Oct 1-Nov Date 1-Mar 1-Apr 1-May 1-Jun 1-Jul 1-Aug 1-Sep 1-Oct 1-Nov Date

FigureFigure 10.10. PercentagesPercentages ofof ground-levelground-level surfacesurface withwith directdirect sunlightsunlight forfor threethree oror moremore hourshours ((leftleft)) andand sixsix oror moremore hourshours ((rightright)) fromfrom MarchMarch toto November November for for study study sites sites A A to to F F when when FAR FAR is is 3.5. 3.5.

4.6.4.6. FARFAR == 4.04.0 UnlikeUnlike previousprevious scenarios,scenarios, EE presentedpresented thethe highesthighest percentagespercentages throughoutthroughout thethe wholewhole periodperiod whenwhen thethe FAR FAR was was set set to to 4.0, 4.0, exceeding exceeding 70 70 percent percent in in summer, summer,summer, as asas shown shownshown in Figureinin FigureFigure 11. 11.11. It was ItIt waswas followed followedfollowed by F,by A, F, and A, and C. B C. and B and D were D were again again almost almost identical, identical, staying staying below below 50 percent 50 percent for most for of most the nine-monthof the nine- period.month period. A, C, and A, FC, also and dropped F also dropped below 50 below percent 50 percent in spring in and spring fall. and fall. ForFor thethe thresholdthreshold ofof sixsix oror moremore hours,hours, itit waswas difﬁcultdifficult toto makemake anyany judgementjudgement onon whichwhich sitesite outperformedoutperformed the the others, others, as as all all percentages percentages genera generallylly remained remained below below 15 percent 15 percent for the for whole the whole nine- nine-monthmonth period. period. Among Among them, D them, and B D showed and B showedthe lowest the percentages lowest percentages for most of for the most time, of remaining the time, remainingbelow 10 percent. below 10 percent.

100 100 A A 90 90 B B 80 80 C C 70

70 (%) Percentage Percentage (%) Percentage 70 D

70 D (%) Percentage Percentage (%) Percentage D D

60 E 60 E F F 50 50

40 40

30 30

20 20

10 10

0 0 1-Mar 1-Apr 1-May 1-Jun 1-Jul 1-Aug 1-Sep 1-Oct 1-Nov Date 1-Mar 1-Apr 1-May 1-Jun 1-Jul 1-Aug 1-Sep 1-Oct 1-Nov Date

FigureFigure 11.11. PercentagesPercentages ofof ground-levelground-level surfacesurface withwith directdirect sunlightsunlight forfor threethree oror moremore hourshours ((leftleft)) andand sixsix oror moremore hourshours ((rightright)) fromfrom MarchMarch toto November November for for study study sites sites A A to to F F when when FAR FAR is is 4.0. 4.0.

4.7.4.7. EstimationEstimation ofof CropCrop YieldYield BasedBased onon aa seriesseries ofof simulationssimulations carriedcarried outout soso far,far, wewe provideprovide anan estimationestimation ofof annualannual yield per housinghousing unitunit ofof carrotcarrot asas anan exampleexample for each study site at the six density levels as shown in in Table Table 44.. WeWe appliedapplied yieldyield estimatesestimates perper areaarea datadata normalizednormalized forfor eacheach dayday providedprovided byby thethe KoreanKorean StatisticalStatistical InformationInformation Service Service in in the the calculation calculation (See (See Table Table1). It1). is It clear is clear that lowerthat lower FAR levelsFAR levels result inresult more in carrot more carrot yields in both three hours and six hours cases and that the estimates vary by site typology. We also noted that the amount of fruit vegetable yields fromfrom atat leastleast sixsix hourshours ofof directdirect sunlight,sunlight, whichwhich may enhance produce quality, is significantly lower than three hours.

Energies 2018, 11, 2030 12 of 15 yields in both three hours and six hours cases and that the estimates vary by site typology. We also noted that the amount of fruit vegetable yields from at least six hours of direct sunlight, which may enhance produce quality, is signiﬁcantly lower than three hours.

Table 4. Estimation of crop yields (grams per housing unit) for each study site at the six density levels.

