Assessing the Determinants of Adopting Urban Tree Planting as Climate Change Mitigation Strategy in Enugu Metropolis, Nigeria

Abstract

This study sought to explore the determinants of adopting urban tree planting as a method of reducing climate change in the metropolis of Enugu. The 823 respondents were chosen using a multistage random selection process. Logistic regression and descriptive statistics were employed in analysing the data. The study results indicated that the majority (53%) of the respondents were male, with an average age of 36 years. The majority of the households were aware of climate change, since they perceived extreme weather events like drought, a decrease in rainfall, and a rise in temperature. Moreover, the majority of the households experienced a decline in agricultural productivity, a reduction in farm returns, and a rise in unemployment during peak seasons. The mitigation strategy adopted by households for climate change is urban tree planting, and this contributes positively to livelihood improvement. Furthermore, the study results showed that the price of the tree, access to information on the changing climate, access to water, use and access of trees, and occupation positively influenced households’ decisions in adopting urban tree planting. Therefore, we recommend that stakeholders such as governments must promote the delivery of agricultural extension and advisory services by improving their climate information systems, among other strategies to boost their all-inclusive adaptation to the effects of climate change.

1. Introduction

Climate change is among the profound socio-environmental menaces confronting the food and agriculture sector [1]. In order words, it has had an important effect on food production and agriculture worldwide. The greatest environmental threat facing the globe now is climate change, particularly in Africa [2,3]. Climate change is expected to result in volatile weather patterns, unstable growing seasons for agriculture and damaged ecosystems. With severe consequences on natural resources and humans, there is no doubt that the climate is changing [4,5]. Truly, as the Earth experiences heat, rainfall profiles drift and lead to extreme occurrences such as flooding (excessive rainfall) and aridity (droughts), coupled with the burning of forests, which recurs continually, thereby resulting in necessitous and unforeseeable harvests [1]. Evidence of these changes includes temperature changes, erosion, flooding, changes in rainfall patterns, water scarcity, desertification, the outbreak of diseases, land degradation, rising sea levels, poor crop yields, hunger, starvation, decline in economic activities, and pasture losses, among others [6,7,8,9,10]. According to Newsham et al. [11], climate change has serious consequences for the environment and human health, thereby imposing distressing impacts worldwide. The noticeable consequences of climate change in developing countries, especially Nigeria, have resulted in weak and uncertain yields, leaving farmers increasingly anxious and more vulnerable, as they have a low adaptive capacity and rely on agriculture for living [6,12,13,14,15]. As a result, climate change has an unswerving effect on production because of the agricultural systems, which are climate-dependent in nature. Consequently, to address this climate change phenomenon, the adoption of urban tree planting is one of the effective approaches that are useful for reducing climate change. According to Ogunkalu et al. [16], planting trees is an authentic approach for reducing the effects of climate variability.

Urban trees can be defined as all public and privately-owned trees referred to as individual trees along streets, backyards, and stands of residue trees in urban settings and rural areas. These trees are among the planet’s longest-living things and serve as link between the present, future, and past generations. According to Gayo [17] and Pataki et al. [18], urban trees are essential for protecting the environment because they provide ecosystem services like clean air, stormwater management, habitat creation and atmospheric cooling. The planting of urban trees is not only for protecting the environment, but also very important in providing health benefits and perceived living infrastructure that offers multiple environmental benefits for residents [19]. The planting of urban trees is among the best adaptation and mitigation approaches implemented in many developing nations to restrain the undesirable effects of the changing climate [20]. These reimbursements are called ecosystem services, because they are the means through which urban trees effectively regulate or combat the activities of climate change. The practice of urban tree planting is an exhaustive approach to land-use management, and it is gainful for agricultural production.

Regarding its economic value, urban tree planting is essentially beneficial to the environment and helps in providing abundant tangible and intangible ecosystem services such as nutrient cycling, sink functions of wetlands, and the hydrological cycle [21]; this, in turn, enhances the maintenance of the environmental quality, ecosystem regulation, and livelihood for both rural and urban dwellers, often at no cost [22,23]. According to Burnham [24], urban tree planting reinforces a range of ecosystem functions and is essential to ecosystems’ ability to adapt. Urban trees dispel climate change through the removal of greenhouse gases (GHGs), changing the urban microclimates through reducing temperatures, altering the patterns of wind, and reducing emissions from energy generation [25,26,27]. Urban tree planting assists in regulating temperatures, reducing energy consumption, improving urban air quality, and reducing wind speeds. Additionally, urban trees help to increase property values, facilitate recovery, and reduce fatigue [26,27]. According to Taylor [28], urban tree planting is the best mitigation approach, given it is more cost-effective than other mitigation approaches as it provides aboveground carbon storage, and cities are advised to adopt it given that cities are recognized as the greatest emitters of CO₂. Despite its contribution, there is a growing experimental understanding of the limitations of urban tree planting as a climate change mitigation strategy.

