تجزیه و تحلیل تجارت کردن خدمات اکوسیستم در شرق اروپا

In this paper we assess trade-offs between ecosystem services in a spatially explicit manner. From a supply side perspective, we estimate opportunity costs, which reflect in monetary terms the trade-offs between ecosystem services due to a marginal land use change. These are based on estimation of the frontier function, which gives the feasible bundles of ecosystem services that can be generated. For this, a two-stage semi-parametric method is applied and spatial data are used on agricultural revenues, cultural services, carbon sequestration and biodiversity for 18 Central and Eastern European countries. Based on the estimates, we assess which regions are most suitable for expanding any of the ecosystem services. Where opportunity costs are low, a further expansion of any of the ecosystem services is cost-effective. If areas are targeted carefully, joint improvement of several ecosystem services can be reached. If carbon sequestration levels are to be increased, it is best to focus on areas already having high sequestration levels because opportunity costs of carbon sequestration decrease with increasing sequestration levels. For biodiversity and cultural services the pattern is less clear as low opportunity cost were found both in areas rich and poor with these services.

Since the Millennium Ecosystem Assessment (2005), ecosystem services research has gained momentum. Step by step, it is becoming better understood how ecosystem functions and services are interrelated and which factors affect the provision of ecosystem services (see e.g. Daily et al., 2009, Haines-Young and Potschin, 2010, Isbell et al., 2011 and UK National Ecosystem Assessment, 2011). Gaining insight into where particular services are weak or strong is important for making land use decisions (Daily et al., 2009). At different levels of decision making. maps can be generated, quickly and transparently showing the bundles of ecosystem services that can jointly be supplied (e.g Daily and Matson, 2008, Naidoo et al., 2008, Haines-Young, 2009, Maes et al., 2012, Martínez-Harms and Balvanera, 2012 and Schulp et al., 2012). Similarly, awareness of the importance of maintaining ecosystem services for human welfare has increased (see e.g. US Environmental Protection Agency, 2009, Haines-Young and Potschin, 2010, TEEB, 2010 and Bateman et al., 2011). The economics of the natural environment is receiving increased attention owing to numerous initiatives including the TEEB studies (TEEB, 2010), assessments in the UK (UK National Ecosystem Assessment, 2011) and USA (National Research Council, 2005 and US Environmental Protection Agency, 2009) and attempts by the WAVES Partnership and the UN SEEA to integrate the value of ecosystem services into national accounts and economic growth plans.1 As a result of these initiatives, the notion that natural resources have economic value is increasingly finding its way into policy analyses and government decision processes.2 However, at national, regional or global scales, much remains still unknown about the trade-offs between ecosystem services resulting from land use changes. Trade-offs are location specific. Thus it is pertinent to understand where changes in land use can improve total food production, biodiversity levels, climate change mitigation, etc. in the most cost-effective way. To answer such questions, trade-offs need to be known in monetary terms. The objective of this paper is to present a method to estimate the trade-offs between the different ecosystem services due to a land use change. The tradeoffs are to be spatially explicit and in monetary terms. With these results, the method aims to answer two questions of practical policy relevance: first, which regions should decision makers target to achieve national and international biodiversity objectives in a cost-effective way? and second, is it better to jointly generate ecosystem services in a region or to specialize in one of them? The novelty of this paper is derived from the way in which the reported approach combines a supply side perspective for ecosystem services with a non-parametric method to estimate transformation functions and opportunity costs and a unique data set. Most directly related to our work are recent studies at the micro level (e.g. Macpherson et al., 2010, Bostian and Herlihy, 2012 and Sauer and Wossink, 2013). We are not aware of studies that use a non-parametric methods to estimate transformation functions for the analysis of ecosystem services and apply this to analyse supply at sub-national to global levels. By following a supply side approach, we assess the expected change in ecosystem services supply due to a land use change. In contrast to most existing supply side analyses, that quantify trade-offs of land use changes in biophysical terms (see e.g. Millennium Ecosystem Assessment, 2005 and Maes et al., 2012), we quantify these trade-offs in monetary terms. Studies that evaluate the monetary