Measuring Environmental Impacts, Subjectivity, and Sustainability

Transcription

1 Measuring Environmental Impacts, Subjectivity, and Sustainability A Breakthrough in Impact Assessment and Natural Resource Allocation Decisions Richard B. Shepard, Ph.D Applied Ecosystem Services, Inc. Troutdale, OR

2 The Problems What This Presentation Considers Problems with the traditional approach to NEPA compliance. How to solve these problems. Assurance that the new approach works.

3 The Problems What This Presentation Considers Problems with the traditional approach to NEPA compliance. How to solve these problems. Assurance that the new approach works.

4 The Problems What This Presentation Considers Problems with the traditional approach to NEPA compliance. How to solve these problems. Assurance that the new approach works.

5 The Problems Value of New Approach Works without statutory change. Addresses and resolves all identified concerns with the current approach. Provides higher quality decisions, is efficient, and is cost effective. Scalable from local needs to global considerations. Objectivity benefits industry, regulators, society, and environment.

6 The Problems Value of New Approach Works without statutory change. Addresses and resolves all identified concerns with the current approach. Provides higher quality decisions, is efficient, and is cost effective. Scalable from local needs to global considerations. Objectivity benefits industry, regulators, society, and environment.

7 The Problems Value of New Approach Works without statutory change. Addresses and resolves all identified concerns with the current approach. Provides higher quality decisions, is efficient, and is cost effective. Scalable from local needs to global considerations. Objectivity benefits industry, regulators, society, and environment.

8 The Problems Value of New Approach Works without statutory change. Addresses and resolves all identified concerns with the current approach. Provides higher quality decisions, is efficient, and is cost effective. Scalable from local needs to global considerations. Objectivity benefits industry, regulators, society, and environment.

9 The Problems Value of New Approach Works without statutory change. Addresses and resolves all identified concerns with the current approach. Provides higher quality decisions, is efficient, and is cost effective. Scalable from local needs to global considerations. Objectivity benefits industry, regulators, society, and environment.

10 The Problems Traditional Approach Descriptive No particular method is used to determine components. Words are used to present existing conditions. Alternatives are explained by words. Problems 1 Not all stakeholder and public values considered. 2 Alternatives are not equally assessed. 3 Cannot objectively equate current and future conditions. 4 All decisions are subjective. 5 The decision-making process is inconsistent.

11 The Problems Traditional Approach Descriptive No particular method is used to determine components. Words are used to present existing conditions. Alternatives are explained by words. Problems 1 Not all stakeholder and public values considered. 2 Alternatives are not equally assessed. 3 Cannot objectively equate current and future conditions. 4 All decisions are subjective. 5 The decision-making process is inconsistent.

12 The Problems What The New Approach Does Includes everyone s values and beliefs during scoping. Quantifies and characterizes environmental, economic, and social conditions. Evaluates all alternatives with the same criteria. Eliminates subjectivity as a factor in decision-making. Measures the concept of sustainability based on societal values.

13 The Problems What The New Approach Does Includes everyone s values and beliefs during scoping. Quantifies and characterizes environmental, economic, and social conditions. Evaluates all alternatives with the same criteria. Eliminates subjectivity as a factor in decision-making. Measures the concept of sustainability based on societal values.

14 The Problems What The New Approach Does Includes everyone s values and beliefs during scoping. Quantifies and characterizes environmental, economic, and social conditions. Evaluates all alternatives with the same criteria. Eliminates subjectivity as a factor in decision-making. Measures the concept of sustainability based on societal values.

15 The Problems What The New Approach Does Includes everyone s values and beliefs during scoping. Quantifies and characterizes environmental, economic, and social conditions. Evaluates all alternatives with the same criteria. Eliminates subjectivity as a factor in decision-making. Measures the concept of sustainability based on societal values.

16 The Problems What The New Approach Does Includes everyone s values and beliefs during scoping. Quantifies and characterizes environmental, economic, and social conditions. Evaluates all alternatives with the same criteria. Eliminates subjectivity as a factor in decision-making. Measures the concept of sustainability based on societal values.