As a matter of fact, there are a challenge associated with urban tree planting, which underscores it as the perpetual depletion of urban trees in communal landscapes, resulting in a loss of urban trees due to extreme harvests [29]. From their study findings, Gayo [17] and Scheid et al. [30] specifies that majority of the respondents in the region submitted that drought was prolonged as a result of climate change thereby leading to water scarcity and this which limit water intake by urban trees. This further increased the risk of desertification, as well as threatens the planting initiative of urban trees. Few studies conducted on urban tree planting as an approach to further mitigate climate change and to evaluate urban tree planting practices to provide local persons with the capacity and understanding to anticipate productivity. Few studies conducted have focused on farmers’ access to trees and their willingness to participate in tree planting initiatives. Thondhlana and Ruwanza [31] quantified that there is scanty information existing on the challenges hindering the factors influencing adequate adoption of urban tree planting practices among urban and rural dwellers. Unplanned urbanization and deforestation, among many challenges, have hindered urban tree planting, giving little recognition [32,33]. The lack of knowledge about urban trees is also contributing to the limited plantation of urban trees as residents utilize fast-growing, multipurpose tree species, a practice that has been related to failed tree planting attempts and failed attempts to reduce climate change. Hence, this study further focused on the determinants of urban tree planting as a strategy to mitigate climate change.

Thus, there is an urgent need to explore a more sustainable deliberate tree planting approach in the study area with appropriate mechanisms for improving environmental health. It is therefore pertinent to note that no study has been carried out on urban tree planting as a climate change mitigation strategy by households. Hence, the study focused on household-level determinants of urban tree planting as a climate change mitigation strategy in Enugu Metropolis. This study therefore sought to investigate the factors that influence households’ decision in adopting urban tree planting as a climate change mitigation strategy in the region as that would enhance households’ and farmers’ capacity for its adaptation to mitigate climate change issues to necessitate formulation of policies.

2. Literature Review

Theoretical Framework

The protection motivation theory (PMT) is a framework, which comprises threat and coping assessments as multidimensional determinants of motivation. The study is built around the PMT with the main aim of assisting households and farmers in responding to climate change threats on food security and agricultural productivity, as well as motivating households to respond to threats by adapting to climate change issues. This theory was developed by Rodgers in 1975 with the main aim of providing some assistance for households and individuals in response to climate change crises, thereby motivating them to marshal-out mitigation strategies. The PMT theory provides insight into the variables affecting a household’s incentive to respond to perceived risks from climate change. The objective of PMT acknowledges and evaluates the threat posed by climate change, and then respond to the evaluation with effective and efficient mitigation. The idea underlying PMT’s assertion is that families are more likely to respond and adapt to risk-reducing situations in order to protect themselves from crisis situations such as climate change [34]. According to the threat and coping appraisal theories of protection motivation, households defend themselves against risk-mitigating issues. To a large extent, the agriculture sector is threatened by climate change, which must be addressed through adaptative measures. PMT was found to be a suitable model to guide studies on climate change.

The first stage of PMT is threat appraisal, which measures the severity of the state and inspects how serious the situation is. Keshavarz and Karami [35] stated that households perceive climate change threats as a global phenomenon that adversely affects households’ livelihoods. The threat appraisal encompasses a cognitive process in which households and farmers frequently contemplate and individually evaluate the extent of climate change, among other imminent threats. This appraisal is concerned with understanding and addressing the perceived effects of climate change whether they are adversely affecting farmers and farming households. This evaluation includes the perceived seriousness of a potentially dangerous event and the likelihood that it will occur, as well as the perceived susceptibility to the perceived threat of climate change in this study. The PMT model’s threat appraisal emphasizes the danger’s basis, as well as the elements that affect the likelihoods that adaptive behaviours will occur.