value of ecosystem services, commonly follow a demand side approach (see TEEB, 2010 for a review of the literature) and in that way evaluate how people appraise the changes. Ideally, a combined supply–demand side approach should be adopted in which it is shown how supply changes due to a land use change and how people value these changes. This makes it possible to evaluate whether the changes are welfare improving. However, demand side valuation analyses are less reliable for studies at higher spatial scales.3 For that reason, we refrain from demand side valuation techniques and approach cost-effectiveness of land use changes from a supply side. We assess trade-offs between ecosystem services that are jointly produced in a given area in monetary terms, i.e. we estimate opportunity costs of land use changes, with which it can be evaluated whether land use changes are cost-effective (see Diaz-Balteiro and Romero, 2008). Such analyses at higher spatial scales are rare. We derive opportunity costs from transformation functions, which summarize the feasible bundles of ecosystem services generated in a region (see e.g. Smith et al., 2012). The estimated transformation functions are then used to show the effects of the land use choices available to authorities—where to develop agriculture, where to preserve biodiversity, where to keep a multifunctional landscape. Trade-offs are contingent on the curvature of the frontier function at each point. The transformation functions are estimated empirically using a two-stage, semi-parametric, distance function approach. Hof et al. (2004), Bellenger and Herlihy (2010) and Macpherson et al. (2010) also adopt non-parametric or semi-parametric estimation techniques (though different from the approach adopted by us) to select the areas that jointly produce multiple environmental outputs in the most efficient way. However, whereas these existing studies focus on efficiency, we extend the approach by explicitly considering the opportunity costs of land use changes as a basis for selecting the areas most appropriate for particular land uses. This extra dimension results in trade-off information in monetary terms which is essential but often missing in supply side analyses. For our application we use spatial data on agricultural revenues, cultural services, carbon sequestration and biodiversity for 18 Central and Eastern European countries on the level of grid cells of a size of 0.5×0.5 degree. The data originate from the integrated assessment model IMAGE (Bouwman et al., 2006), biodiversity model GLOBIO (Alkemade et al., 2009) and additional ecosystem services models (Schulp et al., 2012).4 These models give the state-of-the-art knowledge of the interrelations between land use, agricultural production and ecosystem functioning and results are used extensively in e.g. OECD Environmental Outlook (OECD, 2012), UNEP GEO4 (UNEP, 2012) and several other global assessments of environmental change (see e.g. Van Vuuren and Faber, 2009, Brink et al., 2010 and PBL, 2012). These models, however, do not directly yield information on trade-offs or effects of a marginal land use change. Yet, their results can be used in the semi-parametric method as set up in this paper, to recover from the data the transformation function and derive the opportunity costs of a marginal land use change. Using model data is the only feasible option for our analysis because of the lack of reliable observations of the relevant variables at higher spatial scales. Our work differs from other recent studies at the aggegated level, for example those that use bio-economic models. Examples are the InVEST model (see e.g. Daily and Matson, 2008, Polasky et al., 2008, Nelson et al., 2009 and Keeler et al., 2012), the bio-economic models used to derive cost-effective ecological restoration of the Murray Darling basin in south-east Australia (Crossman and Bryan, 2009, Bryan, 2010 and Bryan et al., 2011) and bio-economic models by e.g. Hauer et al. (2010) and Barraquand and Martinet (2011). Other examples of spatially explicit trade-off analyses for ecosystem services use GIS or heuristic routines to combine ecological and economic concepts (Bateman, 2009, Bateman et al., 2011 and White et al., 2012) or do not estimate trade-offs in monetary terms (Naidoo et al., 2008, Raudsepp-Hearne et al., 2010 and Maes et al., 2012). Most of these analyses, however, are less suitable for doing trade-off analysis at sub-national to global scales. Hussain et al. (2011) also base their analysis on IMAGE and GLOBIO data (Brink et al., 2010). They however employ benefit transfer methods to evaluate values of changes in ecosystem services provision. Such methods remain controversial especially when applied at high spatial scales. The remainder of this paper is set up as follows. In Section 2 we briefly discuss the economic model. The data used are discussed in Section 3. In Section 4, we present estimation results. Finally, Section 5 ends with a discussion and conclusions.

