Climate Urbanism as a New Urban Development Paradigm: Evaluating a City’s Progression towards Climate Urbanism in the Global South

Abstract

Climate change impacts, the resulting spatiotemporal changes, and growing uncertainty exert pressure on city leaders and policy makers to create climate adaptive development strategies worldwide. This article introduces climate urbanism as a new development paradigm that advocates for a climate adaptive urban development process, safeguarding urban economics and infrastructure, and ensuring equitable implementation of related strategies. The objective of this article is to determine how far a climate vulnerable city in the Global South has progressed toward climate urbanism. The study employs Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to develop a conceptual framework. Afterward, the analytical hierarchy process (AHP) and indexing are used to develop a multicriteria decision analysis (MCDA) method to assess the selected climate sensitive factors related to climate urbanism. Findings reveal that the city of Khulna’s climate urbanism index score is 0.36, which is extremely low and denotes subpar urban performance. ‘Climate Conscious Governance’ and ‘Climate Smart Infrastructure’ contribute little, while ‘Adaptive and Dynamic Urban Form’ and ‘Urban Ecosystem Services’ contribute even less. The binary logistic regression analysis reveals the significant indicators of (transformative) climate urbanism. The article provides a critical lens for stakeholders to evaluate climate urbanism and promote urban sustainability in the face of climate change.

1. Introduction

Rapid urbanization has made cities essential for climate change adaptation and mitigation initiatives, as well as for developing growth paths that are resilient to the effects of climate change [1,2]. A school of scholars report that the majority of the world’s cities are located in climate vulnerable regions, despite the fact that they generate approximately 80% of the world’s richest [3,4,5,6]. According to recent studies, cities will experience economic impacts from climate change ranging from 2.3% to 5.6% of their total GDP by the end of the century, with the worst-affected cities losing close to 11% of their GDP (related to sea level rise, health and water resources, emergency services, and infrastructure rehabilitation) [5,6]. An estimate suggests that climate resilient infrastructure investments over the next 15 years could cost up to USD 90 trillion [7]. As a result of such reactive defense mechanisms and the protection of ecological enclaves, urban inequalities are exacerbated and new ones are created, leading to the growth of urban slums, the socio-economic vulnerability of low-income people, and urban poverty [8,9,10]. Consequently, the cascading effects of climate change have an impact on all levels of development, from the local to the national. According to current practices, securitization against the effects of climate change is prioritized for large cities and regions with the highest development potential, at the expense of smaller cities and rural areas [11]. This unbalanced regional development produces urban sprawl, climate migration, climate apartheid, and systematic segregation. These strategies disregard the regions and populations that are most vulnerable to climate change and bear the least responsibility for it [11]. These marginalized and vulnerable people instinctively migrate to safer cities, which exacerbates the effects of climate change by causing the growth of urban slums, rapid resource depletion, and the deterioration of urban space and infrastructure. People in marginalized regions are more vulnerable to the effects of climate change when dependency ratios, labor shortages, investment withdrawals, and unequal resource distribution worsen. In addition, research demonstrates that the majority of governments and municipal authorities, notably in the Global South, are unaware of the significance of cities as transformative actors in the process of adjusting to a changing climate [12]. Scholars have repeatedly noted that the global agreement to limit climate change will influence internal politics, the decision-making process for socio-economic development, bilateral and multilateral relations between nations, and future industrialization and marketing processes. Therefore, urban dynamics must be completely reevaluated in order to maintain urban sustainability and resilience in the face of climate change, as climate urbanism is required for this.

Bangladesh is one of the four countries most vulnerable to climate change and natural disasters, posing a significant and long-term challenge to its development [13,14,15]. Due to its location and geomorphological formation, it is one of the countries most affected by climate change, particularly sea level rise [16,17,18]. The polity is often ravaged by disastrous climatic events including cyclones and storm surges, floods, saline water intrusion, thunderstorms, and river erosions, described as the most prevailing and pervasive [19,20]. The country experiences at least one cataclysmic cyclone every three years [20,21]. A study by the Intergovernmental Panel on Climate Change [22] says that the country’s coastline will be underwater by 2050, if the current trend of global warming keeps going [23]. Scholars forewarn that sea level rise will heighten cyclone-generated storm surges and frequency of flooding, putting the coastal regions at greater risk [24,25]. Bicknell et al. [26] say that the reasons for urbanization, the limitations and inabilities of governments, and the growth and development of cities in high-risk areas all make people living in cities more vulnerable to climate change and natural disasters. Researchers are concerned that rising temperatures due to global warming may reduce food output in the United States by around 30% [26]. Without a doubt, these consequences will disproportionately affect low-income persons [27]. They will be forced to live in climate vulnerable areas or relocate to cities with better job opportunities and climate protection, jeopardizing their livelihoods and causing major problems (such as rapid resource depletion, increased pressure on infrastructure, falling living standards in cities, and more unhealthy and unsafe food). Climate change must therefore be a key concern in urban planning, if important urban issues such as climate migration, severe climate apartheid, and city climate vulnerability are to be addressed. This can be accomplished by institutionalizing transformational gains and building resilience to climate change.

