تعیین کمیت در وابستگی های متقابل بین سیستم های اقتصادی و خدمات اکوسیستم: مدل ورودی خروجی اعمال شده برای دهانه رود سن
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|8629||2011||12 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Ecological Economics, Volume 70, Issue 9, 15 July 2011, Pages 1660–1671
The aim of this paper is to assess the possible contribution of an input–output model towards two of the basic principles of the sustainability strategy of integrated coastal zone management (ICZM) and Post-Normal Science. According to these principles, decision-support tools should offer a holistic perspective and handle high uncertainty. The difficulties in reaching sustainability are due partly to the prevailing use of “narrow-system-boundary” tools that are non-holistic. Consequently, they fail to capture important ecosystem services and ignore interdependencies between them. To comply with the basic principles, our method allows environmental assets to be evaluated in multiple units and integrates results from recent researches in natural sciences. Both enable coverage of interdependencies between ecosystem services. Thereby, we enlarge input–output modelling from the two conventional ecosystem services of sink and provisioning to the most vital ones: the supporting services. An application to the Seine estuary addresses the impacts of maritime transportation infrastructures on nursery habitats for commercial fish. The ecosystem services covered are life support and resource provisioning. Our results show that the restoration of a total of 73.7 km2 of nursery areas over the period 2004–2015 would result in a stock of sole in 2015 that exceeds the “business as usual” scenario by 44.2% (uncertainty range: 35.9%–69.9%). In spite of high restoration costs, the negative macro-economic impact is very low. However, on the sector level, a trade-off results between nurseries and three economic sectors. The quantification of such trade-offs in our model is particularly useful to public participation in decision-making.
The European guidelines for the implementation of the Water Framework Directive recommend the use of cost–benefit analysis (CBA) to identify water bodies in which environmental measures present disproportionate costs (European communities, 2009). For water bodies where costs exceed benefits, decision makers can ask the European Commission to postpone environmental targets or make them less stringent. The European Commission will have to consider such requests carefully as it seems that the difficulties in reaching sustainability are due partly to the prevailing use of “narrow-system-boundary” tools (e.g. CBA). Such tools are useful but they are not the panacea, because they are non-holistic, non-participative and exclude complex environmental issues with too high uncertainty. They represent only one kind of tool among others and should be complemented by other tools and approaches. This has been the problem of the recent decades. “Narrow-system-boundary” tools came in support of sector-related and individual resource-based policies in environmental management. As a result, environmental impacts have been analysed separately, whereas holistic analyses were required, as with European coastal zones ( Belfiore, 2000, O'Hagan and Ballinger, 2009 and Stojanovic and Ballinger, 2009). Consequently, numerous efforts have failed to achieve sustainability. The prevailing use of “narrow-system-boundary” tools such as CBA is partly responsible for the failure to achieve sustainability in European coastal zones. Many investigations on CBA limitations might explain this issue (inter alia van den Bergh, 2000, Maréchal, 2007 and Munda et al., 1994). One limitation is that CBA is an analytical approach rather than a holistic one ( Ackerman, 2004, Gallopin et al., 2001 and Stirling, 2001). That is to say, it considers a narrow range of causes and consequences and takes into account only a small part of the world. It restricts the scope of issues to a micro-scale and leaves out connections. Consequently, CBA fails to consider interdependencies between ecosystem services. However, interdependencies are important (de Groot et al., 2002), since the ecosystem services that directly benefit human activities and survival – resources provisioning and cultural services – depend on the existence of three vital ecosystem services: supporting, regulating and sink services (Millennium Ecosystem Assessment, 2005). Failing to consider interdependencies is a limitation which is not only inherent in CBA but to all other approaches that use single indicators — e.g. green GDP, genuine saving, ecological footprint, and cost–benefit ratio in CBA. Single indicators cannot encapsulate all the complexity inherent in ecosystems (Ashford, 1981). To solve that problem, Post-Normal Science and Integrated Coastal Zone Management (ICZM) (Box 1) put at their core the basic idea of extended peer communities (i.e. stakeholder participation) to encompass the multiplicity of legitimate perspectives ( Belfiore, 2000, Funtowicz and Ravetz, 1994, O'Hagan and Ballinger, 2009, Ravetz, 2006 and Stojanovic and Ballinger, 2009). They recommend avoiding the hegemonisation of a single indicator in analyses of environmental issues and suggest complementing single indicator methods with tools that offer holistic properties (Giampietro et al., 