Research article

# Citizens' acceptability and preferred nature-based solutions for mitigating hydro-meteorological risks in Ghana

Kirk B. Enu<sup>a, *</sup>, Aude Zingraff-Hamed<sup>b, c</sup>, Yaw Agyeman Boafo<sup>d</sup>, Mohammad A. Rahman<sup>a</sup>, Stephan Pauleit<sup>a</sup>

## ARTICLE INFO

Handling Editor: Lixiao Zhang

## 1. Introduction

Biodiversity loss and climate change-related stresses are among the most significant socio-ecological challenges confronting all regions globally. These challenges increase the level of exposure to hydro-meteorological hazards like floods and heatwaves (IPCC, 2022). Hydro-meteorological hazards have often been most devastating in cities due to their high population densities and large scale land use changes (García-Soriano et al., 2020; Singh et al., 2022). As such, urban areas are tipped to be the most vulnerable to hydro-meteorological hazards that may occur in the foreseeable future due to the worsening climate crisis (Douglas, 2017). Approximately 90% of present urbanization is occurring in the Global South, and sub-Saharan Africa (SSA) is at the forefront (Pauleit et al., 2019). Due to the rapid urbanization with a high degree of informality in the region, city dwellers are precariously vulnerable to diverse socio-ecological challenges, including hazards (Kelsall et al., 2021; Sharma et al., 2022). These realities accentuate how weighty the urban resilience conundrum is in SSA and highlight the need to minimize risks urgently.

People's "beliefs, attitudes, judgments and feelings, as well as the wider cultural and social dispositions they adopt towards hazards and their benefits" define their risk perception (Pidgeon et al., 1992, p. 89). Most of the studies on risk perception stem from psychology and observe that perceptions may be influenced by intrinsic or external factors. Intrinsic factors range from people's dispositions, whether they are hierarchical, egalitarians, fatalists or opportunists (Rayner, 1992); the

heuristics that they apply when making judgments and their cognitions, which is influenced by their level of education (Breakwell, 2014). External factors may include how social and political institutions function and shape the perception of equity and justice in the distribution of resources to different social groups and how risks are communicated (Santoro et al., 2019). Another factor that can be either internal or external to different people is how they perceive the environment. Usually, people that consider the environment to be synonymous to ecological or natural resources may not give the necessary importance to social or political transformations that will impact the environment in which they live and vice versa (Vining et al., 2008).

In hydro-meteorological hazards literature, the factors influencing risk perception, as studied, range from the geographic location in which people live, the characteristics of their residential units, their culture, socio-economic and demographic profiles and their experience of risks (direct or indirect, intensity and level of impact) (e.g., Bera and Danek, 2018; Lechowska, 2018; Santoro et al., 2019). Many of these studies were conducted in formal settlements. Even though studies in informal settlements are burgeoning (e.g., Tasantab et al., 2022), a comparative study of different neighbourhoods types in terms of layout, residential unit type and socio-economic status is lacking despite most cities embodying diverse neighbourhoods. Moreover, the over-emphasis on the perceptions of the at-risk robs scientists and practitioners of broader opinions (e.g., of tax-payers who may not be highly exposed) and a thorough understanding of how risks can be mitigated more inclusively and comprehensively (Birkholz et al., 2014). Since risk perceptions are

<footnote><sup>a</sup> School of Life Sciences, Chair for Strategic Landscape Planning and Management, Technical University of Munich, Emil-Ramann-Str. 6, 85354, Freising, Germany</footnote>

<footnote><sup>b</sup> ENGEES National School for Water and Environmental Engineering, 1 cour des cigarières, CS 61039, 67070, Strasbourg, France</footnote>

<footnote><sup>c</sup> Université de Strasbourg, CNRS, ENGEES, UMR 7362 LIVE (Image, City, Environment Laboratory), 3 rue de l'Argonne, 67000, Strasbourg, France</footnote>

<footnote><sup>d</sup> Center for Climate Change and Sustainability Studies, College of Basic and Applied Science, University of Ghana, P.O. Box LG 25, Accra, Ghana</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: kirk.enu@tum.de (K.B. Enu).</footnote>
socially constructed (Boholm, 2003)—people's understanding and interpretation of risks are influenced by social, cultural and contextual factors besides their own objective assessment of risks—they ought to be studied as part of dynamic and adaptive decision-making processes in resilience building. Such learnings could help to understand how risk perceptions impact cooperation, the urgency with which risk reduction is undertaken (Lemée et al., 2022) and what approaches are preferred for mitigating the risks (Santoro et al., 2019).

Over the years, conventional engineering approaches, which depend heavily on constructed measures, have been used for risks mitigation. However, they have not always been effective, especially in the long-term (Lafortezza et al., 2018). Additionally, they may lead to the loss of biodiversity and serve singular purposes despite being capital-intensive (Lupp and Zingraff-Hamed, 2021). These reasons, along with the knowledge that nature can promote climate risk resilience through the provision of ecosystem services, has instigated advocacy for nature-based solutions (NbS) since the emergence of the term in 2008 (Pauleit et al., 2017). NbS are more cost-effective and can provide about 30% of the cost-effective mitigation required to keep global warming below 2 °C by 2030 (Seddon et al., 2019a,b). They can also deliver up to 85% of profitable hydro-meteorological risk management (Debele et al., 2019). Furthermore, NbS are multifunctional and provide multiple co-benefits (Pauleit et al., 2017). At the global level, NbS have been at the centre of discussions in recent United Nations Climate Change Conferences; regionally, the European Commission is exploring the passage of nature restoration as a law (European Commission, 2022) and nationally, 63% of countries have declared ecosystem conservation and restoration as expected outcomes in their National Adaptation Plans (Seddon et al., 2019a). Despite the nature-centred policy orientation, the acceptance and on-the-ground implementation of NbS is significantly lacking. This is evidenced by the gap in investment of about $133 billion committed against the $4.1 trillion needed to meet climate, biodiversity and land degradation targets by 2050 (UNEP, 2021).

