Perception Towards Forestation As a Strategy to Mitigate Climate Change in Somalia

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Perception towards forestation as a strategy to mitigate climate change in Somalia

Osman M. Jama 1,2, Abdishakur W. Diriye 1,2, Abdulhakim M. Abdi 3

1 School of Public Affairs, University of Science and Technology of China, Hefei, People’s Republic of China-

2 Department of Public Administration, Faculty of Economics and Management Science, Mogadishu University, Mogadishu, Somalia.

3 Centre for Environmental and Climate Research, Lund University, Lund, Sweden.

Abstract

Climate change mitigation strategies need both source reductions in greenhouse gas emissions and a

significant enhancement of the land sink of carbon dioxide (CO2) through photosynthesis. In recent years, there has been growing attention to forests as an effective way to mitigate climate change as they capture and store large quantities of CO2 as phytomass and in the soil. Numerous forestation programs have been rolled out internationally and governments, mostly in the developing world, have been fostering tree planting initiatives. However, there is a paucity of studies that assess local perceptions to these initiatives and there are virtually no studies that do so in countries recovering from long-term conflict such as Somalia. Here, we analyze the factors that motivate or hinder the perception of young people in Somalia about forestation as a means to mitigate climate change. This demographic was targeted because 75% of the population of Somalia is between the ages of 18 and 35. Our results show that biocentric value orientation, which emphasizes environmental preservation and ecosystem maintenance, has significant positive influence on attitudes towards forestation whereas anthropocentric value orientation, which treats forests as an instrumental value to humans, did not show a significant influence. A surprising discovery was that biocentric value orientation also had a direct positive influence on young people’s intentions to adopt forestation as a strategy to mitigate climate. We also found that risk perception has a significant positive influence on attitude towards forestation as it is a critical driver of collective action against human-induced environmental problems. We conclude the paper with insights and recommendations for policymakers.

Keywords: afforestation, forestation, reforestation, post-conflict, Somalia, Somaliland, Puntland, climate change, mitigation

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1. Introduction

There is clear evidence that climate change is caused by anthropogenic activities and threatens global economic, social and environmental sustainability (IPCC 2014, Pachauri et al. 2014, Smith et al. 2014). Climate change represents one of the 21st century’s biggest environmental challenges and to effectively reduce atmospheric carbon dioxide (CO2) levels, mitigation strategies would need both source

reductions in greenhouse gas (GHG) emissions and a significant enhancement of the land sink of CO2 through photosynthesis (IPCC 2007, Pachauri et al. 2014). In recent years, there has been growing attention

to forests as an effective way to mitigate climate change as they capture and store large quantities of CO2 as phytomass and in the soil (Malmsheimer et al. 2008, Bastin et al. 2019). The Intergovernmental Panel on Climate Change (IPCC) reported that the forest sector has a mitigation potential of 0.2–13.8 gigatonnes of equivalent carbon dioxide per year (GtCO2e/year) by 2030 with a cost up of to US$100/tCO2e (Smith et al. 2014).

Numerous studies have examined the potential of forests for climate change mitigation and adaptation at both global and regional scales (Kurz and Apps 1995, Bourque et al. 2007, Lippke et al. 2011, Xu et al. 2018). For instance, a recent study (Bastin et al. 2019) presented global tree restoration as one of the most effective strategies for mitigating climate change by mapping global potential tree density and showing that 4.4 billion hectares of land can be forested. This could result in the worldwide restoration of forests and an increase in the forest area of about 25% without disrupting existing forests, urban or agricultural areas. Bastin et al. (2019) Argued under the current climate conditions, this could store a total of 205 Gt of carbon compared to existing emissions that of 10 Gt of carbon annual from fossil fuels and cement manufacturing. However, blanket forestation in areas that were not previously forested such as grasslands, savannas and open-canopy woodlands, is detrimental to those ecosystems (Wang et al. 2011, Veldman et al. 2015, Bond et al. 2019, Grainger et al. 2019). The role of forestation in biodiversity protection, water management, contribution to rural livelihoods, and poverty reduction through job creation has not been adequately addressed (Lewis et al. 2019a).

Over 760 Mha of land have been determined to be suitable for Clean Development Mechanism (CDM) Afforestation and Reforestation (A/R) operations worldwide (Hansen et al. 2003). This provides opportunities to restore lost forest cover and support the recovery of forest landscapes in several countries that have lost forest cover. These regions would in turn serve as carbon sinks, enhance biodiversity, and provide livelihoods and quality of life for people (Stanturf et al. 2015). In Africa, forests and woodlands occupy nearly 650 million hectares of land, or 21% of the continent, and represent about 17% of the world’s forest cover (FAO 2016). Deforestation, land degradation and extreme climatic events in Africa are critical

2 of 30 Jama et al. Perception towards forestation as a strategy to mitigate climate change in Somalia topics continuously being addressed locally, regionally and internationally (FAO 2016). Nearly 8 million hectares of trees have been planted with a range of purposes including commercial round timber, reforestation of degraded land, environmental conservation, and expanding wood supplies. Notably, an ambitious conservation program (AFR100) is set to plant 100 million hectares of trees in Africa by 2030. Twenty-eight African countries pledged to the (AFR100) initiative, which is under the Bonn Challenge that targets specific area of Africa for afforestation programs. For example, Mozambique pledged one million hectares for afforestation, South Africa pledged 3.6 Mha, Kenya pledged 5.1 Mha, and Cameroon pledged 12 Mha (Lewis et al. 2019b).