Severity refers to the degree of damage from the unnatural behaviour. The threat appraisal evaluates a household’s perception of climate change and variability. This appraisal is measured by a household’s belief that climate change is a threat to agricultural productivity, farm returns, and food security, among other things. To assess a household’s threat levels, the study assessed perceived vulnerability, subtracting the response costs and risk aversion. Janmaimool [36] specified that these insights into vulnerability and gravity push households to adapt climate change response measures, adaptation and mitigation strategies in this study.

Coping appraisal is the second element of PMT. After taking preventive actions, coping appraisal can examine the risks and potential rewards. According to Bagagnan et al. [37], the coping evaluation process assesses one’s capacity to avert the threats and hazards of climate change and involves reaction costs, self-efficacy, and efficacy of responses. The application of the advised behaviour in avoiding potential impairment is known as response effectiveness. The confidence that one can effectively adopt the proposed behaviour is known as self-efficacy. Costs connected to advised behaviour are the reaction costs, while the sum of reaction effectiveness and self-efficacy, less response costs are said to represent one’s level of coping capacity. In order words, the coping appraisal technique focuses on how households have responded adaptively to an individual’s capacity to deal with, as well as avoid climate change threats. Findings from certain study results have indicated that households are capable of adapting to and mitigating climate change issues. Self-efficacy and response efficacy are the two subcomponents of the coping appraisal strategy. Self-efficacy can be described as a household’s evaluation of its capability to assume risk preventive behaviours. This study represents a person’s judgment in using adaptation and mitigation techniques. The perceived capability of engaging in constructive conduct is not a sufficient indicator of the perceived efficacy of one’s own plan, which is also important. The degree to which households view the suggested response measures as effective is known as response efficacy, which reveals when the farmer or household believes that danger may be completely eliminated by protective activity [37,38].

Response cost, which is a purely arbitrary estimate of how much it costs a household to use the advised response behaviours, serves as a proxy for PMT. According to Ihemezie et al. [39], farmers are deterred from implementing adaptation and mitigation techniques because of the perceived high cost of such actions. These two assessments interact and result in a guard motivation choice that is prejudiced by barriers (i.e., ideas about and attitudes regarding the feasibility and effects of a behavioural option) impeding the choice of a particular alternative. To apply agricultural preservation strategies, the mutable of fear was examined in this study as an intermediate of the relationship between perceived severity and susceptibility. The current study will determine whether farmers’ protection motivation influences how they adapt to climate-risk management strategies in the Enugu Metropolis (see Figure 1).

3. Materials and Methods

3.1. Description of the Study

This study was carried out in Enugu, the capital city of the Nigerian State of Enugu. The state is geographically located between latitudes 6°21′ N and 6°33′ N, as well as 7°25′ E and 7°38′ E longitudes on the north-west fringe of south-east Nigeria and bordered by the following states which are Abia, Benue, Anambra, Kogi and Ebonyi. Out of the 17 local government areas (LGAs) that made up the state, the metropolitan city is comprised of 3 LGAs, which are Enugu East, Enugu North and Enugu South and comprises of about 722,664 households [40]. The State’s Forestry Commission under the Ministry of Environment and Solid Minerals is tasked with the duty to manage forestry reserves and tree planting areas. In this regard, this commission has not lived up to this expectation especially in the metropolis, thereby allowing real estate developers and other industrialists the opportunity to determine the topography of the city by uncontrollably felling almost all the trees in a bid to develop the residential or commercial buildings [41]. This act has further exposed the city and its inhabitants to millions of carbons constantly released into the environment with no remedial plans, programmes or policy. Based on the foregoing and the role trees play in carbon sequestration, this study, which aims at exploring the determining factors of the urban households’ tree planting adoption as a climate change mitigation strategy in the metropolis, is eminently important and evidence-based.

3.2. Sampling Procedure, Frame, and Sample Size

Using a cross-sectional methodology, the study assessed the household features, adoption, and uses of urban tree planting, determinants, and challenges of tree planting in the Enugu state, specifically the Enugu Metropolis. The design was used to collect primary data from farming households that were preferentially selected out of 10 rural communities due to their quite high number of urban planted trees.