In implementing NbS, proponents have recommended the co-creation pathway since risk resilience building is a collaborative venture between stakeholders and citizens (Santoro et al., 2019). Understandably, actors like city planners, disaster management agencies, land and water management agencies, among others, have been the go-to due to their functions in planning and resilience building (Zingraff-Hamed et al., 2020), including that they can enable or impede decisions (Hansen and Pauleit, 2014). Co-creation, rather, ensures the deliberate inclusion of citizens, including different demographic groups, in the planning, implementation, management and monitoring of implemented measures since they also have stakes in resilience building. Co-creation is mutually beneficial for decision-makers and citizens alike. While citizens increase their knowledge and build their capacities through the exchange with experts (Paul et al., 2018), decision-makers learn from the lived experiences and initiatives of citizens (Boafo et al., 2016). Co-creation also promotes environmental stewardship and, hence, the long-term sustainability of implemented measures (Anderson et al., 2021), enhances trust between decision-makers and citizens (Gates-rsleben and Uzzell, 2000) and ensures environmental justice (Hansen and Pauleit, 2014). This is mirrored in global policies like the Sendai Framework for Disaster Risk Reduction 2015–2030 which calls for “all-of-society” to be involved in risk mitigation (UNDRR, 2015, p.13). Indeed, studies have revealed that the non-participation of citizens in all stages of NbS rollout had led to undesired outcomes (e.g., Ramí-ez-Agudelo et al., 2022; Wamsler et al., 2020). Therefore, an interro-gation of not only stakeholders’ but also citizens’ acceptability of NbS for mitigating risks is critical for promoting NbS in resilience building.

Nonetheless, existing studies on the acceptability of NbS have focused overwhelmingly on stakeholders (e.g., Anderson et al., 2021; Ferreira et al., 2021; Pagano et al., 2019). Studies focusing on citizens are scanty and reveal a critical knowledge gap in literature. The available studies showed that citizens do not find NbS reliable and prefer hybrid solutions as the safe middle ground between NbS and

conventional engineering solutions (Anderson et al., 2022). Also, even though citizens appreciate the various co-benefits of NbS, they give higher priority to their effectiveness for mitigating risks. These studies were based in Europe, however, which has unique urban morphologies and climatic contexts than Global South regions like SSA and shows a critical population gap in literature.

Another study by Anderson and Renaud (2021) introduced the Public Acceptance-NbS framework with factors that determine the acceptance of NbS among citizens. The asserted factors are costs and available funding, trust in risk managers, a sense of responsibility for managing measures and competing interests with societal needs, which may be more controlled by institutions. The other factors that have a more individual outlook are past experience of risks, perceptions of future risks and perceived efficiency of NbS. As this framework is un-tested, an empirical void exists in literature. Thus, we adopted the Public Acceptance-NbS framework as our theoretical framework and focused on the factors that are more individual in scope since our enquiry focused on citizens. We introduced additional determinant variables—level of education and profession—which have been found to highly influence risk mitigation behaviours in SSA (Twerefou et al., 2020) and also knowledge of ongoing or past NbS since Dai et al. (2021) found it to be a key determinant of NbS acceptance.

Our objective was to gain a better understanding of how citizens from different types of neighbourhoods perceive risks to hydro-meteorological hazards and to assess their level of acceptance of NbS for mitigating the risks. We were also interested in knowing the specific NbS measures they would prefer to be implemented, as part of co-creation. We hypothesized that perceived exposure to hydro-meteorological hazards is determined by experience of hazards while the acceptance of NbS over conventional engineering and hybrid solutions is linked to perceived level of exposure to hazards, level of education, profession (if related to nature), perceived efficient measures and experience with NbS (knowledge of ongoing measures or involvement in same). The empirical data was collected from the Greater Kumasi Metropolitan Area (GKMA) in Ghana to offer insights on the topic for the Global South. Our guiding research questions were 1) what are residents' perceived degree of exposure of hydro-meteorological risks; 2) what is the level of acceptance of NbS for mitigating these hydro-meteorological risks and 3) which specific NbS measures do residents prefer for mitigating hydro-meteorological risks?

## 2. Materials and methods

### 2.1. Case study area

GKMA is the capital city of the Ashanti Region of Ghana and the seat of the Ashanti Empire. It is the fastest urbanizing area in the country. It is an agglomeration made up of the Kumasi Metropolis with a total land area of 254 km², and six sprawled municipalities that altogether make up a land area of 2746 km² (Fig. 1). The population stands at 3.5 million (World Population Review, 2021). There are about 289,051 housing units in the area according to the 2021 population and housing census. GKMA is governed not only by state institutions like the local government administrations but also the monarchy, led by the Asantehene.

