On the Relationship Between the Promotion of Environmental Sustainability and the Increase of Territorial Competitiveness: the Italian Case
Bibliographic record
Abstract
The consequences of climate change urges researchers to investigate the issues of environmental sustainability, and the definition of policies for reducing greenhouse gas emissions (GHG) has become more urgent.In this context, urban areas play a significant role since here, economic, productive and social activities are concentrated and, therefore, the majority of the GHG emissions are produced.For these reasons, many cities worldwide are making big efforts to improve their level of environmental sustainability, but is this a worthy investment in terms of territorial competitiveness?In the era of globalization, indeed, countries, regions and cities cannot underestimate the competition against each other to attract firms, people and skills.Therefore, the aim of this paper is to identify the type of relationship between environmental sustainability and territorial competitiveness at the city level in order to verify whether or not it is possible to positively affect competitiveness by investing on green initiatives.The study measures the level of environmental sustainability and competitiveness of 103 Italian capital cities by constructing two composite indicators and it compares the two performances using a statistical approach, showing that being green also means being more competitive and this positive relationship increases proportionally with the population.In other words, strategies designed to make a city more environmentally sustainable positively affect its level of competitiveness, and this correlation is stronger for bigger cities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".