In East Africa, rural livelihoods are mostly dependent on rain-fed agriculture and food security is vulnerable to climate shocks. Between 1990 and 2015, roughly 1% of the region’s woodlands and forest cover has been removed annually as population grew at an average annual rate of 2% (The World Bank 2017). Somalia, which endures recurrent natural hazards, a degraded natural resource base, and the absence of a functioning state for nearly three decades, has experienced a forest cover loss of 30% in 36 years (from 9,050,000 ha in 1980 to 6,363,501 ha in 2015) (FAO 2014). An example of the land conversion that took place in northern Somalia in the two decades immediately after the start of the Somali Civil War is shown in Figure 1. This is in part due to overexploitation of woody resources for charcoal and fuelwood that ultimately contributes to environmental degradation (Oduori et al. 2011, Rembold et al. 2013). The ecosystems of Somalia are part of the arid and semi-arid belt that stretches across Africa and the trees that provide fuelwood and animal fodder are dependent on the availability of water (Abdi et al. 2017). However, droughts are becoming increasingly more frequent (Masih et al. 2014) and have taken a toll on these ecosystems. Furthermore, the emergence of lawlessness also contributes to the reduction of woody cover and the subsequent degradation of ecosystems in Somalia (FAO 2014).

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Figure 1: Change in the woody cover in northern Somalia (Somaliland and Puntland) between the start of the Somali Civil War in 1991/92 and 2015. Woody cover encompasses all land cover types that have a large woody component (e.g. forests, woodlands, savanna, shrubland). The data source is the European Space Agency’s Climate Change Initiative Land Cover dataset (Radoux et al. 2014).

In view of the importance and scale of deforestation, a large-scale grassroots forest restoration campaigns are required as a matter of urgency to regenerate lost forest (Ministry of National Resources 2013). For any climate change mitigation effort to be successful, it is imperative to understand individual psychological and behavioral factors that contribute to climate action or inaction (Koger and Scott 2007, Gifford 2008, Pelletier et al. 2008, Swim et al. 2009, Gifford 2011). There has been relatively little discussion about the psychological factors that motivate or hinder the perception of young people (between 18 and 35 years of age) about forestation as a means to mitigate climate change in least-developed post- conflict countries such as Somalia because the majority of available studies are primarily from the developed world (van der Linden 2014, 2015). Thus, assessing a different socioeconomic and cultural context would help both researchers and practitioners to understand and compare the perceptions in different scenarios (Oreg and Katz-Gerro 2006). This paper attempts to shed light on our understanding of the factors that influence young people’s perception towards forestation and forest management in Somalia.

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The remainder of this article is organized as follows: In the second section, we elaborate on the conceptual framework and derive from it a set of hypotheses. In the third section, we describe the materials and methods including data collection, survey structure and statistical modeling. In the fourth section, the results are presented. In this section, a measurement model was used to establish whether the model fits the data and then structural path analysis was used to test the hypotheses. In the fifth section, the results are discussed in relation to previous studies. In the sixth section, policy implications are presented and in the seventh and final section, conclusions and limitations are presented.

2. Conceptual framework and hypotheses

Theory refers collectively to values, beliefs, attitudes/social norms, intentions and behaviors as a cognitive psychological process that individuals employ to understand the surrounding environment (Fulton et al. 1996, Vaske and Donnelly 1999). The cognitive hierarchy theory proposes that an individual’s view of the surrounding environment is controlled by a set of hierarchical concepts that consist of values, basic beliefs, attitudes/norms, intentions and behaviors (Ball-Rokeach et al. 1984, Homer and Kahle 1988, Rokeach 2008). These hierarchical cognitions are hypothesized to build upon one another in what looks similar to an inverted pyramid where values lie at the base of the pyramid (Figure 2). The term “general value” signifies the most fundamental social constructs and differs from the other components of the framework as it withstands circumstances and issues (Rokeach 1973). For example, a person who holds “honesty” as virtue would be expected to be truthful when filling a tax form or business transactions or when engaging with friends. Since values are subjective, and it is difficult to link them to a more specific interpretation of behavior, it caused value orientations to be included in the cognitive hierarchical model (Fulton et al. 1996, Vaske and Donnelly 1999, Vaske et al. 2001, Bright et al. 2010). Value orientations are groups of basic beliefs about general objects, which give meaning to more abstract values (Manfredo et al. 2009, Bright et al. 2010). It is a lasting belief in first-order cognition that forms the basis for specific attitudes towards a topic, such as forestation, in the cognitive hierarchy framework (Schwartz 1992, McFarlane and Boxall 2000, Tarrant and Cordell 2002). They are central to the cognitive structure, the fewest in number, relatively stable and are unlikely to change unless under extreme pressure (Allen et al. 2009). For instance, while values are used to evaluate the extent to which people agree with abstract ideas such as altruism or honesty, value orientations on the hand focused patterns of belief on a broad range of items (e.g. wildlife, forests) that are meant to relate to cognitions of underlying values.

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Figure 2: Conceptual representation of a cognitive hierarchical framework of human behavior. Boxes with dashed lines were not included in the analysis. Solid arrows denote the hypothesized paths, dashed paths denote that were not hypothesized. In our model, knowledge, risk perception and value orientations (anthropocentric-biocentric) provide the foundation for higher order attitudes and behaviors. Adopted from McFarlane and Boxall (2000).

A widely used framework of value orientations in the context of natural resources can be organized along the continuum from biocentric to anthropocentric orientations (Vaske and Donnelly 1999, Vaske et al. 2001). An anthropocentric value orientation signifies a human-centered viewpoint of the biosphere (Eckersley 1992). This is the traditional notion of natural resource management which bases policies on a utilitarian concept. It is the idea that the primary aim of environment is to provide the people benefits such as timber, food, and recreation benefits (Pinchot 1910, Fulton et al. 1996, Tarrant and Cordell 2002). Anthropocentric value orientation prioritizes environmental suitability for human beings and fails to recognize nonhumans as important parts of nature that are both valuable and need to exist (Vaske and Donnelly 1999).