In terms of topography, GKMA is generally flat but has about 5% of the total land area made up of gently undulating slopes that rise from 240 to 300 m above sea level. Many of the hills have flat topped inter-fluvial ridges with widths varying from 1500 to 2,500 m. The soil type is forest ochrosol, which is nutrient-rich and supports agriculture. GKMA has a tropical wet-dry climate according to the Köppen classification (Aw) and falls within the forest zone of Ghana. Consequently, it experiences two rainfall seasons and records mean annual rainfall between 1250 and 2,000 mm (Koranteng et al., 2019).

Hydro-meteorological hazards were a rarity in GKMA until the early 2000s and have been driven mainly by anthropogenic factors like wet-lands degradation, building in waterways and greens depletion. For
<figure><img src="image_1.png" /><figcaption>Fig. 1. Map of Greater Kumasi Metropolitan Area and description of surveyed neighbourhoods. (NB: 1- Tafo Nhyiaeso, 2- KNUST/Kentinkrono/Ayeduase, 3- Atonsu-Ahinsan, 4- Danyame/Ridge).</figcaption></figure>

instance, since 2012, floods have increased in incidences and severity (Abass et al., 2020) and about 50% of the area is at moderate to high risk (Korah and Cobbinah, 2017). The types of floods documented in GKMA in literature predominantly comprise flash floods and effluent stream floods (Amoateng et al., 2018a; Campion, 2012). Similarly, temperatures in the area have increased by more than 1 °C since the 1960s and a 0.2 °C increase was recorded between 2000 and 2010 alone (Essel, 2017). Again, the average mean maximum temperature in 1975 was 30.0 °C but increased to 32.1 °C by 2010 (Darkwah et al., 2018). This temperature rise has affected human thermal comfort and agricultural production in the city. While the increase in these hazards may have occurred within the context of the global climate crisis, they have been exacerbated by local factors related to unsustainable urbanization practices such as the aforementioned. Notably, impervious areas in the city increased by 54% at the expense of greens from 1986 to 2016 (Abass et al., 2020) while the spatial extent of wetlands and rivers dwindled

from 70 to 29 km² over the same period (Amoateng et al., 2018b). These changes have contributed significantly to the increasing hazards in GKMA.

Efforts at minimizing risks so far have combined several solutions. Conventional engineering measures, however, dominate, such as an ongoing 30-million-euro city-wide drainage infrastructure improvement project (Fig. 2a) and the canalization of sections of rivers like Subin, Aboabo and Sisai, totalling 23 km (Fig. 2b) (Amoateng et al., 2018a). There have been attempts to increase nature also, such as the Green Ghana Project where thousands of tree seedlings have been planted since June 2021 to increase green cover in the city. Some urban trees have been planted on public lands as well (Fig. 2c).

## 2.2. Survey sample selection

Acknowledging that neighbourhoods have not been evenly impacted

<figure><img src="image_2.png" /><figcaption>Fig. 2. Ongoing efforts for mitigating risks in GKMA. (a) Ongoing drainage improvement project (b) Canalization of river Sisai (c) Afforestation in a parcel of land in city center. (Source: Kirk B. Enu and Stephan Pauleit, March 14, 2023).</figcaption></figure>
by hazards, we reviewed literature on the at-risk locations in GKMA and consulted officers of the Kumasi Metropolitan Assembly to select neighbourhoods purposively for the sample. We also considered neighbourhoods that are representative of GKMA in terms of demographic strata like age, gender and socio-economic status. Tafo Nhyiaeso; Atonsu-Ahinsan; the KNUST, Kentinkrono and Ayeduase enclave and Danyame/Ridge were selected (Fig. 1). All the selected neighbourhoods can be considered formal settlements because they were originally planned, even if their present growth is not well controlled. The sample size (n) was calculated following Gutierrez et al. (2013) using the equations:

$$x = \frac{Zc^2r(100 - r)}{10000} \qquad (1)$$

$$n = \frac{N_x}{(N - 1)E^2 + x} \qquad (2)$$

$$E = \sqrt{\frac{(N - n)x}{n(N - 1)}} \qquad (3)$$

where,

x is the required sample size,

Z is the Z-quantile at 95% confidence level (1.96),

C is the proportion of the population of interest,

r is the expected response rate in the population,

n represents the required sample size,

N is the total population size, and.

E represents the margin of error.

The margin of error (E) was 5% (equation (2) and (3)). The recommended sample size was 385. This despite, the research team could guide 622 respondents to complete the questionnaire to strengthen the statistical power of our analyses and to provide more precise estimates and results.

## 2.3. Data collection

The data collection instrument, the survey questionnaire, was developed learning from past studies, including work on NbS acceptance and NbS measures and their co-benefits (e.g., Enu et al., 2023; Ferreira et al., 2021; Joshi et al., 2023; Ommer et al., 2022). Including demographic and neighbourhood characteristics (11), there were 33 questions in all, with 24 closed-ended and 9 open-ended (Appendix B). For close-ended questions, there were those with single choice responses expected, those with Likert scales and questions with tick boxes. The open-ended questions helped to collect more detailed content about respondents' experiences or opinions.

Six enumerators were trained for the data collection exercise on 28th and December 31, 2022. Familiarization visits and reconnaissance surveys followed to map the routes. The visits were also used to identify and establish the needed contacts and rapports with local stakeholders, like assembly members, who are usually the first point of call in community entry. Subsequently, the questionnaires were administered from January 4–25 2023 with the help of mobile devices through a web-based questionnaire developed on Google Forms. The sample selection was done using a two-stage cluster sampling approach. First, households were selected using the systematic random selection of every third household along the main access routes in the neighbourhoods. Houses were selected on both sides of the routes. In cases where there were multiple households in the selected house as happened a few times, a simple random approach was used to select one household. Second, the selection of respondents was done by the purposive selection of the household head. In their absence, spouses or adults above 18 years were surveyed. It took between 30 and 40 min to survey each respondent.

## 2.4. Variables

The group of variables used for this study were a) demographic characteristics like level of education and profession (professions related to nature were defined as those that directly involve the conservation, restoration or management of landscapes and professions where people directly depend on nature's yield (e.g., food, fish and water) or non-material benefits (e.g., recreation) for income)); b) perceptions of hydro-meteorological risks, including past experiences and perceived exposure to future risks and c) perceptions of NbS like experience with the concept, acceptability, perceived efficient measures for mitigating risks and which specific NbS measures are preferred for mitigating risks of floods and extreme heat hazards and the co-benefits expected. All analyses were conducted in Stata 14.2. Descriptive statistics were first conducted and the hypotheses were tested using the Chi-Square test of independence and the multinomial logit model (Hensher et al., 2005).

## 2.5. Data analysis

The Chi-Square test of independence was used to test the relationship between past experience of hydro-meteorological hazards and perceived exposure to future hazards (H₁). The relationship between the intensities of past hydro-meteorological hazards and future hazards was also tested. The Chi-Square test was deemed appropriate because the responses for the survey questions on respondents' perceptions of hydro-meteorological hazards were categorical and assumptions (e.g., the data being collected independently) were met (Tasantab et al., 2022).

The multinomial logit model was used to test the factors that might be determining the preference for NbS. The model is typically used when there is a categorical dependent variable with more than two categories, and one seeks to model the relationship between this dependent variable and a set of predictor variables. Also, assumptions like the independence of irrelevant alternatives, independence of irrelevant attributes, that there should be no perfect predictions, that each observation is independent of others and that explanatory variables are exogenous, must be met (Hensher et al., 2005). As these were satisfied, this model was deemed the most appropriate for testing H₂. NbS was used as the base category to predict the probability of respondents selecting it over conventional engineering and hybrid solutions. Formulars (4) and (5) were used for the calculation.

$$P(Y_i = k) = \frac{e^{\beta_k} x_i}{1 + \sum_{j=1}^{k} e^{\beta_j} x_i}, \text{ where } k = 1, 2 \text{ and} \qquad (4)$$

$$P(Y_i = 3) = \frac{1}{1 + \sum_{j=1}^{3} e^{\beta_j} x_i} \qquad (5)$$

where,

$P(Y_i = k)$ represents the probability that an observation or event i belongs to category k, where k can be either 1 or 2. $k = 1$ represents "conventional engineering solutions" and $k = 2$ represents "hybrid solutions."

$\beta_k$ represents the parameter associated with category k. Thus, $\beta_1$ is the parameter for "conventional engineering solutions" and $\beta_2$ is the parameter for "hybrid solutions". These parameters are estimated from the data and indicate how much each category is influenced by the predictor variable $X_i$.

$X_i$ is the predictor variable for event i. It is the explanatory variable (s) that influences the outcome (category) of event i.

The sum in the denominator, $1 + \sum_{j=1}^{1} e^{\beta_j} x_i$, represents a summation over all categories (j) and their respective parameters ($\beta_j$), where j ranges from 1 to 1. This denominator is used to normalize the probabilities to ensure that they sum up to 1.

$P(Y_i = 3)$ represents the probability that the observation or event i belongs to category 3, which is NbS.
The probabilities of each choice were estimated based on the selected predictor variables and base choice. The coefficients (β) were also estimated using the maximum likelihood method to describe how the explanatory variables influence the probabilities of choosing each category. The category with the highest predicted probability was considered the most likely choice for a respondent. To avoid multicollinearity, the categories "10–20 years" and "> 20 years" as well as "no opinion" and "none of these solutions" under the variables "perceived flood and extreme heat exposure" and "perceived efficient measures", respectively, were combined. The goodness-of-fit of the model was assessed using the log-likelihood, pseudo-R-squared (pseudo R2) and the p-value associated with the Chi-Square test (prob > chi2).

## 3. Results

This section presents the findings from the survey and analyses. Majority of the respondents (64%) were within the 25–34 and 35–44 age ranges. The average age was 40 years. Educational levels were diverse. A significant percentage were graduates (26%) while others had studied up to the junior (22%) or senior high school (21%) levels. Most respondents also worked in the services and commerce sectors as civil servants (n = 36), entrepreneurs (n = 52) and traders (n = 138). Only a quarter had professions that were directly related to nature, such as crop and pastoral farmers (Table S1).

### 3.1. Perceived exposure to hydro-meteorological risks

Perceptions on the level of exposure revealed recent flood experiences with variations across the studied neighbourhoods. For instance, most respondents from Atonsu-Ahinsan (67%) and Kentinkrono/ KNUST/Ayeduase (47%) reported floods in the last "2 years or less". These past flood hazards were reported to be quite severe, with respondents from Atonsu-Ahinsan (95%) and those from Kentinkrono/ KNUST/Ayeduase (76%) reporting "very intensive" or "quite intensive" floods. Many respondents across all four studied neighbourhoods were

"unsure" of their exposure to floods, such as in Danyame/Ridge (65%) and Tafo Nhyiaeso (27%), hence, expect low intensity floods (Fig. 3). Those expecting flood hazards, such as most respondents from Kentinkrono/KNUST/Ayeduase (52%), anticipate floods in "2–5 years" time.

Extreme heat hazards were generally not as widely reported as floods but respondents that had experienced it reported it as a new development. The examples of Atonsu-Ahinsan (47%), Tafo Nhyiaeso (34%) and Kentinkrono/KNUST/Ayeduase (30%) where they were reported to have occurred in "2 years or less" underline this. Reported intensities of past extreme heat hazards varied across the neighbourhoods. While most respondents from Danyame/Ridge (98%) and Tafo Nhyiaeso (87%) reported "not intensive" and "little intensive", those from Kentinkrono/ KNUST/Ayeduase and Atonsu-Ahinsan reported a wider range of perceptions between high and low intensities. Perceptions for future extreme heat hazards mirror experiences from the past. Most of the respondents from Danyame/Ridge (98%) and Tafo Nhyiaeso (83%) expect "not intensive" or "little intensive" extreme heat hazards in the next "2–5 years" while the other neighbourhoods have more varied perceptions.

The similarity of perceptions for past hazards and future perceived risks and their intensities were confirmed in the Chi-Square tests as they returned with relatively high Chi-Square values (H₁) (Table S2).

### 3.2. Acceptability of nature-based solutions

Present priority interventions were reported to be mostly conventional engineering solutions (62%) compared to 27% NbS and 6% being hybrid solutions. Only the case of KNUST/Ayeduase/Kentinkrono deviates from this trend with 40% of respondents reporting NbS to be the priority intervention compared to 37% for conventional engineering solutions and 13% for hybrid solutions (Fig. 4). In mitigating future risks, only 18% of respondents would prefer NbS, compared to 62% opting for conventional engineering, 12% for hybrid solutions and 6% choosing on a case-by-case basis. Respondents from Tafo Nhyiaeso

<figure><img src="image_3.png" /><figcaption>**Fig. 3.** Perceived exposure to hydro-meteorological risks (a) Past experience and perceived future exposure to hazards (b) Intensity of flood hazards (c) Intensity of extreme heat hazards. (NB: 1- Tafo Nhyiaeso, 2- KNUST/Kentinkrono/Ayeduase, 3- Atonsu-Ahinsan, 4- Danyame/Ridge).</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Fig. 4.** Acceptability of NbS (a) Perceptions on present priority measures being implemented (b) Preferred measures for mitigating future risks. (NB: 1- Tafo Nhyiaeso, 2- KNUST/Kentinkrono/Ayeduase, 3- Atonsu-Ahinsan, 4- Danyame/Ridge).</figcaption></figure>

(31%) and Danyame/Ridge neighbourhoods (26%) had higher preferences for NbS than the other neighbourhoods.

The high log-likelihood of the multinomial logit model (−208.89071) suggested a reasonably good fit for our analyses (Table S3). The Pseudo R² of 0.6959 also suggested that the model explains approximately 70% of the variance in the dependent variable relative to a null model. Furthermore, the prob > chi² = 0.000 means that the tested coefficients are highly statistically significant. Thus, the model appeared to be a good fit for explaining the observed outcomes.

Fig. 5 details the results of the multinomial logit model which was used to test H₂ on what factors determine the acceptability of NbS. Against conventional engineering solutions, statistical significance was observed for respondents who were “unsure” about their flood hazard exposure and those expecting flood hazards in “less than 2 years,” making them one and a half times more likely to prefer NbS. However, respondents expecting flood hazards in “2–5 years” and “5–10 years” were almost four times and more than two times less likely to choose NbS, respectively. For extreme heat exposure, no statistical significance was found. In terms of education, graduates were a little more than two times less likely to prefer NbS compared to those with lower education levels.

When considering between NbS and hybrid solutions, statistical significance was found for those expecting flood hazards in “less than 2 years” and almost two times more likely to select NbS. However, respondents expecting hazards in “2–5 years”, particularly those with primary education and graduates, were less likely to choose NbS, with coefficients indicating around eight, four and two times less likelihood, respectively. The other variables, like profession, knowledge of ongoing NbS and perceived efficient measures, showed no statistical significance.

### 3.3. Preferred nature-based solutions

Respondents could choose up to three measures each for mitigating flood and extreme heat risk. The most preferred NbS measures for flood risk mitigation were creating lawns (n = 459), river restoration (n = 367) and wetland restoration (n = 303), varying by neighbourhood (Fig. 6a).

For extreme heat risk mitigation, the most preferred measures were tree-planting (n = 583), reforestation (n = 339) and green/open spaces (n = 279), with gardens (n = 270) following closely after. The neighbourhood preferences also differed, except in KNUST/Ayeduase/

Kentinkrono (Fig. 6b).

Respondents were also asked which co-benefits they expect to obtain from their preferred NbS measures, with the possibility to choose up to nine options (Fig. 7). The most selected co-benefits were cooling homes and cities (n = 412), aesthetic value (n = 411), air purification (n = 361), shade (n = 338), food (n = 314) and improving human health (n = 270).

