Benefits From Water Related Ecosystem Services in Africa and Climate Change

1 Benefits from water ecosystem services in Africa and adaptation to climate 1 change. 2 Laetitia Pettinottia,*, Amaia de Ayalaa and Elena Ojeab 3 a Basque Centre for Climate Change (BC3), Sede Building 1, 1st floor, Scientific Campus of the University 4 of the Basque Country, 48940 Leioa, Spain. 5 b Future Oceans Lab, Universidad de Vigo, Spain. 6 *Corresponding author at: Basque Centre for Climate Change (BC3), Building 1, 1st floor, Scientific7 Campus of the University of the Basque Country, 48940 Leioa, Spain. Tel.: +34 944 014 690 8 E-mail addresses: l.pettino[email protected]k (L. Pettinotti), [email protected] (A. de Ayala),9 [email protected] (E. Ojea) 10 11 Abstract: The present study collects original monetary estimates for water related ecosystem service 12 benefits on the African continent from 36 valuation studies. A database of 178 monetary estimates is 13 constructed to conduct a meta-analysis that, for the first time, digs into what factors drive water related 14 ecosystem services values in Africa. We find that the service type, biome and other socioeconomic 15 variables are significant in explaining benefits from water related services. In order to understand the 16 importance that benefits from water related ecosystem services have for climate change, we explore 17 the relationship between these benefits and the countries vulnerability and readiness to adapt to 18 climate change. We find that countries face synergies and trade-offs in terms of how valuable their 19 water related ecosystem services are and their potential vulnerability and adaptation capacity. While 20 more vulnerable countries are associated with lower benefits from ecosystem services, countries with a 21 This document is the Accepted Manuscript version of a Published Work that appeared in final form in: Pettinotti, L.; de Ayala, A.; Ojea, E.. 2018. Benefits From Water Related Ecosystem Services in Africa and Climate Change. Ecological Economics. 149. DOI (10.1016/j.ecolecon.2018.03.021). © 2018 Elsevier B.V. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 3.0 license http://creativecommons.org/licenses/by-nc-nd/3.0/ 2 higher readiness to adapt are also associated with lower ecosystem services values. Results are 22 discussed in light of natural capital accounting and ecosystem-based adaptation. 23 Keywords: Adaptation; Africa; Ecosystem Services; Meta-analysis; Natural Capital; ND-GAIN; Readiness; 24 Valuation; Vulnerability; Water. 25 JEL classification: N57: Africa • Oceania. O13: Agriculture • Natural Resources • Energy • Environment • 26 Other Primary Products. Q57: Ecological Economics: Ecosystem Services • Biodiversity Conservation • 27 Bioeconomics • Industrial Ecology. Q54: Climate • Natural Disasters • Global Warming.28 3 29 1 Introduction 30 The concept of ecosystem services (ES), understood as the contribution of the benefits derived passively 31 or actively from ecosystems towards current and future human well-being (Fisher et al., 2009), has 32 gained increasing recognition in the last decade. Mainstreamed by the Millennium Ecosystem 33 Assessment (MA) Program (2005), ES were at the focus of the United Nations Environment Programme 34 (UNEP) led study on The Economics of Ecosystems and Biodiversity (TEEB, see de Groot et al., 2012), and 35 are still evolving under the currently developing Intergovernmental Science-Policy Platform on 36 Biodiversity and Ecosystem Services (IPBES) initiative (Díaz et al., 2015). The conservation and 37 improvement of ecosystems has been identified as a central challenge to sustaining livelihoods for the 38 XXIst century (Gleik et al., 2003; Guerry et al., 2015), and research programs as well as conservation 39 initiatives have been launched at local, national and international levels (Díaz et al., 2015). In this 40 context, research to synthetize available evidence on ES monetary values is of prime importance, and 41 understanding what drives these values and how they relate to countries’ climate vulnerability can 42 provide policy guidance regarding the potential of ES for climate change adaptation. 43 The present paper focuses on water related ES in Africa and their links to climate change vulnerability 44 and adaptation. Water-related ES are understood as the services provided by biomes that are river flow 45 impacting or river flow dependent (see the concept of natural infrastructure in Mul et al., 2017) a . In 46 other words, biomes that impact or are predominantly dependent on river flow, as opposed to being 47 predominantly rain fed, deliver water related ES. This landscape approach considers biomes as the entry 48 point to identify the set of ES produced. The water related ES category draws on the MA and TEEB 49 classifications (MA, 2005; de Groot et al., 2012) and encompass more ES than hydrological services 50 a For more on this distinction, please see the WISE UP project http://www.waterandnature.org/initiatives/wise-climate 4 (Grizzetti et al., 2016). Figure 1 presents the biomes included in the present study which interact with 51 surface river flow and provide water related ES. 52 Previous research has paid a lot of attention to water related ES in other regions mainly due to the 53 development of Payment for Ecosystem Services (Lele, 2009), but no previous studies have analysed the 54 values of water related ES in relation to climate vulnerability and adaptation. In this paper, the focus is 55 on the African continent, for three main reasons: 1) River flows are pivotal to the delivery of ES crucial to 56 millions of livelihoods (WWAP, 2016); 2) the African continent presents in general a high climate change 57 vulnerability exacerbating the need for immediate policy solutions (World Bank, 2007), and; 3) water 58 related ES in Africa continue to be inadequately investigated with very poor coverage (Lele, 2009). 59 Figure 1: Water related services from biomes linked to river flows. 60 61 Source: adapted from Mul et al., 2017 and the MA, 2005. 62 5 Water related ES are affected by a very high variability of all climate and water resources characteristics 63 - in turn exacerbated by climate change (Faramarzi et al., 2013; IPCC, 2014). Understanding the benefits 64 of water related services delivery through economic valuation and the factors that affect these 65 economic benefits can provide guidance for water resources management and climate change 66 adaptation. 