Direct Sunlight Study Site Crop FAR Duration ABCDEF 1.5 459.95 299.50 328.71 319.82 603.00 400.24 2.0 313.24 195.03 220.14 206.26 423.67 273.18 Root 2.5 230.17 133.87 161.03 139.69 313.89 195.21 vegetables 3 or more hours 3.0 175.92 91.94 119.84 103.94 247.78 149.11 (Carrot) 3.5 138.98 69.80 92.67 81.28 201.31 117.44 4.0 95.44 55.08 73.48 65.91 166.43 94.45 1.5 208.41 50.73 124.72 85.56 207.57 198.50 Fruit 2.0 119.49 26.00 69.20 47.38 124.12 154.51 vegetables 2.5 76.10 14.66 45.61 27.35 76.92 122.14 6 or more hours (Cucumber) 3.0 51.04 8.33 29.28 17.79 54.35 98.68 (Eggplant) 3.5 36.09 5.85 19.90 12.16 40.90 80.65 4.0 18.43 4.24 14.25 8.82 31.61 66.25

5. Discussion The results of the solar environment analyses for the six study sites at six different density levels have important implications for the potential of urban agriculture in high-density residential complexes. First, among study sites A and B, the two parallel cases, A generally demonstrated higher percentages of ground-level surface with direct sunlight throughout the six density scenarios. Given that their densities were similar, we interpret that the orientation of the buildings plays a critical role in determining the amount of direct sunlight that reaches the ground level. Second, study sites C and D, the two grid cases, did not demonstrate any impressive results throughout the density. Between the two, D consistently had the lowest percentages of direct sunlight at ground level. While other characteristics of the two remain similar, the taller buildings in D may have contributed to blocking direct sunlight. Third, study sites E and F, the two tower cases, resulted in interesting findings, as they seemed to perform better than others as the density increased, demonstrating this typology’s potential for facilitating direct sunlight at ground level in a hyper-dense setting. Between the two, E performed better in general, presumably because of its relatively lower density. Based on a more comprehensive observation of the results, it was clear that, as density increased, the amount of ground-level open space with direct sunlight decreased in all cases. This suggests that to achieve a residential environment with sufficient direct sunlight to support urban agricultural practices, density has to be sacrificed. Lastly, it was difficult to come up with generalized rankings of the three site typologies. However, it was evident that, in lower-density settings, parallel typologies with buildings running east–west could secure the most direct sunlight. The tower typology also performed better as density increased. The grid typology performed the poorest in all scenarios.

6. Concluding Remarks This study explored the potential for utilizing ground-level open spaces for urban agriculture in high-density residential environments. Six cases with differing typologies in Seoul were selected for study. A series of parametric analyses of the duration of direct sunlight on ground-level open space for each site at six density levels using DIVA-for-Rhino identified the density levels and site typologies that best support urban agriculture. This analysis suggested the trade-off between urban density and direct solar access for urban agriculture, yet high-density development is a popular approach in many Asian countries with high population density to efficiently provide housing. Developers also Energies 2018, 11, 2030 13 of 15 tend to maximize housing density to achieve maximum profit. However, careful building layouts that consider location, direction, and relationship with adjacent buildings can improve the solar accessibility of high-density developments, as the simulation results showed considerable differences in percentages of ground-level surface with direct sunlight between sites. Thus, solar-accessible urban and architectural design to harness unused solar energy for crop yields should contribute to improving environmental sustainability. However, there are several shortcomings. First, the six sites, despite being highly representative of Seoul’s residential complexes, could produce case-specific findings that are not transferrable. Second, although DIVA-for-Rhino is a favored tool among researchers and has cutting-edge capabilities, it may be necessary to validate its simulation results to make the findings more convincing. Third, the study focuses on a subset of crops grown by locals, and therefore the findings may not be relevant to those interested in the potential cultivation of other crops. Nonetheless, this study’s contributions could benefit Seoul’s residents and policymakers. It is one of the very first attempts to link the design of high-density built environments with urban agriculture in the Asian context. It also analyzes existing cases, rather than simplified prototypes that do not exist in the real world, therefore presenting more practical findings. Lastly, it provides guidance for local urban design practices, which may actively incorporate space for urban agriculture in high-density residential areas in the near future. Our future research will develop a user-friendly solar accessibility simulation tool that planers and architects can use during initial design process to assess solar potential of their design. We anticipate the new tool to encourage sustainable architecture and urban design by making it easy for designers to use simulations tools, and ultimately contribute to sustainable urban environments.

Author Contributions: H.K. and K.S.L. suggested the initial idea and conducted the data analysis. S.L. conducted the simulation. J.S.L. advised the research design and theoretical framework and edited the manuscript. All authors have approved the final manuscript. Funding: This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 18TBIP-C144203-01). This work was also supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2018R1A2B6005938). Conflicts of Interest: The authors declare no conflicts of interest.

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