International Journal of Environmental Research and Public Health Article Planning for Sustainable Green Urbanism: An Empirical Bottom-Up (Community-Led) Perspective on Green Infrastructure (GI) Indicators in Khyber Pakhtunkhwa (KP), Pakistan Muhammad Rayan 1,* , Dietwald Gruehn 1 and Umer Khayyam 2 1 Research Group Landscape Ecology and Landscape Planning (LLP), Department of Spatial Planning, TU Dortmund University, 44227 Dortmund, Germany 2 Department of Development Studies, School of Social Sciences and Humanities (S3H), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan * Correspondence: muhammad.rayan@tu-dortmund.de Abstract: Rising vulnerability of the urban green infrastructure (UGI) is grabbing global attention, for which inclusive urban landscape and greening policies (ULGP) and frameworks are crucial to support green growth. As such, this research intends to explore the local community’s perspective to assemble sustainable UGI indicators for vital taxonomy of the urban green space (UGS) elements, aiming to develop a multi-functional and sustainable UGI-indicator-based framework that is eco-friendly and supports green-resilient cities in Khyber Pakhtunkhwa (KP) province, Pakistan. An in-depth household survey was executed in three KP districts: Charsadda, Peshawar, and Mardan, placing self-administered 192 questionnaires while covering themes around climate change adaptation, urban resilience, and UGI. Relative importance index (RII) and the interquartile range (IQR) methods were Citation: Rayan, M.; Gruehn, D.; set up for data analysis that revealed excellent reliability (α > 0.88) and internal consistency. The Khayyam, U. Planning for results confirmed community-based UGI indicators with a focus on promoting green-energy-saving Sustainable Green Urbanism: An Empirical Bottom-Up strategies as e-imp (level 9, RII = 0.915), while other (ten) UGI indicators as important (RII = 0.811– (Community-Led) Perspective on 0.894) and (eleven) as moderately important (RII = 0.738–0.792). These UGI indicators were found to Green Infrastructure (GI) Indicators be enhanced by UGS elements (RII ≥ 0.70). These findings provide a foundation for urban policy in Khyber Pakhtunkhwa (KP), change and the development of a sustainable UGI framework to build an eco-regional paradigm for Pakistan. Int. J. Environ. Res. Public greener growth. Health 2022, 19, 11844. https:// doi.org/10.3390/ijerph191911844 Keywords: climate change; adaptation; urban green infrastructure; community participation; KP, Pakistan Academic Editor: Paul B. Tchounwou Received: 11 August 2022 1. Introduction Accepted: 7 September 2022 Urbanization leads to the shrinkage of urban green spaces, which directly contributes Published: 20 September 2022 to extreme climatic hazards such as flooding, drought, urban heat island effect, etc. These Publisher’s Note: MDPI stays neutral hazards then further result in the degradation of ecosystem functions (ESF) and the loss with regard to jurisdictional claims in of biodiversity and affect human health/well-being [1–5]. The experts anticipate that the published maps and institutional affil- climate change observed today and in the foreseeable future will be influenced by the iations. variability of anthropogenic forcing [6]. If we cannot limit global climate change, there will be far-reaching repercussions on nature and society [7]. The global vulnerability of cities to climate-related hazards and stresses is expected to increase due to an increased built-up footprint compared to the population growth rates. According to research, it is estimated Copyright: © 2022 by the authors. that the urban population will grow by 72% from 2000 to 2030, while the built-up area of Licensee MDPI, Basel, Switzerland. This article is an open access article cities (with 100,000 residents) will grow by 175 percent [8]. The incremental trend of the distributed under the terms and world population and anthropogenic activities has changed the land cover and contributed conditions of the Creative Commons to the greying of the natural landscape. These harmful impacts of urbanization and the Attribution (CC BY) license (https:// corresponding high pressure on the natural environment, at an unprecedented rate, are creativecommons.org/licenses/by/ badly hampering urban growth in the major cities of Pakistan. In Pakistan, the urbanization 4.0/). rate is amplified from 32.98% (year 2000) to 36.91% (2019), with further projections to Int. J. Environ. Res. Public Health 2022, 19, 11844. https://doi.org/10.3390/ijerph191911844 https://www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2022, 19, 11844 2 of 29 reach 50% by 2025 [9]. This upsurge is transforming the local attitude towards green spaces; thus, urban centers receiving less consideration become unsafe [10] and evolve multidisciplinary climatic challenges. Amongst other challenges, ‘urban flooding’ remains the most threatening climatic hazard with the power to endanger human safety and frighten natural resources and ecosystems. Furthermore, jeopardizing the socio-economic fabric of urban (flood-affected) inhabitants stays common. These issues call for urban green infrastructure (UGI) (UGI planning is defined as “a network composed of open spaces, waterways, gardens, forests, green corridors, trees on streets, and open spaces, bringing many social, Economic and ecological benefits” [11]. Another UGI version exists as “an interconnected network of natural areas and other open spaces that conserves natural ecosystem values and functions sustains clean air and water and provides a wide array of benefits to people and wildlife” [12]) to enhance ur- ban sustainability [13]. UGI is perceived as a nature-based and cost-effective solution to achieve resilience in the land use planning process to mitigate the ever-rising climate uncertainties [14,15], a revamp of all existent contemporary ideas concerning green space planning [16,17], an approach to enrich the health of the ecosystem, minimizes the surface- water runoff, improves water infiltration rate [18] and a cost-effective strategy to mitigate urban floods. Thus, UGI planning is already testified and declared important in countries such as Germany, the UK, and the Netherlands, where it is encouraged to promote inno- vative nature-based green solutions for climate change mitigation and adaptation [19–22]. Hence, it is confirmed that planning instruments (such as UGI) play an imperative role in minimizing the urban flooding effects, thereby enhancing the socio-ecological well-being of any region. Based on its strengths, this nature-based green (NBGI) approach stands as an applicable instrument for sustainable climate-risk management (SCRM) in cities [23], yet importantly, in bitterly climate-affected countries such as Pakistan. 1.1. Establishing a Niche: Climate Change Impacts Pakistani urban areas pose multifaceted climatic encounters [24]. The consistent urban flooding events observed in recent years are putting lives and livelihoods at stake [25]. It is these growing incidences of floods, strong monsoon circulation, surface temperature rises, etc., that are making the country highly vulnerable and positing it (as per the climate risk index-CRI) (CRI is research that is centered on a comprehensive and accurate database of climate hazard effects observed in all countries in the world. In addition, low-income countries need to utilize the index as a warning signal to equip themself completely for future catastrophic disasters. www.germanwatch.org/en/cri, (accessed on 11 June 2021)) the eighth most vulnerable country to climate hazards [26]. Therefore, the disastrous impacts on ecosystems, biodiversity, agriculture, human settlements, human health, etc., are profound with different levels of adaptability [27,28]. Such devastating disasters for a country such as Pakistan (an agrarian economy) directly hamper the agricultural sector, which contributes 21.9% of GDP and employs 45% of local labor [29,30]. These disturbing lives and livelihoods remain prominent in the major/mega cities, which is further linked to massive and unplanned settlements at the expense of decreasing forest cover [4,31]. Other factors contributing to this issue are high population density, building of new colonies or expansion of physical infrastructure, etc., that are removing the green cover and urban green-spaces and rising air pollution [32,33]. These aforementioned problems prevail due to non-existent UGI planning in the existing urban plans and policies of the country, where such effective strategies are perceived only as a luxury urban activity. It is mainly associated with beautification (though not an essential urban amenity) to influence urban resilience against climatic hazards [10,34,35]. The whole alarming situation is linked to the regional non-resilient outlook toward unbalanced and reactive urban planning policies [36] that leads to unplanned settlements– further enlarging the environmental issue in the country [37]. Aside from the planning deficiencies (as outlined above), other contributing factors are inadequate ULGP, weak laws and enforcement, un-due influence, lack of scientific knowledge, lack of awareness, Int. J. Environ. Res. Public Health 2022, 19, 11844 3 of 29 non-existence of PP is recognized as the effective tool to promote community stewardship in the planning and decision-making process to bolster nature-based-green infrastructure (NBGI) initiatives in land-use