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In contrast, biocentric value orientation is a nature-centered approach and emphasizes environmental preservation and ecosystem maintenance (Eckersley 1992, Vaske and Donnelly 1999, Vaske et al. 2001). Here, the importance of all ecosystems, species and natural resources is raised to the center stage. Human desires and values continue to be significant but are seen from a broader perspective. This approach presupposes both the intrinsic and instrumental importance of the environment and asserts that human economic activities are not inherently the most important. Such universal principles are to be valued and maintained in the field of natural resource management, even if they contradict human-centered values (Thompson and Barton 1994). Meanwhile, value orientations are typically predictive of attitudes and preferences of specific issues or behavioral actions; for example, they can provide essential and accurate measures of whether the proposed forest polices and legal practices in the national forestry plan would be acceptable to the public (Fulton et al. 1996, Vaske and Donnelly 1999, Allen et al. 2009, Clement and Cheng 2011, Steg et al. 2014). The next level, attitude, is more specific in the cognitive hierarchy model. Attitude is defined as an individual’s assessment of negative or positive cognitive evaluations, emotional experience, or behavioral patterns that are regularly present in specific circumstances (Ajzen and Fishbein 1977, Oskamp and Schultz 2005). Similarly, norms are judgments acceptable in specific situations (Ajzen 1991a). The hypothesis in the cognitive hierarchy theory suggests specific attitudes affect behaviors, and value orientations have their influence on behaviors indirectly through specific attitudes (Vaske and Donnelly 1999, McFarlane and Boxall 2000, 2003). Nonetheless, research has shown a mediating influence of attitudes that generally restricts the analysis to constructs of value, attitude, and behavior and has not included possible mediators such as knowledge, risk perception, and antecedent factors that may also affect the value-attitude behavior relationship (McFarlane and Boxall 2003). A significant argument in the literature on human behavior has focused on risk perceptions as one of the main factors of the public’s willingness to mitigate climate change (Leiserowitz 2006, Spence et al. 2011). The core of this argument states that people who perceive negative environmental effects as threats will be more prepared to take mitigation and adaptation actions (O'Connor et al. 1999). Both early and more recent studies have reported favorable positive correlations between risk perception and attitude towards climate change (O'Connor et al. 1999, Hung et al. 2007, Kwon et al. 2019), though some studies have revealed that risk perception and attitude have a negative relationship (Lo 2013, Jørgensen and Termansen 2016). Nevertheless, experiences of a climate-related natural hazard can cause an increase in the probability of climate change being perceived as severe risk (Spence et al. 2011, Broomell et al. 2015) given the fact that risk perception is a prerequisite to people’s intentions to perform collective action (Etkin and Ho 2007).

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Knowledge is defined as the individual’s aptitude to distinguish the concepts and behaviors that are related to environmental protection (Laroche et al. 2001). Early models of environmental psychologies suggested a linear relationship between environmental attitudes and behaviors (Burgess et al. 1998). These models argued that the knowledge of environmental problems would provoke concern for the environment, which in response inspires people to participate in pro-environmental behaviors (Kollmuss and Agyeman 2002). However, such models were identified as inconsistent, and labeled (information) deficit models of public understanding and action (Burgess et al. 1998). Thus, further determinants to explain the behavioral intentions or the behavior itself are needed. Even though it has been theoretically argued that knowledge accounts for a substantial part of pro-environmental behaviors, the empirical indicators available is not robust (Laroche et al. 2001, Kaiser and Fuhrer 2003). Applying this assumption, studies in developing countries have reported climate change information and individuals’ awareness of climate change impacts play positive but possibly indirect roles in forming intentions to address them (Ogunbode and Arnold 2014).

The final level of the cognitive hierarchy theory is behavioral intention or behaviors as predicted by an additive amalgamation of higher-order attitudes towards the behavior, social norms concerning specific attitude objects, or behaviors that are the direct antecedents of behavioral intentions. This study tested behavioral intentions to adopt forestation because of the complexity in identifying the behaviors linked to climate change that was generally applicable and contextually relevant. Prior studies have shown that behavioral intentions closely align with actual behavior (Ajzen 1991a, Kollmuss and Agyeman 2002). As such, in our anticipation by behavioral intention to forestation as strategy to mitigate climate change, actual action would presumably be achieved once the determinants of the intentions of performing the actual behavior are identified (Fishbein and Manfredo 1992). From a theoretical perspective, this study did not include some traditional constructs such as fundamental values, norms and antecedent factors that usually include in the cognitive hierarchy model. We put our focus on the causal interplay of forest value orientations, knowledge, attitude and behavioral intention, and as suggested by Vaske and Donnelly (1999) there is need to expand and include some further determinants. Thus, considering the study context we included the specific belief of risk perception in our proposed cognitive hierarchy model. In summary, we propose to test a model in which knowledge, risk perception, forest value orientations of (anthropocentric/biocentric) influence specific attitudes and specific attitudes, in turn, predict behavioral intentions (Figure 2). This paper proposes five hypotheses based on the principle of cognitive hierarchy model:

H1: Attitude towards forestation has a significant positive influence on behavioral intentions to mitigate climate change. H2: Climate change knowledge has a significant positive influence on attitude towards forestation

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H3: Risk perception has a significant positive influence on attitude towards forestation. H4: Anthropocentric value orientation has a significant positive influence on attitude towards forestation H5: Biocentric value orientation has a significant positive influence on attitude towards forestation

2.1. Study area and context

Somalia is located in the Horn of Africa between the latitudes of 2oS and 12oN and the longitudes of 41oE and 51oE with a landmass of 636,240 km2 (Figure 3). It is a type of dryland with low seasonal average rainfall (approximately 282 mm) and a semi-arid landscape (Omuto and Vargas 2009). The north- south movement of the inter-tropical convergence zone determines the high degree of spatial and temporal rainfall variation that comprises two distinctive rainy seasons known “Gu” from mid-March to June and “Deyr” from mid-September to November. The agricultural and pasture production relies on irregular rainfall which is affected by climatological interactions such as the El Nino-Southern Oscillation (Abdi et al. 2016) and the Indian Ocean Dipole (Marchant et al. 2007).

