The strategy of payment for environmental services (PES) has been increasingly present in current environmental policies, due to the acknowledgment that new mechanisms are needed to stimulate the conservation and maintenance of life-supporting services, such as the services of water provision to populations and to agricultural purposes. Nevertheless, some difficulties related to the lack of consistent methodologies to analyze the efficiency and water quality are verified. The chapter applies a methodology based in an adaptive neutral fuzzy inference system (ANFIS) approach to assess water quality. With this purpose, a water quality index is developed through a fuzzy reasoning. The relative importance of water quality indicators involved in the fuzzy inference process is modeled using a multi-attribute decision-aiding method. In recent years, fuzzy-logic-based methods have demonstrated to be appropriate to address uncertainty and subjectivity in environmental problems.
TopIntroduction
In the last hundred years there have been great changes in human society and in the planet's environmental resources. There was a rapid growth in economic activity, with a tenfold increase in the real value of the global Gross Domestic Product (GDP) (Dowrick & DeLong, 2003). At the same time, the Millennium Ecosystem Assessment (2003) presented many negative environmental trends that led to the decline of most of the environmental services1, approximately 60% of the evaluated environmental services are being degraded or used in an unsustainable way.
A worldwide analysis of 106 river basins evaluated that in one-third of them, more than half the area had been converted to agriculture or urban-industrial use. In Europe, 13 river basins have lost at least 90% of their original plant cover. In the Indus river basin, over 90% of forest areas have been converted to other uses, as well as practically all forest areas in Senegal and the Lake Chad basin in sub-Saharan Africa (Postel & Thompson, 2005).
Given these losses, there is an urgent need for, first and foremost, greater implementation of policies and incentives to promote the conservation and sustainable use of environmental resources and the environmental services, and secondly, a more efficient use of the financial resources available in existing conservation programs of environmental resources. In this sense, in terms of environmental policy, Payments for Environmental Services (PES) arise in a flexible way, which has the potential to act in these two strands (Bishop & Hill, 2014).
Since the 1990s, the PES have gained prominence as a market instrument to enable environmental protection (Wunder, 2005), and have been incorporated into public policies in several countries, especially in Latin America. This instrument emerges in a context of economic liberalization, to address the deficiencies of states and to find new sources of funding for conservation and development (Engel, Pagiola, & Wunder, 2008).
The PES justification lies in the criticisms of the instruments of regulation (also called of command and control) and the integrated conservation and development projects that marked the decades of 1970s to 1990s, especially in developing countries (Ezzine-de-Blas, Wunder, Ruiz-Pérez, & del Pilar Moreno-Sanchez, 2016; Ferraro & Kiss, 2002; Pagiola, Landell-Mills, & Bishop, 2002; Pesche, Méral, Hrabanski, & Bonnin, 2013).
While numerous case studies claim that PES programs have been successful in achieving the desired results, especially social and economic outcomes (Galati, Crescimanno, Gristina, Keesstra, & Novara, 2016; Leimona & Lee, 2008; Wegner, 2016; Wunder, 2007), the environmental additionality is not proven or demonstrated.
The attempts to measure environmental additionality in PES involve difficulties of approach by the traditional methods of mathematics and statistics. This is due to the fact that ecosystems are multidimensional. There are many types of observations that can be made in the ecosystem, and these observations may not represent environmental damage. The ecosystem data are rarely available which leads to environmental generalizations. Much information is qualitative or in discrete categories and, therefore, it is difficult to evaluate the environmental results (Bosserman & Ragade, 1982).
The fuzzy logic theory provides conditions for dealing with vague, inaccurate, and imperfect data. This theory, which may be less restrictive, may be considered more adequate for the treatment of information provided by humans than that of probabilities (Sandri & Correa, 1999). Using fuzzy logic is very convenient in assessing environmental issues, since it can adequately address the ambiguities and subjectivities inherent in these problems.