planning; effectively tackling socio-environmental problems at grassroots levels [38–42] approach, etc., all contributing to the transformation of green- spaces into urban functions/activities [10,35,43]. This exerts constant pressure on land cover, so deterioration of UGS elements. These issues declare Pakistani mega cities highly vulnerable to natural calamities, with no exception of the northwest territories of KP [44]. At a national level in Pakistan, KP province suffered predominantly from consistent flooding events in the last decade [25], which marks this area as highly vulnerable and risky to in-daunting events, accounting for massive economic and human losses [22,36,45] Generally, these damages are linked to region geographic position and topographical features. The area lies on the bank of Swat Kabul, Kunhar, and Panjkora rivers’ basin that originates from the high mountains of Hindukush, Himalayas, and Karakoram ranges. Being at water banks, it enhances the catchment area’s vulnerabilities to urban flooding (Figure 1). In addition, the issue also stresses the built environment service, which leads to the over-exploitation of natural green barriers [10], thereby endangering the urban ESF and human health/well-being in urban settlements [46–48]. Therefore, tackling the underlying causes and destructive effects of climate change in this region requires an immediate effort to examine the nexus between the UGI and climate-resilience notions to be incorporated (holistically) in the land-use planning process [26–29]. It is to develop a rich, multi-functional/inclusive/sustainable UGI-indicator-based (framework) model structured according to the local built environment. Such a model should be grounded on the (native) community perspective or the PP process—that further leads to strengthening Int. J. Environ. Res. Public Health 2022, 1   the 9,c xli mate-resilient strategies, green spaces (GS), ecosystem functions (ESF), and4 houf m28a  n well-being in catchment areas.   FiFgiugurere1 .1.F Flolooodd-a‐affffeecctteedd ddiissttrriiccttss ooff KKPP pprroovviinnccee.. MMaapp SSoouurrccee: :[2[244].] . 1.2. PP for UGI and UGS  PP (as an effective tool) can facilitate community stewardship in the planning and  decision‐making process, though so far less considered within the Pakistan planning con‐ text  [11,12,30–34,49,50]. Consequently,  PP  deems more  effective  in  understanding  the  complexity of interactions among the ecosystems and humans [38,51,52]. Hence, PP facil‐ itates  in  drawing  ‘human‐nature’  studies/concepts  for  not  only  finding Nature‐Based  Green Infrastructure (NBGI) solutions [53] but, in turn, enabling human societies to en‐ hance their adaptative capacity and build resilience against (ever‐rising) environmental  hazards, e.g., urban flooding [44,45]. To further add, a (bottom‐up) PP approach help for  a successful transition to Green Action Plans (GAP) [38]  It is established that there is a dearth of research studies in Pakistan towards devel‐ oping theoretical and empirical foundations for UGI planning and implementation, which  is a prerequisite for an eco‐friendly and climate resilience environment in the country (in  general) and in KP (in particular). Though planning authorities in Pakistan adopt spatial  technologies in‐order to develop land‐use maps of major urban districts, these interven‐ tions are still in their infancy [54,55] and usually require more time and financial resources  [34,56], especially  in  the non‐collaborative and unilateralism environment  (with undue  influence) that Pakistan possesses [43,49].  Hence, to bridge this gap, this (novel) research study intends to develop a rich body  of  multi‐functional  conceptual  UGI‐indicator‐based  framework/model,  which  can  be  grounded upon “Triple Bottom Line” (TBL) (The triple bottom line (TBL) refers to sus‐ tainabilityʹs environmental, socio‐cultural, and economic dimensions. It is the most com‐ monly accepted model used in most urban sustainability applications [46,47,57]) sustain‐ ability and adapted to the local context. Such a potential framework encompasses a set of    Int. J. Environ. Res. Public Health 2022, 19, 11844 4 of 29 1.2. PP for UGI and UGS PP (as an effective tool) can facilitate community stewardship in the planning and decision-making process, though so far less considered within the Pakistan planning context [11,12,30–34,49,50]. Consequently, PP deems more effective in understanding the complexity of interactions among the ecosystems and humans [38,51,52]. Hence, PP facilitates in drawing ‘human-nature’ studies/concepts for not only finding nature-based green infrastructure (NBGI) solutions [53] but, in turn, enabling human societies to enhance their adaptative capacity and build resilience against (ever-rising) environmental hazards, e.g., urban flooding [44,45]. To further add, a (bottom-up) PP approach help for a successful transition to green action plans (GAP) [38]. It is established that there is a dearth of research studies in Pakistan towards develop- ing theoretical and empirical foundations for UGI planning and implementation, which is a prerequisite for an eco-friendly and climate resilience environment in the country (in general) and in KP (in particular). Though planning authorities in Pakistan adopt spatial technologies in-order to develop land-use maps of major urban districts, these interventions are still in their infancy [54,55] and usually require more time and financial resources [34,56], especially in the non-collaborative and unilateralism environment (with undue influence) that Pakistan possesses [43,49]. Hence, to bridge this gap, this (novel) research study intends to develop a rich body of multi-functional conceptual UGI-indicator-based framework/model, which can be grounded upon “Triple Bottom Line” (TBL) (The triple bottom line (TBL) refers to sustain- ability’s environmental, socio-cultural, and economic dimensions. It is the most commonly accepted model used in most urban sustainability applications [46,47,57]) sustainability and adapted to the local context. Such a potential framework encompasses a set of core sustainable UGI indicators and vital taxonomy of UGS elements. To bottom-up oriented framework/model testify and validate the margin between essential and inessential po- tential UGI indicators and green space elements. Here, the local community evaluates the significance of the sustainable UGI indicators and their relationship with multiple UGS elements, according to the native built-in environment. It is because (i) the effectiveness of UGS structures (usually) depends on the spatial contextual factors (socio-cultural and economic) of any region where they are examined [15,50,58–60] and (ii) not all the UGS ele- ments had an excellent functional linkage to improve the resilience of respective sustainable UGI indicators while coping with the gradual climate change. In this sense, this research study is inception that breeds the PP approach to develop an inclusive, sustainable UGI-indicator-based framework to build green and resilient cities in the KP province. It is also imperative that this UGI model, developed through this study, can be adapted to the native spatial environment-will provide a proactive and long-term way for ULGP and guidelines for CC mitigation/adaptation. This may lead to a well- balanced relationship between anthropocentrism and eco-centrism activities for KP and beyond. Moreover, such a framework will lead to encouraging innovative green grass-root initiatives, with the mandate to build a new eco-cultural paradigm to enhance the adaptive capacity in sustained human settlements. This will inevitably open up a new domain of study to gradually probe more deeply into innovative community PP approaches when planning nature-based green adaptation techniques for climate change adaptation. 1.3. Study Aim and Research Questions This research study aims to analyze the community perspective (through the bottom- up PP approach) to gauge the locals’ insightful view regarding UGI-indicator-based frame- work/model. It is to obtain a greater consensus among the local community to find a relationship between sustainable UGI indicators and (potential) taxonomy of UGS ele- ments, as per the native built environment. This then leads to validating the sustainable UGI framework/model, which fits best in the local socio-economic and cultural context. Such an effort would contribute to enhancing green-spaces, besides alleviating vulner- ability towards climate hazards. They also improve regional socio-ecological resilience. Int. J. Environ. Res. Public Health 2022, 19, 11844 5 of 29 It also builds climate-resilient cities in KP territory under a community participation approach–promoting a sense of community ownership. Therefore, the study intends to find answers to the following three research questions: i. What is the level of the local community’s understanding of Climate Change and UGI? ii. Which essential UGS elements strengthen the resilience of (sustainable) UGI indicators? iii. What type of UGI-indicator-based model contributes to building a green climate- resilient city-state? 