The population for this study was sampled using a multi-stage sampling technique. This strategy makes sense and provides an accurate depiction of the intended population. The first stage of multi-stage sampling involved selecting a study site, which is Enugu State. The second stage involves selecting a metropolis made up of the Enugu East, Enugu North, and Enugu South local government areas (LGAs) due to many households’ adapting to climate change, and they are using urban tree planting as a mitigation approach to climate change. Afterward, ten communities were randomly picked from each of the LGAs and six streets were randomly picked from each of the ten communities. The third and last stage involved selecting fifteen households in each of the six selected streets of the ten selected communities that practice urban tree planting on their farms. These households were selected randomly in these 10 communities as they provide different features and benefits of urban tree planting. The sample size for this study was 900 households involved in urban tree planting. However, 823 questionnaires were used for the analysis having discarded 77 for inconsistency. The unit of analysis was farming households.

3.3. Data Collection

In the study, both primary and secondary data were used. Primary data were collected using copies of structured questionnaires to obtain data on households’ characteristics, adoption, and uses of urban tree planting, determinants, and challenges of tree planting. The questionnaire was pre-tested before the actual time of data collection to check its appropriateness and reliability. The researchers made use of well-experienced scholars and extension officers to validate the questionnaire used to gather information. The pre-testing of the questionnaire was also used to train the enumerators who were used to administer the questionnaires. The secondary data were used to write literature and to support the findings of the study. The secondary data used were obtained from peer-reviewed literature, government ghazals, books, and the Department of Agriculture’s records. The data were collected from May to July 2021.

Figure 2, Figure 3, Figure 4, Figure 5, Figure 6 and Figure 7 demonstrate peri-urban, urban area and rural areas of Enugu State during data collection by enumerators and researchers. The urban tree plantation in these areas was implemented by the State’s Forestry Commission under the Ministry of Environment and Solid Minerals, which is tasked with the duty to manage forest reserves and tree planting. This was also on the basis of mitigating climate change impact as urban tree planting is providing various benefits. All of three areas have implemented urban tree planting for various reasons depending on the household.

3.4. Data Analysis

Data were coded in MS Excel and transported into SPSS version 23 and STATA 15 for analysis. The study further made use of both descriptive and inferential statistical tools in analysing data. Descriptive statistics were used to estimate households’ characteristics, their uses, the benefits of urban tree planting, as well as the challenges faced by the households. An appropriate econometric model in the form of the Logit model was employed to estimate the factors influencing the adoption of urban tree planting as a mitigated approach to climate change.

3.5. Inferential Analysis and Descriptive Statistics

Using descriptive statistics and t-statistics, it was possible to describe the study variables employed in the logistic regression model to show household problems and understanding of urban tree planting. In order to present the findings of this investigation, frequency tables, cross-tabulations, and graphs were used. In order to analyse the continuous and categorical variables, respectively, descriptive statistics like mean and standard deviation, as well as percentages, were used.

3.6. Analytical Framework

To estimate factors influencing the adoption of urban tree planting as a mitigated approach to climate change in the study area, the Logit regression model was used. The regression model is the best-fit model to estimate the factors when the decision is binary [42,43]. Logit regression is a dichotomous analysis frequently used in adoption studies as they are dichotomous. In this study, the logit model was used to analyse households’ decisions in either adopting or not adopting urban tree planting methods as a mitigation strategy for climate change. Logit regression is a statistical method used to predict the relationship between the response or dependent variable in adopting urban tree planting, which has two or more categories with one or more explanatory variables on a category or interval scale.

Comparatively, to discriminant examination, the Logit regression model is more applicable to a larger range of exploration conditions. Due to its superior comparative mathematical simplicity, this model was adopted over the Probit model [43,44]. The ability of this model to more effectively address the primary research objectives as well as the characteristics of the data and sample (association between variables, slope indicating how the log odds ratio is in favour of adoption urban tree planting) led to its selection. Furthermore, the significant explanatory variables do not influence household adoption decisions to the same extent. According to Atube et al. [45], the logit regression model enables binary choice analyses, such as whether or not a household modifies its behaviour to combat the consequences of climate change. This allows the estimation of choice probabilities for the various categories.