The total forest cover in Somalia is about 14% (90,000 km2) of which extensive Acacia and Commiphora dry deciduous woodland and thicket cover around 2.4% (Ullah and Gadain 2016). The mist forests of the Golis Mountains are biodiversity hotspots and hubs of endemism (UNEP 2005, Ullah and Gadain 2016). Tropical floodplain forests that previously existed along the Shabelle River were cleared and replaced with smallholder farmers, including sugar and banana plantations. Some of the native forest products that Somalia exports include frankincense, myrrh, gum Arabic, and yicib nuts (Cordeauxia edulis). For example, in 1985, Somalia was the largest exporter of incense with over 2,000 tons annually (UNEP 2005, Ullah and Gadain 2016). The country is also home to about 3,028 higher plant species, of which 17 are considered to be endangered. Deforestation that resulted from charcoal production and unsustainable land use practices has had a significant effect on the composition trees species and the density of the forest cover (UNEP 2005, Ullah and Gadain 2016).

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Figure 3: Overview of the location of Somalia relative to its neighbors and the African continent in the inset. The internal borders of the semi-/autonomous regions of Puntland and Somaliland are constantly changing and have thus been intentionally excluded from this map. Data source is Natural Earth Data (www.naturalearthdata.com).

Somalia is amongst the world’s most climate-vulnerable countries (Busby et al. 2014). Communities frequently experience droughts and floods, which have increased the impact of extreme climatic events on

10 of 30 Jama et al. Perception towards forestation as a strategy to mitigate climate change in Somalia the already fragile arid and semi-arid regions in the country. In terms of environmental degradation, the country experienced deforestation, desertification and land degradation owing to several factors such as lack of functioning central government, week socio-political disorder, population growth, and absence of robust community participation in the environmental decision making and implementation (Jama et al. 2020). Charcoal production drives deforestation and increases soil erosion thereby limiting the capacity of the land to withstand or avoid natural disasters, which in turn worsens the impact of disasters such as floods, sandstorms, and droughts (FAO 2013). A growing population in this dryland environment creates pressure on land resources to meet the demand (Sallaba et al. 2017). Thus, control of natural resources and their use has further aggravated internal tensions and mass migration as natural resources became increasingly scarce (Dehéz 2009). Climate projections suggest an increase of extreme climatic events since the mitigation of, and adaption to, extreme climate events are poorly integrated into the national adaptation and mitigation framework (Ajuang Ogallo et al. 2018).

3. Materials and methods

3.1. Data collection and survey participants

Primary and secondary data sources were used in this study. Primary data was used for a survey questionnaire to empirically test the hypotheses and the secondary data was collected through online resources (Web of Science, Scopus, Google Scholar, and grey data from Somali government and UN reports). A self-administered closed-ended survey questionnaire with organized sections was deployed to a sample of three universities in Somalia. The universities are Mogadishu University in Mogadishu (South Central Somalia), Puntland State University in Garowe (Puntland), and Amoud University in Borama (Somaliland). We purposively selected these universities as it is relatively safe to collect data there and their student body is representative of the entire country as students move from their home regions to the cities where the universities is located. According to UNFPA (2014), 75% of the Somali population is under the age of 30 (Figure 4), and is projected to continue growing (Boke-Olén et al. 2017), which encouraged us to target the young educated population for this study.

Participants were students who agreed to attend a workshop held in each of the three universities. The workshop was on the importance forests and forestation and the relationship between forests and climate change. The researchers surveyed the students using an on-site questionnaire at the end of the workshop. The survey was administered between March and May 2019 during which a total of 500 copies of questionnaires were distributed and 457 of them were returned answered. This represents a 91.4% response rate. After cross-checking, unanswered and unengaged responses were discarded. A total of 434

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(86.8%) valid responses, of which 157 were from Somaliland, 145 from Puntland and 132 from South

Variable Category N=434 Percentage (%)

Gender Male 280 64.5 Female 154 35.5 Age Average 26 – Maximum 30 – Minimum 19 – Somaliland 157 36.2 Regions Puntland 145 33.4 South central Somalia 132 30.4 Total 434 100 Central Somalia, were prepared for further analysis.

Table 1: Demographics of the survey participants.

Figure 4: Population pyramid of the age distribution in the Somali population according to gender. There is a general pattern of decrease as the population ages. The lower estimate of the 0-4 age group is probably due to underreporting.

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This figure is reproduced from an open-access report from the United Nations (UNFPA 2014) that permits reproduction with acknowledgment and citation.

The results of the three samples were merged into one dataset to evade selection bias due to non- significant demographic differences such as gender, awareness of climate change, the importance of forest management, and willingness to participate in forestation campaign (Supplementary Table 2). Of the 434 valid responses, 64.5% were males (n = 280) compared to 35.5% females ( n = 154). The oldest respondent was 30 years old, the youngest was 19 years old, and the average age was 26 years (Table 1). The majority of the respondents were either moderately aware 46.5% (n = 202) or somewhat aware 31.1% (n = 135) of climate change (see Figure 5). Similarly, majority of the respondents reported importance of forest management as either important (20.3%, n = 88) or very important (61.5%, n = 267) (Figure 6). Finally, a fairly high number of respondents indicated their willingness to participate in forestation campaigns at either somewhat willing (38.5%, n = 167) or totally willing (38.7%, n = 168) to participate (Figure 7).