Global literature has made substantial contributions to the ontological and epistemological development of urbanism [28,29,30,31,32,33,34] and to the climate change portfolio [28,29,30,31,32,33,34,35,36,37,38,39,40,41]. The prevailing body of climate change studies has predominantly emphasized the consequential challenges and prospective solutions for addressing and adapting to climate change, while largely neglecting the pivotal significance of a systematic socio-economic transformation essential for achieving urban sustainability and effectively mitigating climate change risks. Despite the need for guaranteeing urban sustainability in the face of global climate change scenarios and increasing spatiotemporal uncertainties, rethinking urbanism in the context of climate change and uncertainty has received little attention. This research therefore undertakes the challenge to appraise urbanism focusing on its dynamic and transformative nature. A school of thought has made an important contribution to this new urbanism paradigm, notably climate urbanism (see for example [6,42,43]). Still, much more study is required, particularly in the form of theoretical and analytical frameworks that can be used to advance climate urbanism research. As a result, the purpose of this research is to develop a novel and robust analytical framework that uses climate urbanism principles to assess the performance of cities in a local context. This work also adds to our understanding of the dimensions and elements that contribute to climate urbanism, particularly in the setting of coastal Bangladesh. It could make a significant difference by assisting Bangladesh’s climate policy and attempts to adapt to and mitigate the effects of climate change. This study examines how far a city in the Global South has progressed toward climate urbanism through a case study of a vulnerable city in Bangladesh. It assesses Khulna’s performance in creating and executing equitable climate action. The paper proposes a comprehensive technique for measuring urban performance in accordance with this idea by using the concepts of climate urbanism. It has prioritized the development of relevant metrics for assessing urban performance and measuring the characteristics of climate urbanism.

2. Understanding Climate Urbanism: A Systematic Review and Meta-Analysis

For constructing the conceptual framework of climate urbanism, this study followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for the selection of appropriate literature to investigate (see Figure 1 for details). To identify relevant studies, searches of five electronic databases were conducted. In order to increase the likelihood of selecting the most pertinent records, no exclusion criteria regarding study area, study scope, data source and collection, or publication date were applied. The review references international journal articles that have been reviewed by experts. The evaluation was conducted using the four sequential procedures outlined below.

Climate Urbanism

Urbanism is defined as both the organization of social life in cities and the dominant concepts of urban development in the minds of urban planners and administrators [43]. The sociological assertions made by Wirth [34] regarding the structure, density, and composition of social groups in urban environments gave rise to the concept of urbanism as a means to describe how cities (or settlements) are inhabited. Other American scholars attempted to expand Wirth’s definition of urbanism, arguing that urbanism encompasses not only the social activity in cities, but also the material conditions that define those cities [28]. Urbanism is the study of how cities are lived in, experienced, governed, conceived, designed, and planned. Consequently, climate urbanism can be defined as a new paradigm of urbanism concerned with shifting urban governance and the transformation of urban life in response to climate change scenarios and growing spatiotemporal uncertainties [43]. It is a subset of critical urban theory in that it emphasizes the politically and ideologically mediated, socially contested, and therefore malleable nature of urban space—that is, its continuous (re)construction as a site, medium, and result of historically specific social power relations [44]. The literature of climate urbanism has referred to three schemes of climate urbanism: reactive, entrepreneurial, and transformative [12]. Reactive climate urbanism denotes the efforts in cities in response to the visible effects of climate change [43], criticized by scholars for exacerbating social inequalities by prioritizing the safeguarding of essential economic functions and favoring wealthy stakeholders at the expense of marginalized groups and, thus, resulting in a new form of ‘climate apartheid’ [12]. Entrepreneurial climate urbanism, driven by neoliberal principles, embraces climate change as an opportunity to enhance cities’ economic competitiveness, despite exacerbating disparities and competition between cities, leading to new forms of exclusion and inequality referred to as climate gentrification [12]. Transformative climate urbanism denotes the progressive endeavor by multiple stakeholders to use cities as platforms for a broader transformation through diverse modes of experiment with technologies and social life or through mutinying forms of activism that lead to broader social mobilization to deal with the climate change impacts [12]. This modality is argued to have the most potential to foster environmentally just forms of urban development through challenging social and environmental injustices and facilitating new economic opportunities. In this research, the jargon ‘climate urbanism’ refers to the blended concept of the aforementioned modalities, which allows us to develop a robust analytical framework of climate urbanism to evaluate urban performance of the study area more comprehensively.

The foundation of climate urbanism is the understanding that cities are the main drivers of socio-economic progress and that climate change has already had and will continue to have an impact on this expansion [6]. Three pillars serve as its foundation: (i) carbon management; (ii) climate smart infrastructure; and (iii) climate politics. As they contribute to climate change and present serious risks to humanity, carbon and other greenhouse gas (GHG) emissions are prioritized for reduction in climate urbanism [6]. Globally, socio-economic vulnerability has been made worse by the spatial and temporal uncertainty brought on by climate change. Building climate robust infrastructure and making sensitive places resilient are also essential to ensuring urban sustainability as the effects of climate change become more apparent [43]. In order to deal with climatic calamities and uncertainties, climate urbanism emphasizes the construction of adaptable and robust urban forms and infrastructure in cities. The competition between nations and cities has substantially intensified as a result of globalization. Decisions made by one nation or city have a big effect on others. According to academics [42,45], the global climate change order will influence domestic politics, planning, and decision making. Changes in investment requirements and bilateral and multilateral diplomatic links will improve the socio-economic features of cities. In such a dynamic and complicated situation, climate urbanism focuses on climate conscious governance at all levels, from local to international [42,45], acknowledging that knowledge politics is the primary force behind this new urbanism paradigm [42,43]. According to one school of thinking, informality can be seen as the rule rather than the exception in climate urbanism, and as a result, urban environments can be changed even through informal systems [43]. Urban area governance could change if marginal stakeholders are mainstreamed into decision-making processes as a result of the cohabitation of informal practices and official settings.

Lynch’s framework, which makes reference to a basic normative theory based on shared human values, can be used to comprehend climate urbanism. It includes the broad objectives of efficiency and justice in addition to a number of performance criteria, such as fit, vitality, access, control, and sense [46]. While achieving local particularities that correlate to the varied local geographies of climate vulnerability, these traits are applicable to every cultural environment [46]. The definition of urbanism in this theory, which is ‘the spatial arrangement of people doing things, the spatial flows of people, goods, and information that follow, and the physical characteristics that alter space in ways significant to those actions […]’, [47] (p. 48) also takes into account Wirth’s (1938) [34] complex physical, social, and behavioral perspectives, space management and perception, as well as cyclical and secular fluctuations in spatial distributions […]. Lynch’s theory cannot be used in this study’s scope to provide a thorough explanation of climate urbanism. It should be emphasized that Lynch’s philosophy was considered while determining the scope and fundamental principles of climate urbanism. Based on the systematic review, meta-analysis, and consideration of Lynch’s framework, this research has created six pillars of climate urbanism (see Figure 2) and ninety-six underlying variables (see

Table A2. Dimensions and underlying variables of climate urbanism.