67 Africa is not the continent with the largest ES valuation literature (for details on ES valuation methods 68 see de Groot et al., 2012; Pascual et al., 2010). Only 19% of the valuation studies referenced in TEEB are 69 located in Africa. Most studies are located in the Americas (33%) and Asia (26%) (based on Mc Vittie and 70 Hussain, 2013). Moreover, the valuation literature in Africa is geographically disparate: Southern and 71 Eastern Africa gather the highest number of studies while North, West and central sub-Saharan Africa go 72 under-represented. Valuation studies on water related ES in Africa represent 28% of all water related ES 73 valuation studies globally. The most frequently valued water related ES are raw materials and food 74 provision, mainly due to two different reasons: 1) these services are relatively easy to value using the 75 direct market pricing method (Van der Ploeg et al., 2010) and; 2) dependence on provisioning services is 76 high and proportionally larger in African developing countries than in developed countries, hence an 77 early focus on estimating values for this type of service (Egoh et al., 2012; Mc Vittie and Hussain, 2013). 78 Indeed, ES’ consumptive outputs (e.g. crops, fish etc.) contribute to subsistence livelihoods and 79 constitute a very important share of households’ income in African developing countries, thus 80 participating in poverty alleviation and reducing vulnerability to negative shocks (Egoh et al., 2012; Suich 81 et al., 2015). 82 The role of ES in reducing vulnerability and in contributing to adaptation is particularly important in the 83 face of climate change (Jones et al., 2012; Munang et al., 2013a). Adaptation to climate change can be 84 rooted in ES sustainability - known as ‘ecosystem based adaptation’ (Ojea, 2015). It is defined as an 85 6 approach that “harness the capacity of nature to buffer human communities against the adverse 86 impacts of climate change through the sustainable delivery of ES” and is expected to provide cost87 effective adaptation resulting in resilient socio-ecological systems (Jones et al., 2012). Such adaptation 88 option is hailed as particularly beneficial as carbon sequestering ecosystems b such as forests, wetlands 89 and peatlands can contribute to achieving mitigation targets set under the 2015 Paris agreement as well 90 as the sustainable development goals of the United Nations while delivering on adaptation to climate 91 change (Munang et al., 2013b). Early evidence on ecosystem based adaptation supports this is the case 92 (Doswald et al., 2014). However, little is known yet on the linkages between adaptation and the value of 93 ES at a regional scale (Ojea et al., 2015). Indeed, ecosystem-based adaptation approaches have not been 94 mainstreamed yet, with only little evidence in the literature (Jones et al., 2012). Indeed, ES valuations 95 are mostly conducted in isolation of climate change and adaptation considerations. To fill this gap, one 96 feasible approach is to explore to what extent water related ES values are related to higher (or lower) 97 vulnerability and higher (or lower) leverage to adapt to climate change in countries. The present paper 98 addresses these questions to explore the potential links between the value of water related ES and 99 countries vulnerability and potential to adapt to climate change. 100 To do this, the paper synthesises water related ES values elicited for Africa in the last three decades 101 using a meta-analysis. Meta-analyses – the analysis of analyses as defined by (Glass, 1976) - have been 102 increasingly used in the field of environmental valuation (Brander et al., 2006; Ghermandi et al., 2008) 103 as it allows for a rigorous testing of a central tendency across a large number of studies while controlling 104 for the effect of several parameters (Nelson and Kennedy, 2009). In this context, a meta-analysis for 105 water related ES values is carried out to: 1) provide a quantitative answer to what factors drive water 106 b Recent review highlights that much of the claimed climate regulation benefits of EbA, beyond carbon sequestering ecosystems, relate to local temperature regulation rather than mitigation (McVittie et al., 2017). 7 related ES values in Africa and; 2) understand the relationship between climate change vulnerability and 107 readiness to adapt and the benefits obtained from ES. 108 Next section introduces the methodology, outlines the data selection, standardization and coding 109 carried out in order to perform the meta-regression. Section 3 presents the model specification and 110 section 4 its associated results. Section 5 discusses the result implications before the concluding section. 111 2 Methodology 112 2.1 Existing meta-analyses of water-related ES 113 Studies aimed at understanding the benefits from ES have so far conducted meta-analyses focused on 114 one ecosystem type, such as coral reefs (Brander et al., 2007; Ghermandi and Nunes, 2013), coastal and 115 marine ecosystems (Liu and Stern, 2008), wetlands (Brander et al., 2006; Brouwer et al., 1999; 116 Chaikumbung et al., 2016; Ghermandi et al., 2008; Woodward and Wui, 2001), forests (Barrio and 117 Loureiro, 2010; Ojea et al., 2016), or mangroves (Brander et al., 2012). Other studies focus on one or a 118 bundle of ES for a specific ecosystem, such as recreational services from forests (Ojea et al., 2015; 119 Zandersen and Tol, 2009); water ES from forests (Ojea et al., 2015; Ojea and Martin-Ortega, 2015); 120 regulating services from wetlands (Brander et al., 2013) and non-carbon services from forests (Ojea et 121 al., 2016). The geographic coverage of these meta-analyses is slightly biased towards North America, 122 especially if the study is focused on wetlands (Ghermandi et al., 2008). Most studies have adopted a 123 global coverage while a few have specifically focussed on developing or emerging economies (wetlands 124 in developing countries in Chaikumbung et al., 2016; water and recreation services from forests in 125 central America in Ojea et al., 2015; and water services from forests in central and south America in 126 Ojea and Martin-Ortega, 2015). 