Figure 5: Climate change awareness of survey respondents. These figures show a largely moderate awareness of the climate change.

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Figure 6: Respondent perception towards their importance of forest management. The majority responded that forest management is very important to them.

3.2. Survey structure and measurements

The survey questionnaire was structured into three parts. The first part attempts to clarify the research purpose and the procedure of filling the questionnaires. The second part was used to gather demographic information and basic understanding of the relationship between climate change and forest management. The third and final section was used to understand the psychological and behavioral factors of the respondents towards forestation as a strategy to mitigate climate change. All the constructs in the current study were latent variables and measured with multiple measurement scales as proposed by Churchill Jr (1979) and Kline (2011). An initial list of measured items was drawn from an extensive literature review of previous studies in applied socio-psychological theories (Supplementary Table 1). A pre-test was first implemented to a small group of the target respondents (n = 30) before starting the full data collection to enhance the reliability and validity of the survey. The responses to the questionnaires were based on a five-point Likert-scale from (5 = strongly agree to 1 = strongly disagree).

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Figure 7: Respondent willingness to participation forestation campaigns. Majority of the respondents were either somewhat willing or completely willing to participate the forestation campaigns.

3.3. Statistical analysis

Explanatory factor analysis was first performed using SPSS version 23 (IBM Corp 2015) to identify factor loadings with the extraction method of maximum likelihood and varimax rotation. Structural equation modeling was then run using the SPSS module AMOS version 24. This software combination has been used to test hypothesis involving multivariate relationships between observed and unobserved variables (Nunkoo and Ramkissoon 2012, Nunkoo et al. 2013). The present study applied the two-stage testing approach suggested by Anderson and Gerbing (1988). In the first stage, confirmatory factor analyses (CFA) were used to estimate the reliability and validity of the measurement model. In the second stage, structural (path) model was used to explore the associations between the latent variables (Anderson and Gerbing 1988) This study used chi-square (χ2) and root mean square error of approximation (RMSEA), standardized root mean square residual (SRMR), comparative fit index (CFI), and the Tucker-Lewis index (TLI) as the defining fit indices to show that the model fits the data. Following the suggestions of Hu and Bentler (1999), CFI and TLI scores are satisfactory at a level higher than 0.9 (and preferably greater than 0.95), SRMR scores below 0.06 and RMSEA scores below 0.08 indicates a good model fit. Other measures applied for baseline comparisons to identify how the model fits the data were normed fit index (NFI),

15 of 30 Jama et al. Perception towards forestation as a strategy to mitigate climate change in Somalia relative fit index (RFI) and incremental fit index (IFI), which for all the acceptable score is higher than 0.9 (Bentler and Bonett 1980, Jöreskog and Sörbom 1984). In addition, according to Hu and Bentler (1999), the cutoff criteria of best goodness of fit statistics were considered to be χ2 / df < 3. Thus, the present study considered that as the guiding assessment of the goodness of fit statistics (see Table 4). Before CFA data was screened to check a possible violations of underlying assumptions and no statistical violations were found (see supplementary materials). Skewness/kurtosis scores ranged within the required ±1 and ±3 except three Skewness’s items, which according to Bagozzi and Yi (2012) is not a concern of an extreme non- normality. Furthermore, collinearity statistics (tolerance/VIF) and multivariate normality and outliers, hit the acceptable assumptions (Bagozzi and Yi 2012). Mediation analysis was carried out using non-parametric multivariate bootstrapped method developed by Preacher and Hayes (2008). In this study, 2000 bootstrap samples taken from the original data were used. Bootstrapping takes large number of samples from original data, sampling with replacement, and computes the standard error of the indirect effect in each sample (Preacher and Hayes 2004). This is a well-established approach used in mediation research (MacKinnon et al. 2007) to analyze both the total effect and the indirect effect of the antecedent while controlling for the other factors (Preacher and Hayes 2008).

4. Results

4.1. Measurement model analysis

4.1.1. Confirmatory factor analysis

Stage one of the Anderson and Gerbing (1988) two-way approach was conducted to establish the reliability and validity of the latent variables underlying each measurement parameter items. Before establishing the validity and reliability of the constructs, we examined the measurement model goodness fit using the maximum likelihood estimation method. The results indicated that the model fit indices were satisfactory (χ2 = 558.618, P < 0.0, df = 237, χ2/df = 2.357, RMSEA = 0.056, SRMR = 0.045, CFI = 0.964) and acceptable according to Hu and Bentler (1999). However, a number of modification indices were suggested by the AMOS module as possible improvements to the model. After adding two covariance modifications to error components of the measurement indices for attitude and behavioral intention, the goodness fit indices were substantially improved (χ2 = 413.342, P < 0.000, df = 235, χ2/df = 1.759, RMSEA = 0.042, SRMR = 0.043, CFI = 0.98, IFI = 0.980, TLI = 0.976, NFI = 0.955, RFI = 0.947). All the scores met the cutoff criteria suggested in the literature (Anderson and Gerbing 1988, Hu and Bentler 1999, Hair Jr et al. 2014) except the P-value which is sensitive to sample size greater than 200 (Schermelleh-Engel et al. 2003).

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4.1.1. Validity and reliability test

Cronbach's alpha value was higher than the minimum threshold standard of 0.7 (Table 2), indicating the consistency of the entire scale with no less than 70% of the variance in measurement is captured by the construct variance (Fornell and Larcker 1981, Nunnally and Bernstein 1994). The standardized factor loading value of the measurement items were above the acceptable point of 0.7. Similarly, the composite reliability (CR) and average variance extracted (AVE) were above the lowest level of 0.6 and 0.5 respectively, indicating a satisfactory convergent validity for the structural model (Hair Jr et al. 2014). Overall, Table 2 provides a strong indication of construct validity in relation to reliability of unidimensionality and convergent validity. Moreover, discriminant validity should be satisfactory (Anderson and Gerbing 1988, Kline 2011). The present study satisfied this condition by applying the new discriminant validity with a superior performance called Heterotrait-monotrait ratio of correlations (HTMT, Table 3) (Henseler et al. 2014).

