What does this research mean for the field?

Urban sustainability disclosures exhibit heterogeneous greenwashing patterns—with high-rated cities tending toward symbolic overpromising and lower-rated cities showing structural weaknesses—which are shaped by institutional pressures and governance capabilities. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To develop an explainable AI framework that evaluates the credibility of city-level climate sustainability disclosures and detects greenwashing risks.

August 28, 2026Sustainable FuturesOpen Access

AI-Enabled governance for sustainable cities: evaluating the credibility of urban climate disclosures and greenwashing risks

View Full Paper

Ask AI

Bookmark

Authors

CHCao HongNational Taiwan Normal University HCHan-Wei ChenNational Taiwan University CCChialin ChenInternational Monetary Fund

Discussion

Member takes

Overview

Computational evaluation reveals distinct greenwashing patterns across 537 municipal climate disclosures, highlighting disparities between symbolic commitments and implementation capabilities.

Key Points

To develop an explainable AI framework that evaluates the credibility of city-level climate sustainability disclosures and detects greenwashing risks.
Evaluated N=537 municipal responses from the Carbon Disclosure Project (CDP) Cities dataset using an AI framework combining large language models (LLMs) and explainable AI (XAI).
Executed a three-stage workflow featuring LLM-based scoring, surrogate XGBoost modeling interpreted through SHAP analysis, and validation via chi-square analysis.
The surrogate XGBoost model demonstrated high predictive fit with LLM credibility scores (R² = 0.995), with SHAP analysis showing governance, assessment, monitoring, and citizen engagement were the primary drivers of higher scores.
Chi-square testing uncovered divergent greenwashing profiles, characterized by symbolic overpromising in A-List cities and structural or procedural implementation deficits in Non-A-List cities.

Cite This Study

Hong et al. (2026) studied this question.

synapsesocial.com/papers/6a9145ead15324a1df3a9178 https://doi.org/10.1016/j.sftr.2026.102113

View Full Paper

Ask AI

Bookmark

August 28, 2026Sustainable FuturesOpen Access

AI-Enabled governance for sustainable cities: evaluating the credibility of urban climate disclosures and greenwashing risks

View Full Paper

Ask AI

Bookmark

Authors

CHCao HongNational Taiwan Normal University HCHan-Wei ChenNational Taiwan University CCChialin ChenInternational Monetary Fund

Discussion

Member takes

Overview

Computational evaluation reveals distinct greenwashing patterns across 537 municipal climate disclosures, highlighting disparities between symbolic commitments and implementation capabilities.

Key Points

To develop an explainable AI framework that evaluates the credibility of city-level climate sustainability disclosures and detects greenwashing risks.
Evaluated N=537 municipal responses from the Carbon Disclosure Project (CDP) Cities dataset using an AI framework combining large language models (LLMs) and explainable AI (XAI).
Executed a three-stage workflow featuring LLM-based scoring, surrogate XGBoost modeling interpreted through SHAP analysis, and validation via chi-square analysis.
The surrogate XGBoost model demonstrated high predictive fit with LLM credibility scores (R² = 0.995), with SHAP analysis showing governance, assessment, monitoring, and citizen engagement were the primary drivers of higher scores.
Chi-square testing uncovered divergent greenwashing profiles, characterized by symbolic overpromising in A-List cities and structural or procedural implementation deficits in Non-A-List cities.

Cite This Study

Hong et al. (2026) studied this question.

synapsesocial.com/papers/6a9145ead15324a1df3a9178 https://doi.org/10.1016/j.sftr.2026.102113

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

View Full Paper

Ask AI

Bookmark