2006). This would enable a globalising approach where various elements, usually dissected into parts, are instead gathered to be studied together with their interactions inside a system (Gallopin et al., 2001). However, if the advantage of holistic tools is to reflect ecosystem complexity in a better way, the disadvantage is that complexity causes high degrees of uncertainty in turn (Gallopin et al., 2001 and Munda et al., 1994). The degree of uncertainty is so high that it often takes the form of “indeterminacy”, which means that it is impossible to perform an accurate prediction of the future state of the system. That is to say, no statistical correlation can be established between a cause and an effect. As a result, statistic and probability theory do not apply (Giampietro et al., 2006). This is usually due to the inherent ecosystem complexity, the lack of scientific knowledge and the absence of good data. Consequently, some scientists might be tempted to exclude issues from their analysis that are too uncertain. However, addressing issues with indeterminacy is important because high uncertainty is rather a rule than an exception in environmental issues (Giampietro et al., 2006, Munda et al., 1994, Refsgaard et al., 2006 and Stirling, 2001). To prevent scientists from putting aside issues with indeterminacy, Post-Normal Science and ICZM suggest an approach that allows high uncertainty to be handled. It consists in using decision-support tools within a stakeholder participation process (Belfiore, 2000, O'Hagan and Ballinger, 2009 and Stojanovic and Ballinger, 2009). The recognition of the importance of stakeholder participation stems from the strong belief in Post-Normal Science that it is impossible to define in absolute terms what should be considered as enough scientific evidence to make a decision. Inherent ecosystem complexity and our limited understanding mentioned above explain why data and knowledge will always be incomplete (Gallopin et al., 2001 and Munda, 2004). For instance, in many environmental issues, scientists encounter difficulties in distinguishing the contribution of each cause to an effect or even in considering the multiple effects of one cause (Gobin et al., 2004, Maxim et al., 2009 and Refsgaard et al., 2006). Moreover, even when the “cause–effect” relationship can be quantified reliably, the individual appraisal of impacts caused by policy measures is inherently subjective (Stirling, 2001). As a result, a part of individual judgment and common sense will always remain. Consequently, the transparent inclusion of divergent public perspectives and value judgment is important. Interesting techniques in this sense are “social multi-criteria evaluations” addressed by Giampietro et al., 2006 and Stirling, 2006. This approach consists in a multi-criteria evaluation – in which stakeholders give scores to policy options – combined with a quality assurance to guarantee the reliability of the final result. The purpose of this paper is to build on the methodological developments from Leontief, 1970, Victor, 1972, Carpentier, 1994 and Jin et al., 2003, with the aim of developing an input–output (I–O) model based on commodity-by-industry tables and assess the contribution the model can bring to the two basic principles of Post-Normal Science and ICZM. The two basic principles consist in developing methodological approaches for the analysis of environmental issues that i) allow high uncertainty issues to be addressed (Funtowicz and Ravetz, 1994) and ii) demonstrate holistic properties (Giampietro et al., 2006, O'Hagan and Ballinger, 2009 and Cheong, 2008) which take into account interdependencies between ecosystem services. With these aims in mind, we have developed an ecological–economic I–O model enabling the identification and quantification of trade-offs (e.g. between environmental and economic targets, and between two economic sectors). The model has been applied to a real case study: the restoration of estuarine nurseries for sole juveniles in the Seine estuary (Haute-Normandie region, France), which is located in the Eastern channel (fishing zone VIId extending from the South of England to North of France). The interdependency between a provisioning ecosystem service (fish resources) and a life supporting service (nursery habitat for fish juveniles) is considered as well as its impact on human activities. Thereby, this paper contributes to extending I–O modelling from the conventional ecosystem services of sink (accumulation of pollutants emitted into the ecosystem) and provisioning (consumption of natural resources) to supporting ecosystem services, the most vital category, since all ecosystem services depend on them ( de Groot et al., 2002 and Millennium Ecosystem Assessment, 2005). The remainder of the paper is organised as follows. Section 2 describes the conceptual model of the ecological–economic system analysed in this study. Section 3 formalises the ecological–economic I–O model developed to simulate the system. Data collection is discussed in Section 4. The next two sections apply the model to three policy scenarios of nursery restoration programmes (Section 5) and provide results and discussion on the limitations (Section 6). The final section concludes with the contributions of our model to I–O modelling and to the basic principles of ICZM and Post-Normal Science.