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Table 2: Validity and reliability test. Internal reliability Convergent Validity Average Cronbach’s Item-Total Factor Composite Variance Constructs Alpha Items Correlation Loadings Reliability Extracted (CA) (AVE)

Climate Change CCK1 0.902 0.824 0.868 0.906 0.712 Knowledge CCK2 0.852 0.896 (CCK) CCK3 0.866 0.918 CCK4 0.598 0.648 Risk Perception RP1 0.920 0.748 0.789 0.922 0.747 (RP) RP2 0.879 0.926 PR3 0.826 0.871 PR4 0.811 0.841 Anthropocentric ACV1 0.939 0.868 0.901 0.940 0.795 Value (ACV) ACV2 0.839 0.866 ACV3 0.877 0.916 ACV4 0.838 0.874 Biocentric Value BCV1 0.917 0.865 0.907 0.919 0.740 (BCV) BCV2 0.851 0.893 BCV3 0.798 0.834 BCV4 0.726 0.734 Attitude (ATT) ATT1 0.913 0.833 0.864 0.906 0.708 ATT2 0.8 01 0.834 ATT3 0.854 0.891 ATT4 0.725 0.761 Behavioral BIF1 0.941 0.899 0.942 0.934 0.782 Intention (BI) to BIF2 0.806 0.922 adopt forestation BIF3 0.844 0.801 BIF4 0.890 0.750

Table 3: Hetero trait-mono trait ratio of the correlations analysis.

1 2 3 4 5 6

Climate Change Knowledge ---

Risk Perception 0.069 ---

Anthropocentric Orientation 0.088 0.015 ---

Biocentric Orientation 0.111 0.127 0.073 ---

Attitude Towards Forestation 0.082 0.150 0.104 0.138 ---

Behavioral Intention 0.136 0.188 0.013 0.357 0.318 ---

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Table 4: Fit indices: the table shows the comparisons of the measurement model hypothesized model and the final model. CMIN = Chi-square, DF = Degrees of Freedom, CFI = Comparative Fit Index, SRMR = Standardized Root Mean Square Residual, RMSEA = Root Mean Square Error of Approximation, TLI = Tucker-Lewis Index, NFI = Normed Fit Index, RFI = Relative Fit Index, IFI = Incremental Fit Index.

Goodness of Fit Measurement Model Hypothesized Model Final Model Threshold

CMIN 413.342*** 471.718*** 416.202*** --

DF 235 239 237 --

CMIN/DF 1.759 1.974 1.756 Between 1 and 3

CFI 0.980 0.974 0.980 >0.95

SRMR 0.043 0.047 0.046 <0.08

RMSEA 0.042 0.042 0.043 <0.06

TLI 0.976 0.970 0.976 >0.900

NFI 0.955 0.948 0.954 >0.900

RFI 0.947 0.940 0.947 >0.900

IFI 0.980 0.974 0.980 >0.900

R2 0.110 0.227

Note: *** = P < 0.001

4.2. Structural path model

The second stage of the Anderson and Gerbing (1988) two-way approach yielded satisfactory goodness of fit study (χ2= 471.718, P < 0.0, df =239, χ2/df = 1.974, RMSEA= 0. 047, SRMR = 0.077, CFI = 0.974, IFI = 0.974, TLI = 0.97, NFI= 0.948, RFI = 0.94) that is in line with earlier research (Hu and Bentler 1999, Hair Jr et al. 2014). Unexpectedly, the suggestions of the modification indices from AMOS indicated potential improvement to the model. If we add two extra paths from climate change knowledge and biocentric value orientation to behavioral intentions, the goodness of fit of the indices improved substantially with minimal effect on the hypothesized model (χ2 = 416.202, P < 0.0, df =237, χ2/df = 1.756, RMSEA = 0.042, SRMR = 0.046, CFI = 0.98, IFI = 0.98, TLI = 0.976, NFI = 0.954, RFI = 0. 947). Therefore, we maintained the adjusted structural model (χ2/df = 1.756) as the final model because it

19 of 30 Jama et al. Perception towards forestation as a strategy to mitigate climate change in Somalia exhibited a superior fit than the hypothesized model (χ2/df = 1.974). The results of the chi-square difference showed that the two models differed significantly (∆χ2 = 55.516, P < 0.0, ∆df =2). Equally, the final model (R2 = 0.227) indicated a higher ability of explaining variance in behavioral intentions than the hypothesized model (R2 = 0.11). Table 4 summarizes the comparisons between models.

Table 5: Hypothesis testing.