2. Research Methodology This research has adopted the UGI (conceptual base) framework model developed by the author [48], grounded on two conceptual frameworks: (i) Driver pressure state impact response (DPSIR) framework that aims to conceptualize the relationship between UGI elements and anthropogenic activities to build climate-resilient cities and (ii) incor- poration of the model, proposed by [14], which is further enhanced by inserting three additional components, (a) climate resilience strategies, (b) eco-system function—ESF and then (c) the UGI elements as suggested by [15,17,59,61], but revised to build a strong cor- relation among them [48] (for details, see Appendix A: Figures A1 and A2). Additionally, semi-structured discussions with multi-stakeholder (planning experts and community) in Pakistan were conducted regarding the potential role of NBGI initiatives’ in promoting an eco-friendly and climate-resilient environment, resulting in nine cross-cutting themes [34] (see Appendix B: Table A1). The consolidated integration of both models and concepts intends to build a cohesive, sustainable UGI framework. This framework is perceived to enhance urban resilience against (ever-rising) environmental hazards and (simultaneously) minimize the degra- dation of the urban ecosystem health. These conceptual models and new cross-cutting themes (an innovation of this study) are regarding the potential role of UGS infrastruc- ture in addressing SCRM. This, in-tern, assists in determining the (potential twenty-two, some placed under main headings) UGI indicators that were classified into three main sustainability categories (i.e., ecological, socio-cultural, economical). It was conducted along with the ten (community garden (CG); botanical garden (BG); urban park (UP); forest (FO); green streets (GR); rain garden and bio-swale (RG); green and permeable parking area (GPA); wetland (WL); green roof and green wall (GRW); and horticulture (HO)) vital UGS (quantitative) elements to accomplish the research questions. Of course, there could be other indicators, such as institutional and political. However, that is out of the scope of this research and can be considered in future research. The potential UGI indicators and green elements are mainly quantitative, and the relative importance index (RII) and interquartile range (IQR) analysis technique employed applied to calculate the relative significance of each sustainable UGI indicator, as well as the UGS elements, as analyzed by the local community (within the real-life context). This method is recognized best approach for ordinal-scale surveys [62–65]. 2.1. Study Area, Sampling Technique, and Survey Design Multi-stage sampling technique is used. Firstly, selection of the municipalities (Tehsil) (Based on the higher population, the municipalities (Tehsil) in each district are selected (Table 1)) in each study district (Peshawar, Mardan, and Charsadda) (Table 1), and secondly; the selection of sub-municipalities (Union Council-UC) in each tehsil in the KP province, which is based on population census datasets [66]. (For determining the UC, this research integrated the interquartile range (IQR) technique with criteria 1. IQR is an efficient method for determining cut-off points [64,67–70]; based on population census datasets [66] and above the cut-off point (mid-point), all UCs were selected for the field survey (Figure 2). This methodology was adopted as there is no official list of the residential houses affected by climatic uncertainty within UCs exist. Moreover, each municipality has a minimum of 20 and a maximum of 37 sub-municipalities (called Union Councils-UCs). So, the most floods affected, time (cost) efficient areas, and safer strategies (in time of COVID-19 pandemic) Int. J. Environ. Res. Public Health 2022, 19, 11844 6 of 29 were selected for study purposes). Thirdly: the flood-affected Households (HHs) were consulted in the community survey (Table 2 and Figure 2) executed in the case study area from October 2020 to mid-December 2020. Table 1. Population census of three districts of KP province. Precipitation District Tehsil Town Population Geographic Data Climate (mm) (1999–2018) Mardan Tehsil 1,403,394 34.2883◦ N, 72.1890◦ E Mardan Katlang Tehsil 343,144 34.3521◦ N, 72.0764◦ E Takht Bhai Tehsil 626,523 34.3314◦ N, 71.9046◦ E 400.3 Charsadda Tehsil 804,194 34.2165◦ N, 71.7148◦ E Charsadda Shabqadar Tehsil 383,765 34.2186◦ N, 71.5546◦ E Tangi Tehsil 428,239 34.3040◦ N, 71.6555◦ E Humid subtropical Town 1 759,595 Town 2 547,807 Peshawar Tehsil Peshawar ([71]) Town 3 821,059 33.9437 ◦ N, 71.6199◦ E 546.075 Town 4 435,940 Peshawar cant. 70,741 Source: Authors’ compilation from the KP Bureau of Statistics (2018) [66], KP Local Government [71] and Pakistan Metrological department (2019) [72]. Table 2. Sampling size for the community survey. Union Council Tehsil Population Total No Average No of (Selection (Selection Grounded on a of Sample HH Size No HHs Sample District Grounded on a Tehsil High Urban Population High Urban Population Population (Source: KP 399.6/6.2 with the Integration of the (with 95 CI and Bureau of 399.5/7 Population) Interquartile Range + 5 MoE) Statistics) 339.7/5.6 Technique (IQR) Mardan Mardan 1,403,394 411,148 399.6 6.2 64 Charsadda Charsadda 804,194 350,483 399.5 7 57 Peshawar Town3 821,059 575,409 399.7 5.6 71 Source: Authors’ own elaboration, compilation the [66]. In total, 192 HHs [with 95%confidence level (CI), ±5% margin of error (MoE)] in which 64 HHs belong to Mardan, 57 HHs to Charsadda, and 71 HHs from Peshawar tehsils. A total of 1198.8 sample population (community) were consulted to study the subject trends, as per Cochran (1977) (Table 2), succeeded over from pilot testing to check the independence of various indicators and necessary modification in the questionnaire, as per the inputs (from local govt. officials, two expert consultants, three academicians, and three community members), which were conducted to check its feasibility, inclusiveness, and precision. This approach helped to do prior minor amendments (see Appendix C) questions’ appropriateness and time efficiency [73,74]. In general, the acknowledgment level remained acceptable for generalizing the results over the whole study sample population [75,76]. IInntt.. JJ.. EEnnvviirroonn.. RReess. .PPuubblilcic HHeeaaltlthh 22002222, ,1199, ,x1 1844 8 7oof f2289      FFiigguurree 22. .DDisitsrtircitc, tt,etheshils,i la,nadn udnuionnio cnouconucinl cmilamp aopf MofarMdaanrd, aCnh,aCrshaadrdsaad, adnad,  aPnedshPaewsahra. wSoaur.rcSeo: uAruce‐: tAhourtsh’o crosm’ cpoimlaptiiolant ifornomfr o[2m4,[6264],.6 6]. ITnh  teoctaolm,  1m9u2n HityH-sb a[swedithe m95p%ircicoanlfsidtuednycee mlepvelol y(eCdI)t,h  ±e5s%n omwabragllinte ochf neirqruore  i(nMtohEe)c] aisne  wsthuidchy t6o4 idHeHnsti fbyeslopnegc ifitoc MHHarsd,awnh,  i5c7h HseHrvse  tdo aCshaarresafedrdean,c eanbde n7c1h mHaHrsk ,frsoelmec tPinesgheavwearry  tfeohusritlsh. HA Htotfarol mof 1th1e98r.e8f esraemnpcelep pooinptualastiaonnH (cHoms smamunpiltey)t owoebreta cionnfisuelldtedda ttoa s. tTuhdiys tmhee tshuobd‐- joeclot gtryenwdass, dase ppleory Cedocshinracne n(1o97o7ffi) c(Tiaalblilset 2s)o, fsuflcocoede-daefdfe cotveedr rferosimde pnitlioatl theostuinsegs teox cishtewcki tthhien  iUndCesp. eTnodceonllceec totfh veasrtiuoduys dinadtaic,aatostrrsu actnudr endecsuesrsvaeryy qmueosdtiifoincantaioirne wina tshdee qsiugensetidonwnitahirteh, raese  pSeerc ttihoen sinAp–uCts. ((“frSoemcti oloncaAl gwoavstl. aobfefilcleiadlsa,s twthoe dexepmeorgt rcaopnhsiucltinanfotrsm, tahtrieoen ,aacaimdeinmgictoiavnasl,i danadte  tthhreeere csopmonmduennittyp rmofiemleb, kernso),w wlehdicghe ,waenrde lcoocnadtiuocnt.edT htoe cdhivecekrs ietsc faetaesgiobriylit“yt,r ianncslu”shivasenbeesesn,  ainncdl updreedciasisoant. hTirhdisg aenpdperorascinhc ehethlpeesdta  ttoe adpop rporvioerd mit.inPoarr taBmceonmdpmreehnetsn d(sse4e qAupespteionndnixa irCe)s  qdueessigtinoends’ taopvperroifpyritahteenneastsi vaendco tmimmeu enfiftiyci’esnvciye w[7s3o,7n4]t.h Ienp goetnenertaial,l tdheefi ancitkinoonws olefdcglimmeantte  lcehvaenl greem(CaiCn)e,da dacacpetpattaiobnlet ofoCr Cge,nuerrbaalinzirnegsi ltiheen crees,ualntsd oUvGerI ,thaes ewxhpolalein setdudiny Asapmppelned pixopD‐; uPlaarttioCnw [7a5s,7d6iv].i d ed into three sub-parts (environmental, socio-cultural, and economic), eachcomprising several queries to define and determine the significance of each UGS element and iTtshere claotmiomnsuhnipityw‐bitahsseuds etaminpaibrilceaUl sGtuI dinyd eicmatpolrosy. eTdh ethPeo stnenotwiablaUllG teIcinhdniicqauteo risn atnhde cUaGseS  setluedmye ntots iwdeenretifdye vspeleocpifeidc bHyHths,e wauhtihcho rsseirnvepdre acesd ai nrgefreerseenacrec hbsetnucdhimesa”r[k4,8 s]e. l“eTchtiinsgp erovceersys  froeusurtlhte HdHin fsroelmec tthineg rethfeerevnitcael ptaoxinotn aosm ayn oHfHthSe sUamGpSleel teom oebnttasinth faietledn dhaatnac. eTdhitsh me qetuhaolidty‐ osltoagnyd awrdasa dnedplhoeyaeldth sionfcree nspo eocftfiivceiaUl lGistIsi nofd filcoaotodr‐safafencdtewd oruesliddebnutiiladl hthoeusuersb eaxnisitn wteirtfhaicne  UinCtsh. eToK cPorlelegcito tnheth satutdisyr desaitlaie, na tstarguacitnusrtecdo snusrtvaenytl qyureissitniognennaviriero wnams ednetsailgtnherde awtsitshu tchhreaes  Suercbtaionnfls oAo–dCin. g(“”S[e3c4ti]o),nw Ah iwchasc olanbseilsltedof acsl othseed daenmdogorpaepnh-eicn idnefdorqmuaetsiotino,n asim(Fiinggu rteo 3v)a. liA‐ dLaiktee rtth-es craelsepaopnpdreonatc hprwofaislea, dkonpotwedle[d7g7e–,7 a9n]dto lroecgaitsiotenr. tThheep darivtiecripsea ncatst’ergeosrpyo “ntsreasnsso” thhaast  btheeenn ianticvluedceodm ams uan tihtyirpde grespnedcetri vseinocfe( pthoet esntatitael aapnpdrosuvsetda iint.a Pblaer)t UBG cIominpdriceahteonrds sa n4d qtuheesi‐r