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Record W3123329564 · doi:10.22004/ag.econ.230597

Assessing SDGs: A New Methodology to Measure Sustainability

2016· article· en· W3123329564 on OpenAlexaboutno aff
Lorenza Campagnolo, Carlo Carraro, Fabio Eboli, Luca Farnia

Bibliographic record

VenueAgEcon Search (University of Minnesota, USA) · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainable Development and Environmental Policy
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityStrengths and weaknessesRanking (information retrieval)Sustainable developmentNormalization (sociology)Environmental Sustainability IndexComposite indexComputable general equilibriumEnvironmental economicsIndex (typography)Environmental resource managementComputer scienceComposite indicatorManagement scienceEnvironmental planningPolitical scienceEconomicsGeographyEconometricsMacroeconomics

Abstract

fetched live from OpenAlex

The FEEM project APPS – Assessment, Projections and Policy of Sustainable Development Goals – focuses on the quantitative assessment of the seventeen Sustainable Development Goals (SDGs), adopted by the United Nations at the end of September 2015. The project consists of two phases. The first, retrospective, computes indicators for all SDGs in 139 countries and then derives a composite multi-dimensional index and a worldwide ranking of current sustainability. This allows informing on strengths and weaknesses of today socio-economic development, as well as environmental criticalities, all around the world. The second phase, prospective, aims at evaluating the future trends of sustainability in the world by 2030. The assessment of the SDGs is carried out by means of an extended version of the recursive-dynamic computable general equilibrium ICES macro-economic model that includes social and environmental indicators. The final goal is to highlight future challenges left unsolved in next 15 years of socio-economic development and analyze costs and benefits of specific policies to support the achievement of proposed targets. This paper presents the methodology and the results of the retrospective assessment. Five main steps are described: i) screening of indicators eligible to address the UN SDGs; ii) data collection from relevant sources; iii) organization in the three pillars of sustainability (economy, society, environment); iv) normalization to a common metrics; v) aggregation of the 25 indicators in composite indices by pillars as well as in the multi-dimensional index. The final ranking summarizes countries’ sustainability performance. As expected, Middle-North European countries are at top of the ranking (Sweden, Norway and Switzerland the first three), with the most industrialized European countries such as Germany and UK, however, penalized by insufficient environmental performance. Other highly developed countries are between 24th (Canada) and 52nd place (United States). The emerging nations are scattered in our sustainability ranking. Brazil (43rd) and Russia (45th) precede China (80th) and India (102nd), the latter two especially penalized because of their social complexity. The worst performances, in terms of overall sustainability, are in Sub-Saharan Africa (Comoros, the Central African Republic and Chad occupy the last places in the ranking).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.709
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.072
GPT teacher head0.294
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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