Type of Credit: Elective
Credit(s)
Number of Students
Sustainable development has been ardently advocated by numerous international institutions, governments, NGOs, and corporations in recent years. Despite its growing prominence, the concept of sustainable development is broad and complex. One illustrative example of its scope is the United Nations’ 17 Sustainable Development Goals (SDGs). These cover a wide array of interconnected objectives, ranging from poverty eradication and climate action, to ensuring inclusive education and beyond.
Navigating these multifaceted and often nebulous concepts can pose a significant challenge. The tasks of defining these goals, assessing progress, and crafting effective strategies are no small feats. Yet, the advent of advanced data analysis tools and techniques provides a promising pathway to address these complex issues.
能力項目說明
This course is designed to illuminate this intersection. Its goal is to equip students with the necessary knowledge and skills to apply data analysis in the context of sustainable development. Through this journey, students will cultivate a robust understanding of how to define, evaluate, and address sustainable development goals using data-driven approaches.
This course is designed to leverage data analysis in order to evaluate the ways in which various groups and classes mobilize to promote their agendas, ultimately impacting the progress of sustainable development. These social actors encompass corporations, trade associations, social classes, racial and ethnic groups, as well as the state. Through this course, you will embark on a journey into a dynamic and rapidly evolving field that has the potential to drive significant change on corporate, national, and even global levels.
| 教學週次Course Week | 彈性補充教學週次Flexible Supplemental Instruction Week | 彈性補充教學類別Flexible Supplemental Instruction Type |
|---|---|---|
Week 1: Introduction of the Course (2024/9/12)
Introduction: Relationships between Sustainable Development and Data Analysis
UN Global Pulse (May 2012) Big Data for Development: Challenges and Opportunities.
Week 2: Data Sources: Governmental Data, Private Data, and Citizen Data (2024/9/19)
Section 1: Corporations and Sustainable Development
Week 3: Organizations and Environment (2024/9/26)
Grant et. al. “Organizational Size and Pollution: The Case of the U.S. Chemical Industry.” American Sociological Review, 67:389-407.
Prechel, Harland and Lu Zheng. 2011. “Corporate Characteristics, Political Embeddedness, and Environmental Pollution by Large U.S. Corporations.
Week 4: Corporations, Climate Change, and Carbon Emissions (2024/10/3)
Prechel, Harland.2021. “Neoliberal Organizational and Political-Legal Arrangements and Greenhouse Gas Emissions in the U.S. Electrical Energy Sector.” The Sociological Quarterly, 62:2, 209-233
Pien, Chao, and Chou. Forthcoming 2023. “The Developmental State’s Legacy and Corporate Carbon Emission Performance: Evidence from Taiwanese Firms between 2014 and 2018.” Climate and Development.
Week 5: Double Ten Day Break (2024/10/10)
Week 6: Practices in Corporation Analysis (2024/10/17)
Section 2: Spatial Factors and Sustainable Development
Week 7: Spatial Analysis and Social Inequality (2024/10/24)
Mollalo, Vahedi, and Rivera. 2020. "GIS-based spatial modeling of COVID-19 incidence rate in the continental United States." Science of the total environment 728 (2020).
Chetty, Friedman, Hendren, Jones, Porter. “The Opportunity Atlas: Mapping the Childhood Roots of Social Mobility” NBER wp, 2018
Week 8: Spatial Analysis and Climate Change (2024/10/31)
Chang, H.S., Su, Q. and Jheng, D.C., 2022. “Comparing the spatial patterns of flooding and individual risk perception: A case study of Yunlin, Taiwan.” Urban Climate, 45
Pien et. al. Draft. 2023. "Local Energy Transition Practice’s Political Impacts: Evidence from Taiwanese Nuclear Referendums in 2018 and 2021"
Week 9: Practices in Spatial Analysis (2024/11/7)
Section 3: Neo-liberalism and Income
Week 10: Income Evaluation (2024/11/14)
Rose, Stephen. 2018. “How Different Studies Measure Income Inequality in the US: Piketty and Company Are Not the Only Game in Town.” Urban Institute.
Atkinson, Piketty, and Saez. 2011. “Top incomes in the long run of history.” Journal of economic literature, 49(1), pp.3-71.
Week 11: Income Inequality after the 1980s (2024/11/21)
Nau. 2013. Economic Elites, Investments, and Income Inequality. Social Forces, 92:2
Chu et al. 2023. Distributional National Accounts of Taiwan, 1981–2017. Taiwan Economic Review, 51:2
Week 12: Practice in Income Analysis (2024/11/28)
Section 4: The State and International Efforts and Sustainability
Week 13: Foreign Aid (2024/12/5)
Burnside, Craig, and David Dollar. 2000. "Aid, Policies and Growth." American Economic Review 90 (4):847-68
Dunning, Thad. 2004. "Conditioning the Effects of Aid: Cold War Politics, Donor Credibility, and Democracy in Africa." International Organization 58 (2):409-23
Week 14: The State and Renewable Energy Development (2024/12/12)
Dani Rodrik. 1998. “Why Do More Open Economies Have Bigger Governments?” Journal of Political Economy 106 (1998), 997-1032
Alves et. al. 2019. From a breeze to the four winds: A panel analysis of the international diffusion of renewable energy incentive policies (2005–2015). Energy policy, 125.
Week 15: Practice in State and International Institutions (2024/12/19)
Week 16: Final Proposal Presentation I (2024/12/26)
Week 17: Final Proposal Presentation II (2025/1/2)
Week 18: Self-learning and Final Report Writing (2025/1/9)
1. It is essential that you complete all required readings before attending the classes.
2. This course is structured into different sections, each consisting of both literature classes and practice classes.
3. In the literature classes, students are expected to write critique papers. For the short paper, students should review all the assigned readings for a given week. The critique should include a clear identification of the research question(s), the cause, the effect, theoretical argument, empirical design, and findings of the readings. It is important to focus on identifying one central theoretical limitation or one central empirical limitation that has significant conceptual implications for the analysis. Furthermore, students should provide a thorough analysis of the weakness and present a plausible argument for possible improvements. Each short paper should be two single-spaced pages and must be submitted by 5 pm prior to the day of the course. Additionally, students are expected to read and provide feedback on other teams' critique papers. Authors are responsible for sharing their short papers with the entire class.
4. During the practice classes, the instructor will assign several projects for the students. Students should prepare the necessary data and codes before the classes and complete the projects during the practice sessions.
5. All students are required to utilize data analysis techniques to develop individual final project papers on a topic within the field of sustainable development. Students should carefully follow the guidelines provided by the instructors to effectively develop their final proposals and papers.
Generative AI Utility Policy
Conditional Acceptance: