Guidelines for a participatory Smart City model to address Amazon’s urban environmental problems
Bibliographic record
Abstract
Climate change is a global challenge, and the Brazilian Amazon Forest is a particular concern due to the possibility of reaching a tipping point that could amplify environmental crises. Despite many studies on the Amazon Forest, this research was conducted in Manaus, the capital of Amazonas state, to address five gaps, including the lack of local citizen consultation on urban environmental issues, Smart Cities, decarbonization, and disruptive technologies. This study holds significance for the academy community, government bodies, policymakers, and investors, as it offers novel insights into the Amazon region and proposes a model to engage citizens in Smart Cities. This model could also guide other municipalities aspiring for participatory sustainable development with a decarbonization focus, mitigating future risks, and protecting future generations. Basically, it is an explanatory and applied study that employs mixed methods, including literature, bibliometric and documentary reviews, two questionnaires, and descriptive statistical approaches, organized in four phases to reach the following goals: (a) provide information on the main challenges facing humanity, the Brazilian Amazon state, and the city of Manaus; (b) identify the best Smart City approaches for engaging citizens in solving urban problems; (c) contextualize and consult Manaus City Hall about the effectiveness of the Smart City project; (d) investigate the perceptions of citizens living in Manaus on the main city’s environmental problems, as well as their level of knowledge and interest on issues related to Smart Cities, decarbonization, and disruptive technologies; (e) propose a participatory Smart City model with recommendations. Among the result, the study found that the term “Smart City” dominates scholarly publications among nineteen urban-related terms, and the five main environmental problems in Manaus are an increase in stream pollution, garbage accumulation, insufficient urban afforestation, air pollution, and traffic congestion. Although citizens are willing to help, the majority lack knowledge on Smart City and Decarbonized City issues, but there is a considerable interest in training related to these issues, as well as disruptive technologies. It was found that Amsterdam, Melbourne, Montreal, San Francisco, Seoul, and Taipei all have a formal model to engage citizens in solving their urban problems. The main conclusion is that, after 6 years, the Smart City Project in Manaus is a political fallacy, as no model, especially with a citizen participatory approach, has been effectively adopted. In addition, after conducting a literature and documentary review and analyzing 25 benchmark Smart Cities, the P5 model and the Citizen Engagement Kit model are proposed with 120 approaches and guidelines for addressing the main environmental problems by including Manaus’ citizens in the Smart City and/or decarbonization journey.
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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 | 0.038 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".