17 Fuzzy Logic: Background Outline 1 The Problems 2 Fuzzy Logic: Background Fuzzy Logic: Application Fitting the New Paradigm in the NEPA Framework 3 The Assurance 4 Summary

18 Fuzzy Logic: Background Linguistic Variables Definition A linguistic variable uses words to describe a concept that is inherently imprecise, uncertain, or fuzzy. The variable may have an underlying measurement, but is itself not directly measured. Most variables we encounter are represented by numbers. Examples Height (Tall or Short), Distance (Far or Near), Weight (Heavy or Light), Size (Large or Small); Pretty, Wealthy, Significant, Acceptable.

19 Fuzzy Logic: Background Sets: Crisp and Fuzzy I Definitions A set is a collection of similar things. Crisp sets have two states: member or non-member with a threshold between them. Fuzzy sets have degrees of membership along the continuum of 0.0 to 1.0. A linguistic variable is defined by several, overlapping fuzzy sets, and a measured value can be a member of two fuzzy sets simultaneously. Example The linguistic variable Distance can have fuzzy sets of Adjacent, Close, Moderate, Far, and Excessive.

20 Fuzzy Logic: Background Sets: Crisp and Fuzzy II The Crisp Set "Tall" The Fuzzy Set "Tall" Membership, µ(x) 0.50 Not set members Set members Membership, µ(x) Height (in feet) Height (in feet)

21 Fuzzy Logic: Background Boolean Logic Definition Boolean logic is expressed by truth tables of crisp sets and exclude the middle ground. Example Google searches of the World Wide Web use Boolean logic. You enter terms such as x AND y, OR z, but NOT a. The returned results are those that fit the constraints. Example Probability statement: There is a ninety percent chance of rain tomorrow. Tomorrow we will learn if the prediction is correct or not.

22 Fuzzy Logic: Background Fuzzy Logic Definition Fuzzy logic represents qualitative relations among fuzzy sets. Example Possibility Statement: There is a ninety percent chance of heavy rain tomorrow. Here we have a probability statement ( ninety percent ) with a fuzzy set ( heavy rain ).

23 Fuzzy Logic: Background Approximate Reasoning Models Approximates the way humans make decisions. Uses fuzzy sets and fuzzy logic to accommodate uncertainties. Benefits from middle ground between AND and OR. Built on IF-THEN rules. Output reflects the influence of all the rules. Results are truth values between [0, 1] with compatibility index.

24 Fuzzy Logic: Background Approximate Reasoning Models Approximates the way humans make decisions. Uses fuzzy sets and fuzzy logic to accommodate uncertainties. Benefits from middle ground between AND and OR. Built on IF-THEN rules. Output reflects the influence of all the rules. Results are truth values between [0, 1] with compatibility index.

25 Fuzzy Logic: Background Approximate Reasoning Models Approximates the way humans make decisions. Uses fuzzy sets and fuzzy logic to accommodate uncertainties. Benefits from middle ground between AND and OR. Built on IF-THEN rules. Output reflects the influence of all the rules. Results are truth values between [0, 1] with compatibility index.

26 Fuzzy Logic: Background Approximate Reasoning Models Approximates the way humans make decisions. Uses fuzzy sets and fuzzy logic to accommodate uncertainties. Benefits from middle ground between AND and OR. Built on IF-THEN rules. Output reflects the influence of all the rules. Results are truth values between [0, 1] with compatibility index.

27 Fuzzy Logic: Background Approximate Reasoning Models Approximates the way humans make decisions. Uses fuzzy sets and fuzzy logic to accommodate uncertainties. Benefits from middle ground between AND and OR. Built on IF-THEN rules. Output reflects the influence of all the rules. Results are truth values between [0, 1] with compatibility index.

28 Fuzzy Logic: Background Approximate Reasoning Models Approximates the way humans make decisions. Uses fuzzy sets and fuzzy logic to accommodate uncertainties. Benefits from middle ground between AND and OR. Built on IF-THEN rules. Output reflects the influence of all the rules. Results are truth values between [0, 1] with compatibility index.

29 Fuzzy Logic: Application Outline 1 The Problems 2 Fuzzy Logic: Background Fuzzy Logic: Application Fitting the New Paradigm in the NEPA Framework 3 The Assurance 4 Summary

30 Fuzzy Logic: Application Solving Two Traditional Problems Several problems with traditional approach to NEPA compliance mentioned earlier. The modern approach to two of these are illustrated. 1 Not all stakeholder and public values considered. 2 Cannot objectively equate current and future conditions.