Dimension	Indicators
Climate Conscious Governance	Pro-poor climate adaptation, monitoring and reporting, continuous R&D, climate change advisor, research cell, action to reduce GHGs [43,76], ensuring co-benefit of climate actions [42,118,119], informing community, publishing progress and implementation reports [110,120,121], public engagement in decision making [42], understanding of locale, evidence-based climate action, stakeholders’ knowledge, understanding climate change narratives and language [122], upper level budget allocation, local fund for climate change and uncertainty, 3rd sector investment [123], pro-climate leadership [42,124,125,126,127], partnership with local actors and researchers [128,129,130], presence of local climate agenda, autonomy of local government [12], recognition of local government as key climate actor [45], active community participation [42,131], PPP for climate actions, active climate conscious civil society, climate conscious community [42], self-organization of local community, collaboration and coordination among multiple stakeholders [42,132,133].
Adaptive and Dynamic Urban Form	Percentage of open space, greeneries [110], land use efficiency [111,112], mixed use, net residential density [111,134], climatic consideration [12], accessible to immediate neighborhoods, basic services and resources [6,47], level of desirability [112], misuse of land, preferable density, productive urban form (authors’ inclusion).
Resilient Economy	Per capita disposable income [109], employment rate, income level [107], location advantage, resource dependency, income–expenditure ratio [105], multiple income sources [72,74,135], community-based emergency fund [37], income security, economic diversity, investment potentials, economic strength, self-production (authors’ inclusion).
Just and Equitable Society	Access to basic medical services, to decision-making process, to electricity, and to equitable water supply, food security, low-income housing provision, gender equity, women’s education and participation, equitable mobility options, consideration of low-income groups and dependent groups, primary education for all [77], access to information [79], most vulnerable first (MVF), local food hub [136], connection with institutions (NGOs, COs, GOs, etc.) [78].
Urban Ecosystem Services	Access to green areas/parks, utilization of adjacent waterbodies [107,137], green barriers, household vegetation [92], knowledge about ecosystem services, adoption of nature-based solution, per capita land consumption (authors’ inclusion).
Climate Smart Infrastructure	Green rooftop/vertical gardening, green building materials, smart energy (solar rooftop) installation, natural cooling system, fossil fuel uses, rainwater harvesting, innovative water-saving strategy, safe and secure water supply, neighborhood-scale waste-water treatment plant, proper sanitation, climate conscious housing, solar-powered street lighting, smart footpaths [79], use of non-motorized transport (NMT) [77,79], access to proper drainage system [75,79], daily vehicle miles traveled (VMTs) [67,77,138], per capita energy consumption for traveling [77], informal settlements [78], annual per capita damage recovery cost and household fossil fuel consumption, disaster risk index (authors’ inclusion).

in the Appendix A for details). See Appendix A.1 for concise description of the identified dimensions of them and arguments supporting their relevancy as the pillars of climate urbanism.

3. Materials and Methods

3.1. Study Area

Due to its unique geophysical characteristics, alarming susceptibility to climate change impacts, and distinct socio-economic profile, the city of Khulna has been designated as the case study area for this research (see Figure 3). It is situated between 22°46′ and 22°54′ north latitudes and between 89°28′ and 89°35′ east longitudes [48]. Its total land area is 45.65 km² and its population is approximately 1.5 million [49]. The city is situated in the southwestern coastal region of Bangladesh, on the banks of the Rupsha and Bhairab rivers, two to four meters above sea level [50]. Khulna experiences an average annual rainfall of 1630 mm and a temperature of 26.37 °C, with a yearly increase of 4.960 mm and 0.005 °C, respectively [51]. Flooding, saline intrusion, sedimentation, river erosion, a rise in temperature, the intensity of cyclones, and waterlogging are the most significant challenges presented to city residents by climate change. The city of Khulna is subdivided into 31 wards, which are home to approximately 1,566,183 households [48]. The population is steadily rising due to both natural growth and migration from rural and small urban fringe areas. The city’s major economic sectors comprise commercial retail, automotive servicing, logistics and transportation, manufacturing, hospitality and catering, and education [52]. Statistics shows that Khulna’s contribution to the national GDP in 2020 amounted to approximately USD 53 billion, while its purchasing power parity (PPP) reached USD 95 billion. It is the third largest industrial metropolis and the second largest seaport is located near the city. The city is a significant naval command center for the Bangladesh Navy [50] and plays a vital role in facilitating economic functions in the southwestern coastal region of the country. Khulna achieves an HDI score of 0.678, similar to the national score of 0.661, reflecting slow but incremental progress in socio-economic sectors [53]. The anticipated impact of the Padma Bridge’s recent development on the city’s socio-economic landscape necessitates prompt and well-structured reformulation of development policies and strategies to ensure sustainable growth.

3.2. Data Collection and Methods

Household Questionnaire Survey: This study’s quantitative primary data were gathered through a household survey in the study area. As a result, a questionnaire with both closed- and open-ended queries was created. The questionnaire has predominantly focused on indicators of the dimensions of climate urbanism (see Supplementary File S1 for details). Prior to the final survey of the study area, the necessary preliminary tests and adjustments were conducted. The sample size was determined with a 10% error margin using the formula of [54]. The estimated number of samples was 100. In this study, stratified sampling was used for sampling purposes. Using the Create Fishnet tool in ArcGIS 10.8, a number of grids were generated for Khulna (KCC), from which twelve grids were chosen at random for the household survey. Finally, the samples were distributed among the selected grids with household density in mind.