trieolnantiaoinreshs idpewsigitnhemd utolt vipelreifvyi ttahlet naxaotinvoem coymgmreuennietyleʹsm veinewtssi nonth tehelo pcoalteunrtbiaaln deenfviniritoinonmse onft  cclaimn abtee ecahsailnygeex (pClCor)e, dad. aptation to CC, urban resilience, and UGI, as explained in Appen‐   Int. J. Environ. Res. Public Health 2022, 19, 11844 8 of 29 The demographic characteristic affirmed 65.6% as male, 22.4% as female, 12% preferred not to mention their gender, and no participant was from the third-gender category; this option was provided as the government of Pakistan officially recognizes “trans” as a third gender [80–82]. The participation percentage of Masculine is high compared to feminine participation because most of the KP region’s households are mainly male-headed [66]. Moreover, the other reasons behind this relatively low no are (i) the female HHs are very low, and the majority of HH are male-headed in the area; (ii) local social and cultural norms are challenging to gain access to females; (iii) society does not allow outsider males (authors) to directly interact with females in their areas—yet this study tried to include the maximum possible female as HH heads through volunteer enumerators understanding the local culture and knowledge of the study. Regarding the participants’ educational background, 73.4% had tertiary/higher ed- ucation levels, 19.3% were intermediate, and 7.3% had secondary education (Table 3). The study also signifies nearly all representatives from the major four age groups had participated in the survey that shows the engagement of community individuals from all age groups. The age group with the highest frequency was 30–40 years (43.8%), followed by 34.4% in the age bracket of 20–30 years (The socio-demographics presented here only project all segments of the society, across all age groups, gender categories, and income and education levels were duly consulted. Their separate relationship for the study variables was neither intended to be covered nor they have influenced the broader findings of this study) (Table 3). The participants came from an array of socioeconomic backgrounds. Table 3. Socio-demographic analysis. Socio-Demographics TotalParticipants Ratio Gender-specific Male 126 65.6 Female 43 22.4 Diverse (the government of Pakistan recognizes the identification of “trans” as a third gender [80–82]) 0 0 Prefer not to say 23 12 Location Charsadda 56 29.1 Mardan 46 24 Peshawar 67 34.9 Not mention 23 12 Literacy No education to elementary 0 0 Secondary education (SSC) 14 7.3 Intermediate 37 19.3 Higher education 141 73.4 Other (informal) 5 2.6 Age 15–20 years 0 0 20–30 years 66 34.4 30–40 years 84 43.8 40–50 years 42 21.9 50–60 years 14 7.3 Source: Authors’ calculation, using field data. Int. J. Environ. Res. Public Health 2022, 19, x  9  of  28    dix D; Part C was divided  into three sub‐parts (environmental, socio‐cultural, and eco‐ nomic), each comprising several queries to define and determine the significance of each  UGS element and its relationship with sustainable UGI indicators. The Potential UGI in‐ dicators and UGS elements were developed by the authors in preceding research studies”  [48]. “This process resulted in selecting the vital taxonomy of the UGS elements that en‐ hanced the quality standard and health of respective UGI indicators and would build the  urban interface in the KP region that is resilient against constantly rising environmental  threats such as urban flooding” [34]), which consist of closed and open‐ended questions  (Figure 3). A Likert‐scale approach was adopted [77–79] to register the participants’ re‐ sponses so that the native community perspective of (potential and sustainable) UGI in‐ dicators and their relationship with multiple vital taxonomy green elements in the local  urban environment can be easily explored.    The demographic characteristic affirmed 65.6% as male, 22.4% as female, 12% pre‐ Int. J. Environ. Res. Public Health 2022f, e1r9r, exd   not to mention their gender, and no participant was from the third‐gender cat1e0g oofr y2;8     this option was provided as the government of Pakistan officially recognizes “trans” as a  third gender [80–82]. The participation percentage of Masculine is high compared to fem‐ inine participation because most of the KP region’s households are mainly male‐headed  [T66ab].l eM 3o. rSeoocivoe‐dr,e tmhoeg ortahpehri cr aenaasolynssis .b ehind this relatively low no are (i) the female HHs are  very low, and the majority of HH are male‐headed in the area; (ii) locaTlo stoacli a l and cultural  norms are challenginSgo tcoi goa‐Dine amccoegsrsa tpoh fiecms ales; (iii) society does nPoatr atillcoipwa onutst sider R matailoe s  (Gauetnhdoersr‐)s tpoe dciifriecc tly interact with females in their areas—yet this stud y tried to incl ude  the maximuMma lpe ossible female as HH heads through volunteer enume1r2a6to  rs unders6t5a.n6 d‐ ing the locaFle cmulatluer e and knowledge of the study.    43  22.4  RegardDing the  participants’ educational background, 73.4% had tertiary/higher edu‐cation levels,i v1e9r.3se%( twhee rgeo ivnetrenrmmeednita otef ,P aankdis 7ta.3n% r ehcaodg nseizceosn tdhaery educat0i on (Table 3).0 T  he  sitduednyt iafilcsaot isoignn oiffi “etsr naneas”rl yas a all  trheiprdre gseenntdaetirv [e8s0 –fr8o2m])  the major four age groups had partici‐ pated in thPer seuferrv neyo tt thoa st asyh ows the engagement of community indivi2d3u als from al1l 2a ge  gLroocuaptiso. nT he age group with the highest frequency was 30–40 years (43 .8%), followed   by  34.4% in thCe hagaers bardadcak et of 20–30 years (The socio‐demographics presen5t6e d here onl2y9 p.1r o‐ ject all segmMeanrtdsa onf  t he society, across all age groups, gender categorie4s6,  and income2 4a nd  education lPeevsehlsa waerr e duly consulted. Their separate relationship for t6h7e  study var3ia4b.9l es  Int. J. Environ. Res. Public Health 2022,w19a,s1 1n8e44itherN iontt emnednetdio tno  be covered nor they have influenced the broad2e3r   findings o91f 2ot fh2i9s  sLtuitdeyra) c(yT able 3). The participants came from an array of socioeconomic  backgrounds.   No education to elementary  0  0  Secondary education (SSC)    14  7.3  Intermediate  37  19.3  Higher education  141  73.4  Other (informal)  5  2.6  Age      15–20 years  0  0  20–30 years  66  34.4  30–40 years  84  43.8  40–50 years  42  21.9  50–60 years  14    7.3  FSioguurrcee 3: .A Cuotmhomrsu’ ncaitlyc uHlaHti osun,r vuesyin sgt rfaieteldg yd.. aStoau. rrcce:: [[344]]..    22.2.2.. Daattaa AAnnaallyyssiissa annddS Suurrv  veeyyR Reelilaiabbiliiltityy  DDaattaaw weerreee exxaamminineeddt hthrroouugghhM MicicrorossooftftE Exxcceel.l.S SeecctitoionnssB Ba annddC C( (aasso offF Figiguurree3 3))o offt thhee  ssuurrvveeyyq quueestsitoionnnaiariereu suesdeda qau qeusteisotnio-bna‐bseadsecdo dcoindginaglg aolrgitohrmithtmo s teog rsegarteeganted aenxdam exinaemtihnee  ctohme mcoumnmityurneistyp ornesepso.nCsreosn. bCarcohn’bsaaclhp’hsa a(lαp)hwa a(αs )e xweacsu teexde,caunteddα, -avnadlu αes‐v(a>l0u.e7s)  c(o>n0.fi7r)m coend‐ dfairtma reedl idabatilai tryel(iFaibgiulirtey 4(F).igure 4).      FFiigguurree4 4. .C Crroonnbbaacchh’s’sa alplphhaar reelilaiabbiliiltiyty. .S Soouurcrcee: :A Auuththoorsrs’ ’c caalclcuulalatitoionnu ussininggfi feieldldd daatata. .  AAss tthhee pprrooppoosseedd UUGGII ininddicicaattoorrss aanndd UUGGSS eelelemmeennttss aarreeq quuaannttititaattivivee,, tthheerreeffoorree tthhee  rRelealtaivtieveim  Ipmoprtoarntacnecien dIenxd(eRxI I()RtIeIc)h  tneicqhuneiqwuaes  ewmaps loeymepdlotoyedxa  tmo ineexacmominme ucnoimtymreusnpiotyn srees‐ to build a composite (potential) UGI framework/model. It is to determine community satisfaction of UGS elements and UGI indicators, according to the local built-in environment, as executed in similar research studies for ordinal-scale surveys [62–65,83].   Based upon RII (outcomes), the importance level of each UGI indicator and UGS element was calculated. To ensure a rational quantity of UGI indicators and vital UGS ele- ments (for respective UGI indicators from RII), two interrelated strategies were introduced: (a) adaptation importance scale criterion, as proposed by Chinyio (1998) [62] and Akadiri (2011) [83], yet inserting ‘four new levels’, so accounting for (in-total) nine-point impor- tance levels [34] (Table 4). The scale ranges from “extremely unimportant” to “extremely important”, utilizing both Positive and negative weights (Table 5). The values substituted into formula 1 (Table 5) are from Table 6. It is to find variance in the significance levels amongst the UGI indicators and UGs elements because not all the GS elements positively enhanced the efficacy of the sustainable UGI indicators. Int. J. Environ. Res. Public Health 2022, 19, 11844 10 of 29 Table 4. Criterion of 9-point scale. 