Course Introduction
Environmental Planning and Management
Environmental Planning and Management — 114-2 Required (3.0 credits).
✦ Course Information
| Course title | Environmental Planning and Management |
|---|---|
| Semester | 114-2 |
| Designated for | College of Engineering · English-Taught Intelligent Engineering and Technology Undergraduate Program |
| Curriculum Number | EnvE7090 |
| Curriculum Identity Number | 541EM4050 |
| Class | — |
| Credits | 3.0 |
| Full / Half Yr. | Half |
| Required / Elective | Required |
| Remarks | The upper limit of the number of students: 30. |
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Class Section
| Class | Instructor | Time | Location |
|---|---|---|---|
| — | Hsing-Jui Wang | Monday 7, 8, 9 (14:20–17:20) | — |
Course Description
Environmental problems are inherently characterized by multiple objectives, open system interactions, and various sources of uncertainty. Environmental planning and management aims to address identified environmental issues through a two-stage process of planning and management, with the ultimate goal of achieving effective environmental quality control.
This course focuses on the application of systematic analytical frameworks to environmental problems, emphasizing optimization-based decision analysis, quantitative methods, and systems analysis. Students will learn how to structure complex environmental problems, integrate environmental data, and evaluate alternative management strategies to support informed and rational decision-making under uncertainty.
Course Objective
- Systematically analyze environmental problems and describe them using both qualitative and quantitative approaches.
- Develop an overview of commonly used environmental decision-analysis methods, including optimization and systems-based techniques.
- Apply the methodologies introduced in the course to integrate environmental data with real-world problems and formulate systematic analyses and optimization-based decision recommendations.
Course Requirement
Students are expected to have:
- Fundamental knowledge of environmental engineering, and
- Basic understanding of statistics and environmental economics.
- Student Workload (Expected weekly study hours before and/or after class): —
- Office Hours: Appointment required.
- Designated reading: 待補
- Adjustment methods for students: —
- Teaching methods: —
- Assignment submission methods: —
- Exam methods: —
- Others: Negotiated by both teachers and students
References
Lecture notes
Barrow, Christopher J., 2006, Environmental management for sustainable development 2nd ed., Routledge, Taylor & Francis Group, London and New York
Lein, James K, 2003, Integrated environmental Planning, Blackwell Science Ltd., Oxford.
Randolph, John, 2012, Environmental land use planning and management 2nd ed., Island press
Department for Communities and Local Government, London, 2009, Multi-criteria analysis: a manual
Chang, Ni-Bin, 2011, System analysis for sustainable engineering, theory andapplication, the McGraw-Hill Companies, Inc.
Grading
| No. | Item | % | Explanations for the conditions |
|---|---|---|---|
| 1. | Assignments & Attendance | 40% | — |
| 2. | Mid-term Exam | 25% | — |
| 3. | Final Projects | 35% | — |
評量方式
NTU has not set an upper limit on the percentage of A+ grades.
等第制
NTU uses a letter grade system for assessment. The grade percentage ranges and the single-subject grade conversion table in the NATIONAL TAIWAN UNIVERSITY Regulations Governing Academic Grading are for reference only. Instructors may adjust the percentage ranges according to the grade definitions. For more information, see the Assessment for Learning Section.
Progress
| Week | Date | Topic |
|---|---|---|
| Week 1 | 2/23 | Course Introduction |
| Week 2 | 3/02 | Introduction to Environmental Planning & Management and Basic Data Analysis using Python I |
| Week 3 | 3/09 | Introduction to Environmental Planning & Management and Basic Data Analysis using Python II |
| Week 4 | 3/16 | Cost-Benefit Analysis |
| Week 5 | 3/23 | Statistical Decision-Making Analysis |
| Week 6 | 3/30 | Analytic Hierarchy Process (AHP) |
| Week 7 | 4/06 | Public Holiday |
| Week 8 | 4/13 | Mid-term exam |
| Week 9 | 4/20 | Principal Components Analysis (PCA) |
| Week 10 | 4/27 | Factor Analysis (FA) |
| Week 11 | 5/04 | Fuzzy Theory |
| Week 12 | 5/11 | Bayesian Inference System |
| Week 13 | 5/18 | Bayesian Hierarchical Theory |
| Week 14 | 5/25 | Paper Discussion |
| Week 15 | 6/01 | AI application in EPM |
| Week 16 | 6/08 | Term Project Presentation |