Spatial Data Collection: The accumulation of spatial data has allowed for the identification of the city’s diverse land uses. For this purpose, Sentinel-2 satellite imagery was retrieved from the archive of the United States Geological Survey (USGS).

Secondary Data Collection: Secondary data were collected from government (i.e., Khulna City Corporation, Khulna Development Authority, and Bangladesh Bureau of Statistics) and non-government organizations, as well as national and international documents including census reports from the Bangladesh Bureau of Statistics (BBS), working papers, and seminar reports of various development organizations such as the World Bank, UNDP, IPCC etc., and published peer-reviewed journal articles and books on climate urbanism, climate change adaptation, and migration. These data were essential for correctly conducting the study.

3.3. Data Analysis Methods

3.3.1. Assigning Weightage to the Dimensions Using Analytical Hierarchy Process (AHP)

The analytical hierarchy process (AHP) is a semi-quantitative method developed by Saaty [55] for determining the relative importance of differential variables adopted in a multicriteria decision-making problem via pairwise comparison based on experts’ opinions [56,57,58,59]. It is preferred to other weighting tools in MCDA due to its procedural flexibility, evaluation that incorporates both subjective and objective perspectives, and consistency throughout the process [25,60,61]. This study utilized AHP to weigh and evaluate the dimensions of climate urbanism based on the opinions of three experts with extensive knowledge of climate urbanism, climate change, and urban sustainability. Using the lens of climate urbanism, the AHP-generated weights of the dimensions were used to assess the urban performance of Khulna.

In the AHP, weights for each of the criteria considered in the analysis are determined through three successive stages. In the first step, a pairwise comparison matrix was created based on the experts’ opinions on a scale of 1–9, which is defined as the scale of importance established by [55], where 1 denotes the equal importance of two factors and 9 denotes the extreme importance of one variable over another. In the matrix, the evaluation of one criterion corresponds to that of another.

In the second phase, the sum of the values in each column of the comparison matrix is calculated in order to divide each entry of the respective columns in order to produce a normalized matrix. Following this procedure, all columns are normalized, with the sum of the normalized values for each column being 1.

In the final stage, the consistency of the experts’ decisions is validated by applying the consistency ratio measure using Equations (1) and (2). A CR score above 0.10 indicates inconsistency in the comparison matrix and unreliability in the results. Therefore, the pairwise comparison must be repeated until it achieves admissible consistency (CR < 0.1).

$$\mathrm{Consistency} \mathrm{Index}, \mathrm{CI}=\frac{{\lambda }_{max}-n}{n-1}$$

(1)

$$\mathrm{Consistency} \mathrm{Ratio}, \mathrm{CR}=\frac{CI}{RI}$$

(2)

where ${\lambda }_{max}$ is the largest eigenvalue of the matrix, n denotes the dimension of the matrix, and RI refers to random inconsistency index. The RI value is determined based on the number of conditioning factors (n) adopted in the analysis.

The AHP model developed for this study has a consistency ratio (0.07) of less than 0.1, meeting the threshold for a consistent matrix. The AHP-produced weights of the dimensions of climate urbanism are further used to assess the urban performance of the study area.

Table 1. AHP-generated weight for each dimension of climate urbanism.

AHP-Generated Weight
Dimension	Weight
Ms1 = Climate Conscious Governance	Ws1 = 0.40
Ms2 = Just and Equitable Society	Ws2 = 0.25
Ms3 = Urban Ecosystem Services	Ws3 = 0.03
Ms4 = Resilient Economy	Ws4 = 0.18
Ms5 = Climate Smart Infrastructure	Ws5 = 0.07
Ms6 = Adaptive and Dynamic Urban Form	Ws6 = 0.07

presents this study’s AHP-generated weight of each dimension.

3.3.2. Generating the Indices

In order to generate the climate urbanism index score, all the indicators (63) under the six dimensions of climate urbanism are normalized using Equation (3) in the case of a positive functional relationship and Equation (4) in the case of a negative functional relationship (see Appendix A:

Table A3. Overview of the condition of different parameters of climate urbanism.