127 8 The present work is, to our knowledge, the first meta-analysis study on the economic valuation of water 128 related ES focussed on the African continent. For this, an original dataset is constructed based on 129 secondary data from published literature, gathering information on the ES, its monetary value, and 130 additional socioeconomic variables following our understanding of the context where the values occur 131 (section 2.2). A meta analytical model is estimated (section 3.3) to explain the observed variations in 132 water related ES economic values while controlling for a set of study and context characteristics (Stanley 133 et al., 2013). 134 2.2 Context for variable selection 135 The selection of potential variables affecting ES values in the meta-analysis is guided by previous studies 136 (e.g. Brander et al., 2012; Ghermandi et al., 2008; Ojea et al., 2010; Richardson and Loomis, 2009) and 137 the understanding of the system and processes where the ES occur (Figure 2). The water system (the 138 biome) supports the delivery of ES (categorized as surface area of production c and type of ES), which 139 yields a benefit to people that can be measured in monetary terms and could potentially depend on the 140 valuation methodology used and the authors’ familiarity with the case study area. This monetary or 141 economic value is also dependent on the wider context where it occurs, and will be influenced by 142 context variables on a larger scale, including socio economic and demographic factors (e.g. population, 143 GDP, education level), biodiversity richness, and climate change adaptation readiness and vulnerability. 144 At the same time, the ES economic value also impacts the water system. In turn, it can have a feedback 145 effect on the delivery of ES (depletion, for example) as well as on the context (e.g. reduced poverty). 146 147 c Standardization by production unit area is necessary to allow comparability across estimates. 9 148 Figure 2: Potential variables affecting ES values in the meta-analysis 149 150 Previous meta-analytical approaches for ES support this reasoning. These studies include variables 151 related to the context, the study and the ecosystem, that are impacting the economic values of the ES - 152 the dependent variable (Brander et al., 2013; Chaikumbung et al., 2016; Ojea et al., 2010; Ojea and 153 Martin-Ortega, 2015). The next sub section details the selection process for the dependent variable. The 154 full list of variables and their summary statistics are presented in Table 1. In addition, a more detailed 155 definition of each variable is given in Appendix 1. 156 2.3 Database building 157 A peer reviewed literature search was conducted through electronic journal databases including EVRI d , 158 SCIENCEDIRECT and Google Scholar during the months of March to August 2014 using all different 159 combinations of the keywords “Economic Valuation”, “Africa”, “Valuation”, “Ecosystem” and 160 d Accessible at http://www.evri.ca/en 16 Table 1: Variable description and summary statistics 233 Variable Type Description Variable name Coding Number of observations Mean (Std. Dev.) Range [Min; Max] Dependent variable lnVAL Numeral Natural logarithm of the ES value in international $/ha.year (2014 value) 178 3.84 (3.00) [-4.35; 11.35] Explanatory variables Study variables BIO Dummy Type of biome where the service is provided B_IWT Inland wetlands (=1) 64 0.36 (0.48) [0; 1] B_CWT Coastal wetlands (=1) 45 0.25 (0.44) [0; 1] B_FWT Freshwaterg (=1) 20 0.11 (0.32) [0; 1] B_WDL Woodlands (=1) 14 0.08 (0.23) [0; 1] B_TRO Tropical forest (=1) 27 0.15 (0.36) [0; 1] B_GRAS Grasslands (=1) 8 0.04 (0.21) [0; 1] SERV Dummy Type of ecosystem service as per the TEEB classification PROV Provisioning (=1) 113 0.63 (0.48) [0; 1] REG Regulating (=1) 35 0.18 (0.39) [0; 1] SUPP Supporting (=1) 15 0.08 (0.28) [0; 1] CULT Cultural (=1) 18 0.10 (0.30) [0; 1] logHA Numeral Log of the surface area of the ES in hectares logHA 178 10.35 (3.60) [-.47h; 18.19] METD Dummy Original valuation method used in the primary valuation METD_M Market –based methods: direct market price, cost-based, factor income (=1) 158 0.90 (0.31) [0; 1] METD_NM Non-marketbased methods: Contingent valuation and travel cost (=1) 19 0.10 (0.31) [0; 1] g Freshwater biomes include rivers, lakes and floodplain in line with the categorisation of the TEEB (2010) h The negative values are due to the <1ha figures for certain ES. 17 Variable Type Description Variable name Coding Number of observations Mean (Std. Dev.) Range [Min; Max] LEAD Dummy Whether the lead author of the study is based in a local or international institution located in Africa. LEAD First author based in Africa (=1) other (=0) 178 0.80 (0.40) [0; 1] Context variables Socio economic and demographic PMRY_ENROL Numeral Primary school enrolment rate, both sexes, in percentage (World Bank, 2015) 177 102.94 (17.13) [30.61; 131.27] GDP Numeral GDP per capita in thousands of 2014 PPP USD (World Bank, 2015) 178 3.30 (3.27) [0.61; 12.3] POP_R Numeral Percentage of rural population (World Bank, 2015) 178 74.86 (12.00) [23.56; 88.17] POVTY_R Numeral Rural poverty headcount ratio at national poverty line in percentage (World Bank, 2015) 175 50.17 (19.09) [22.4; 92.2] Biodiversity GEF Numeral Composite index by the Global Environmental Facility of relative biodiversity potential for each country. (Global Environmental Facility, 2015) 178 9.37 (6.67) [0.31; 23.52] Climate change VUL Numeral Composite index scoring the vulnerability of each country to climate change. (Notre Dame University, Canada, 2016) 178 1.01 (0.024) [0.98; 1.11] READ Numeral Composite index scoring the readiness of a country to leverage investment in climate change adaptation policies. (Notre Dame University, Canada, 2016) 178 0.99 (0.06) [0.88; 1.09] 18 Study-specific variables include the methodology applied in the original valuation exercise and other 234 characteristics of the case studies. Biome (BIO) is based on what is defined in the original publication 235 and can be an inland wetland (B_IWT), a coastal wetland (B_CWT), a freshwater system i.e. river, lake, 236 floodplains (B_FWT), woodlands (B_WDL), tropical forest (B_TRO), or grassland (B_GRAS) (Table 1). The 237 number of observations for terrestrial and aquatic ecosystems is 49 and 129, respectively. Both types of 238 biomes present similar average values per hectare with 2014 PPP USD 1,457 for terrestrial and 1,469 for 239 aquatic biomes. Ecosystem services (SERV) are classified following the MA and TEEB categorisation into 240 provisioning (PROV), regulating (REG), habitat or supporting (SUPP) and cultural (CULT) services (Table 241 1). The valuation method (METD) can be market-based (METD_M), i.e. direct market pricing, cost based 242 methods, factor income and production function; or non-market based (METD_NM) i.e. contingent 243 valuation and travel cost. The surface area is included in log-transformed hectares (logHA) and refers to 244 the area of the ES provision. Finally, information on the lead author is collected to identify any 245 “authorship effect” (Brouwer et al., 1999), recording if the lead author has an affiliation to a research 246 centre or an international organisation based in Africa (LEAD) i . The literature shows mixed evidence on 247 authorship effects. On one side, one can expect that first authors affiliated to an institution located in 248 Africa might be more likely to report higher ES values, as they may have better knowledge of the local 249 context and of the communities where the market and non-market based valuation methods are 250 implemented, therefore providing more accurate and comprehensive estimates. On the other side, in 251 the case of market-based methods, these local authors may have access to finer scale market data that 252 could lower the estimates (Brander et al., 2006), and have better knowledge of cultural and social norms 253 that may help design unbiased non-market preference elicitation approaches. 254 Context variables related to socio-economic traits, biodiversity level, vulnerability and adaptation to 255 climate change at the national level are also expected to influence the value of water related ES (see 256 i It is assumed that affiliation between publication time and research time has not changed. 19 Figure 2) and were included in the dataset. Each data point for the context variables corresponds to the 257 study’s country and year j . First, socio-economic and demographic variables such as GDP per capita 258 (GDP), education level as the percentage of the population of official primary education age enrolled in 259 primary school (PMRY_ENROL), rural population share expressed as the percentage of population living 260 in rural areas (POP_R) and rural poverty, the percentage of rural population living below the national 261 poverty lines (POVTY_R). The last two variables above are at rural level to reflect that ES provision in the 262 dataset mainly occurs in rural areas. All variables relate to a country’s development levels and can 263 potentially explain data heterogeneity. Indeed, it can be expected that more developed countries would 264 tend to present higher ES values as highlighted in previous meta-analyses (Barrio and Loureiro, 2010; 265 Brander et al., 2006; Ghermandi et al., 2008; Ojea et al., 2010). 266 Second, a variable reflecting the country’s biodiversity status is also included with the biodiversity 267 richness indicator elaborated by the Global Environmental Facility (GEF). Indeed, biodiversity 268 fundamentally underpins ecosystems, supporting their capacity to provide services to humans 269 (Cardinale et al., 2012; Ojea et al., 2010). Higher biodiversity levels are associated with water related ES 270 (Balvanera et al., 2014). However, given that a single service may result from multiple functions, positive 271 and negative effects of biodiversity richness can counteract each other and the resulting net effect is still 272 unknown (Balvanera et al., 2014). Less evidence is available regarding the effect of biodiversity on the 273 economic value of those ecosystem services and the present study wants to contribute in this respect. 274 Third, climate change indices developed by Notre Dame University k for vulnerability to climate change 275 and readiness to adapt are also considered (VUL and READ). These indices are included to explore the 276 extent to which ES values are related to climate change vulnerability and potential adaptation leverage 277 in study countries. It is expected that higher ES values are associated with less vulnerable and more 278 j As an example, for a 2012 study in Uganda, GDP per capita and all other context variables will correspond to year 2012 for Uganda. k ND Gain country index http://index.gain.org/ 20 ready to adapt countries, as a high value ES can reflect the state of the ecosystems and the associated 279 level of benefits society receives. Each index considers several dimensions of a country’s vulnerability 280 and readiness (see Appendix 1). The adjusted for GDP indices are used, they measure the actual 281 performance of the country compared to its expected performance given its GDP. A detailed 282 explanation on all context variables and their sources is available in Appendix 1. Care was taken when 283 selecting the variables to minimize potential collinearity l . The tests for collinearity produced a diagnostic 284 of no correlation problem as the Variance Inflation Factors (VIF) returned values lower than 6 for all 285 variables m (Ojea et al., 2010). Correlation coefficients between each variable are available in Appendix 4. 286 3.2 Model specification 287 The dependent variable in the models ( in yln ) is a vector of the water related ES monetary values 288 converted to 2014 international US$ per hectare per year. It is expressed in logarithmic terms (see Table 289 1) based on the analysis of the histograms of the dependent variable in log and non-log form as well as 290 on the result of the Box-Cox model test (Cameron and Trivedi, 2009, chapter 3) n . Semi-logarithmic 291 regression is also the resulting functional form in previous meta-analyses of ES values (Barrio and 292 Loureiro, 2010; Brander et al., 2007; Johnston et al., 2005; Lindhjem, 2007; Liu and Stern, 2008; 293 Richardson and Loomis, 2009; Rolfe and Brouwer, 2012; Woodward and Wui, 2001). The explicit 294 specification of the meta-regression model can be described as follows: 295 ,ln ,,,,, jicjicsjisji u X X y ++++=  (1) 296 l For example, the adjusted for GDP ND gain indices were chosen over the non-adjusted ones to limit collinearity. m Mean VIF for model 1 is 2.36 ranging from 1.25 to 3.84 and 2.69 for model 2, ranging from 1.36 to 4.95. n The Box-Cox test resulted in a value of – 1038 ( 2  = 129.45) hence the null hypothesis of no difference between semi-log and linear model was rejected at a 1% significance level (i.e. models are significantly different at 99% confidence level in terms of goodness of fit). In addition, we obtain an estimate of 04.0=   , which gives much greater support for a log-linear (or semi-log) model )0( =  than the linear model )1( =  (see Cameron and Trivedi, 2009, chapter 3). 