Necessity to sustain resources has risen in recent years with significant number of people affected by lack of access to essential resources. Framing policies that support environmental sustainability is necessary for addressing the issue. Effective policies necessitate access to a framework which assesses and keeps track of sustainability. Conventional frameworks that support such policy-making involve ranking of countries based on a weighted sum of several environmental performance metrics. However, the selection and weighing of metrics is often biased. This study proposes a new framework to assess environmental sustainability of countries via leveraging unsupervised learning. Specifically, this framework harnesses a clustering technique and tracks progressions in terms of shifts within clusters over time. It is observed that using the proposed framework, countries can identify specific ways to improve their progress towards environmental sustainability.
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/9034088 |
| Date | 15 August 2019 |
| Creators | Aiyshwariya Paulvannan Kanmani (7036478) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/A_data-centric_framework_for_assessing_environmental_sustainability/9034088 |
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