Dimension	Indicator	Unit of Measurement	Xmax	Xmin	Index Score
Climate Conscious Governance	Understanding of locale	Likert	---	---	0.60
	Evidence-based climate action	Binary	---	---	0.00
	Stakeholders’ knowledge about climate change drivers, impacts in locale	Likert	---	---	0.60
	Understanding climate change narratives and language	Likert	---	---	0.40
	Continuous R&D	Binary	---	---	0.00
	Allocation of budget from upper level	Binary	---	---	0.00
	Local fund for climate change and uncertainty	Binary	---	---	0.00
	Investment (*FDI, PPP, NGOs, IOs)	Binary	---	---	0.00
	Pro-climate leadership	Binary	---	---	0.00
	Climate change advisor	Binary	---	---	0.00
	Research cell	Binary	---	---	0.00
	Partnership with local actors and researchers	Binary	---	---	0.00
	Presence of local climate agenda	Binary	---	---	0.00
	Autonomy to make local decisions	Binary	---	---	0.00
	Recognition of local government as key climate actor	Binary	---	---	0.00
	Active community participation	Binary	---	---	0.00
	PPP for climate actions	Binary	---	---	0.00
	Active climate conscious civil society	Likert	---	---	0.40
	Climate conscious community	Likert	---	---	0.20
	Self-organization (of local community)	Likert	---	---	0.20
	Collaboration and coordination among multiple stakeholders	Likert	---	---	0.20
	Actions to reduce climate change contributors (GHGs)	Binary	---	---	0.00
	Pro-poor climate adaptation (readiness, preparedness, barriers and enablers, policy credibility, quality)	Binary	---	---	0.00
	Monitoring and reporting	Likert	---	---	0.40
	Ensuring co-benefit of climate actions	Likert	---	---	0.20
	Informing community about contracts, budget, account	Binary	---	---	0.00
	Publishing progress and implementation report	Binary	---	---	0.00
	Public engagement in decision making	Likert	---	---	0.20
Urban Ecosystem Services	Access to green areas/parks	Binary	1	0	0.28
	Green barriers	Binary	1	0	0.28
	Knowledge about ESSs	Binary	1	0	0.60
	Utilization of adjacent waterbodies	Binary	1	0	0.13
	Per capita land consumption	Ratio	8	0	0.79
	HH vegetation	Binary	1	0	0.66
Resilient Economy	Per capita disposable income	Ratio	12,500	0	0.51
	Income level	Ratio	40,000	2154.64	0.38
	Multiple income sources	Count	3	1	0.44
	CBEF *	Binary	---	---	0.42
	IER	Ratio	2.5	1	0.41
	Location advantage	Likert	100	20	0.46
Resilient Economy	Income security	Ratio	100	20	0.47
	Self-production	Ratio	1	0	0.40
	Resource dependency	Likert	80	40	0.10
Climate Smart Infrastructure	Green rooftop/vertical gardening	Binary	1	0	0.47
	Solar-powered street lighting	Binary	1	0	0.34
	Daily vehicle miles traveled (VMTs)	Count	35	1	0.74
	Access to proper drainage system	Binary	1	0	0.87
	Annual per capita damage recovery cost	Taka	37,500	0	0.95
	Per capita HH fossil fuel consumption	Ratio	6	0	0.59
	Informal settlements *	Ratio	---	---	0.25
Adaptive and Dynamic Urban Form	Preferable density	Likert	100	20	0.73
	Accessible to immediate neighborhoods	Likert	100	60	0.58
	Level of desirability	Likert	100	40	0.69
	% Of open space *	Ratio	---	---	0.06
	% Of greeneries *	Ratio	---	---	0.15
	Misuse of land *	Ratio	---	---	0.01
	Net residential density *	Ratio	---	---	0.24
Just and Equitable Society	Women’s participation	Binary	100	20	0.62
	Food security	Binary	1	0	0.51
	Connection with institutions	Binary	1	0	0.38
	MVF *	Binary	---	---	0.00
	Low-income housing provision *	Binary	---	---	0.00
	Consideration of dependent groups *	Binary	---	---	0.00

It is important to note that the ‘Climate Conscious Governance’ variables in this study were evaluated using a Likert scale ranging from 1 to 5 and a binary scale (0 for absence/no, 1 for presence/yes). As these scales are unitless, normalization is not required, and the minimum and maximum values (at household level) are irrelevant due to the consistent responses across the city level. These variables are assessed directly at city level. Here, * indicates that these variables are assessed directly at city level and, thus, the minimum and maximum values (at household level) are irrelevant due to the consistent responses across the city level.

for details).

$$\mathrm{Index} X_s=\frac{X_s-X_{min}}{X_{max}-X_{min}}$$

(3)

$$Index X_S=\frac{X_{max }-X_s}{X_{max}-X_{min}}$$

(4)

In this step, the index score for each dimension of climate urbanism is calculated using Equation (5).

$$M_s=\frac{{{\displaystyle \sum }}_{i=1}^{n}Index{X}_{s^i}}{n}$$

(5)

Here, $M_s$ = index score for any dimension of climate urbanism.

${\displaystyle \sum }_{i=1}^{n}Index{X}_{s^i}$ = sum of normalized index score of all the indicators under the dimension.

$n$ = number of indicators under the dimension.

Finally, the composite index (i.e., climate urbanism index) is measured using Equation (6).

CUI = (M_s1 × W_s1) + (M_{s2 ×} W_s2) + (M_s3 × W_s4) + (M_s5 × W_s5) + (M_s6 × W_s6)

(6)

Here, M_s1 = Climate Conscious Governance index and W_s1 = weight for M_s1; M_s2 = Just and Equitable Society and W_s2 = weight for M_s2; M_s3 = Urban Ecosystem Services and W_s3 = weight for M_s3; M_s4 = Resilient Economy and W_s4 = weight for M_s4; M_s5 = Climate Smart Infrastructure and W_s5 = weight for M_s5; M_s6 = Adaptive and Dynamic Urban Form and W_s6 = weight for M_s6.

3.3.3. Application of Binary Logistic Regression (BLR)

This research has adopted a binary logistic regression (BLR) model for identifying the significant indicators having the potential for influencing climate urbanism’s transformative nature in the study area. BLR is a statistical modeling technique used to analyze the relationship between multiple predictors and a binary response variable [62]. Logistic regression outperforms discriminant analyses when independent parameters encompass categorical, continuous, or mixed types, while also demonstrating robustness against violation of the multinormality assumption [62]. Considering the characteristics of the variables treated as the factors of transformative modality of climate urbanism in this study, a BLR model is therefore appropriate to examine the extent of influence of the selected parameters to drive transformative climate urbanism.

We have utilized SPSS Statistics 25 software to run the BLR using 19 indicators which are not included in the climate urbanism indexing process. It is important to note that the variables registered with a single response for all households, including all variables under climate conscious governance and some other variables from other dimensions, are not included in the BLR model due to singularity and multicollinearity issues. Another reason is that it would also violate the assumption of independent variables. That is why only 19 among the 96 identified indicators are selected for the BLR model. Another important point is that although the climate urbanism index score could have been used as the dependent variable in the BLR model by classifying it into two classes, this approach was not employed due to the direct calculation of the climate index score at the city level, which limits the possibility of dichotomous classification for the study area’s index score. Thus, in our study, we employed ‘climate consideration in housing’ as a representative dependent variable in the BLR model to capture the essence of transformative climate urbanism. This choice was motivated by the literary recognition of this parameter as a transformative action in addressing the impacts of climate change, along with its greater correlation with the climate urbanism index. All the 19 indicators selected for the BLR model represent transformative modality of climate urbanism.