21 where i denotes each specific study ,N i )...,,2,1( = j refers to the value estimate reported in the 297 study )...,,2,1( i M j = ,  is the usual constant term or intercept and the  vectors are the 298 coefficients to be estimated in the meta-analysis. Each  coefficient is associated to a type of 299 explanatory variable: either study specific ( s X ) or context specific ( c X ) (see Table 1). Where each 300 study i provides a single estimate ,j then 1 Mi= and i  collapses into j u . However, where a study 301 gives more than one value estimate, it is necessary to account for the common error across estimates 302 )( j u and the individual-specific effect or panel error within a study ).(i  303 3.3 Model estimation 304 There are several approaches to estimating this model depending on assumptions regarding the error 305 variance-covariance matrix (Lindhjem, 2007). Table 2 presents the different estimators used in recent 306 meta-analysis literature in environmental economics. These include Weighted Least Squares (WLS), 307 Generalized Least Squares (GLS), explicit specifications of panel models with fixed or random effects, 308 and Ordinary Least Squares (OLS) usually applied with Huber-White adjusted standard errors clustered 309 by study. This last estimator has been most commonly used in the environmental economics literature 310 (see Table 2). Meta-regression models dealing specifically with data heterogeneity, heteroscedasticity 311 and correlated observations are described in Nelson and Kennedy (2009). 312 Table 2: Models estimated in meta-analysis studies 313 Estimation technique Study OLS Brander et al., 2012; Ghermandi et al., 2008; Lindhjem, 2007; Liu and Stern, 2008; Loomis and White, 1996; Ojea et al., 2016, 2010; Richardson and Loomis, 2009; Shrestha and Loomis, 2001 OLS with Huber–White adjusted SE Barrio and Loureiro, 2010; Brander et al., 2006; Ghermandi and Nunes, 2013; Johnston et al., 2003; Lindhjem, 2007; Woodward and Wui, 2001; Zandersen and Tol, 2009 Weighed OLS with Huber White Ghermandi and Nunes, 2013 Multi-level OLS Bateman and Jones, 2003; Brander et al., 2007; Brouwer et 22 al., 1999; Ghermandi et al., 2008; Johnston et al., 2003 GLS Ojea et al., 2015; Ojea and Loureiro, 2011 Fixed GLS Ojea and Martin-Ortega, 2015 RE GLS Chaikumbung et al., 2016; Ojea and Loureiro, 2011 GLS cluster SE Chaikumbung et al., 2016 Weighed GLS with cluster SE Chaikumbung et al., 2016; Johnston et al., 2003 Note: some studies estimate more than one model and hence are reported multiple times. Generalized Least Square (GLS), 314 Ordinary Least Squares (OLS), Fixed Effects (FE), Random Effects (RE), Standard Errors (SE). 315 316 Since most studies in the database report more than one monetary value estimate - a panel of 317 observations - estimates from the same study are likely to be correlated. Therefore the meta-regression 318 specification defined in (1) can be estimated with data-panel structure (Nelson and Kennedy, 2009). The 319 appropriateness of including the study specific error term i  was tested by applying the Breusch Pagan 320 Lagrange Multiplier test for random effects (Torres-Reyna, 2007; Zandersen and Tol, 2009) o . The null 321 hypothesis of no panel effect was rejected at 5% significance level ( 2  value of 6.92 with 322 Prob.> 2  = 0.0043). In addition, the Hausman test was used to determine whether the random effects 323 model (as opposed to the fixed effects one) is the correct specification. This procedure tests whether a 324 significant correlation between unobserved individual-specific random effects ( i  ) and the explanatory 325 variables ( i X ) exists (Cameron and Trivedi, 2009, chapter 8; Wooldridge, 2002, chapter 10). Under the 326 null hypothesis, i  in (1) is purely random, implying that it is uncorrelated with regressors i X in (1). The 327 Hausman specification test resulted in a 2  value of 11.46 with Prob. > 2  = 0.32, yielding to not reject 328 the null hypothesis of non-correlation at 5% significance level, and therefore supporting the adoption of 329 a random effects model. Cluster-robust standard errors were specified for the random effects panel 330 data models estimated in section 4 (Cameron and Trivedi, 2009,chapter 8). 331 o This test helps choosing between a random effects regression and a simple OLS regression (Torres-Reyna, 2007) 23 4 Results 332 To better explain the variations in the value observations and check for the robustness of the results 333 obtained, Model 1 and extended Model 2 with a focus on climate change vulnerability and readiness to 334 adapt are estimated. In addition, cross-products of variables are computed to further interpret the 335 results (section 4.2). 336 4.1 Model 1 and 2 337 Both models are random effects panel data models with cluster-robust standard errors and are 338 estimated in STATA (V.14.1) p . The two models perform well with reasonable R square for this type of 339 study q . The estimated coefficients along with their standard errors and 95% confidence intervals are 340 presented in Table 3: 341 342 p A GLS model corrected for heteroscedasticity and an OLS with cluster robust standard errors were also estimated for both models (model 1 and model 2) and similar results were obtained in terms of coefficients significance and behavior. q The overall R-sq is in line with previous published work using the same model (Mattmann et al., 2016) as well as with other model results (Brander et al., 2012, 2006; Brouwer et al., 1999; Chaikumbung et al., 2016; Ghermandi et al., 2008; Ojea et al., 2010, 2015; Shrestha and Loomis, 2001; Woodward and Wui, 2001). 