1 Extremely unimportant (e-unimp) (0 ≤ RI < 0.2) 2 Moderately unimportant (m-unimp) (0.2 ≤ RI < 0.3) 3 Slightly unimportant (s-unimp) (0.3 ≤ RI < 0.4) 4 Unimportant (unimp) (0.4 ≤ RI < 0.5) 5 Low (l) (0.5 ≤ RI < 0.6) 6 Slightly important (s-imp) (0.6 ≤ RI < 0.7) 7 Moderately important (m-imp) (0.7 ≤ RI < 0.8) 8 Important (imp) (0.8 ≤ RI < 0.9) 9 Extremely important (e-imp) (0.9 ≤ RI ≤1) Source: [34]. Table 5. Equation (1) A sample estimating the RII value of increasing pervious surfaces to optimize the stormwater management indicator. RII = ΣW/(N × A) . . . (1) W = Likert scale weights: assigned by participants to each indicator (1 to 9). N = Total number of samples A = The highest value on a Likert scale. RII = (9 × 93) + (8 × 42) + (7 × 21) + (6 × 18) + (5 × 8) + (−4 × 3) + (−3 × 3) + (−2 × 2) + (−1 × 2)/(192 × 9) = 0.834 (as rated by a community member) Source: Authors’ calculation using field survey data. The second methodology was the adaptation of the Interquartile Range (IQR) tech- nique to identify a specific cut-off point in the RII Values (RIV) of UGS elements. The IQR is a viable and effective technique to determine the difference between the median of the lower (Q3) and upper (Q1) quartile of the RII data set [64,68–70]. It also enables the identifi- cation of a vital (manageable) number of UGS elements for the respective UGI indicators according to the native spatial environment. The value of 0.70 is considered a cut-off point, which assists in determining the key UGS elements for each specific UGI indicator. This enhances the urban system’s ability to withstand climate threats of anthropogenic changes. The cut-off-point (RI = 0.7) is based on the Likert scale (Table 4), ranging from Moderately important to Extremely important. The cut-off point ((RI < 0.7) implies that not all the individual UGS elements had an excellent functional connection with the (respective) UGI indicators to fight climate change in the study areas. Int. J. Environ. Res. Public Health 2022, 19, 11844 11 of 29 Table 6. RII value of sustainable UGI indicators. Relative Cut-Off Point. Rank Level of Particpants Overall Approved UGI Order Based SignifanceCategories Urban Green Infrastructure Indicators (n) Weight Index Interquartile Range Indicators on (9-Point ScaleRII = ΣW/(N × A) Technique (IQR) (RII ≥ 0.80) RII Value Criterion) “Optimize storm water management” i. “Increasing pervious surfaces” 192 1441 0.834 0.80 yes 9 8 ii. “Minimize, retain and organically purified rainwater runoff”. 192 1364 0.789 0.80 no 14 7 “Decreasing the impact of urban heat islands” iii. “Enhanced the quantity of the green spaces”. 192 1517 0.878 0.80 yes 3 8 iv. “Use of evaporative materials on the roofs, walls and floors”. 192 1287 0.745 0.80 no 19 7 “Enhancing air quality (e.g., extracting impurities)”. v. “Growing more green trees and installing a green barrier in a roadway”. 192 1339 0.775 0.80 no 16 7 “Enhancing noise quality”. Ecological vi. “Use a green sonic wall to reduce the minimum and maximum noise pollution. (i.e., thick hedges could be provided with a small meadow for minimum noise and for 192 1347 0.780 0.80 no 15 7 maximum noise reduction wide layers of bamboo and deciduous trees could be provided)”. “Lower emissions of carbon (e.g., elimination of greenhouse gas emissions through greenery)” vii. “Grow greater density of trees as shading and evaporating fabric for the paved surfaces”. 192 1513 0.876 0.80 yes 4 8 “Enhancing building energy performance”. viii. “Promote green energy-saving strategies”. 192 1581 0.915 0.80 yes 1 9 “Improved soil fertility and degradation condition”. ix. “Increase previous areas and plant trees to enhance soil stabilization”. 192 1473 0.852 0.80 yes 6 8 “Improved and safeguard urban ecology”. x. “Improve and strengthen the urban green network connectivity”. 190 1430 0.836 0.80 yes 8 8 Int. J. Environ. Res. Public Health 2022, 19, 11844 12 of 29 Table 6. Cont. Rank Level of Particpants Overall Relative Cut-Off Point. Approved UGI Order Based Signifance Categories Urban Green Infrastructure Indicators (n) Weight Index Interquartile Range Indicators on (9-Point ScaleRII = ΣW/(N × A) Technique (IQR) (RII ≥ 0.80) RII Value Criterion) i. “Agri-production (e.g., home gardening; urban farming; and community farming)”. 192 1411 0.817 0.80 yes 10 8 “Enhancing social wellness”. ii. “Optimizing the recreation, and socialization activities”. 192 1402 0.811 0.80 yes 11 8 Socio- iii. “Improved city’s appeal (through various green cultural elements)”. 192 1275 0.738 0.80 no 21 7 iv. “Enhancing the mental and physical health (e.g., visual and physical exposure to open green areas has a beneficial 192 1509 0.873 0.80 yes 5 8 effect on stress and anxiety reduction)”. v. “Provide ecological areas for research & education”. 192 1304 0.755 0.80 no 18 7 vi. “Enhance connectivity of green areas to promote walking & biking opportunities”. 192 1287 0.745 0.80 no 19 7 i. “Enhanced the value of property”. 192 1244 0.720 0.80 no 22 7 ii. “Minimize healthcare expense”. 192 1369 0.792 0.80 no 13 7 iii. “Decrease energy use (e.g., heating & cooling requirements)”. 192 1448 0.838 0.80 yes 7 8Economic indicators iv. “Minimize the risk of flood disasters”. 192 1544 0.894 0.80 yes 2 8 v. “Decreasing the utilization of private cars by encouraging walking and biking opportunities (i.e., 192 1377 0.797 0.80 no 12 7 changing modes of transportation)”. vi. “Value of eliminating of air pollutants”. 192 1331 0.770 0.80 no 17 7 Source: Authors’ calculation using field survey data. Significance level keys: 1—extremely unimportant; 2—moderately unimportant; 3—slightly unimportant; 4—unimportant; 5—low; 6—slightly important; 7—moderately important; 8—important; 9—extremely important. Int. J. Environ. Res. Public Health 2022, 19, x  13  of  28    lower (Q3) and upper (Q1) quartile of the RII data set [64,68–70]. It also enables the iden‐ tification of a vital (manageable) number of UGS elements for the respective UGI indica‐ tors according to the native spatial environment. The value of 0.70 is considered a cut‐off  point, which assists in determining the key UGS elements for each specific UGI indicator.  This enhances the urban system’s ability to withstand climate threats of anthropogenic  changes. The cut‐off‐point (RI = 0.7) is based on the Likert scale (Table 4), ranging from  Moderately important to Extremely important. The cut‐off point ((RI < 0.7) implies that  not all the individual UGS elements had an excellent functional connection with the (re‐ Int. J. Environ. Res. Public Health 2022,s1p9,e1c1t8i4v4e) UGI indicators to fight climate change in the study areas.  13 of 29 3. Results and Findings  3. Results and Findings The results of  the community‐based  (bottom‐up) empirical  field study were eluci‐ datedT,h feirrsetsluyl, ttshoef athneswcoemr tmo uRnQit1y:- bcoamsemd (ubnoitttyo’ms u-unpd)eermstapnirdicinalgfi oefl dclsimtuadtye wchearengeleu, caiddaaptetda‐, fitirosntl yto, t chleimanatsew cehratnogRe,Q u1r:bcaonm remsiulineintyce’s, aunndd eurrsbtaann dgirnegeno fincflrimasatrtueccthuarne gthe,eamdeasp. tTahtieo nnetxot  csleimctiaotensc htaacnkglee ,(uRrQb2a nanreds RiliQen3)c,e d, eatnedrmurinbianngg kreeye nUiGnfSr aesletrmuectnutrse ththaet mstreesn. gTthheenne sxutssteacintiaobnlse  tUacGkIl ein(dRiQca2toarnsd, tRheQre3b),yd ceotnertmribinuitninggk teoy aU (pGrSobealebmlee) nUtGs tIh faratmsterewnogrtkh/emnosdueslt. aTinhaeb alteteUmGpIt  itnod diceavteolrosp,  tahne eresbseynctioanl trreisbiuliteinncget aogaai(npsrto tbhaeb elen)vUiroGnImfreanmtaelw choarkll/enmgoeds einl. tThhe enaatttempt todevelop an essential resilience against the environmental challenges in the natiivvee bbuuiilltt-‐ iinn ccoonntteexxtt..  33..11.. SSeeccttiioonn AA:: UUnnddeerrssttaannddiinngg tthhee LLooccaall PPeerrssppeeccttiivvee  TThhee rreessuullttss rreepprreesseenntteedd tthhaatt ooppttiioonnss oonnee,, ttwwoo,, ssiixx,, aanndd sseevveenn aarree mmoorree eeffffeeccttiivvee tthhaann  ootthheerr ooppttiioonnss,, whhiicchh aacchhiieevvee aa llooweerr ppeerrcceennttaaggee.. TThhee ssttaattiissttiiccaall iinnvveessttiiggaattiioonn rreepprreesseennttss  tthhaatt moorreet hthaannt hthreree-ef‐ofuorutrhtshso fotfh tehceo cmomumnuitnyitbye lbieevlieevthe atthaant ainnc rineacsreeainset hine tghloe bgalloabnanl uaanl‐ mnueaaln mteemanp eteramtupreeraptousrees psoesveesr esewveeraet hweeraetvheenr tesvseuncths asuscrhis iansg risseiangle sveeal sleavnedls,  tahnedre, uthpeorne‐, duapmona,g desamthaegeecso tlhoeg ieccaollohgeaiclathl hoefabltoht hoft hbeotuhr bthaen uarsbwane lalsa swreullr aals arrueraasl (aFriegausr (eF5ig).urTeh 5is).  lTeahdiss lteoaednsd taon egnedriannggheurimnga nhuhmeaaltnh /hweaelltlh-b/weienlgl‐bsaefientgy saanfdetdye asntrdu cdteiosntrutoctuiorbna tno eucrob-sayns teecmo‐ fsuynsctetimon  sfuinctthioensst uind ythdeis  tsrtiuctdsy.  Fduisrtthriecrtsr. eFsulrttshaebr oruetsu“altds aapbtaotuiot n“atodacplitmataioten chtoa ncgliem”aitne  tchheangatei”v ein/ ltohcea lncaotinvte/xlot cfaolu cnodnttehxatt fooputniodn tsh: aotn oep, ttiwonos,: tohnree, ,twfoou,r ,tharnede, sfeovuern, arenmd asienveedn  erxetmreamineelyd imexptroermtaenlty– reimcepivoirntganmt–orreecethivainng75  %moSraet istfhaacnti o7n5A%p  pSarotivsafalcVtoioten (SAApVp)ro(Fviaglu  rVeo6t)e.  T(ShAisVse) t(oFfigfiuvree i6m).p Tohrtiasn stevt aorfi afibvles imheplposrttaonot uvtalirniaebtlhees ihmeplpesr attoiv oeuctolincee tphteo ifm“apdearpatiavteio cnotno‐ ccleimpta otef “cahdanapgtea(tCioCn) t”oi ncliamnainted cihgeanogue s(CcoCn)t”e ixnt ,abne isnideigsecnoonutrsi bcuontitnegx t,o bsetsriednegst choentirnigbuloticnagl   atdo asptrteivnegtchaepnaicnitgy .loTchael yadalaspotiavses icstapinacbiutyil.d Tinhgeyr easlisloie nascseistto wina brdusiledninvgir orensmilieenntcael htoawzaarrdss  (ei.nev.,iurornbamneflnotaold hianzga).rds (i.e., urban flooding).  Int. J. Environ. Res. Public Health 2022, 19, x    14  of  28    FFiigguurree 55.. DDeefifinniinngg cclliimmaattee cchhaannggee ((CCCC)).. SSoouurrccee:: AAuutthhoorrss’’ ccaallccuullaattiioonn uussiinngg fifieellddd daattaa..    Figure 6. Defifining climate change adaptation.. Source: Authors’ calculation using fifield data..  