31 Fuzzy Logic: Application Quantifying Values and Beliefs I 1 Group participants in the scoping process by broad values; e.g., local residents, project supporters, project opponents. 2 Solicit candidate components to be included in the assessment; group by major category: 1 Environmental 2 Economic 3 Social 3 Have each person compare the relative value to them of candidate components pairwise.

32 Fuzzy Logic: Application Quantifying Values and Beliefs II Importance Value Definition 1 Equal importance 3 Weak importance of one over another 5 Strong importance of one over another 7 Demonstrated importance of one over another 9 Absolute importance of one over another 2, 4, 6, 8 Intermediate values between two definitions

33 Fuzzy Logic: Application Quantifying Values and Beliefs III 4 Run a computer program that collates answers in a matrix, calculates the principal eigenvector, and displays the weight of each candidate component: λ = Wt Cn = C 1 C 2 C 3

34 Fuzzy Logic: Application ECI: Environmental Condition Index I Definition An Environmental Condition Index is a number between 0 and 1 that characterizes all components of the environment based on local societal values of desirability, acceptability, or goodness. It is applied to both existing conditions and predicted future conditions under each alternative.

35 Fuzzy Logic: Application ECI: Environmental Condition Index II Calculating an ECI 1 Group components into higher-level policies (e.g., Wildlife habitat, Wetlands, Water quality). 2 Determine objective criteria for each policy (i.e., what constitutes a desired or acceptable condition). 3 Fuzzify measurements and add subjective observations. 4 Calculate consequent fuzzy set of overall policy goodness. 1 For alternatives, incorporate significance of changes from baseline conditions. 5 Aggregate all policies into output fuzzy set of goodness. 6 Defuzzify output fuzzy set to crisp number in the range [ ].

36 Fuzzy Logic: Application ECI: Environmental Condition Index III Start Create Policies Components (environmental, economic, societal) Goodness Criteria? YES Fuzzify Data Hedges NO Define Objectives More Components? YES Policy Consequent Set Rules NO Fuzzy Inference Engine ECI Output Fuzzy Set Defuzzify ECI STOP

37 Fuzzy Logic: Application ECI: Environmental Condition Index IV Rainfall Moderate Heavy µ(x) cm/hr Steep Very Steep 0.65 Slope Vegetation_cover µ(x) Percent Low Medium 1 µ(x) Percent µ(x) 0 0 Erosion_risk Soil_erodability 1 µ(x) 0 Low High

38 Fuzzy Logic: Application Significance Components I The fundamental purpose for conducting an environmental assessment is to determine if impacts are significant. The technical literature defines eight components of significance: Likelihood of occurrence Direction and magnitude Areal extent or distribution Whether impact can be mitigated; if so, to what degree Impact reversibility Duration Timing of occurrence relative to project phases Geographic impact scale

39 Fuzzy Logic: Application Significance Components II Some components can be measured, but all are treated as fuzzy sets. For each impact and each alternative, the eight components are assigned membership grades in each fuzzy set. IF-THEN rules express the semantics of the underlying concerns and are used to calculate a collective value of significance.

40 Fuzzy Logic: Application Quantifying Sustainability No consensus definition. Unmeasurable directly. A Type-2 fuzzy set, or psychometric linguistic variable. Universe of Discourse spans complete range of years. Allows all values and beliefs to be quantified and included in analyses. Any subjective concept can be quantified and used in an approximate reasoning model.

41 Fuzzy Logic: Application Quantifying Sustainability No consensus definition. Unmeasurable directly. A Type-2 fuzzy set, or psychometric linguistic variable. Universe of Discourse spans complete range of years. Allows all values and beliefs to be quantified and included in analyses. Any subjective concept can be quantified and used in an approximate reasoning model.

42 Fuzzy Logic: Application Quantifying Sustainability No consensus definition. Unmeasurable directly. A Type-2 fuzzy set, or psychometric linguistic variable. Universe of Discourse spans complete range of years. Allows all values and beliefs to be quantified and included in analyses. Any subjective concept can be quantified and used in an approximate reasoning model.

43 Fuzzy Logic: Application Quantifying Sustainability No consensus definition. Unmeasurable directly. A Type-2 fuzzy set, or psychometric linguistic variable. Universe of Discourse spans complete range of years. Allows all values and beliefs to be quantified and included in analyses. Any subjective concept can be quantified and used in an approximate reasoning model.