24 343 Table 3: Meta-analysis regression model 1 and 2 results. 344 The coefficients for the dummy variables can be interpreted as constant proportional changes given an 345 absolute change in the variable. 346 Model 1 Model 2 Variable Coefficient (Std. Error) 95% CI Coefficient (Std. Error) 95% CI B_FWT -1.086** (0.376) [-1.822 -0.349] - 1.023** (0.337) [-1.684 -0.363] PROV -1.481* (0.859) [-3.165 0.204] -1.461* (0.868) [-3.163 0.241] REG -0.166 (0.713) [-1.564 1.232] -0.215 (0.727) [-1.639 1.210] SUPP -1.668* (0.951) [-3.531 0.196] -1.810** (0.914) [-3.603 -0.019] logHA -0.357*** (0.084) [-.523 -0.192] -0.295*** (0.083) [-0.458 -0.133] METD_M -0.617 (0.852) [-2.288 1.053] -0.587 (0.859) [-2.271 1.097] LEAD 1.949** (0.817) [0.349 3.550] 2.044** (0.749) [0.575 3.512] PMRY_ENROL -0.035 (0.031) [-0.096 0.027] -0.0342 (0.026) [-0.085 0.017] GDP 0.311* (0.189) [-0.060 0.681] 0.367** (0.143) [0.087 0.648] POP_R 0.039 (0.044) [-0.048 0.126] 0.0482 (0.040) [-0.029 0 .126] POVTY_R -0.040** (0.017) [-0.074 -0.007] -0.044*** (0.014) [-0.071 -0.018] GEF -0.019 (0.075) [-0.166 0.128] -0.139* (0.074) [-0.284 0.005] VUL -46.302*** (12.166) [-70.147 -22.458] READ -12.971** (6.840) [-26.377 0.435] Constant 9.985** (3.930) [2.282 17.688] 9.950** (4.165) [1.785 18.114] Observations 174 174 Groups 34 34 R-sq: 0.3917 0.4818 Note: ***, **, *: Significance at the 1%, 5% and 10% levels, respectively. CI: Confidence Interval Other combinations of variables were tried but gave no significant result If the regressions had included METD_NM instead of METD_M the coefficients for this variable would have been the reversed of the ones presented here i.e. 0.617 and 0.587 for Model 1 and 2, respectively. 25 For the study characteristics, freshwater ecosystems (B_FWT) resulted into a negative and significant 347 coefficient indicating that freshwater ecosystems have in general, lower ES benefits than other types of 348 biomes in the dataset (grasslands, wetlands, tropical forests and woodlands). Provisioning (PROV) and 349 habitat or supporting services (SUPP) display significant negative coefficient estimates, with respect to 350 cultural services as the omitted variable (CULT). This result indicates that provisioning and habitat 351 services are, in general, related to lower ES monetary value as compared to cultural services. One 352 explanation could be that revenues from international tourism can be substantially larger than the 353 economic value derived from generally low value provisioning goods (e.g. fish catch), as obtained in 354 other analyses (UNEP, 2010). Indeed, international users may place higher values than local users on 355 services such as tourism, but lower values on regulation services, while local users may do the opposite. 356 Most original studies included in the database did not provide explicit information on end users. 357 However, it can be expected that end users of cultural services are most often foreign visitors, who are 358 wealthier than end users of provisioning and regulating services, who are mostly local communities. 359 Another potential explanation lies in the common use of the market price valuation method for 360 provisioning services valuation, which in the literature is recognized for providing slightly lower 361 estimates than other methodologies (e.g. Brander et al., 2006). 362 Regarding the valuation method of the primary studies, market-based valuation methods (METD_M) 363 seem not to be significantly different from non-market methodologies in our dataset. However, 364 environmental valuation literature generally shows higher values with non-market valuation techniques 365 than with market-based valuation methods (Brander et al., 2006). 366 The coefficient for surface area is also negative and significant, showing that, on average, the larger the 367 area where the ES is produced, the lower the marginal benefit per hectare. This tendency is in line with 368 other studies on environmental valuation and is due to decreasing marginal returns with size (Brander et 369 al., 2006; Chaikumbung et al., 2016; Ghermandi et al., 2008). 370 32 research should look in this direction to explore trade-offs between natural and non-natural capital in 501 adaptation. 502 Further research should address what drives ES values at the local scale by combining observed values 503 with spatial information that can explain variation at a finer scale. This was not possible for exploring 504 biodiversity, adaptation and vulnerability to climate change in the African case studies. But it may be a 505 necessary approach to understand the specific dynamics of the service users and providers, the area of 506 the ecosystems producing the services and potential seasonal variations in the service provision that 507 may have an effect on their value. 508 33 Appendices Appendix 1 – List of variables ACRONYM VARIABLE DESCRIPTION UNIT TYPE VAL VALUE ES value in 2014 purchasing power parity (PPP) $ per hectare (ha) and year 2014 PPP $ per ha and year Quantitative ECOLOGICAL VARIABLES BIO BIOME Type of biome in which the service is provided n.a. Qualitative SERV SERVICE Type of ecosystem service considered as per the TEEB classification in 4 categories: provisioning (PROV), regulating (REG), supporting (SUPP) and cultural (CULT) Source: http://www.teebweb.org/resources/ecosystem-services/ n.a. Qualitative STUDY VARIABLES METD METHOD Original valuation method used for obtaining the value estimate of the ES. Note: Benefit transfer valuation method was replaced by the original valuation method of the original study for Seyam et al., (2001) and Turpie et al.,(2000). n.a. Qualitative HA SURFACE AREA IN HA Surface area in hectares where the ES is delivered Hectares Quantitative LEAD LEAD Whether the lead of the paper (first author) is affiliated to either an organisation located in Africa, either an international organisation with offices in Africa at the time of publication. n.a. Qualitative SOCIO ECONOMIC INDICATORS PMRY_ENROL GROSS ENROLMENT RATIO, PRIMARY BOTH SEXES, PERCENTAGE Total enrolment in primary education, regardless of age, expressed as a percentage of the population of official