Standardized Path Standard Hypothesis t-value Outcome Coefficient Error H1: Attitude Behavioral Intention  0.262*** 0.047 5.635 Supported

H2: Climate Change Knowledge  0.085 0.043 1.652 Rejected Attitude

H3: Risk Perception Attitude  0.140** 0.053 2.731 Supported

H4: Anthropocentric Value Attitude  0.099 0.055 1.949 Rejected

H5: Biocentric Value Attitude  0.138** 0.06 2.677 Supported

H6: Biocentric Value Behavioral Intention  0.318*** 0.055 6.872 Discovered

H7: Climate Change Knowledge  0.119** 0.038 2.614 Discovered Be§havioral Intention

Note: *** = P < 0.001; ** = P < 0.01

4.3. Hypothesis testing

The final model based on the cognitive hierarchy model is shown in Figure 8. The relationships of the hypothesized constructs were tested, including the two additional paths revealed from the structural path model. Table 6 summarizes the hypothesis testing of the final model, and the results indicated that three hypotheses were accepted and two were rejected. The two discovered hypothesis after the modifications of the original model were also supported. Regarding H1, we found that attitude towards forestation has a significant positive influence on behavioral intentions to mitigate climate change (H1: β

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= 0.262, t = 5.635, P < 0.0). H2 and H4 hypothesized that climate change knowledge and anthropocentric value orientation have a significant positive influence on attitude towards forestation, and these hypotheses were rejected (H2: β = 0.085, t = 1.652, P > 0.05), (H4: β = 0.099, t = 1.949, P > 0.05). Similarly, H3, and H5 hypothesized that risk perception, and biocentric value orientation have a significant positive influence on attitude towards forestation and these hypotheses were all supported (H3: β= 0.140, t = 2.690, P <0.01), (H5: β = 0.138, t = 2.677, P < 0.001). Meanwhile, the two additional paths discovered revealed interesting links with behavioral intention. Path D1 showed that biocentric value orientation has a direct significant positive influence on behavioral intention (D1: β = 0.318, t =6.872, P < 0.0), and path D2 revealed that climate change knowledge has a direct significant positive influence on behavioral intention. (D2: β = 0.119, t = 2.614, P < 0.001). These two additional paths were retained in the final model. Table 5 summarizes the results of the hypothesis testing.

Table 6: Mediation results and total standardized effects on Intentions to adopt forestation as a strategy to mitigate climate change: The table shows total standardized effect on response variable (c), direct effects on response variable after controlling the mediator (c´), and the indirect effects (c- c´) which is equivalent to a×b, where a is the effect of independent variable X on the mediator M, and b is the effect of M on the dependent variable Y after controlling for X. CCK = Climate Change Knowledge; RP = Risk Perception; ACV = Anthropocentric Value Orientation; BCV = Biocentric Value Orientation; ATT = Attitude

Independent Mediator Direct Effects Indirect Effects Standardized Outcome Variables (M) (c´) (a×b) Effects (c)

CCK ATT 0.113* 0.021 (ns) 0.135** no mediation

RP ATT 0.076 (ns) 0.035** 0.111* Full mediation

ACV ATT -0.013 (ns) 0.025* 0.012 (ns) Full mediation

BCV ATT 0.310** 0.035** 0.345*** Partial mediation

ATT 0.252*** 0 0.252*** n/a Note: *** = P < 0.001; ** = P < 0.01; * = P < 0.05; ns = “not significant”; n/a= “not available”

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4.4. Common method bias

A common methodological bias may occur if the same respondent is asked to answer all questions through a self-reporting questionnaire to measure their intentions. This is usually observed in studies examining behavioral willingness of respondents (Podsakoff et al. 2000). To determine whether it occurs in this study, we first ran Harman’s single factor test. We observed that the maximum variance explained by a single factor was 17.56%, which is considerably less than the threshold of 50% indicating that our data does not suffer from common method bias. Furthermore, we ran specific bias tests to determine whether the dataset has zero bias and equal bias. The results of chi-square test for zero constrained (χ2 = 86.114, df = 24, P < 0.0) and equal constrained (χ2 = 86.114, df = 23, P < 0.0) models were significant, meaning that the test of equal specific bias showed unevenly distributed bias which according to Pallant (2013) will not cause a problem if the simple size is greater than 40.

Figure 8: Final proposed model based on cognitive hierarchy model. The basic logic of the model denotes several layers where knowledge and risk perception and value orientations (anthropocentric-biocentric) were hypothesized as predictors of specific attitude and behaviors. Causal relationships among latent variables, represented by single-headed arrows and co-variances among several of the residuals by dual-headed arrows. β = standard regression weight, * = P < 0.05, ** = P < 0.01, *** = P < 0.001, t-values are in the parenthesis, ns = “not significant”. CCK = Climate Change Knowledge; RP = Risk Perception; ACV = Anthropocentric Value Orientation; BCV = Biocentric Value Orientation; ATT = Attitude; BI = Behavioral Intention to adopt forestation. (χ2 = 416.202, P < 0.0, df =237, χ2/df = 1.756, RMSEA = 0.042, SRMR = 0.046, CFI = 0.98).

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Overall, the mediation results show that attitude towards forestation did not fully mediate the relationship between the variables in the cognitive hierarchy model. Similarly, it is imperative that the biocentric value orientation have the highest total standardized effects on Intentions compared to attitude which most of the previous studies (Ajzen and Fishbein 1977, Ajzen 1991b) stated to have the highest direct effect on intention. This finding is interesting and it indicates a greater concern of environmental conservation.

5. Discussion

In this study, we investigated the psychological factors that influence young people’s intentions to adopt forestation as a strategy to mitigate climate change. The study specifically examined the influence of climate change knowledge, risk perception, forest values orientations, attitude, and behavioral intention from an applied perspective. The study applied a cognitive hierarchical model to understand the influence of these psychological factors on attitude towards forestation and predict the young people’s behavioral intention. Overall, the results of this study indicate the final model for young people’s intentions to adopt forestation is favorable. The findings of the hypothesis testing supported three of the five proposed hypotheses (H1, H3, and H5) and rejected two hypothesis (H2 and H4). Furthermore, two paths were discovered after suggested modifications to the model.

