Winning the SDG battle in cities: how an integrated information ecosystem can contribute to the achievement of the 2030 sustainable development goals
Bibliographic record
Abstract
Abstract In 2015, the United Nations adopted an ambitious development agenda composed of 17 sustainable development goals (SDGs), which are to be reached by 2030. Beyond SDG 11 concerning the development of sustainable cities, many of the SDGs target activities falling within the responsibility of local governments. Thus, cities will play a leading role in the achievement of these goals, and we argue that the information systems (IS) community must be an active partner in these efforts. This paper aims to contribute to the achievement of the SDGs by developing a conceptual model to explain the role of IS in building smart sustainable cities and providing a framework of action for IS researchers and city managers. To this end, we conduct grounded theory studies of two green IS used by an internationally recognized smart city to manage water quality and green space. Based on these findings, we articulate a model explaining how an integrated information ecosystem enables the interactions between three interrelated spheres – administrative, political and sustainability – to support the development of smart sustainable cities. Moving from theory to practice, we use two real‐world scenarios to demonstrate the applicability of the model. Finally, we define an action framework outlining key actions for cities and suggest corresponding questions for future research. Beyond a simple call‐to‐action, this work provides a much‐needed foundation for future research and practice leading to a sustainable future for all.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.027 | 0.027 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".