Using logit regression, which also satisfies the general normal probability distribution, the heteroscedasticity problem is resolved. The logistic model was consequently used for this inquiry. Let i stand for the likelihood of success. Think of the collection of explanatory variables x = (x₁, x₂,…, x_n) as well. These variables could be continuous, discrete, or a combination of the two. In that case, Equation (1) provides the logistic function for π_i:

$$Logit{\pi }_i=log\left(\frac{{\pi }_i}{1-{\pi }_i}\right)={\beta }_0+{\beta }_1{X}_1+{\beta }_2{X}_2+\dots \dots +{\beta }_n{X}_{i,n}$$

(1)

where:

$${\pi }_i=\frac{exp({\beta }_0+{\beta }_1{X}_1+{\beta }_2{X}_2+\dots \dots +{\beta }_n{X}_{i,n})}{1+exp({\beta }_0+{\beta }_1{X}_1+{\beta }_2{X}_2+\dots \dots +{\beta }_n{X}_{i,n})}=\frac{exp\left(x_i^{\prime }\beta \right)}{1+exp\left(x_i^{\prime }\beta \right)}=\wedge \left(x_i^{\prime }\beta \right)$$

(2)

where:

It is customary to refer to “success probability” as “π_i”, where π_i is the possibility that a sample would falls into a particular dichotomous response variable category, and 0 > π_i > 1. The logistic cdf is represented as Λ(.), where λ(z) = e z/(1 + e − z) = 1/(1 + e − z) and β signifies a vector of variables that need to be evaluated. The odds ratio is the formula $\left(\frac{{\pi }_i}{1-{\pi }_i}\right)$.

3.7. Estimation and Likelihood Ratio Test

Although researchers can utilize the least-squares method, the maximum likelihood method is recommended to evaluate β due to its preferred improved statistical features. The logistic function of the single predictor variable X in the following logistic model should be expressed as follows:

$$\pi \left(X\right)=\frac{exp\left(x_i^{\prime }\beta \right)}{1+exp\left(x_i^{\prime }\beta \right)}$$

(3)

The goal of the study is to identify the estimations when βˆ plugged onto the model for π(X), and will yield a number near to 1 if urban tree planting is adopted and 0 otherwise. The likelihood function is denoted mathematically by Equation (4):

$$L({\beta }_0,{\beta }_1)={\prod }_{i:Yi=1}\pi \left(xi\right){\prod }_{i\prime :Yi\prime =1}(1-\pi (xi0\left)\right)$$

(4)

The estimated values of βˆ are chosen to optimize this probabilistic function. The study uses the logarithm on both sides to compute and use the log-likelihood function for an estimate. In the investigation, the probability ratio was used to check whether any subset of estimations β is zero. Suppose that p and r denote the corresponding numbers of β for the entire model and the simplified model. The likelihood ratio test statistic is shown below:

$$\wedge \mathrm{\ast }=2\left[l\right({\beta }^{\left(0\right)})-l(\beta \left)\right]$$

(5)

where:

l(βˆ) and l(βˆ(0)) are the log-probabilities of the entire and simplified models, respectively, as determined at the maximum likelihood estimation (MLE) of that simplification. Λ*~χ² n − r; n and r are the number of variables in the entire and simplified models, respectively.

4. Result and Discussion

4.1. Demographic Features of Farmers

The outcomes of the demographic features of the household heads in

Table 1. The households’ demographic features.

Variables	Description	Mean
Gender	1 for male	0.53
Age	Number of years	36.67
Education	dummy; 1 for eight years and above education	0.84
Marital status	1 for married	0.69
Residence owner	1 for owner	0.31
Tenant	1 for yes	0.69
Occupation	1 for civil servant	0.28
Access to tree	1 for yes	0.18
Cost of trees	1 for N500 and above	0.84
Access to climate change information	1 for yes	0.43
The adoption rate of tree planting	1 for yes	0.58

indicate that the majority (53%) of the household heads interviewed were males within the average age of 36 years. The study outcomes agree with the findings of [15,17,45], that farming households are dominated by men because of the nature of farming operations, which can be physically demanding, and cultural beliefs in Africa and that can permit men to be engaged in farming, while the women provide some care, handle chores and management of their households. The average age of the respondents stipulates that farming in the study area is practiced by young people who are energized and ready to participate in both environment and developmental preservation. These results concur with the study findings of [17,31], that tree planting is practiced by young farming households who are vested with much knowledge about it compared to elderly farmers. The majority (84%) of the farmers attained an educational level of eight years, which is equivalent to secondary years. This explains why the household heads that spent eight years in school have improved abilities to read, as well as their knowledge on agricultural activities, which enhanced their awareness and ability to engage in diverse kinds of farm practice such as tree planting to mitigate climate change. These results align with that of [46,47], that being literate as a farming household is beneficial for adopting innovative farming techniques utilized in improving agricultural productivity, since the households sampled can understand, and read the stated instructions. The study results showed that the majority (69%) of the farming households were married and this played a huge role in decision-making, as married farmers thought about the well-being of their households, as well as assisted in the provision of family labour. These results were in line with the findings of [44]. Household size was employed as a proxy for measuring household labour with the study results indicating that the mean household size was five persons per household, which played an important role in tree planting.

