Research/ai/urban_Arindam_Font size adjustment

_research/ai-models/air-pollution/2026-low-cost-sensor-calibration.md

@@ -276,6 +276,7 @@ This calibration framework can support:
 - **Tutorial:** <a href="{{ '/software/tutorials/urban-lcs-calibration/' | relative_url }}">Low-cost sensor calibration tutorial</a>
 
 - **Data:** <a href="http://ora.ox.ac.uk/objects/uuid:66fbe8c1-4b63-4124-bf0d-a78cbc9e1408">OxAria low-cost air-quality sensor dataset</a>
+
 ---
 
 ## References

_research/ai-models/air-pollution/2026_air_pollution.md

@@ -2,44 +2,93 @@
 layout: post
 category: "Data-driven & Urban Sustainability"
 topic: "Air Pollution"
-thumbnail: "assets/img/Gappy.png"
-tldr: "A Robust Cross-Efficiency DEA Model with Undesirable Outputs for Urban Energy Efficiency Evaluation."
-title: Data-driven 
+thumbnail: "assets/img/2026-workshop-li-urban-1.jpg"
+tldr: "Urban Energy Efficiency Assessment under Uncertainty."
+title: Sustainable Cities & Energy Systems 
 subtitle: Our studies focused on urban sustainability
 ---
 <p class="post-meta">
   Posted on 18 June 2026
 </p>
 
 ## Introduction
-As industrialisation and urbanisation continue to advance, energy consumption—whilst driving economic growth and social development—has also given rise to severe resource constraints and environmental pollution. For a long time, fossil fuels, primarily coal, oil and natural gas, have underpinned the rapid expansion of China’s urban economies; however, issues such as carbon dioxide emissions, smog pollution and ecological degradation caused by high levels of energy consumption have become increasingly prominent. Particularly against the backdrop of the ‘dual carbon’ goals, achieving a balanced development between economic growth, energy utilisation and environmental protection has become a critical issue for the sustainable development of Chinese cities. As the primary vehicles for energy consumption and pollution emissions, the energy efficiency levels of cities not only relate to high-quality regional economic development but also directly impact the achievement of national energy conservation and emission reduction targets. Therefore, scientifically evaluating urban energy efficiency and revealing its patterns of change under conditions of uncertainty holds significant practical importance for promoting sustainable development.
+Rapid urbanization and economic growth have significantly increased energy demand across Chinese cities, creating new challenges related to sustainability, resource management, and environmental performance. Under the framework of China's carbon neutrality objectives, understanding how efficiently cities utilize energy resources has become increasingly important for supporting sustainable development policies.
+This study evaluates the energy efficiency of 280 Chinese prefecture-level cities under uncertainty conditions, providing a robust framework to assess urban energy performance while accounting for data variability and measurement uncertainty.
 
 ## Study Area
-The selection of 280 prefecture-level cities in China as the research subjects was primarily based on the following considerations. 
+The analysis considers 280 prefecture-level cities distributed across China's seven major regions: North China, Northeast China, Central China, East China, South China, Southwest China, and Northwest China.
+This large-scale dataset enables the investigation of regional disparities in energy efficiency and the identification of long-term development patterns across different economic and geographical contexts.
 <p align="center">
-  <a href="https://github.com/modelflows/modelflowsapp/blob/dev/assets/img/2026-workshop-li-urban-1.jpg?raw=true">
-    <img src="https://github.com/modelflows/modelflowsapp/blob/dev/assets/img/2026-workshop-li-urban-1.jpg?raw=true"
+  <a href="https://github.com/modelflows/modelflowsapp/blob/dev/assets/img/2026-workshop-li-urban-6.png?raw=true">
+    <img src="https://github.com/modelflows/modelflowsapp/blob/dev/assets/img/2026-workshop-li-urban-6.png?raw=true"
          alt="vel_plot"
          width="650">
   </a>
 </p>
 