4. Results

The research reveals three key findings: (I) the extent to which Khulna is characterized by climate urbanism; (II) the identification of the key factors that influence the transformative nature of climate urbanism using principal component analyses (PCAs); and (III) the measurement of the factors’ contribution to transformative urbanism using binary logistic regression (BLR). These findings can provide policy makers and urban planners with insights for enhancing the sustainability and resilience of a city.

4.1. Assessing Performance of Khulna towards Climate Urbanism

Khulna’s climate urbanism index (urban performance) score is low at 0.36 in a possible range of 0 to 1, indicating poor urban performance, according to the study’s findings (see Figure 4). Among the six dimensions of climate urbanism, ‘Climate Conscious Governance’ received with the lowest score (unweighted) of 0.12, indicating the inefficiency and improvidence of the urban governing system of the city. Despite being highly susceptible to climate change and catastrophe risks, both the ‘Adaptive and Dynamic Urban Form’ and ‘Climate Smart Infrastructure’ dimensions exhibit surprisingly high performance levels with index scores (unweighted) of 0.67 and 0.66, respectively. In contrast, the dimensions of ‘Resilient Economy,’ ‘Urban Ecosystem Services’, and ‘Just and Equitable Society’ have lower index scores (unweighted) of 0.41, 0.52, and 0.50, respectively. These findings indicate the need for targeted interventions and enhancements in these areas in order to enhance the sustainability and resilience of the city as a whole. Figure 5 depicts the individual contribution of each dimension of climate urbanism to urban performance of the study area. The analysis indicates that the ‘Just and Equitable Society’ sector has the highest contribution to the urban performance in the city, followed by ‘Resilient Economy’. ‘Climate Conscious Governance’ and ‘Climate Smart Infrastructure’ have a low contribution, while ‘Adaptive and Dynamic Urban Form’ has even less. ‘Urban Ecosystem Services’ has the lowest contribution among all dimensions. These findings suggest a need for targeted interventions and improvements in these areas to enhance the city’s overall sustainability and resilience.

4.2. Key Factors Influencing Transformative Nature of Climate Urbanism

A total of 19 climate urbanism variables were selected for a PCA model with orthogonal rotation (i.e., varimax) in order to identify the factors that substantially contribute to climate urbanism’s transformative nature (see

Table 2. Loadings of the most influential variables of climate urbanism.

Dimension	Influential Factor	Loading Score
Just and Equitable Society	Access to decision-making process	0.547 (n1)
	Gender equity	0.717 (n2)
	Women’s education	0.751 (n2)
	Access to necessary information	0.802 (n7)
Adaptive and Dynamic Urban Form	Provision of mixed land use	0.839 (n5)
	Utilization of adjacent waterbodies	0.563 (n5)
	Smart urban form	0.807 (n6)
Urban Ecosystem Services	Adopting nature-based solution	0.509 (n1)
Climate Smart Infrastructure	Adopting smart water-saving strategies	0.612 (n1)
	Adopting smart energy option	0.623 (n1)
	Using green building materials	0.748 (n1)
	Access to safe and secure water supply	0.795 (n3)
	Utilizing non-motorized transport (NMT)	0.699 (n4)
	Use of fossil fuel for domestic activities	0.593 (n6)
	Access to proper sanitation facilities	0.750 (n3)

Here, n refers to the component number.

for details). Due to singularity issues, the PCA model could not include variables with a single response for all households, including those associated with climate conscious governance. As a consequence, the statistical model used only 19 parameters to identify influential factors in climate urbanism.

In addition, the PCA model’s eligibility criteria did not exclude any variables when determining the extent of the influential variables’ influence on urbanism’s transformative characteristics. Instead of relying solely on the first principal component (a one-factor model), we utilized all seven PCA-extracted factors to increase the model’s explanatory power. According to Beynon et al. [63], this approach is more trustworthy.

The PCA was verified as statistically valid through the Kaiser–Meyer–Olkin (KMO) and Bartlett’s tests [64,65]. The KMO measure of the PCA (see

Table 3. KMO and Bartlett’s test of the PCA.

Kaiser–Meyer–Olkin Measure of Sampling Adequacy		0.567
Bartlett’s Test of Sphericity	Approx. Chi-Square	289.103
	df	171
	Sig.	0.000

) has verified the sampling adequacy for the analysis with a value greater than the acceptable limit (0.5).

Additionally, the Bartlett’s test of sphericity (see Table 3) shows that the p value (0.000) is much lower than 0.05, which means the correlations between the variables are statistically significant. The average communality (0.605) of the PCA is also recorded to be greater than the acceptable value of 0.500. Figure 6 shows the number of extracted factors in this PCA model. The seven factors retained in the PCA model together explain 60.56 of the total variances that could be by all the 19 variables. It is important to note that the PCA model is applied to highlight the significant factors, but it cannot render insights about the contribution or extent of influence of all the prominent parameters of a factor. Therefore, a binary logistic regression (BLR) has been performed to measure the extent of influence of the factors on shaping transformative urbanism.

4.3. Influence of Underlying Parameters on Transformative Climate Urbanism

Following data sufficiency, collinearity, and multicollinearity tests, 19 indicators were selected for entry into binary logistic regression (BLR) to assess the impact of variables driving the transformative paradigm of climate urbanism. It is important to note that these indicators were selected as the variables of transformative climate urbanism based on the literature review and required data were derived following the same process as applied to the rest of the parameters under the six dimensions of climate urbanism. Based on [66], 4 out of 19 variables were removed from the BLR model due to strong collinearity (r > 0.60) identified through bivariate correlation analysis. The variables registered with a single response for all households, including all variables under climate conscious governance and some other variables from other dimensions, were not included in the BLR model due to singularity and multicollinearity issues. Another reason is that it would also have violated the assumption of independent variables. The results of the BLR model are shown in

Table 4. Significant parameters driving transformative paradigm of climate urbanism.

