EvK2CNR
Information
  • Date submitted: 28 Oct 2011
  • Stakeholder type: Major Group
  • Name: EvK2CNR
  • Submission Document: Download
Keywords: Data (1 hits),

Full Submission

Mountains around the world supply a considerable number of ecosystem services of which we know partially the potential. In Italy, for example, mountain regions contribute about 20% to GDP. In order to recognize, valuate and capture of benefits deriving from mountain ecosystem services, and anticipate the threats to which they might be exposed it is important to constantly monitor in a rigorous and scientific way the natural capital from which these services are produced in order to preserve their flow. Italy, through Ev-K2-CNR, is contributing to this endeavour at the international level with the SHARE Project (Stations at High Altitude for Research on Environment).

Mountains host a wide range of ecosystems, different in quantity and quality and generally in a good state of conservation. These differences are so many that they are recognizable from regional scale down to valley level. This kind of richness in terms of diversity stands as a warning light to escape from the temptation to find monocausal explanations.

Considering the need of a reliable valorisation and preservation of the Natural Capital in mountains and also the abovementioned ecological, economic, and cultural diversity (Human Capital) of the global mountain systems, it is recommended to establish regional centres of excellence and knowledge since approaches to Green Economy in mountains shall be designed according to local conditions and must be context appropriately, taking into account the specificities such as environmental fragility, vulnerability and low economies of scale. The creation of a network of multilevel integrated databases on global, regional and local scale with standard, reliable and certified Data on Natural and Human Capital will represent a fundamental tool for the calculation of the economic values and the identification of the different development models.
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