## 4. Discussions

### 4.1. Perceived exposure to hydro-meteorological risks

The result of our survey showed that floods in GKMA appear recent, evidenced by most residents having experienced floods only within the last 2 years. The citizen perceptions are consistent with literature, as Abass et al. (2020) posits that floods have become devastating year after year in GKMA since 2012. Interestingly, while low-income and high-income neighbourhoods reported low flood exposure, middle-income neighbourhoods seem highly exposed to flood hazards. For the low-income neighbourhood, this is likely because of the relatively high elevation of the area (Korah and Cobbinah, 2017). The high-income neighbourhood is also one of the few neighbourhoods in GKMA that is not densely built up and green spaces as well as blue natural networks remain in pristine conditions. On the other hand, the middle-income neighbourhoods had experienced more frequent and intense floods. This is also consistent with literature, especially for Atonsu-Ahinsan (e.g., Amoateng et al., 2018a; Campion, 2012). Their severe experiences are possibly because of their proximity to rivers and wetlands and the fact that many of these ecosystems have been degraded (Korah and Cobbinah, 2017). Considering how recent floods were reported across all the studied neighbourhoods, floods are no longer confined to specific neighbourhoods but is a real and increasing risk at the city scale.

Extreme heat hazards also appeared recent but are generally not very intensive. Temperatures in GKMA have shown an increasing trend (Darkwah et al., 2018; Essel, 2017). But the impact on thermal comfort in GKMA may have been less severe than expected, unlike in other regions where a four times spike in heat strokes and related mortalities have been recorded (Alahmad et al., 2023; Marchand and Gin, 2022). Also, the low intensity of felt extreme heat hazards could be because the population of GKMA, like SSA, is youthful and younger people tend to be more resilient than the aged (Mo Ibrahim Foundation, 2019). Beyond
<figure><img src="image_5.png" /><figcaption>Fig. 5. Coefficient plot of multinomial logit model results (a) NbS and conventional engineering (b) NbS and hybrid solutions.</figcaption></figure>

age, the minimal experience of extreme heat reported in the low-income neighbourhood, which is more densely built up, can also be explained by the neighbourhood construction allowing proper air flow and ventilation. Another reason is its location on the windward direction of the slopes in GKMA. In the high-income neighbourhood, it is understandable that extreme heat will be lowly reported because of the abundance of nature in the area and the fact that wealthier citizens often have the resources for investing in good ventilation. Only the middle-income

neighbourhood (KNUST/Ayeduase/Kentinkrono) reported widespread and intense experiences of extreme heat with concerns of further intensification in the future. This is startling since the area has significant green cover. However, living very close to a university, the residents may be more risk aware as most have higher education, being either students, researchers or working in academia. Altogether, these results suggest high resilience against extreme heat exposure.

Perceptions on exposure to future flood and extreme heat hazards
<figure><img src="image_6.png" /><figcaption>**Fig. 6.** Preferred measures for mitigating hydro-meteorological risks across the neighbourhoods (a) Flood risk (b) Extreme heat risk. (NB: 1- Tafo Nhyiaeso, 2-KNUST/Kentinkrono/Ayeduase, 3- Atonsu-Ahinsan, 4- Danyame/Ridge).</figcaption></figure>

and their intensities were found to have been strongly influenced by past experiences across all the four studied neighbourhoods. Hence, we reject the null hypothesis of H₁ that, perceived exposure to hydro-meteorological hazards is not influenced by experience of hazards. This aligns with the literature about informal settlements (Tasantab et al., 2022). Experiences in western France showed that when risk perceptions are high, commitment to risk reduction is also high (Lemée et al., 2022). Therefore, the results mean that respondents that are more aware of possible future hazards are likely committed to improving their resilience. The sheer number of respondents who were uncertain about their risk exposures is partly a reflection of the uncertainty that surrounds the present climate crisis and a revelation of the little awareness on risks possibly because of the unavailability of risk evaluation studies, reliable information and communication on risks.

## 4.2. Acceptability of nature-based solutions

Only citizens who were unsure about their flood risk exposure and those expecting flood hazards in less than two years would accept NbS over conventional engineering and hybrid solutions. In literature, stakeholders had been found to accept NbS when they had been involved in or had knowledge of ongoing NbS interventions (Dai et al., 2021). In the end, risk perception shapes preferences for risk mitigation strategies and usually, high risk perception leads to preference for conventional engineering solutions (Lafortezza et al., 2018). This study shows that, at least, in the Global South, NbS may be preferred instead if there is high or uncertain risk perception, especially with floods, in the short-term. Further studies should put light on the link between perceived high risk exposure and the neglection of conventional solutions as we hypothesize a “feeling of betrayal” by conventional engineering solutions.

Aside from the KNUST/Ayeduase/Kentinkrono enclave (middle-income with high proportion of academia) where majority of respondents
<figure><img src="image_7.png" /><figcaption>**Fig. 7.** NbS measures and the expected co-benefits (a) Number of times all co-benefits were selected (b) Number of times co-benefits for mitigating flood risk were selected (c) Number of times co-benefits for mitigating extreme heat risk were selected.</figcaption></figure>

selected NbS to be the priority intervention, conventional engineering was reported to be the prevailing priority intervention across all the neighbourhoods. These results align with current ongoing risk mitigation efforts by authorities and past studies that indicate massive investment in built infrastructures, including the canalization of sections of some of the major rivers in GKMA (Fig. 2b) (Amoateng et al., 2018a). This deviates from the case in Europe where hybrid measures are more preferred by citizens (Anderson et al., 2022), whereas GKMA residents give little priority to green spaces during planning (Diko, 2022). The overwhelming preference for conventional engineering portrays the need for environmental education and NbS advocacy to address misconceptions surrounding the concept and to gain the support of citizens for widespread adoption given the importance of NbS for achieving climate resilience (Pauleit et al., 2017; Seddon et al., 2019b; UNEP, 2021). Advocacy is critical because even in China where the Sponge City Program has been implemented for about a decade, some citizens still find such nature-based interventions “useless” (Chan et al., 2023, p.8). Indeed, the outlier result given by KNUST/Ayeduase/Kentinkrono, can

be explained by the presence of the Kwame Nkrumah University of Science and Technology campus and its effort in conserving and increasing nature (Fig. 1).