Interestingly, the farming households were working in groups, and this played an important role in enhancing their climate change awareness and adaptation measures available. The study results indicated that the majority (58%) of farming households attained the land tenure status, implying that they were land custodians. The study outcomes further indicated that about 43% of farming households had limited access to extension services, which was a significant factor in their lack of access to information on climate change and effective mitigation measures. The mean farm size of farmers was two hectares and this implies that the households were practicing farming at a small-scale level. These results agree with the findings of [42] that the majority of smallholder farmers usually have a small farm size. Access to trees was poor (18%) due to its high cost of NGN 500 (0.63 USD) and above (84%) or the high tenancy rate among the farmers. The results found access to climate change information (43%) was very poor and this contributed to the lack of adoption measures in the study. The adoption rate of tree planting as a strategy was found to be 58%, which is low due to the limited availability in their knowledge about climate change, as well as the lack of visibility of extension personnel in the research area.

4.2. Knowledge of the Changing Climate and Its Impact

The results on farming households’ knowledge of the changing climate as perceived by the variations in rainfall and temperatures are represented in

Table 2. Knowledge of climate change.

Variable	Percentage (%)
Increase Temperatures (Yes)	0.58
Decrease in rainfall (Yes)	0.60
Increase in extreme weather (Drought)	0.72
Impact	Percentage (%)
Decrease in production output	0.66
Reduce farm income	0.34

. The table shows the changes farming households have perceived about climate change. Findings from the study further show that farming households have experienced an increase in temperature, with households experiencing hotter days than they normally perceived in the past years. Farming households have perceived a reduction in rainfall patterns, which has resulted to changes in planting dates. Lastly, farmers have perceived an increase in extreme weather events (drought) and this has negatively impacted tree planting. In their study findings, refs. [6,48] made similar observations that these three are the variations from climate change noticed by the farmers.

Farming households have experienced the negative effects of changing climate. Households have also experienced a decrease in production output due to the changing climate. This is due to a reduction in rainfall patterns, which is important for irrigation. These results agree with the findings of Agbugba et al. [1] and Mdoda [2]. The decrease in productivity output will eventually lead to a reduction in farm income.

4.3. Uses and Perceived Benefits of Urban Tree Planting by Farming Households

As shown in

Table 3. Respondents’ perception of planted trees uses.

Variable	Percentage (%)
Timber	0.14
Fuelwood	0.10
Fruit tree	0.12
Fodder	0.20
Windbreaker	0.12
Fencing	0.08
Soil improvement	0.24

, the majority of the farming households indicated that they were aware of the changing climate factors and their effects on agricultural productivity as it reduces their output and farm returns. Based on the adverse effects, households decided to adapt to variations on climate change by planting urban trees as the mitigation approach. Adoption of urban tree plantation in the study area was well received as socio-economic results pointed out that majority (58%) of the farming households adopted tree planting to mitigate the effects of climate change. Results also indicated that farming households planted various trees due to various benefits, whereas urban trees were planted to prevent the effect of high winds from destroying vegetables and other crops, to protect fodder for livestock keepers, and for building strong shelters for livestock during severe rainfalls, among other reasons. The most understood reason for urban tree plantation was to improve the soil (24%), so that agricultural productivity can be enhanced with possible farm returns. This was also carried out to protect the soil from being eroded during extreme weather conditions as it becomes the permanent soil cover. Findings showed that timber (14%), fodder (20%) and windbreakers were dominant factors in the study area as they provided farmers with counterpart effects of blowing winds, cooling high temperatures and also provided some shelter for livestock. These results were in line with [17] that tree planting is very important in agriculture, as it yields better benefits for crops, vegetables and livestock keepers. Fruit trees (12%) were planted as they improved some vitamin A status of livestock through grasses and forages besides lowering sensitivity to climate change risks in the area. The results support the findings of Msalilwa et al. [49], which indicates that this approach is good for livestock keepers; and as a result, their vulnerability to climate change decreases with an increase in output, which will enhance household food security. Moreover, the last reason for tree planting explains why fuel wood and fencing by households since they are widely used under minimal circumstances for income generation purposes.