primary education age. The ratio can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition. Note: Data is not always available for the study year. When this was the case the closest year with data available was entered. Source: World Bank indicator, can be accessed at http://data.worldbank.org/indicator/SE.SEC.ENRR/countries Percentage Quantitative GDP GDP PER CAPITA IN THOUSANDS OF 2014 PPP $ GDP per capita based on purchasing power parity (PPP). Data are in current international dollars based on the 2011 ICP round. Note: For year 1982 in Zambia, data was not available in 2014 PPP. The current 2014 USD data was taken from the World Bank. Current 2014 USD is equivalent to PPP 2014 USD. Source: World Bank indicator, can be accessed at http://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD 2014 PPP USD Quantitative POP_R PERCENTAGE OF RURAL POPULATION Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population. Aggregation of urban and rural population may not add up to total population because of different country coverages. Source: World Bank indicator, can be accessed at http://data.worldbank.org/indicator/SP.RUR.TOTL.ZS Percentage Quantitative 34 ACRONYM VARIABLE DESCRIPTION UNIT TYPE VULNERABILITY/ ES RELIANCE INDICATORS POVERTY INDICATOR POVTY_R RURAL POVERTY HEADCOUNT RATIO AT NATIONAL POVERTY LINE IN PERCENTAGE Rural poverty headcount ratio is the percentage of the rural population living below the national poverty lines. Source: World Bank indicator, can be accessed at http://data.worldbank.org/indicator/SI.POV.RUHC Percentage Quantitative ENVIRONMENTAL INDICATOR GEF GEF BENEFITS INDEX FOR BIODIVERSITY GEF benefits index for biodiversity is a composite index of relative biodiversity potential for each country based on the species represented in each country, their threat status, and the diversity of habitat types in each country. The index has been normalized so that values run from 0 (no biodiversity potential) to 100 (maximum biodiversity potential) Source: World Bank indicator, can be accessed at https://www.thegef.org/gef/sites/thegef.org/files/documents/GBI_Biodiversity_0.pdf Index Quantitative CLIMATE CHANGE INDICES VUL VULNERABILITY INDEX ADJUSTED FOR GDP The adjusted for GDP Notre Dame Global Adaptation Index (ND-GAIN) for vulnerability is an index assessing the vulnerability of a country by considering six life supporting sectors: food, water, health, ES, human habitat, infrastructure. Each sector is represented by six indicators that span the three cross cutting components of vulnerability: - exposure to climate related hazards; - sensitivity of that sector to climate related hazards; - adaptive capacity of the sector to cope with these impacts Index ranges from - 0.989 to 0.222. Lower scores indicate lower vulnerability. We used the adjusted for GDP version of the index as there is a correlation between the ND-Gain scores and GDP per capita. The adjusted for GDP score is defined as “the distance of a country's measured ND-GAIN score and its expected value based on the regression of ND-GAIN and GDP”. Source ND-GAIN website, can be accessed at http://index.gain.org/ Index Quantitative READ READINESS INDEX ADJUSTED FOR GDP The adjusted for GDP Notre Dame Global Adaptation Index (ND-GAIN) for adaptation is an index measuring readiness by considering a country's ability to apply economic investments to adaptation actions. It considers three components: - economic readiness; - governance readiness; - social readiness Index ranges from -0.387 to 1.228. A lower score indicates a lower performance. We used the adjusted for GDP version of the index as there is a correlation between the ND-Gain scores and GDP per capita. The adjusted for GDP score is defined as “the distance of a country's measured ND-GAIN score and its expected value based on the regression of ND-GAIN and GDP”. Source ND-GAIN website, can be accessed at http://index.gain.org/ Note: Education in the index is the enrolment rate at tertiary school level, not primary school like the variable we used in our model. Index Quantitative n.a: not applicable 35 Appendix 2 – Studies in the database 1. Acharaya, G. and Barbier E.B., 2000, Valuing groundwater recharge through agricultural production in the Hadejia-Nguru wetlands in northen Nigeria. Agricultural Economics 22(3): 247-259. 2. Adekola, O., Moradet S., de Groot R. and Grelot F., 2008, The economic and livelihood value of provisioning services of GaMampa wetland, South Africa. In: 13th IWRA World Water congress, 1 - 4 September, 2008, Montpellier, France. 3. Arntzen, J., 1998, Economic valuation of communal rangelands in Botswana: a case study. IIED, London, UK. 4. Barbier, E.B., Adams W.M. and Kimmage K., 1991, Economic valuation of wetland benefits: the Hadejia-Jama floodplain, Nigeria. IIED, London, UK. 5. Emerton L, 1994, An Economic Valuation of the Costs and Benefits in the Lower Tana Catchment Resulting from Dam Construction, A Report Prepared For Acropolis Kenya Limited 6. Emerton, L., 1998, Djibouti biodiversity - economic assessment. IUCN, Gland, Switzerland. 7. Emerton L., 1999, Balancing the opportunity costs of wildlife conservation for communities around lake Mburo National Park, Uganda, Evaluating Eden Series Discussion Paper No.5 8. Emerton L., 2002, The return of the water: Restoring the Waza Logone floodplain in Cameroon, IUCN Wetlands and Water Resources Programme 9. Emerton, L (ed), 2005, Values and rewards: counting and capturing ecosystem water services for sustainable development. IUCN Water, Nature and Economics Technical Paper No. 1, IUCN — The World Conservation Union, Ecosystems and Livelihoods Group Asia. 10. Emerton, L. and A. Asrat, 1998, Eritrea biodiversity - economic assessment. IUCN, Gland, Switzerland. 11. Emerton, L., Iyango L., Luwum P. and Malinga A., 1998, The present economic value of Nakivubo Urban Wetland, Uganda. National Wetlands Conservation and Management Programme; IUCN: Biodiversity economics for Eastern Africa. 12. Emerton, L. and Muramira E., 1999, Uganda biodiversity - economic assessment. Prepared with National Environment Management Authority, Kampala. IUCN, Gland, Switzerland. 