Parameter (n = 100)	B (Beta Coefficient)	Exp (B): Odds Ratio	Wald Chi-Square	Standard Error	Sig.
Constant	−33.531	0.000	0.000	40,193.006	0.999
Smart urban form	5.862	351.489	5.394	2.524	0.020 *
Access to necessary information	2.977	19.621	4.933	1.340	0.026 *
Using green building materials	3.124	22.739	4.443	1.482	0.035 *
Smart water-saving strategy	2.912	18.386	4.264	1.410	0.039 *
Utilizing NMT	3.427	30.786	5.378	1.478	0.020 *
Adopting nature-based solution	1.519	4.566	0.623	1.923	0.430
Utilizing adjacent waterbodies	−0.898	0.407	0.223	1.900	0.637
Access to proper sanitation	20.968	1.27 × 1010	0.000	15,892.443	0.999
Safe and secure water supply	2.286	9.838	0.000	43,220.927	1.000
Using fossil fuel	0.369	1.447	0.028	2.213	0.867
Adopting smart energy option	−0.117	0.890	0.010	1.138	0.918
Women’s education	0.761	2.141	0.128	2.132	0.721
Gender equity	−0.539	0.583	0.085	1.849	0.771
Provision of mixed land use	−21.085	0.000	0.000	17,576.864	0.999
Access to decision-making process	−1.195	0.303	0.589	1.557	0.443

Note: All the independent variables having significant influence on the transformative paradigm of climate urbanism are marked as bold. Dependent variable: ‘no climatic consideration in housing’ and ‘climatic consideration in housing’ (used as referenced category). This variable is used as the representative of transformative climate urbanism. Significance level: * significant at p ≤ 0.05.

.

The table shows that the BLR model has significant predictive power (X²(15) = 40.12, pseudo-R2 (Nagelkerke) = 0.579, p < 0.05) to estimate the influence of underlying variables of climate urbanism on the transformative paradigm, indicating a 57.9% success rate in prediction. It shows that ‘smart urban form,’ ‘access to necessary information,’ ‘green building materials,’ ‘smart water saving strategy,’ and ‘use of non-motorized transport (NMT)’ are the predictor parameters that influence the transformative potentials of climate urbanism to more notable extents than the others.

The results indicate that access to climate change information is strongly associated with a 20-fold increase in interest in climate resilient housing (i.e., transformative climate urbanism), while the presence of sustainable urban form is linked to a 352-fold increase. Similarly, the odds of households being transformative are 22.74, 18.38, and 30.78 times higher for those who adopt eco-friendly building materials, smart water-saving strategies, and non-motorized transportation, respectively. These findings suggest that promoting sustainable practices and providing information on climate change can play a crucial role in fostering the transformative nature of climate urbanism, which is critical to deal with global climate change impacts and related spatiotemporal uncertainties.

5. Discussion

Climate urbanism is regarded as the new urban philosophy that emphasizes how urban populations live and are governed, as well as how urban policies and physical development thinking about the climate emergency are undertaken [6,43]. In light of this, the study set out to assess how well a major city in the Global South prepared for, carried out, and directed societal changes while taking into consideration climate change and the possibility of extreme weather occurrences. The progress of the case study city in the Global South, Khulna, in terms of climate urbanism depends on the city’s performance in various dimensions. By examining the contribution of common components to the index value, it is possible to comprehend the role played by each climate urbanism facet in the climate urbanism of Khulna. According to the findings, the performance of the city is significantly impacted by the ‘Just and Equitable Society’ sector (35.20%) and the ‘Resilient Economy’ (20.91%). However, other common factors with low contributions include ‘Climate Conscious Governance’ (13.46%), ‘Climate Smart Infrastructure’ (13.16%), and ‘Adaptive and Dynamic Urban Form’ (12.89%). The dimension with the lowest contribution is ‘Urban Ecosystem Services’ (4.50%).

Scholars such as Long and Rice [6], Castán Broto et al. [12], and Castán Broto and Robin [43] note that climate change exacerbates urban inequalities as affluent urban residents move to low-risk climate zones, forcing the urban poor to seek refuge on vulnerable lands. In the decision-making process of city affairs, the urban impoverished have no voice, which is reflected in the findings of this study. It is discovered that limited access to decision making and disregard for marginalized groups impede the overall urban development progress of the city in the case study. However, urban impoverished populations are found to be organized and empowered, as well as to have networks with institutional actors such as GOs and NGOs, in order to improve their access to urban services. Due to factors such as increased women’s empowerment and education, social cohesion, and access to essential services, the urban poor have performed moderately well in the ‘Just and Equitable Society’ dimension of climate urbanism, contributing to the city’s performance. ‘Climate Conscious Governance’ performs the worst, with issues such as limited capacity and a lack of evidence-based development actions. As the performance of urban ecosystem services in Khulna is unsatisfactory, it is also alarming that the city’s planning and development did not prioritize the green city concept. Access to green areas/parks, green barriers, and utilization of adjacent waterbodies are among the indicators of urban ecosystem services for which the city’s performance is alarming. This is because both residents and city officials have limited knowledge of ESSs. Overall, the city’s performance in all six dimensions of climate urbanism is poor, leaving it highly vulnerable to risks and uncertainties resulting from climate change.

Climate urbanism examines an emergent mode of imagining new cities, as climate change influences not only how people envision the future of cities but also how individuals and institutions plan and design cities to foster urban resilience [43]. Therefore, this study identified nineteen indicators that city authorities could consider as promoting climate urbanism that contributes to transformational changes in cities, including effective land use management, energy efficiency, access to information, green networks, nature-based solutions, access to basic services for poor and marginalized populations, women’s empowerment, poor and marginalized access to the decision-making process, etc. However, only a few indicators were found to be significant contributors to Khulna’s performance towards a paradigm shift towards climate urbanism. These indicators included smart urban form, access to required information, the use of green building materials, an intelligent water conservation strategy, and the implementation of NMT. The research’s findings and global best practices suggest the following suggestions for enhancing the urban performance of Khulna in the face of global climate change and related uncertainties.