In this paper we assessed trade-offs between ecosystem services in a spatially explicit manner. The estimated opportunity costs represent the trade-offs between on the one hand biodiversity, cultural services or carbon sequestration and on the other hand agricultural revenues (provisioning services) resulting from a marginal change in land use. These opportunity costs are based on estimates of the transformation function. This function shows the feasible bundles of ecosystem services that can be generated in a region depending on a number of regional and agro-climatical characteristics. For this, a two-stage semi-parametric robust, conditional FDH method is set up and spatial data are used on agricultural revenues, cultural services, carbon sequestration and biodiversity for 18 Central and Eastern European countries. The objectives of this paper were twofold. The first aim was to analyze trade-offs between ecosystem services in a spatially explicit manner in order to assess in which regions ecosystem services can be increased cost-effectively. The second aim was to assess whether it is better to jointly generate ecosystem services or to specialize in one of them. As for the first objective, we indicated which areas are most suitable for expanding provision of each of the ecosystem services. If areas are targeted carefully, joint improvement of biodiversity, cultural services and carbon sequestration can be reached. Moreover, in certain areas expansion of agricultural production only leads to a marginal loss of biodiversity or one of the other ecosystem services. Generally, it is more cost-effective to target regions with low opportunity costs. For increasing carbon sequestration, targeting areas already having high sequestration levels is cost-effective. Opportunity costs decrease at a decreasing rate when carbon sequestration levels increase. For biodiversity and cultural services this pattern is less clear. We also found that in general opportunity costs increase if biodiversity levels increase, even though at a decreasing rate. In some biodiversity rich areas opportunity costs start to decrease again if biodiversity levels cross a certain threshold level. It is noted that these opportunity costs indicate the gross agricultural benefits foregone due to a marginal increase of biodiversity, cultural services or carbon sequestration. They do not indicate whether society is willing to pay for these foregone benefits. Nevertheless, these results do provide valuable information on effects of land use changes which is relevant for making land use decisions. As for the second objective, we conclude that the transformation function is not quasi-concave. As a result, the assumption often made in economic analysis that the cost of producing an additional unit of a certain good or service gradually increases, does not apply for all cases. Especially for the relation between gross agricultural revenues (provisioning services) and carbon sequestration, specialization in one of the ecosystem services seems to be cost-effective. The relationship between agricultural revenues and biodiversity or cultural services is more complex. In most areas combining bundles of ecosystem services is cost-effective. But especially if biodiversity levels are high, focusing on biodiversity conservation, instead of combining agricultural production and biodiversity, becomes cost-effective. The approach presented here provides interesting insights in the interactions between ecosystem services if land use changes are to be proposed. Showing the trade-offs between ecosystem services helps to understand the linkages within ecosystems. An important observation is that these linkages may not follow a concave relationship, an assumption made in several related studies. If concavity of the frontier is imposed in a situation where this is in fact non-concave, flawed inference will follow and thus the conclusions on the suitability for multifunctional land use or specialization will be incorrect. In addition, the shape of the frontier depends on the variables included. Polasky et al. (2008) estimate a concave frontier for the relation between agricultural income and number of species. A rough estimate of MSA can be obtained by translating the acreages of their land use categories to MSA values and then drawing the frontier, instead of the number of species. It turns out that in that case the shape of their frontier would be different and be much more comparable to the shape found in our study. Moreover, results also depend on the services included. Sensitivity analysis showed that deleting one of the ecosystem services did not change much the relationship between the remaining variables. Similarly, adding another ecosystem services is not expected to alter the relationships between the services currently analyzed. The interactions with the newly added service, however, may show unexpected new insights. Adding more variables will also make the analysis more complex. Furthermore, this analysis only included one input variable, land allocation, and adopted a grid size of 50×50 km. A grid size of 50×50 km is a large scale for ecosystem services analyses because it misses local heterogeneity which is important for some ecosystem services. Currently it is not yet feasible to obtain data at a lower resolution, but this will be possible in the near future when IMAGE will be based on smaller grid cells. Note, however, that the grid data in fact are aggregates of sub-grid information on shares of each grid cell covered with a particular land use type and the ecosystem services generated by each of these land use types. Including more input variables may also become possible in the near future. Data on regulating services like pollination, erosion protection and pest control are already available, but they should be complemented with data on human inputs or land use intensity in order to be able to properly show the trade-off between using human or natural inputs. This is left for future research. Finally, the lessons learned on the interactions between the different ecosystem services may also complement and improve demand side environmental valuation studies searching for human preferences for land use changes. They may complement these studies as the results show which combinations of ecosystem services actually are feasible and what trade-offs of proposed land use changes will be. It may also improve valuation studies. In these studies respondents are asked to evaluate a proposed change without them actually knowing what the effects will be. In that case, it can be wondered whether values expressed truly reflect preferences. The trade-off information from our analysis provides insights about these effects which may in the end shape and sharpen preferences. Therefore, the relevance of the method and results presented here goes beyond its direct results but also feeds into other, related fields of research.