4.4. Determining Factors of Households’ Urban Tree Planting Adoption Decision

The study employed logistic regression to evaluate the factors that affect farming households’ decision to adopt tree planting in the Enugu metropolis. Findings as indicated in

Table 4. Factors affecting households’ decision to adopt urban tree planting.

Variable	β	P > z	Marginal Effect
Education	1.926	0.001 ***	0.023
Price of tree seedling	1.189	0.008 ***	0.042
Access to climate change information	0.441	0.032 **	0.030
Age	−0.432	0.018 **	−0.034
Access to water availability	0.512	0.015 **	0.019
Use and access of tree	0.783	0.002 ***	0.056
Occupation (informal employed)	1.234	0.004 ***	0.042
Number of Observation = 823	LR Chi2 (10) = 323.53	Pseudo R2 = 0.723	Prob > Chi2 = 0.001

Note: ** and *** are significant levels at 1% and 5%, respectively.

showed that sixteen parameters were used in the model, while only eight parameters were discovered to be significantly related to the decision to implement urban tree planting by farming households at different probability levels. Table 4 displays specifics of relevant factors from this model. The corrected R² of the regression was 0.723, which indicates that the regressors included in the model can account for 72% of the dependent variable’s variation. The more explanatory and better the model matches the sample, the higher the R² value. The influences on household adoption of urban tree planting are shown in Table 4.

Education had a coefficient that was positive and was found to be statistically significant at a 1% level of confidence, indicating that an extra year of education for households will result in a rise in the adoption of urban tree planting. This, therefore, implies that an increase in education will enhance households’ knowledge about innovative techniques that are employed in agriculture to mitigate climate change. This is because educated households have more environmental consciousness and a higher knowledge level attained through the formal education system. These results agree with the findings of Lockwood and Berland [48], which indicated that educated households in Central Indianapolis are vested with more knowledge about urban tree planting as a climate change mitigation strategy as it enhanced their productivity. The marginal effect implies that an extra year of education for the farmers will result in a 2.3% rise in the number of households adopting urban tree planting as an approach to mitigate climate change.

It is worth noting that the price of tree seedlings had a positive coefficient, which was found to be significant at the 1% level. These outcomes imply that a 1% rise in the price of tree seedlings will result in a rise in urban tree planting adoption by the households. Hence, the price of tree seedlings determines the quality of the urban trees that households purchase. A low-priced urban tree is associated with poor quality, which means the households will not enjoy their benefits unlike when the quality is good. Gayo [17] made a similar observation that the cost of urban tree seedlings determines the sustainability of the urban tree, as well as the quality of the tree and that will enable households to enjoy productivity in their agricultural practices. The marginal effect implies that there is a 1% increase in the price of tree seedlings, which will lead to a 4.2% increase in households’ chances in adopting urban tree planting as an approach to mitigate climate change.

Regarding access to climate change information, it indicated a positive coefficient was found to be significant at 5% confidence level. This implies that a 1% rise in access to climate change information by households will result in households’ urban tree adoption as an approach to reduce climate variability. This is because households are equipped with the necessary knowledge about climate change, which enhances their decision to adapt to it by adopting sustainable strategies that enhance agricultural productivity and farm returns. In order words, access to climate change awareness has improved the adaptation rate and improved the level of understanding of the approaches to be used by households. The marginal effect infers that a 1% rise in climate change information access by the households will lead to the rise in their urban tree planting adoption, which is an antidote approach to reduce climate variability by 3.0%.

More so, the use and access to trees had a positive coefficient and was found significant at a 1% confidence level, which infers that a 1% increase in understanding the use and access to trees by the household will induce an increase in adopting urban tree planting as a strategy to reduce climate variability. This is because households understand urban tree benefits from other farmers and have access to such information, which makes it easier for them to adopt urban tree planting as a formidable strategy. Understanding the benefits of urban tree planting will make it easier for households to adopt them as they are well informed of the benefits. The marginal effect implies that a 1% increase in the use and access of trees will increase households’ chances in adopting urban tree planting as an approach to mitigate climate change by 5.6%. In his study on tree planting, Gayo [17] made a similar observation.