نتیجه گیری انگلیسی
In this paper, we develop an ecological–economic I–O model to estimate the impacts of the restoration of half of the fish nurseries with high juvenile densities (73.7 km2) that have been largely destroyed between 1834 and 2004. Our results show that such a measure, applied on the basis of the precautionary principle advocated in the Marine Strategy Framework Directive (European Parliament and Council, 2008), can be implemented in the Seine estuary with only a slight decrease in macro-economic indicators compared to a situation without restoration (BAU scenario). The amount of sole biomass generated by the restoration exceeds the BAU scenario by 44.2%, while the GDP, the total gross operating surplus, the total compensation of employees, and the total employment are all below the BAU by 0.2%. However, these results must be considered as partial. If all costs of nursery restoration were included in the model, a part of ecosystem services could not be assessed because appropriate data and knowledge do not exist yet. Only two ecosystem services are evaluated by the model. The first is life support for juvenile sole, an overfished species whose population is at risk in the Eastern channel (ICES, 2008). The second is the provisioning service of sole for human consumption, which is an important service provided to the economy, given the high commercial value of sole. If, without considering all the benefits from ecosystem services, our results show little negative macro-economic impact, extending the assessment to the five other ecosystem services mentioned in Section 6.2 would probably demonstrate that nursery restoration has positive macro-economic impacts. The model is operational and ready for extension to these other services as soon as appropriate data becomes available. The results also show that a vast nursery restoration programme generates significant economic impacts on the sector level rather than on the macro-economic one. This suggests that the restoration costs can be seen more as a problem of cost allocation than as a problem per se. In this perspective, the model helps to identify the sectors which would be the most vulnerable if the “Polluter Pays Principle” were applied, such as recommended in the Marine Strategy Framework Directive. This concerns harbours, the mining sector and the sector of coke manufacturing, refined petroleum products and nuclear fuels (Section 6.3). Identifying vulnerable sectors might help decision makers to adapt this principle and change the cost allocation rules, if they want to avoid a complete collapse of some economic sectors. For instance, indirect polluters such as the tertiary sector and final consumers (households) could participate in restoration costs, since both benefit from commodities transported on water. Moreover, this paper shows possibilities to improve the holistic properties of I–O modelling, one of the two basic principles of Post-Normal Science and ICZM that is tested in this paper. This is achieved by extending the amount of categories of ecosystem services considered. Usually, I–O analyses only address the problem of sink (accumulation of pollutants emitted into the ecosystem) and provisioning services (provision of natural resources for human consumption). In this paper, we extend I–O modelling to supporting services (estuarine nurseries as a habitat for fish juveniles). Such extension is very important because supporting services are the most vital category of ecosystem services, since all other ecosystem services depend on them (de Groot et al., 2002 and Millennium Ecosystem Assessment, 2005). Besides extending the number of ecosystem service categories, holistic properties are also ensured by the use of multiple indicators and units in our I–O model. This fulfils the recommendation from Post-Normal Science advocating the use of a diverse panel of indicators to capture the complexity of ecosystems and ensure holistic properties (Giampietro et al., 2006). Along with the use of recent developments in biology and hydro-sedimentology, this makes it possible to consider interdependencies between ecosystem services in I–O modelling, as recommended by Carpentier (1994) (Section 3.1). This also encounters the definition of “holistic” from Gallopin et al. (2001) (Section 1). If the advantage of holistic properties is a better understanding of the inherent complexity of ecosystems and to enlarge the amount of ecosystem service categories considered, the disadvantage is that great uncertainty is introduced into scientific analyses (Section 3.1). As recommended by Post-Normal Science and ICZM, this uncertainty should be managed. To fulfil this recommendation, we apply the following strategy. Part of the uncertainty – the degree of precision – is handled through a sensitive analysis showing the range of values within which our results may vary, due to the relative indeterminacy on the model parameters (Section 6). This concerns the short to medium term uncertainty as to the capacity of estuarine nurseries to shelter sole juveniles (depending on sediment and water contamination — sink service), the nursery surface area that will remain (life support service), and the marine fish stock in the Eastern channel for human consumption (provisioning service). Another part of uncertainty – the degree of accuracy – is handled through the validation of the economic results given by the model based on economic data observed in the past. The discrepancy between observed data and the results given by the model is used as a measure of the uncertainty due to the architecture of the model. Another part of the uncertainty cannot be analysed through sensitivity analysis because it relates to parameters and variables that could not be inserted into the model. This problem concerns the five ecosystem services mentioned in Section 6.2 for which appropriate data do not yet exist. As a result, uncertainty remains on the interdependencies between these five ecosystem services and the economic system. Until data and knowledge are produced, this is typically the kind of issue for which final decision relies more on individual judgment and common sense than on scientific evidence. This shows the need for transparent inclusion of divergent public perspectives and value judgment in decision-making processes via stakeholder participation techniques (Giampietro et al., 2006 and Stirling, 2006).