Surprisingly, GKMA residents with lower levels of formal education and low-income neighbourhoods had more acceptance of NbS than the other neighbourhoods. This contradicts the case in Europe where preference for green space is positively linked to high level of education (Maas et al., 2006). The reason could be that citizens with lower levels of education or from low-income neighbourhoods trust in nature more than technical solutions. It could also be that they find NbS unsophisticated and feasible to implement, especially given the city authorities' financial and institutional capacity constraints for which reason they are unable to respond to challenges swiftly (Abass et al., 2020; Amoateng et al., 2018a). In comparison, highly educated citizens rather may trust more in science, and consequently, would want to see the evidence of the effectiveness of NbS, which is scant, especially in the Global South. Besides, stakeholders, who usually are highly educated, have called for more quantitative assessment of the effectiveness of NbS in reducing
risks and the expected impacts (Santoro et al., 2019). Therefore, the comparative advantage of NbS over other solutions, such as its use of simple technology, cost-effectiveness and multiple co-benefits, are key messages that should be well communicated and demonstrated in NbS advocacy, including considering these topics for future research.

### 4.3. Preferred nature-based solutions

Creating lawns, river restoration and wetlands restoration were the most preferred NbS measures for mitigating flood risk. Evidently, 53% of residential landscapes in GKMA have been sealed (Abass et al., 2019; Enu et al., 2023), which has negative implications for stormwater infiltration and heat as shown in global studies (Rahman et al., 2022, 2023). Rivers and wetlands have also been some of the most degraded ecosystems in GKMA, including a 58.6% decrease in their spatial extent from 1986 to 2016 (Cobbinah et al., 2021). This, generally, may reflect the causality between environmental degradation and hazards due to rapid urbanization in GKMA and the perception of increasing risk exposure.

In mitigating the risk of extreme heat, the high preference for tree-planting and reforestation could be deduced from the ongoing Green Ghana Project where residents are invited to support greening efforts on public lands since June 2021. Also, the local governments and several environmental non-profits are investing in tree-planting initiatives, making it an increasingly popular intervention in GKMA (Fig. 2c–S1, S2). For green/open spaces, since the city was originally modelled after Ebenezer Howard's Garden City philosophy by Maxwell Fry and Jane Drew in 1945, it had numerous green belts that provided regulatory ecosystem functions; lands for agriculture and space for social functions like festivals, funerals and games (Essel, 2017). However, 80% of these spaces were lost to developments from 1991 to 2019 (Cobbinah et al., 2022). Most respondents would have seen this decline, which has made the city a shadow of its former reputation as the Garden City of West Africa. Studies already underscore the potential of societal memory, like former green cityscapes, for leveraging restoration (Wantzen et al., 2019). Thus, a future with this past may be desired and will be welcomed and supported by citizens.

The specific measures preferred across the studied neighbourhoods varied. The popularity of gardens in the low-income and high-income neighbourhoods is unsurprising because the former may depend on them for provisioning ecosystem services like food and herbs while the latter use them for recreation (Pribadi and Pauleit, 2015). The preference for creation of lawns also indicates citizens' willingness to integrate NbS within their home enclaves and, thus, highlight the need for policy makers to begin exploring opportunities for greening at the micro-scale. Besides, private and semi-private greenery should ordinarily to be the largest share of green spaces in cities (Schmidt and Walz, 2021). These unique preferences across neighbourhoods present the opportunity to develop city plans integrating these NbS measures. It also emphasizes the importance of advancing NbS implementation through co-creation approaches to ensure that interventions are context-specific and accepted by locals to promote stewardship and the sustainability of measures.

Respondents anticipated various co-benefits from their preferred NbS measures, including cooling homes and cities, aesthetic value, air purification, shade, food and improving human health. It is remarkable to see air pollution and nature and human health issues because they are not urban environmental challenges that receive great attention in planning in GKMA (Diko, 2022) but it appears that the residents prioritize these co-benefits. Meanwhile, the lesser citizens are engaged, the less trust they would have in decision-makers leading risk management and, therefore, likely to be less supportive of interventions (Gatersleben and Uzzell, 2000) while more engagement will increase trust (Zingraaff-Hamed et al., 2019) and cooperation (Pagano et al., 2019). Therefore, planning NbS to deliver the co-benefits desired by citizens could foster public support for NbS, making implemented NbS measures more sustainable.

Surprisingly, the selection of preferred NbS measures seldom aligned with the expected co-benefits. For instance, for creating lawns, co-benefits like green jobs creation and controlling erosion are expected but these co-benefits were selected by only 30% of respondents (Fig. 7b and c). Similarly, only 36% of respondents selecting tree-planting also selected their co-benefits. Hence, even though respondents had clear NbS preferences, the preferences not being strongly driven by the expected co-benefits hint of their little knowledge of NbS and their benefits. This was also the case for stakeholders in Portugal (Ferreira et al., 2021). They may also suggest that unlike in Europe (Anderson et al., 2022), SSA citizens give significant importance to NbS co-benefits. These findings show the importance of tailored and effective communication and education to help citizens align their NbS preferences to desired co-benefits.