This research evinces that ‘Climate Conscious Governance’ is the underperforming sector in Khulna, which can be attributed to the absence of an evidence-based climate agenda and proactive civil societies, weak institutional capacity, and limited public participation in decision making. In addition, local government’s knowledge politics are unsatisfyingly shallow, which limits its ability to improve urban performance. Recognizing the role of local government as a key agent of transformative climate urbanism is instrumental to strengthen the city’s climate politics. Enhancing local autonomy, accountability, and transparency is necessary to ensure efficient resource planning and utilization. Facilitating active public participation in decision-making and development processes, while incorporating local scholars from universities and colleges, is crucial for nurturing citizen engagement and realizing greater socio-economic transformation that effectively addresses the fundamental drivers of climate change. Establishing a research unit within the local government to investigate socio-economic transformation, climate change adaptation needs, and evidence-based decision making is crucial. Given limited resources, engaging young professionals, trainees, and social workers can be a cost-effective approach to enhance this sector’s capacity. Additionally, creating a monitoring and evaluation commission, with the aid of an informed civil society, can tackle institutional inefficiencies and corruption.

The rights of marginalized groups are undermined by policy makers, making socio-economic inequity persistent and, thus, demanding targeted interventions and social transformation to uplift urban development. Prioritizing the most vulnerable but least contributing (can be defined as the climate change victims) in development decisions is highly recommended as ignoring them further creates more serious urban issues that cost much more and last for longer periods of time. Policy makers must prioritize social transformation and citizens’ capacity building over simplistic project implementation and aid. Promoting indigenous industries, adaptive practices, and resource flow to strengthen social capital can be impactful to elevate urban performance of the city towards climate urbanism.

Khulna’s economic potential is limited by resource dependency, singular income source, low income levels, limited locational advantages, lack of economic diversity and innovation, and the absence of a community-based emergency fund to deal with uncertain situations like COVID-19 and cyclones, resulting in lower urban performance. Establishing an ‘uncertainty and climate change’ fund for the community may be imperative to deal with such uncertainty. This fund can be controlled by the local government and will be delivered to the community when in need. Encouraging and promoting alternative sources of income such as freelancing or outsourcing could be a useful approach to transforming the unemployed young population into self-employed and self-sufficient individuals. In addition, to reduce economic vulnerability to climate change, it is important to shift towards skill- and knowledge-based employment, rather than relying on resource-dependent industries. Moreover, it is crucial for cities of developing countries to bolster their primary sector and diversify into secondary and tertiary sectors, prioritizing export growth and import reduction.

‘Urban Ecosystem Services’ contributed the least to climate urbanism in the research area because of the poor management of natural resources and the lack of understanding of ecosystem services among both residents and decision makers. Raising awareness among the community about the significance of nature-based solutions and promoting utilization of natural resources for local production is the paramount measure to enhance urban ecosystem services in the study area. Additionally, promoting productive measures such as rooftop and vertical gardening among households can mitigate indoor temperature and enhance oxygen levels, while ensuring safer food production. Encouraging location-specific design practices for community members is also critical to optimize natural resource utilization and align with city plans.

The study suggests that smart urban design and non-motorized transport are crucial in promoting transformative climate urbanism in the area. Compact urban design and effective land use can reduce motorized trips and encourage non-motorized modes like walking and cycling [67,68]. Furthermore, information accessibility, adopting smart water-saving strategies, and green building materials are found to be significant transformative attributes of climate urbanism in the study area. Informed citizens are encouraged to adopt innovative and indigenous strategies to deal with climate-induced uncertainties and calamities, leading to socio-economic transformation, which is instrumental to ensure urban sustainability.

6. Conclusions

This research assesses urban performance of Khulna in the light of climate urbanism, an emerging paradigm of urban development. In doing so, the study develops the conceptual and analytical frameworks of climate urbanism. An analytical hierarchy process (AHP) and indexing aided multicriteria decision analysis (MCDA) methodology has been applied to conduct the research using both primary and secondary data sources. In addition, the most influential factors shaping transformative nature urbanism and their extents of impact have been evaluated using principal component analyses (PCAs) and binary logistic regression (BLR).

The findings of the study elucidate that Khulna has a low urban performance (climate urbanism index) score of 0.36 in a possible range of 0 to 1. ‘Climate Conscious Governance’ has the lowest dimensional score of 0.12 while contributing a little to the urban performance. ‘Adaptive and Dynamic Urban Form’ and ‘Climate Smart Infrastructure’ score 0.67 and 0.66, respectively, making exiguous contributions to the climate urbanism index score of the city. ‘Just and Equitable Society’ contributes the most to climate urbanism in the study area and is characterized by an index score of 0.50. The dimensional index scores of ‘Resilient Economy’ and ‘Urban Ecosystem Services’ are 0.41 and 0.52, respectively. Comparatively, ‘Resilient Economy’ makes the second largest contribution to climate urbanism, whereas ‘Urban Ecosystem Services’ makes the smallest. Furthermore, the BLR-derived results show that access to necessary information, using green building materials, smart water-saving strategy, smart urban form, and utilizing NMT are the most salient factors influencing the transformative nature of climate urbanism in the city.

Further research should explore and establish climate urbanism metrics for national, regional, and local strata. A mixed method research to understand how the transformative paradigm of climate urbanism can promote urban sustainability will be instrumental to establish climate urbanism firmly as an urban theory. Additionally, devising more comprehensive and rigorous analytical approaches for studying climate urbanism, especially to conduct quantitative and mixed method research, is instrumental for furthering the climate urbanism portfolio in the global literature.

Insights from this study can help city planners and decision makers to improve urban sustainability in the face of climate change and increased spatiotemporal uncertainty by focusing on intervention priorities. Further, the findings of the study will help Khulna’s planners craft better climate adaption plans and urbanization policies. In addition, it will pave the way for additional studies on climate urbanism on local levels.