Results suggest that climate change knowledge itself did not have a significant influence on young people’s attitudes towards forestation. Contrary to our findings, previous studies reported a limited influence of climate change knowledge on attitude (McFarlane and Boxall 2000). Remarkably, one of our central findings is the discovered significant path (D1) that predicted climate change knowledge has a direct significant positive influence on intentions to adopt forestation as a strategy to mitigate climate change. This could be as a result of the level of education given that we have targeted university students in our data collection and some studies stipulated that the persons with higher levels of education are more proactive and knowledgeable about specific actions or behaviors such as climate change mitigation (Baker et al. 2011, Muttarak and Pothisiri 2013). Moreover, another reason could be the fact that we measured in our questionnaire the knowledge about the link between climate change and forests after we held a workshop in which we taught the students about the interaction between the two. Enhanced knowledge of climate change causes and consequences is associated with higher level of concern (Shi et al. 2016). As such, this could have increased respondents’ knowledge about this complex issue.

The results revealed that risk perception has a significant positive influence on attitude towards forestation, which in line with earlier studies (Hung et al. 2007, Kwon et al. 2019) This means risk

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perception is a critical driver of collective action against human-induced environment problems. Studies in developing countries have indicated people primarily perceived the risk of climate change more than those in developed countries (So Young Kim 2014). In Somalia, climate-sensitive agricultural production and livestock herding has been particularly hit by recurrent extreme climate events over the past two decades. Thus, we assume individuals who recognize the risks of climate change would be more prepared and willing to take mitigation actions.

Our results were consistent with previous studies (Vaske and Donnelly 1999, McFarlane and Boxall 2000, Vaske et al. 2001, Allen et al. 2009) in that biocentric value orientation has significant positive influence on attitude towards forestation whereas anthropocentric value orientation did not show a significant influence on attitude towards forestation. Unexpectedly, another important finding is the discovered path (D2) that predicted biocentric value orientation to have a direct significant positive influence on young people’s intentions to adopt forestation as a strategy to mitigate climate. This could be a result of personal climate change experience as recent studies suggested that it plays a valuable role in individual decision-making (Weber 2006, Spence et al. 2011). There are ample social-psychological models offering explanations that the relationship between value orientations and behavioral intentions or behaviors are fully mediated by attitude, beliefs or norms. However, our model indicated that attitude partially mediated the relationship between biocentric value orientation, climate change knowledge, and behavioral intentions. A possible explanation is that we targeted younger educated individuals living in urban areas as previous studies stated that these groups hold a stronger biocentric values and eventually support sustainable natural resource management (McFarlane and Boxall 2000). This also validates prior studies that stated affluence is not always a predictor of environmental concern (Plombon 2011). Since Somalia is a low-income country with a high poverty ratio and poverty is the primary determinant of vulnerability that influences dependence on natural resources. Specifically, value orientation indicates higher biocentric orientation than the anthropocentric orientation which means higher environmental concern.

Finally, in support of the hierarchical cognitive model, the findings indicated that young people’s attitude towards forestation had a positive significant prediction on behavioral intention to adopt forestation as a strategy to mitigate climate change. This finding is consistent with existing literature that indicates attitude toward specific forest management has a significant positive influence on forest management intentions or actions (Vaske and Donnelly 1999, Brown and Reed 2000, Karppinen 2005, Tikir and Lehmann 2011, Meijer et al. 2015, Laakkonen et al. 2018). Although the prediction of behavioral intention is not strong in the present study it still displayed a higher significance, which supports the young people’s intention to adopt forestation as a strategy to mitigate climate change and it could be as a result of extreme climate vulnerability and deforestation that exist in the study area (Ajuang Ogallo et al. 2018). A probable

24 of 30 Jama et al. Perception towards forestation as a strategy to mitigate climate change in Somalia reason is due to the fact that individuals in countries that are vulnerable to climate change express higher willingness to support and approve proactive climate change policies such as forestation (So Young Kim 2014).

6. Insights for policymakers and implications

Five policy propositions are recommended in this paper. First, forests should be protected from growing charcoal production with the creation of alternative energy programs and enabling local community members to use fuel-efficient stoves and the promotion of sustainable livelihoods for charcoal producers. Second, forestation should be a sustainable strategy for conservation of forest resources to rehabilitate forest ecosystems and natural resources as well as restore the watersheds and mitigate desertification to improve extreme local climatic conditions. Third, large-scale tree-planting should include native trees where deforestation has taken place and caution should be taken in these efforts to avoid turning grasslands, open-canopy woodlands and savannas into forests. Fourth, relevant authorities should promote the importance of forests and their role in providing several economic and environmental services to enhance local community awareness. Fifth, the creation of federal, state and local forest and biodiversity conservation policies and government recognition of the importance of community engagement when formulating forest regulations and policies.

7. Conclusions

Understanding the theory of how and why psychological factors influence people’s behavioral intention of specific climate change mitigation strategies can help forest managers and policymakers to gain in-depth knowledge about how different forest management priorities and policies may be interpreted by local communities. This study examined a cognitive hierarchy framework in order to analyze the psychological factors that may predict young people’s intentions to adopt forestation as a strategy to mitigate climate change and provide further insights for practitioners and policymakers.

The study investigated the influence of knowledge, risk perception, forest value orientations on people’s attitude, which in turn predicted intentions to adopt forestation as a strategy to mitigate climate change within a post-conflict developing country. However, in terms of limitations, the study could not include all the possible factors that might predict behavioral intention to adopt forestation. Similarly, the study sample is somewhat biased in favor of the younger and more highly-educated members of Somali society, which reduces the generalizability of our findings. Nonetheless, we believe that the cognitive hierarchy model we presented provides a suitable theoretical framework and information useful for the

25 of 30 Jama et al. Perception towards forestation as a strategy to mitigate climate change in Somalia design of effective programs aimed at improving climate change mitigation through forestation and forest management in Somalia.

Acknowledgements: This work was supported by the Chinese Scholarship Council [CSC no. 1741159C74] and the Swedish Research Council [Grant no. 2018-00430].

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