Furthermore, the results emphasize that the local community acknowledged ¾ of the  potential options that are perceived as influential in gauging/defining urban resilience in  the local context. The statistical result shows that options: one, three, four, five, six, and  eight are more effective as  they received >75% SAV  than options  two, seven, and nine  (SAV < 50%) (Figure 7). This  illustrated participants’ understanding and  level of confi‐ dence  in  the potential alternatives, which may  lead  to green‐growth approaches and a  sustainable urban environment in KP. The results further elucidate the perception of the  native respondents on the potential possibilities of UGI, so it is worth noting that option  2 is extremely significant compared to option 14, which received 81% and 23% confidence  votes from the participants. Besides that, the community also endorses options: 1, 3, 4, 7,  8, 9, 12, and 13, though the confidence level varies between 60% and 70% (Figure 8). This  certifies all the nine potential possibilities were viewed as a key standard to define UGI at  the grass‐root level in the study areas.    Figure 7. Defining urban resilience. Source: Authors’ calculation using field data.    Int. J. Environ. Res. Public Health 2022, 19, x  14  of  28    Int. J. Environ. Res. Public Health 2022, 19, 11844 14 of 29 Figure 6. Defining climate change adaptation. Source: Authors’ calculation using field data.  Furrttherrmorre,, tthe rressullttss eempphhaassiizzee ththaat tththe elolcoacla clocmommunuintyit yacakcnkonwolwedlegdegde ¾d  3o/f4 thofe  tphoetepnottieanl toiapltoiopntiso tnhsatth aartea prerpcericveidv eads ainsfilnuflenuteinatli ainl  ignaguaguinggin/dge/fdinefiinngi nugrbuarnb arnesrieliseilniecne cien  itnhet hloeclaolc caol nctoenxtte. xTth. eT shteatsistatitcisatli craeslurelts ushltoswhso wthsatt hoapttiopntsi:o onnse: ,o tnher,eeth, rfoeeu,r,f ofiuvre, , fisvixe,, asnixd,  aenigdhte iagrhet maroerem eofrfecetifvfeec taisv ethaesyt hr ecyeirveecde iv>7e5d%> 7S5A%V StAhaVn thopantioonpst itownos, tswevo,ense, vaennd, naninde  n(SinAeV( S 0.70 (Table 8) have the potential to enhance the efficacy of the UGI indicators against anticipated environmental challenges in the local urban context. These correlations enable to build of an inclusive and sustainable UGI indicator framework, besides supporting the formulation of green-growth strategies in land-use planning. Additionally, highlighting such vital taxonomy of green-space elements can improve the native community’s understanding of NBGI for climate change mitigation/adaptation. Thus, the community-based green strategies can help to build an eco-sustainable and climate-resilient environment in the urban interface of the KP province and beyond. 3.2.3. (b) Identifying the Key UGS Elements The finding further confirmed a functional linkage of each green element with the sustainable indicator, based on IQR values (0.60–0.76) with a cut-point of 0.70 (Table 8). These results highlighted important UGS elements for each UGI indicator, conditioned to local environments and community understanding. Overall, the outcome represents a pattern of variance, which signifies that each potential UGS element is characterized by a distinctive quality, and it does not improve the functional linkage and health of every UGI indicator. It is probably due to regional spatial context. Yet, the determined key green elements (Table 8) that perform a pivotal role in strengthening the resilience of respective UGI indicators also help to mitigate/adapt against the climatic variabilities in the urban settings. It is also established that the city/urban appeal can be improved through various taxonomy of mixed-use green-spaces. It can be achieved more explicitly through the recommended UGS categories CG: “community garden”; BG: “botanical garden”; UP: “urban park”; FO: “forest”; RG: “rain garden and bio-swale”; WL: “wetland”; GRW: “green roofs and walls” and HO: “horticultural” [34]. Similarly, the study further propels the community’s recommended green measures should be considered to plan risk-reducing contingencies against floods and heat island effects. Such bottom-up green initiatives pro- mote community stewardship in green space planning, improve cities’ ecological resilience, and benefit dwellers in times of climate emergencies. These measures need immediate attention by the concerned stakeholders for the mitigative measure, followed by other inclusion of additional UGS elements in the landscape greening policies and planning for adaptive measures. All in all, the findings contribute to achieving an agreement on establishing a sustainable and inclusive UGI framework backed by the community’s under- standing/importance. This may lead to building a new regional paradigm, which would encourage green growth infrastructure, not only in KP province but also in other regions having the same features. It is worth mentioning that if this research outcome is compared with the native experts studied [34], it exemplifies both the native multi-stakeholder’s viewpoints on understanding the functional interlinkage between taxonomy of UGS elements and UGI indicators in the native built environment vary in some optimal possibilities. However, in most cases, the collective level of agreement overlapped/agreed. This reflects the knowl- edge, awareness, and perspective of native experts [34,51] and the community toward the natural green landscape-based (NBLB) approach, a sustainable, cost-efficient, and inno- vative climate change adaptation/mitigation approach for green cities. Additionally, the overall research studies represent a strong tendency to accentuate the holistic and effective multi-stakeholder participatory planning (MSPP) approach in the decision-making process for designing and implementing NBLG initiatives that naturally alleviate the high risk of environmental hazards in the northwest urban region of Khyber Pakhtunkhwa, Pakistan. Int. J. Environ. Res. Public Health 2022, 19, 11844 17 of 29 Table 7. RII values for each urban green space (UGS) element. Relative Index (RII) of UGS Elements RII = ΣW/(N × A) Categories Urban Green Infrastructure Indicators Community Botanical Urban Green Rain Garden Green & Green Roof & Garden Garden Park Forest Streets & Permeable Wetland HorticulturalBio-swale Parking Area Green Wall “Optimize storm water management”. i. “Increasing pervious surfaces”. 0.71 0.72 0.75 0.88 0.63 0.76 0.6 0.81 0.56 0.59 ii. “Minimize, retain and organically-purified rainwater runoff”. 0.66 0.69 0.65 0.82 0.81 0.91 0.71 0.92 0.7 0.65 “Decreasing the impact of urban heat islands”. iii. “Enhanced the quantity of the green spaces”. 0.7 0.73 0.75 0.9 0.67 0.48 0.4 0.6 0.65 0.58 iv. “Use of evaporative materials on the roofs, walls and floors”. 0.65 0.69 0.76 0.86 0.63 0.91 0.82 0.94 0.73 0.53 “Enhancing air quality (e.g., extracting impurities)”. v. “Growing more green trees and installing a green barrier in a roadway”. 0.72 0.73 0.79 0.84 0.74 0.55 0.58 0.65 0.71 0.63 “Enhancing noise quality”. Ecological vi. “Use a green sonic wall to reduce the minimum and maximum noise pollution. (i.e., thick hedges could be provided with a small meadow for minimum 0.69 0.69 0.74 0.91 0.69 0.42 0.37 0.6 0.64 0.61 noise and for maximum noise reduction wide layers of bamboo and deciduous trees could be provided)”. “Lower emissions of carbon (e.g., elimination of greenhouse gas emissions through greenery)” vii. “Grow greater density of trees as shading and evaporating fabric for the paved surfaces”. 0.73 0.76 0.76 0.91 0.72 0.41 0.41 0.63 0.67 0.65 “Enhancing building energy performance”. viii. “Promote green energy-saving strategies”. 0.63 0.57 0.64 0.68 0.64 0.38 0.34 0.51 0.87 0.55 “Improved soil fertility and degradation condition”. ix. ”Increase previous areas and plant trees to enhance soil stabilization”. 0.73 0.77 0.74 0.89 0.66 0.63 0.53 0.70 0.60 0.70 “Improved and safeguard urban ecology”. x. “Improve and strengthen the urban green network connectivity”. 0.71 0.79 0.75 0.93 0.65 0.42 0.44 0.70 0.68 0.70 Int. J. Environ. Res. Public Health 2022, 19, 11844 18 of 29 Table 7. Cont. Relative Index (RII) of UGS Elements RII = ΣW/(N × A) Categories Urban Green Infrastructure Indicators Community Botanical Urban Forest Green Rain Garden Green & & Permeable Wetland Green Roof &Garden Garden Park Streets Green Wall HorticulturalBio-swale Parking Area i. “Agri-production (e.g., home gardening; urban farming; and community farming)”. 0.87 0.66 0.61 0.76 0.53 0.35 0.30 0.45 0.61 0.82 “Enhancing social wellness” ii. “Optimizing the recreation, and socialization activities”. 0.78 0.81 0.81 0.82 0.75 0.41 0.30 0.68 0.71 0.69 iii. “Improved city’s appeal (through various green Socio- elements)”. 0.75 0.78 0.82 0.85 0.79 0.70 0.69 0.77 0.75 0.74 cultural iv. “Enhancing the mental and physical health (e.g., visual and physical exposure to open green areas has a beneficial effect on stress and anxiety 0.79 0.74 0.81 0.89 0.75 0.39 0.38 0.75 0.69 0.61 reduction)”. v. “Provide ecological areas for research & education”. 0.72 0.79 0.68 0.85 0.67 0.45 0.42 0.74 0.72 0.76 vi. “Enhance connectivity of green areas to promote walking & biking opportunities”. 0.69 0.76 0.83 0.89 0.78 0.35 0.35 0.71 0.55 0.58 i. “Enhanced the value of property”. 0.74 0.74 0.85 0.63 0.74 0.51 0.52 0.56 0.82 0.70 ii. “Minimize healthcare expense”. 