We did not use specific NbS measures to assess the acceptability of NbS as we deemed it necessary to begin from the conceptual level before moving to the operational. Despite being systematically sound, this is a potential limitation because the specific NbS measure could make a significant difference if a respondent had to choose between them and other specific conventional engineering measures. Also, although the study was designed to compare results across different neighbourhoods, informal settlements were excluded. This may be a limitation because our conclusions may not be applicable to informal contexts. Lastly, variables in the Public Acceptance-NbS framework related to institutions were not tested since the study focus was on citizens. Future studies can, thus, assess these institutional factors and how they influence NbS acceptance.

### 5. Conclusions

This study provides valuable insights for policymakers, urban planners and environmental advocates in GKMA, Ghana, and other Global South cities. It emphasizes the importance of tailoring risk communication and education efforts to increase awareness and support for NbS, especially among those who have not yet directly experienced hydro-meteorological hazards. Additionally, the study highlights the unique preferences of different neighbourhoods, underscoring the importance of co-creating NbS solutions that align with local needs and priorities.

The first objective was to assess perceived exposure to hydro-meteorological hazards. We observed that most residents of GKMA have experienced flood hazards recently, with varying degrees of intensity while extreme heat hazards are also relatively recent and generally not very intensive. Awareness of future hazards is less pronounced, and many respondents are unsure about their level of exposure. Our study concludes that floods are no longer confined to specific neighbourhoods but is city-wide and citizens are more resilient against extreme heat hazards. But with how recent both hazards were reported, risks are increasing. As risk perceptions had been strongly influenced by past experiences, citizens with high-risk perception are more likely to commit to improving their resilience. However, even though extreme weather regimes are expected, many are unaware of their risks exposures. Studies on risk evaluation and improved communication on risks in practice are, therefore, recommended for timely response.

Our second objective was on the acceptability of NbS. We found that present risk mitigation efforts are dominated by conventional engineering solutions and citizens prefer the same for mitigating future risks. We modified the Public-Acceptance NbS framework by Anderson and Renaud (2021) to ascertain what factors determined the acceptance of NbS using the multinomial logit model. Under a quarter of respondents accepted NbS, likely due to low awareness of NbS; lack of evidence of the effectiveness and benefits of NbS, hence the need for quantitative studies, as well as a possible preference for measures that deliver instant results unlike NbS that may take time. Therefore, the results of the model may have minimal effect in the real-world sense. Notwithstanding, the findings can be leveraged for driving support for NbS.
Having found that those accepting NbS had high or uncertain risk perception and low level of formal education, the comparative advantage of NbS, like its use of simple technology, cost-effectiveness and multiple co-benefits, should be well communicated and demonstrated in NbS advocacy to remove any misconceptions about the concept and prove its validity for achieving climate and risk resilience.

Finally, we were interested in probing what specific NbS measures citizens would prefer for mitigating hydro-meteorological risks as part of co-creation. For flood risk, creating lawns, river restoration and wetlands restoration were the most preferred measures while for extreme heat risk, tree-planting, reforestation and green/open spaces were the most preferred. The findings support a conclusion that co-creation in NbS implementation is more critical than often regarded because it can help promote citizen support and stewardship to make implemented NbS more sustainable. This is because there were unique preferences across each of the studied neighbourhoods, which appeared plausible for their context-specific challenges. The overwhelming preference for lawns creation also reveals citizens' willingness to integrate NbS within their home enclaves and, thus, highlight the need for policy makers to advance policies to guide greening at the micro-scale. We also conclude that there is little knowledge of NbS and their benefits among citizens, evidenced by the disconnect between preferred NbS measures and the selected expected co-benefits. This necessitates tailored and effective communication and education to help citizens align their NbS preferences with the desired co-benefits. Also, the high interest in co-benefits like air purification and improvement in human health reveal critical issues that urban planners may be giving little attention to and planning NbS to deliver these co-benefits will increase public acceptance for NbS.

## Funding

This study has been funded by the Andrea von Braun Stiftung through project "Wetlands and Nature-based Solutions for Climatic Extremes Mitigation" (Grant No. 006/2021). The research of KBE and AZH was supported by the PHUSICOS H2020 project (Grant Agreement No. 776681).

## CRediT authorship contribution statement

**Kirk B. Enu:** Writing – original draft, Visualization, Validation, Methodology, Funding acquisition, Data curation, Conceptualization.
**Aude Zingraff-Hamed:** Writing – review & editing, Visualization, Supervision, Conceptualization.
**Yaw Agyeman Boafo:** Writing – review & editing, Data curation.
**Mohammad A. Rahman:** Writing – review & editing, Validation, Supervision.
**Stephan Pauleit:** Writing – review & editing, Supervision, Funding acquisition, Conceptualization.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Data availability

Data will be made available on request.

## Acknowledgements

We would like to express profound appreciation to Joshua Nortey and Silas Boadu of the Kumasi Metropolitan Assembly, Ghana, for their support in mapping the study area and planning of the data collection for this study. We appreciate research assistants, Agyemang Kwasi Oppong, Andrews Asamoah, Emmanuel Ofori Poku, Isaac Sarfo Junior, Nicholas Opare Obuba and Rickmel Boamah-Bempah as well for helping to administer the questionnaire. Immense appreciation also goes to Dr.

Stephan Haug (Technical University of Munich, Chair of Mathematical Statistics) for his useful insights during the analyses. The pictures used to illustrate different NbS measures in the questionnaire were obtained from pixabay.com and freepik.com.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jenvman.2024.120089.

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