44 Fuzzy Logic: Application Quantifying Sustainability No consensus definition. Unmeasurable directly. A Type-2 fuzzy set, or psychometric linguistic variable. Universe of Discourse spans complete range of years. Allows all values and beliefs to be quantified and included in analyses. Any subjective concept can be quantified and used in an approximate reasoning model.

45 Fuzzy Logic: Application Quantifying Sustainability No consensus definition. Unmeasurable directly. A Type-2 fuzzy set, or psychometric linguistic variable. Universe of Discourse spans complete range of years. Allows all values and beliefs to be quantified and included in analyses. Any subjective concept can be quantified and used in an approximate reasoning model.

46 Fitting the New Paradigm in the NEPA Framework Outline 1 The Problems 2 Fuzzy Logic: Background Fuzzy Logic: Application Fitting the New Paradigm in the NEPA Framework 3 The Assurance 4 Summary

47 Fitting the New Paradigm in the NEPA Framework NEPA Components The new approach enhances every major part of NEPA compliance: Scoping Existing Environment Alternative Description Alternative Analyses Effected Environment Alternative Selection Record of Decision

48 Fitting the New Paradigm in the NEPA Framework Benefits of Extensive Public Involvement Gain local knowledge Incorporate all values and beliefs Remove standing for legal challenge

49 Fitting the New Paradigm in the NEPA Framework Benefits of Extensive Public Involvement Gain local knowledge Incorporate all values and beliefs Remove standing for legal challenge

50 Fitting the New Paradigm in the NEPA Framework Benefits of Extensive Public Involvement Gain local knowledge Incorporate all values and beliefs Remove standing for legal challenge

51 Fitting the New Paradigm in the NEPA Framework Documenting Process and Decision Operate openly as much as possible. Involve public in component and alternatives decisions. Use process audit trails to document how model converted inputs to outputs. Show the ECI of existing conditions and alternative future conditions.

52 The Assurance How do I Know it Works? I Based on the same robust mathematics as control systems in trains, airplanes, anti-lock brakes. Many companies have applyed fuzzy logic, neural networks, and evolutionary computing to successfully solve their business problems.

53 The Assurance How do I Know it Works? II Fuzzy Logic Solution Providers Scianta Intelligence Fuzzy Intelligent Technologies, Inc. Aptronix Fuzzy Zaptron Systems Knowledge System Designs Inform GmbH

54 The Assurance How do I Know it Works? III Corporations Using Fuzzy Logic Solutions Shearson-Lehman (portfolio safety and suitability) Boeing Corporation (manufacturing optimization) BP Corporation (petroleum refining and transportation) Dow Chemical (project risk analysis) IBM (managed healthcare fraud detection) American Express (credit capacity and stress prediction)

55 The Assurance How do I Know it Works? IV Fuzzy Logic Business Applications Risk assessment/management (banks, credit card companies) Fraud detection (medical provider, consumer) Project management Scheduling Drug concentrations for maximum therapeutic value Investment portfolio safety and stability Data mining

56 The Assurance Why Has No One Used This Before? The tools and techniques are not taught to business, engineering, or science students. No one saw this approach as a solution to a recognized problem. Every new technique or method has a beginning. You have not been presented before now with the opportunities this approach opens for your business.

57 The Assurance Why Has No One Used This Before? The tools and techniques are not taught to business, engineering, or science students. No one saw this approach as a solution to a recognized problem. Every new technique or method has a beginning. You have not been presented before now with the opportunities this approach opens for your business.

58 The Assurance Why Has No One Used This Before? The tools and techniques are not taught to business, engineering, or science students. No one saw this approach as a solution to a recognized problem. Every new technique or method has a beginning. You have not been presented before now with the opportunities this approach opens for your business.

59 The Assurance Why Has No One Used This Before? The tools and techniques are not taught to business, engineering, or science students. No one saw this approach as a solution to a recognized problem. Every new technique or method has a beginning. You have not been presented before now with the opportunities this approach opens for your business.

60 Summary Summary Subjective environmental decision making fails everyone, is expensive and takes a long time. Fuzzy sets and fuzzy logic are appropriate solutions that really work, and they are mathematically robust. NEPA compliance can be efficient, effective, objective, and address everyone s concerns.