0.82 0.77 0.80 0.88 0.73 0.42 0.34 0.67 0.70 0.68 iii. “Decrease energy use (e.g., heating & cooling Economic requirements)”. 0.69 0.68 0.75 0.75 0.66 0.45 0.33 0.55 0.90 0.61 indicators iv. “Minimize the risk of flood disasters”. 0.70 0.72 0.74 0.95 0.71 0.73 0.64 0.86 0.61 0.60 v. “Decreasing the utilization of private cars by encouraging walking and biking opportunities 0.66 0.73 0.80 0.84 0.79 0.42 0.44 0.71 0.53 0.53 (i.e., changing modes of transportation)”. vi. “Value of eliminating of air pollutants”. 0.72 0.78 0.79 0.92 0.75 0.43 0.45 0.64 0.76 0.69 Source: Authors’ calculation using field survey data. Int. J. Environ. Res. Public Health 2022, 19, 11844 19 of 29 Table 8. Key Urban Green Space (UGS) elements. Interquartile Range (IQR) Methdology Approved Number Approved Urban Green Categories Urban Green Infrastructure Indicators Cut-OffIQR = (Q3-Q1) of UGS Elements SpaceQ1 Q3 Point.(Median) Mean (RII ≥ 0.70) (UGS) Elements “Optimize storm water management”. i. “Increasing pervious surfaces”. 0.61 0.76 0.72 0.70 0.70 6 CG; BG; UP; FO; RG; WL ii. “Minimize, retain and organically-purified rainwater runoff”. 0.67 0.82 0.71 0.70 0.70 6 FO; GS; RG; GPA; WL;GRW. “Decreasing the impact of urban heat islands”. iii. “Enhanced the quantity of the green spaces”. 0.59 0.72 0.66 0.70 0.70 4 CG; BG; UP; FO. iv. “Use of evaporative materials on the roofs, walls and floors”. 0.66 0.85 0.75 0.70 0.70 6 UP;FO;RG:GPA;WL;GRW “Enhancing air quality (e.g., extracting impurities)”. v. “Growing more green trees and installing a green barrier in a roadway”. 0.60 0.69 0.67 0.70 0.70 6 CG; BG; UP; FO; GS;GRW. Ecological “Enhancing noise quality”. vi. “Use a green sonic wall to reduce the minimum and maximum noise pollution. (i.e., thick hedges could be provided with a small meadow for minimum noise and for maximum noise reduction wide layers of bamboo 0.64 0.75 0.70 0.70 0.70 2 FO; UP. and deciduous trees could be provided)”. “Lower emissions of carbon (e.g., elimination of greenhouse gas emissions through greenery)” vii. “Grow greater density of trees as shading and evaporating fabric for the paved surfaces”. 0.64 0.75 0.70 0.70 0.70 5 CG; BG; UP; FO; GS. “Enhancing building energy performance”. viii. “Promote green energy-saving strategies”. 0.52 0.64 0.60 0.70 0.70 1 GRW “Improved soil fertility and degradation condition”. ix. “Increase previous areas and plant trees to enhance soil stabilization”. 0.64 0.74 0.70 0.70 0.70 6 CG; BG; UP; FO; WL;HO. “Improved and safeguard urban ecology”. x. “Improve and strengthen the urban green network connectivity”. 0.66 0.74 0.70 0.70 0.70 6 CG; BG; UP; FO; WL;HO. Int. J. Environ. Res. Public Health 2022, 19, 11844 20 of 29 Table 8. Cont. Interquartile Range (IQR) Methdology Approved Number Approved Urban Green Categories Urban Green Infrastructure Indicators Cut-OffIQR = (Q3-Q1) of UGS Elements SpaceQ1 Q3 Point.(Median) Mean (RII ≥ 0.70) (UGS) Elements i. “Agri-production (e.g., home gardening; urban farming; and community farming)”. 0.47 0.74 0.61 0.70 0.70 3 CP; FO; HO. “Enhancing social wellness” ii. “Optimizing the recreation, and socialization activities”. 0.68 0.80 0.73 0.70 0.70 6 CG; BG; UP; FO; GS;GRW Socio- iii. “Improved city’s appeal (through various green elements)”. 0.74 0.79 0.76 0.70 0.70 9 CG; BG; UP; FO; GS; RG; cultural GRW; WL; HO. iv. “Enhancing the mental and physical health (e.g., visual and physical exposure to open green areas has a beneficial effect on stress and 0.63 0.78 0.75 0.70 0.70 6 CG; BG; UP; FO; GR; WL anxiety reduction)”. v. “Provide ecological areas for research & education”. 0.67 0.76 0.72 0.70 0.70 6 CG; BG; FO; WL; GRW; HO vi. “Enhance connectivity of green areas to promote walking & biking opportunities”. 0.56 0.78 0.70 0.70 0.70 5 BG; FO; UP; GS; WL. i. “Enhanced the value of property”. 0.58 0.74 0.72 0.70 0.70 6 CG; BG; UP; GS; GRW;HO. ii. “Minimize healthcare expense”. 0.67 0.79 0.72 0.70 0.70 6 CG; BG; UP; FO; GS;GRW. Economic iii. “Decrease energy use (e.g., heating & cooling requirements)”. 0.57 0.74 0.67 0.70 0.70 3 FO; UP; GRWl. indicators iv. “Minimize the risk of flood disasters”. 0.66 0.74 0.72 0.70 0.70 7 CG; BG; UP; FO; GS; RG; WL. v. “Decreasing the utilization of private cars by encouraging walking 0.53 0.78 0.69 0.70 0.70 5 BG; FO; UP; GS; WL. and biking opportunities (i.e., changing modes of transportation)”. vi. “Value of eliminating of air pollutants”. 0.65 0.78 0.74 0.70 0.70 6 CG; BG; UP; FO; GS;GRW. Source: Authors’ calculation using field survey data Keys: CG: “community garden”; BG: “botanical garden”; UP: “urban park”; FO: “forest”; GS: “green streets”; RG: “rain garden and bio-swale”; GPA: “green permeable parking area”; WL: “wetland”; GRW: “green roofs and walls”. Int. J. Environ. Res. Public Health 2022, 19, 11844 21 of 29 4. Discussion This research contributes to building up an inclusive and sustainable UGI framework, thereby connecting the local community (and their perspective) with the multi-functional urban green areas. Such an ecological interaction between humans and nature helps to understand NBGI techniques that reduce environmental hazards and promote urban sustainability [84]. This study also attempts to find UGI indicators, referred to as UGS elements, according to the local built-in context that remains vital to enhancing urban planning. This research establishes that (based on the spatial context), each UGS element has a distinctive characteristic that plays a unique role in improving the quality of respective UGI indicators to fight climatic disasters (e.g., urban floods, drought, etc.). Additionally, the cohabitation of diverse vital taxonomy of green elements and UGI indicators can lead to developing a sustainable UGI framework, which is relevant to the local built environment. It also leads to accomplishing (nature-based) green policies to adapt to climate change through resilient land-use planning [15,85,86]), whereas such green planning approaches further naturally minimize the high risk of urban flooding [23,45,87,88] and build long-term climate-resilient environment. Therefore, developing such resilient strategies remains crucial in areas that are not only highly vulnerable to in-daunting climatic challenges [3,89] but also remain susceptible due to the geographical location, hence requiring a reactive planning system [25,36] in a situation where the expansion of urban functions remains escalated [90,91]. The harsh regional realities continue to put pressure on the land cover and, thereupon, the decline of urban green-spaces [35]. Thus, the regions required adequate and effective urban landscape and greening policies (ULGP). The upgradation of the existent policies and initiation of new urban plans needs to be tapped local community perspective. Such an approach is considered more effective in apprehending the intricacy of human and ecosystem interactions [38,39,51,52]. It stands crucial to identify the vital taxonomy of UGS elements. These elements will have the potential to identify the key/reliable/sustainable UGI indicators according to the native built-in environment. This integration of the local concepts can build a consensus toward an integrated urban landscape and green infrastructure. It is built on the idea of stimulating community partici- pation while considering them important stakeholders in executing the planning/process, though it is not much institutionalized and practiced yet [36,49,92]. So, this study endorses a communal approach to building a UGI indicators’-based model. Only such a model can contribute to building a green, climate-resilient city-state. This approach can better address ecological, socio-cultural, and economic issues in land use. This model facilitates building an eco-regional paradigm that supports the successful transition of green action plans (GAP) and serves the community more effectively at the grassroots level in the urban interface of the Peshawar, Mardan, and Charsadda districts of KP and beyond. 5. Conclusions The empirical study has outlined an explicit quantitative research methodology for de- veloping a rich body of multi-functional UGI-indicator-based framework/model grounded upon TBL sustainability. This scientific UGI model is backed by the local community’s per- spective, and it presents the significance and practicability of UGI indicators and the UGS elements as per the local built-in environment. The results exhibit that ten UGI indicators fall into the categories of “IMP” and the other eleven as “M-IMP”, whereas only one indica- tor received the “E-IMP” level. Furthermore, a varied catalog of vital UGS elements (for UGI indicators) was presented, subject to the building spatial context of the KP region. This depicts community insight and satisfaction level towards the respective green spaces and their relationship with each UGI indicator while coping with climatic hazards. Moreover, this study has emphasized the role of the local inhabitants in establishing a sustainable UGI framework, meeting the standards of a green, climate-resilient city in the north-western region of Pakistan. The participatory planning (PP) approach is recognized as the best tool that effectively promotes and strengthens community stewardship in the planning process for urban green spaces at the grassroots level. All in all, this research study bridges Int. J. Environ. Res. Public Health 2022, 19, 11844 22 of 29 the planning gap and improves collaboration processes among the local inhabitants and relevant government institutions. It is