Quantitative-qualitative assessments of environmental causal networks to support the DPSIR framework in the decision-making process

Abstract The DPSIR framework helps to identify and situate stressors, drivers and pressure variables within a dynamic environmental process composed of cause-effect relations. However, an important aspect related to its structural deficiency implies the use of unidirectional causalities between variables. In this work, we extend the capacities of the DPSIR framework by addressing three important points. Firstly, causal networks are built instead of unidirectional causalities, the former based on paths represented by sequences of cause-effect relations between involved variables. These paths are derived from the population growth as a driving force variable, along with CO 2 emissions, waste, water and loss of vegetation cover as pressure variables. Trends of these paths are combined to determine and quantitatively assess a global environmental state trend whose impacts on the environment require corrective management actions as a response. Secondly, quantitative assessments of environmental trends are transformed into fuzzy-qualitative data to facilitate their interpretation. Thirdly, a method based on weighted environmental management actions is presented to decision-makers who aspire to change current path trends in order to approach desirable scenarios similar to those put forth by the OECD outlook towards 2030. The results obtained applying this framework to the State of Morelos, Mexico, show that it can be a useful support tool in the selection and monitoring of management actions capable of reaching favorable environmental trends.

[1]  D. Bryant,et al.  Environmental indicators : a systematic approach to measuring and reporting on environmental policy performance in the context of sustainable development , 1995 .

[2]  Samuel H. Preston,et al.  The effect of population growth on environmental quality , 1996 .

[3]  Ines Omann,et al.  Climate change as a threat to biodiversity: An application of the DPSIR approach , 2009 .

[4]  C. Vörösmarty,et al.  Global water resources: vulnerability from climate change and population growth. , 2000, Science.

[5]  M. Cropper,et al.  The Interaction of Population Growth and Environmental Quality , 1994 .

[6]  Carlo Giupponi,et al.  Towards the development of a decision support system for water resource management , 2005, Environ. Model. Softw..

[7]  John Icely,et al.  A review of the application and evolution of the DPSIR framework with an emphasis on coastal social-ecological systems , 2015 .

[8]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[9]  Kevin Fong-Rey Liu,et al.  A Qualitative Decision Support for Environmental Impact Assessment Using Fuzzy Logic , 2009 .

[10]  M. Hollick Enforcement of mitigation measures resulting from environmental impact assessment , 1981 .

[11]  R. Kohsaka Developing biodiversity indicators for cities: applying the DPSIR model to Nagoya and integrating social and ecological aspects , 2010, Ecological Research.

[12]  Ramin Nabizadeh,et al.  A novel approach in water quality assessment based on fuzzy logic. , 2012, Journal of environmental management.

[13]  W. Silvert Ecological impact classification with fuzzy sets , 1997 .

[14]  Joseph N. Boyer,et al.  The EBM-DPSER Conceptual Model: Integrating Ecosystem Services into the DPSIR Framework , 2013, PloS one.

[15]  Rudolf de Groot,et al.  A conceptual framework for selecting environmental indicator sets , 2008 .

[16]  J. Walmsley Framework for Measuring Sustainable Development in Catchment Systems , 2002, Environmental management.

[17]  P. Vitousek Beyond Global Warming: Ecology and Global Change , 1994 .

[18]  L. R. Bixby,et al.  Población y deforestación en Costa Rica , 1998 .

[19]  Rudolf de Groot,et al.  Framing environmental indicators: moving from causal chains to causal networks , 2008 .

[20]  Avner Vengosh,et al.  Climate change, water resources, and the politics of adaptation in the Middle East and North Africa , 2011 .

[21]  M. Enea,et al.  Fuzzy approach to the environmental impact evaluation , 2001 .

[22]  Ali Azarnivand,et al.  Adaptive policy responses to water shortage mitigation in the arid regions—a systematic approach based on eDPSIR, DEMATEL, and MCDA , 2015, Environmental Monitoring and Assessment.

[23]  T. Ross Fuzzy Logic with Engineering Applications , 1994 .

[24]  Ignacio Requena,et al.  A qualitative method proposal to improve environmental impact assessment , 2013 .

[25]  Glenn W Suter,et al.  A Framework for Fully Integrating Environmental Assessment , 2008, Environmental management.

[26]  A. Shi,et al.  The impact of population pressure on global carbon dioxide emissions, 1975-1996: evidence from pooled cross-country data , 2003 .

[27]  John A. Meech,et al.  Application of Fuzzy Logic in Environmental Risk Assessment: some Thoughts on Fuzzy Sets , 2000, Cybern. Syst..

[28]  William B. Meyer,et al.  HUMAN POPULATION GROWTH AND GLOBAL LAND-USE/COVER CHANGE , 1992 .

[29]  N. Draper,et al.  Applied Regression Analysis , 1966 .

[30]  A J J Lynch,et al.  The Usefulness of a Threat and Disturbance Categorization Developed for Queensland Wetlands to Environmental Management, Monitoring, and Evaluation , 2011, Environmental management.

[31]  Vincenzo Naddeo,et al.  River water quality assessment: A comparison of binary- and fuzzy logic-based approaches , 2012 .

[32]  Alessandro Ferrarini,et al.  Sustainability at the Local Scale: Defining Highly Aggregated Indices for Assessing Environmental Performance. The Province of Reggio Emilia (Italy) as a Case Study , 2004, Environmental management.

[33]  Jeremy Holland,et al.  Quantitative and Qualitative Methods in Impact Evaluation and Measuring Results , 2009 .

[34]  Igor Linkov,et al.  Weight-of-evidence evaluation in environmental assessment: review of qualitative and quantitative approaches. , 2009, The Science of the total environment.

[35]  Martin O'Connor,et al.  An analysis of risks for biodiversity under the DPSIR framework , 2009 .

[36]  R. Bouwmeester,et al.  Fuzzy Logic Water Quality Index and Importance of Water Quality Parameters , 2009 .

[37]  C. Revenga,et al.  Urban growth, climate change, and freshwater availability , 2011, Proceedings of the National Academy of Sciences.

[38]  Perinaz Bhada-Tata,et al.  Environment: Waste production must peak this century , 2013, Nature.

[39]  Malte Busch,et al.  Potentials of quantitative and qualitative approaches to assessing ecosystem services , 2012 .

[40]  Katharina Helming,et al.  Does research applying the DPSIR framework support decision making , 2012 .

[41]  A. Moridi,et al.  Environmentally Sound Water Resources Management in Catchment Level using DPSIR Model and Scenario Analysis , 2013 .