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2019 International Summer Workshop: Training Course on Environmental Accounting and Management 2019暑期工作坊:环境核算与管理国际课程与培训
发布时间:2019-05-17

2019 International Summer Workshop:

Training Course on Environmental Accounting and Management

2019暑期工作坊:环境核算与管理国际课程与培训

June 24-28 (First Week)

July 1-5 (Second Week)

July 8-12 (Third Week)

School of Environment, BNU, Beijing, China

 

International faculty members国际教师团队

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Mark Brown is a professor of Environmental Engineering Sciences and   director of the Center for Environmental Policy at the University of Florida.   He is the Academic Master of 111 Plan in BNU. His interests in research and   education are centered on several areas that deal with the interface of   humanity and environment including, systems ecology, ecological engineering,   ecological economics, environmental planning, environmental policy,   restoration ecology and wetlands ecology.

Mark Brown教授,佛罗里达大学湿地与环境政策研究中心主任,北京师范大学高等学校学科创新引智基地(111计划)学术大师。师从系统生态学创始人H.T. Odum,主要研究领域为能源与环境政策、生态系统服务评估、湿地生态修复等方面,发表论文140余篇,文章引用5600余次,H指数35。国际能值协会创始人,曾担任美国生态工业协会主席。

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Sergio   Ulgiati is a professor of the Parthenope   University. He is the Academic Master of 111 Plan in BNU. His interests in   research and education are centered on several areas that deal with the emergy   analysis, circular economy, zero emission and urban ecological management.

Sergio   Ulgiati教授,意大利Parthenope大学教授,北京师范大学高等学校学科创新引智基地(111计划)学术大师。2007年至今长期致力于链接中意两国科研力量,研发循环经济理论、零排放技术并运用到管理实践之中。发表论文200余篇,其中“中国循环经济测量”和“中国与世界的可持续、幸福与循环经济”两篇文章发表在顶级期刊Science上,分享循环经济的实践与思考。

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Francesco   Gonella is a Professor at Ca’ Foscari   University of Venice, Italy. He was Director of the International School on   Emergy Accounting, Venice 2013, 2015 and 2018. Research interests include   nano-structured glasses, environmental Physics, systems thinking, and higher   education.

Francesco Gonella,威尼斯Ca Foscari大学教授,外专局高端人才,担任国际能值课程主任。研究兴趣包括环境物理学、系统思维及教育等。

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Giovanni   Baiocchi is an applied environmental   economist. His main research looks at the global and local impact of economic   activity, including trade, urbanization, and lifestyles. He is the Academic   Master of 111 Plan in BNU. Giovanni is a lead author for the IPCC 5th   Assessment for Working Group III, focusing on the drivers, trends, and   mitigation of climate change. He was also selected as a qualified independent   expert for environmental themes by the European Commission.

Giovanni Baiocchi,马里兰大学副教授,环境经济学家,北京师范大学高等学校学科创新引智基地(111计划)学术大师。在Environmental Science &   Technology, Ecological Economics, Journal of Industrial Ecology, Nature   Climate Change等期刊上发表多篇论文。是IPCC第三工作组第五次评估报告的主笔作者,是欧洲环境专委会的独立评估专家。

 

Chinese Teacher Team中方教师团队

Gengyuan Liu 刘耕源 (Beijing Normal University北京师范大学)

Lixiao Zhang 张力小 (Beijing Normal University北京师范大学)

Sai Liang 梁赛 (Beijing Normal University北京师范大学)

Yan Hao 郝岩 (Beijing Normal University北京师范大学)

 

课程培训报名Course registration

本次课程培训将分别安排在北师大本部和北师大沙河校区,本次课程免费,沙河校区可以安排沙河校区内住宿与餐饮(收费,统一安排,开具培训费发票),由于课程教室空间与住宿房间有限,请大家报名参加。报名信息请发送到liugengyuan@bnu.edu.cn,课程时间安排与其他信息将由邮件通知。

 

课程信息:

June 24-28 (First Week)

课程名称:Data Visualization with Processing and Applications in R(基于R的数据可视化)

The aim of this visualization course is to effectively communicate information through graphical means. This course aims to provide an understanding of the theory underpinning the field of data visualisation and to provide an introduction to the practicalities of creating effective graphical representations of data using R. Topics covered include:

setting up R, reproducible research in R, Rstudio, R markdown, and knitr; Basic R. Tidy data; Data Processing with dplyr and tidyr; Critique visualizations and inform its design following Tufte’s and Infographics’ principles; Design effective visualizations that tap into human’s high bandwith channels to the cognitive centers; Grammar of Graphics principles Implementation in R: ggplot; Interactive and dynamic graphics in R

 

July 1-5 (Second Week)

课程名称:Environmental Accounting and Management(环境核算与管理)

Lecture 1: Systems Perspective; Systems language

Discussion 1: What is a system? What is systems thinking? The macroscope. General Systems Theory

Discussion 2: Thinking in systems: the Systems zoo

Lecture 2: Systems diagramming, Numerical evaluation

Discussion 1: Complexity, Stocks & Flows, Interconnections, Feedback, Reducing complexity through aggregation

Discussion 2: Thinking in systems: stocks, flows and feedbacks

Lecture 3: Equations and calibrating models, flows, storages and time

Discussion 1: Dynamic behavior, System dynamics, Purpose, Non-Linear relationships

Discussion 2: Unpredictability of systems behavior 1: Chaos and chaotic systems

Lecture 4: Simulation using

Discussion: Unpredictability of systems behavior 2: Chaos and chaotic systems

Assign Simulation Exercise

Lecture 5: General System Principles – Energy, Emergy, Exergy & Thermodynamics

Discussion 1: Hierarchy theory, Self-organization, Lotka’s maximum power, Zipf ’s least effort.

Discussion 2: Entropy and the Second Principle of Thermodynamics

Lecture 6: Complexity-Complex Models

Discussion 1: Complexity, Chaos, Complex adaptive systems

Discussion 2: Case study: emergy and health structures

Lecture 7: Complex Models-Models of Economic Interface

Discussion 1: Growth, Steady State, Pulsing Paradigm

Discussion 2: Case study: emergy and artistic glass sector

Assign Country Project

Lecture 8: Production & yield; Autocatalytic Modules

Discussion 1: Autocatalysis, Malthus

Discussion 2: Case study: emergy and highways infrastructures

Lecture 9: Design Elements

Discussion: Lotka-Volterra, Loops, cycles, series, control, oscillation

Lecture 10: Ecosystem Concepts-Ecological Principles

Discussion: Diversity /richness – stability, succession, pulsing, persistence, Island Biogeography.

 

July 8-12 (Third Week)

课程名称:Spatial Data Science using R(基于R的空间数据科学)

This course introduces advanced quantitative methods for studies in geographical sciences and other related fields, including (1) multivariate methods, (2) spatial analysis methods, (3) spatial prediction and uncertainty estimation methods, and (4) basic geocomputing. Through this course, students will develop their technical foundations for geographical analysis. They are expected to understand the mathematical, statistical, and computational principles of the methods covered, understand the types of science problems that can be addressed using these methods as well as their strengths and weaknesses, and to demonstrate their ability to apply these methods to address their own research questions using the R statistical language and related tools. Students should gain a thorough understanding of the impacts on spatial and statistical inferences of many issues associated with these methods. Topics covered include: Spatial Autocorrelation, Regression, Interpolation, Cross-validation, Clustering, Classification Methods, and Machine Learning Algorithms.