At a 1% level, occupation had a significant and positive coefficient. This is positive because employed persons have no time to seek knowledge about the tree benefits as compared to self-employed and informal employed. This suggests that a 1% unit increase in informal and self-employed, will induce an increase in adopting urban tree planting as a mitigation strategy for climate change. This is the case as employed persons spent less time on tree planting than self-employed persons and do not seek the benefit of planting trees. Flexible time for self-employed households allows them to have time to understand tree planting as well as to know the requirements for planting trees to enable them to reap the highest benefits. The marginal effect implies that a 1% increase in occupation will increase the chances of households adopting urban tree planting as an approach to mitigate climate change by 4.2%.

On the other hand, respondents’ age is particularly important as it is used as proxy for measuring farm experience. Age had a negative coefficient that was found to be significant at a 5% confidence level. This infers that as households increase annually, it will result in a decrease in the adoption of urban tree planting as a strategy for reducing the effects of the changing climate. This is the case with experienced farmers who are vested with knowledge on the strategy on what suits their farming profession since they have been farming for years and only care about providing food for their households. The study had many inexperienced farming households, which affects the adoption of urban tree planting as they do not know what approach to use in their farm. The marginal effect implies that a 1% rise in the respondents’ age will result in a reduction in households adopting urban tree planting as an approach to mitigate climate change by 3.4%.

Furthermore, at a 5% level of significance, the positive coefficient and statistical significance of access to sufficient water were both present. This suggests that increasing family access to appropriate water will lead to a rise in the usage of urban tree planting as a climate change reduction strategy. This is because water is essential for irrigation purposes as it assists in the growth stages of trees. Therefore, having access to adequate water enhances the growth stages of trees. Hence, the marginal effect implies that a 1% increase in the access to adequate water will result in households adopting urban tree planting as a viable option in mitigating climate change by 1.9%.

4.5. Study Implication to the Broader Overview of Urban Tree Planting

The study presents the current status of urban tree planting and its importance in reducing climate variability impact. The study further revealed that climate variability poses a major threat that poses a negative impact on the livelihood of the peri-urban and urban populations of developing countries, especially Nigeria. Climate change has severe consequences on natural resources as the earth experiences heat, rainfall profiles drift, and utmost occurrences such as aridity (droughts) and floods (excessive rainfall) coupled with the burning of forests, which negatively impacted economic activities in urban and peri-urban areas such as a decline in agricultural practices and land degradation. This decline in economic activities resulted in the loss of income, productivity and employment for many persons who depend solely on agriculture-related careers for living.

Adaptivity to climate change is the only feasible approach in mitigating climate change in developing countries. This approach will provide farmers and farming households with a resilient approach that will enhance productivity, which plays an important role in reducing food insecurity and poverty, which is extremely high in developing countries. Tree planting is one approach that is easy to implement and affordable to any household and farmer. Urban trees seem to be a more visible choice for adapting to pollution and climate change than mitigation measures. Tree planting is not only used as a mitigation strategy, but also for the reproductive capacity of farms and their usage as a source of food. Urban tree planting provides countless ecosystem services and social benefits to urban inhabitants.

5. Conclusions

This study investigated the determinants of urban tree planting adoption as a climate variability reduction strategy in the Enugu metropolis after experiencing the adverse consequences of climate variability that reduced agricultural productivity and farm income, which adversely affected their household livelihoods. Urban tree planting has brought high benefits to households such as soil improvement, fruit trees, fodder, and windbreakers. The econometric regression has shown that the adoption of urban tree planting was positively influenced by education, price of tree seedling, climate change information access, access to water availability, use and/or access of trees and occupation (informal employed), while being negatively influenced by age of the household. Based on the study results, it therefore recommends that the government should prioritize policies on urban tree planting as a climate change mitigation strategy to inform the public of the benefits of planting trees, as well as their roles in reducing greenhouse gas emissions. The large percentage of young persons who were observed planting trees and their high literacy rate recommends the likelihood of future tree planting practice expansion in the metropolis. There is therefore a need for an urgent policy framework for compulsory house-to-house and street tree planting in the region to curb the menace of climate change. This study recommends that the government and non-governmental organisations (NGOs) invest in agricultural extension and advisory services in training and educating farmers and staff, among other persons about urban tree planting benefits to attract more households, especially young persons.