to overcome the gap between technical knowledge and expertise in NBGI to achieve resilience in land-use planning. It will build local capacity to fight climate uncertainties more cost-effectively than the traditional grey infrastructure. In conclusion, these empirical findings highlight the role of native community mem- bers in developing a sustainable UGI-indicator-based framework/model according to socio-cultural and ecological contexts. This will lead to building an eco-friendly and (green) climate-resilient city-state, not only specific to the northwest urban regions of KP province (Pakistan) but having its application to other regions. The research will inevitably open up a new area to study the potential role of innovative and indigenous NBGI initiatives in addressing sustainable climate-risk management (SCRM) strategies according to the native spatial environment. This planning technique can pave the way to meeting the goal of a well-balanced relationship between anthropocentrism and eco-centrism activities, not only specific to the urban interface of the KP region but also across the country. Policy Implications This research proposes essential policies guidelines/changes to create resilient urban- ism against climatic risks (such as urban flooding): (1). Increase awareness and understanding among all the native inhabitants toward a better understanding of UGI planning, a sustainable, cost-efficient, and innovative nature-based climate adaptation strategy for spongy green cities. (2). A need to develop an inclusive policy that supports community participation at all levels, which will then promote community ownership and further strengthens the planning process for UGS. (3). Balanced, proactive planning reforms are essential that encourage collaborations among the decision-makers and the local community. It should be linked with bridg- ing the planning gap and improving the scientific knowledge regarding green ini- tiatives, extending from policy making to decision making and implementation for greener growth. (4). Considering the UGI planning examples of the Netherlands and Germany, there is a high need to incentivize green grass-root initiatives that would foster eco-friendly living practices and local stewardship of green practices to build a sustainable environment. 6. Scope of Future Research (1). Further research can be conducted to study the relationship of the same (and ad- ditional) variables across socio-demographic groups to design micro-level urban greening policies. (2). The social dimension of the sustainable urban landscape and greening policies (ULGP) and frameworks at the macro, micro, and meso levels needs to be investigated that can help build a new cultural paradigm to support and monitor green urbanism. (3). The scalability of urban green space (UGS) elements must coincide with the magni- tude of the climate hazards, knowing the appropriate green)/natural-based climate mitigation and adaptation measures to plan safer, healthier, and climate-resilient urban regions. (4). In studying and analyzing green spaces, it would be interesting to consider different species of green roofs in different climates. It will help to better understand the potential role of green roofs in reducing climatic stress and improving the ecosystems functions (ESF) and health/well-being of inhabitants. Green roofs are becoming increasingly popular, especially in high-density urban clusters, where open spaces are limited. It is easy to implement and monitor, and they offer similar benefits as traditional green spaces. (5). Pandemics (such as COVID-19) though pose less stress and do not degrade the UGI indicator more exclusively; however, this aspect needs to be further explored. There Int. J. Environ. Res. Public Health 2022, 19, 11844 23 of 29 is a need to develop institutional and political indicators, and their potential role in NBGI infrastructure planning to address SCRM should be investigated. Author Contributions: Conceptualization, M.R.; data curation: M.R.; methodology, M.R., D.G. and U.K.; software, M.R.; formal analysis, M.R.; validation.; M.R., D.G. and U.K.; writing—original draft preparation, M.R.; writing—review and editing, M.R., D.G. and U.K.; supervision, D.G. and U.K. All authors have read and agreed to the published version of the manuscript. Funding: This research, as a part of the Ph.D. dissertation, was funded by the Deutscher Akademis- cher Austauschdienst (DAAD), Government of Germany, grant number 57381412. TU-Dortmund University, Germany, funded the Article Processing Charges (APCs). Institutional Review Board Statement: This research study was conducted according to the guide- lines of the Declaration of Helsinki. Ethical review and approval at any stage were waived for this study, due to the reason that no sensitive/personal information (e.g., names, contact details, codes, etc.) were sought/gathered during data collection or at any stage of this research. This research study and the questions asked were limited to context-based questions to generate knowledge about the role of Urban Green Infrastructure (UGI) in planning for climate-resilient cities. The responses were generalized to draw meaningful context-based results. Informed Consent Statement: Informed verbal consent was acquired from each person participant. Publishing consent: The authors state their willingness to publish this work after it has been approved. Data Availability Statement: This manuscript contains all data produced or examined during this investigation. Acknowledgments: The corresponding author declares the financial support was provided by DAAD-Deutscher Akademischer Austauschdienst (Ref. No.: 2018/19-57381412) for doctoral studies in Germany. Other than DAAD, the authors are grateful to TU-Dortmund University for paying Article-Processing Charges (APCs), which helped in the speedy publication of the research findings. I am also indebted to my brother for providing valuable support in collecting data from household field surveys during those challenging COVID-19 times in the Peshawar, Mardan, and Charsadda districts of KP province. Conflicts of Interest: The authors have no relevant financial or non-financial interests to disclose. IntI.nJt.. EJ.n Evnirvoinro. nR.e Rs.ePs.u PbulibcliHc eHaletahlt2h0 22022, 21,9 1, 91,1 x8 44 2243o fo2f 928    Appendix A. Development of Conceptual Base Frameworks   FiFgiugruerAe 1A.1R. eRlaetlaiotinosnhsihpiapm amonogntgh tehaen atnhtrhorpoopgoegneicniacc aticvtiitvieitsieasn adnUd GUIGfoI rforre sreilsieilnietncti tcieitsi.esS.o Suorucerc: e[4: 8[4].8].    IInntt.. JJ.. EEnnvviirroonn.. RReess.. PPuubblilcic HHeeaalltthh 22002222, ,1199,, x1 1844 242 5oof f2289      Figure A2. Conceptual base model: climate resilience strategies, ecosystem functions, human well‐ bFeiignugr,e aAnd2. GCIo enlecempetnutasl. bSaosuercmeo: [d4e8l]:. climate resilience strategies, ecosystem functions, human well- being, and GI elements. Source: [48]. AAppppeennddiixx BB  TTaabbllee AA11.. LLiisstt ooff ccoonncceeppttss eevvoollvveedd ffrroomm tthhee sseemmii-‐ssttrruuccttuurreedd mmeeeettiinnggss wwiitthh nnaattiivvee eexxppeerrttss..    MMitiigtiagtaitoionn ooff cclliimmaattee cchhaannggee  AdAadpatpattaiotinon toto cclilmimaattee cchhaannggee  WWataetre rmmaannaaggeemmeenntt  GGrereenen ssppaaccee nneettwwoorrkkss  EEccoossyysstteemf ufunnctcitoinosnasn adnsde rsveirc‐es WWildilldiflief eaanndd bbiiooddiivveerrssiittyy  Urban resilience Organic fvoiocdesp roduction Energy-efficient building SoUcriablacno hreessiioline/nucnei ty OrganAicg froeeonde pcornoodmucytion  Energy‐efficient building  SourcSeo: c[3ia4]l. cohesion/unity  A green economy    Source: [34].  Appendix C AppeBnadsiexd Co n the inputs provided by the participants in the pilot survey, some minor revisBioanssedw eorne  tmhaed  ien,pauimtse pdrtoovmidaekde bthye  tshuer vpeayrtdiceispiganntms oinre  tahpe ppriolpotr iasutervaneyd, tsimome-ee fmficiineonrt : r•evisi“oSnesc twioenreA m: Iandteh, eaipmaretdic tiop amntakper otfihlee ,suardvievye rdseescigante gmoorryeh aapdparolsporbiaeteen ainndc otirmpoer‐aeftfeid‐ cient:i nto the question of gender class. In Pakistan, the government officially recognized  ““tSreacntiso”na sAt:h Ien tthhierd pgaretnicdiepra[n8t0 p–8ro2f]i”l.e, a diverse category had also been incorporated  • i“nItno stehcet iqounebstitohnre oef- pgoeinndteLri kclearstss. cIanl ePwakaisstuanp,d tahtee dgoinvteornamfievnet- pooffiincti,alalny dreicnoSgencitzieodn  “Ctrfianvse”-p aosi nthteL tihkierrdt gsceanldeewr a[8s0t–r8a2n]s”f.o  r med into a nine-point, aimed to achieve more  “vIanr isaebcitliiotyn abm thornege‐tphoeirnets Lpioknedrte nsctailnep wutass aunpddpatreedci siniotno ain fitvhee‐preosiunltt,s a”n. d in Section C  • f“iTvoe‐mpoitiingta tLeiktheerta mscbailge uwityasa mtraonnsgfothrempeadr tiincitpoa an tn’sinfeee‐dpboainckt,,  caeimrtaeidn qtou earcihesieovfes emctoioren  vcawriearbeilaitlyso arme-opnhgr athseed r”e.spondent inputs and precision in the results”.     “To mitigate  the  ambiguity  among  the  participant’s  feedback,  certain Sqouuercriee:s[ 3o4f] . section c were also re‐phrased”.  Appendix D Source: [34].      Identifying and validating the perspective, knowledge, beliefs, attitudes, and prefer- ences of the native communities regarding the proposed definitions and possibilities of the UGI, urban resilience, climate change (CC), and adaptations to CC.   Int. 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