13. Hatfield R. Malleret King D., 2007, The economic value of the mountain gorilla protected forests - The Virungas and Bwindi Impenetrable National Park, IGCP 14. Hoberg J., 2011, Economic Analysis of Mangrove Forests: A case study in Gazi Bay, Kenya, UNEP report 15. Kairo J, Wanjiru C and Ochiewo J, 2009, Net Pay: Economic analysis of a Replanted Mangrove Plantation in Kenya, Journal of Sustainable Forestry, 28:3,395 — 414 16. Kakuru W., Turyahabwe N., and Mugisha J., 2013, Total Economic Value of Wetlands Products and Services in Uganda, The Scientific World Journal, Volume 2013 17. Karanja, F., Emerton L., Mafumbo J. and Kakuru W., 2001, Assessment of the economic value of pallisa district wetlands, Uganda. Biodiversity Economics for Eastern Africa & Uganda's National Wetlands Programme, IUCN Eastern Africa Programme. 18. Kipkoech A., Mogaka H., Cheboiywo J. & Kimaro D., 2011, The total economic value of Maasai Mau, Trans Mara and Eastern Mau Forest blocks of the Mau Forest, Kenya, Environmental Research and Policy Analysis 36 19. Lannas Kathryn S. M. and Turpie Jane K., 2009, Valuing the Provisioning Services of Wetlands: Contrasting a Rural Wetland in Lesotho with a Peri-Urban Wetland in South Africa, Ecology and Society 14(2): 19 20. Ly, O.K., Bishop J.T., Moran D. and Dansohho M., 2006, Estimating the Value of Ecotourism in the Djoudj National Bird Park in Senegal. IUCN, Gland, Switzerland, 34pp. 21. Naidoo, R. and W.L. Adamowicz (2005) Biodiversity and Nature-Based Tourism at Forest Reserves in Uganda, Environment and Development Economics 10(2): 158-178. 22. Navrud, S. and Mungatana E.D., 1994, Environmental valuation in developing countries: The recreational value of wildlife viewing. Ecological Economics 11(2): 135-151. 23. O’Farrell P.J., W.J. De Lange a, D.C. Le Maitre a, B. Reyers a, J.N. Blignaut b, S.J. Milton c, D. Atkinson d, B. Egoh e, A. Maherry a, C. Colvin a, R.M. Cowling, 2011, The possibilities and pitfalls presented by a pragmatic approach to ecosystem service valuation in an arid biodiversity hotspot, Journal of Arid Environments 75 - 612:623 24. Schaafsma, M., Morse-Jones, S., Posen, P., SwetnamR.D., Balmford, A., Bateman, I.J., Burgess, N.D., Chamshama, S.A.O., Fisher, B., Freeman, T., Geofrey, V., Green, R.E., Hepelwa, A.S., Herna´ ndez-Sirventm, A., Hess, S., Kajembe, G.C., Kayharara j, G., Kilonzo, M., Kulindwa, K, Lund, J.F., Madoffe, S.S., Mbwamboq, L., Meilby, H., Ngaga, Y.M., Theilade, I., Treue, T., van Beukering, P., Vyamana, V.G. & Turner, R.K.. 2014. The importance of local forest benefits: Economic valuation of Non-Timber Forest Products in the Eastern Arc Mountains in Tanzania. Global Environmental Change, Volume 24, Pages 295-305 25. Seyam, I.M., Hoekstra A.Y., Ngabirano G.S. and Savenije H.H.G., 2001, The value of freshwater wetlands in the Zambezi basin. Value of Water Research Report Series No. 7, IHE Delft, The Netherlands. 26. Spurgeon, J., 2002, Socio-economic assessment and economic valuation of Egypt’s mangroves, FAO report 27. Turpie, J., 2000, The use and value of natural resources of the Rufiji Floodplain and Delta, Tanzania. Rufiji Environmental Management Project, Technical report No. 17. 28. Turpie, J.K. (2003) The existence value of biodiversity in South Africa: how interest, experience, knowledge, income and perceived level of threat influence local willingness to pay. Ecological Economics 46(1-2): 199-216. 29. Turpie J., 2005, The Valuation of Riparian Fisheries in Southern and Eastern Africa, Tropical river fisheries valuation: background papers to a global synthesis by Neiland, A. E.; Béné, C. 2006. Tropical river fisheries valuation: A global synthesis and critical review. Colombo, Sri Lanka: International Water Management Institute. 45 pp. (Comprehensive Assessment of Water Management in Agriculture Research Report 15) 30. Turpie, J. et al. 2010. Estimation of the Water Quality Amelioration Value of Wetlands A Case Study of the Western Cape, South Africa 31. Turpie J., Barnes J., Arntzen J., Nherera B., Lange G., Baleseng Buzwani B., 2006, Economic value of the Okavango delta, Botswana, and implications for management, Report 32. Turpie, J.K., B.J. Heydenrych and S.J. Lamberth, 2003. Economic value of terrestrial and marine biodiversity in the Cape Floristic Region: implications for defining effective and socially optimal conservation strategies. Biol. Conservation 112: 233251. 33. Turpie, J., Joubert A., 2000, Estimating potential impacts of a change in river quality on the tourism value of Kruger National Park: An application of travel cost, contingent and conjoint valuation methods, Water SA, vol 7-3 34. Turpie, J., Smith B., Emerton L. and Barnes J., 1999, Economic value of the Zambezi Basin Wetlands. Zambezi Basin Wetlands conservation and resource utilization project. IUCN Regional Office for Southern Africa. 37 35. Turpie, J., Day E., Ross-Gillespie V., Louw A.., 2010, Estimation of the Water Quality Amelioration Value of Wetlands A Case Study of the Western Cape, South Africa, Environment for Development Discussion Paper Series 10-15 36. Wasswa H., Mugagga F., Kakembo V., 2013, Economic Implications of Wetland Conversion to Local People’s Livelihoods: The Case of KampalaMukono Corridor (KMC) Wetlands in Uganda, Academia Journal of Environmental Sciences 1(4): 066-077 38 Appendix 3 – Cross tabulation of the value of water related ES and biomes in 2014 PPP USD Biome\ES sub type Climate regulation Aesthetics Erosion prevention Extreme event protection Food Genepool Medicinal resources Nursery Pollination Raw materials Recreation Soil fertility Waste mediation Freshwater provision Inland Wetlands 239 (375) 2,304 (3,058) 10,735 (10,785) 444 (908) 235 (491) 97 16 912 (2,112) 518 (648) 3,862 512 (1,111) Coastal Wetlands 476 (270) 6,541 1,775 (2,865) 271 (247) 5 28,406 (49,043) 191 (451) 337 (203) Freshwater 52 (68) 2 (4) 622 (582) 43 (53) Woodlands 43 (50) 2 (1) 1 254 (574) Tropical forest 42 (33) 287 9 (16) 20 (13) 39 69 (76) 1,260 (1,488) 150 233 (319) Grasslands 3 (0.2) 0.1 (0.1) 1,012 5,552 (7,478) 51,552 39 Appendix 4 – Correlation matrix The figures in the matrix correspond to the correlations significant at the 5% level. 40 References Balvanera, P., Siddique, I., Dee, L., Paquette, A., Isbell, F., Gonzalez, A., Byrnes, J., O’Connor, M.I., Hungate, B.A., Griffin, J.N., 2014. 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