 ## Research Methodology
 
-Data Envelopment Analysis (DEA) is a widely recognized non-parametric method for evaluating the relative efficiency of Decision-Making Units (DMUs). However, traditional DEA models rely heavily on deterministic input and output data, rendering their efficiency scores highly sensitive to measurement errors or data fluctuations. To address this limitation, this study integrates Robust Optimization (RO) principles into the conventional DEA framework. By constructing a bounded uncertainty set (such as a polyhedral or budget uncertainty set), the proposed Robust DEA model accounts for the worst-case scenario within a predefined protection level. 
+Research Methodology
+Robust Data Envelopment Analysis (RDEA)
+Data Envelopment Analysis (DEA) is a widely used non-parametric approach for evaluating the relative efficiency of Decision-Making Units (DMUs). However, conventional DEA models assume deterministic input and output variables, making their results sensitive to measurement errors and data uncertainty.
+To overcome this limitation, this work integrates Robust Optimization (RO) into the DEA framework through the Robust Data Envelopment Analysis (RDEA) methodology.
+By introducing uncertainty sets and protection levels, RDEA evaluates efficiency under worst-case scenarios, producing more reliable and stable efficiency estimates.
+Two robust formulations are considered:
 
-## Results
+- CU-RDEA (Conservative Uncertainty Robust DEA)
+- CEU-RDEA (Conservative Extended Uncertainty Robust DEA)
 
-The energy efficiency results of CU-RDEA and CEU-RDEA. A comparison of city maps across China's seven major regions reveals that the energy efficiency performance of Chinese cities is highly sensitive to data uncertainty. Empirical results show that the efficiency scores derived from both the CU-RDEA and CEU-RDEA models exhibit a marked downward trend as the perturbation level T increases from 0 to 0.10. This indicates that robust DEA methods can effectively mitigate efficiency bias issues caused by data fluctuations, changes in energy demand, and policy uncertainty.
-From a spatial perspective, there are significant differences in energy utilization efficiency among various regions in China. The East China region exhibits a distinct gradient of competition, while the Northwest and Southwest regions have a lower overall efficiency level. In terms of regional distribution, the 2010 CU-RDEA and CEU-RDEA scores reveal that early high-efficiency cities were predominantly concentrated in specific resource- or policy-oriented regions such as Ordos and Suihua; by 2024, however, core central cities such as Beijing, Chongqing, Wuhan, and Jiangmen have emerged as leaders under the CEU-RDEA model, occupying leading positions across their respective regions. This indicates that China’s urban energy development model is gradually shifting from a traditional resource-driven approach to one driven by the synergy of technological innovation, industrial upgrading, and green, low-carbon initiatives.
-![vel_plot](https://github.com/modelflows/modelflowsapp/blob/dev/assets/img/2026-workshop-li-urban-2.png?raw=true)
-![vel_plot](https://github.com/modelflows/modelflowsapp/blob/dev/assets/img/2026-workshop-li-urban-3.png?raw=true)
-![vel_plot](https://github.com/modelflows/modelflowsapp/blob/dev/assets/img/2026-workshop-li-urban-4.png?raw=true)
-![vel_plot](https://github.com/modelflows/modelflowsapp/blob/dev/assets/img/2026-workshop-li-urban-5.png?raw=true)
+This approach allows the assessment of how uncertainty affects urban energy efficiency rankings and regional performance.
+<p align="center">
+  <a href="https://github.com/modelflows/modelflowsapp/blob/dev/assets/img/2026-workshop-li-urban-7.png?raw=true">
+    <img src="https://github.com/modelflows/modelflowsapp/blob/dev/assets/img/2026-workshop-li-urban-7.png?raw=true"
+         alt="vel_plot"
+         width="700">
+  </a>
+</p>
+
+The diagram should show how urban energy data are processed through DEA and Robust Optimization to build the CU-RDEA and CEU-RDEA models. The goal is to communicate the novelty of the methodology rather than reproduce the mathematical formulation presented in the article.
+
+## Main Findings
+
+The results reveal that urban energy efficiency is highly sensitive to data uncertainty.
+As the perturbation level (Γ) increases, both CU-RDEA and CEU-RDEA efficiency scores show a systematic decline, demonstrating the importance of incorporating uncertainty into efficiency assessments.
+The analysis also highlights substantial regional disparities across China:
+
+- East China exhibits the highest overall efficiency levels.
+- Northwest and Southwest China show comparatively lower efficiency performance.
+- Significant spatial heterogeneity exists among cities within the same region.
+
+Furthermore, the results indicate a progressive transition from traditional resource-driven development models toward growth supported by technological innovation, industrial upgrading, and green low-carbon strategies.
+<p align="center">
+  <a href="https://github.com/modelflows/modelflowsapp/blob/dev/assets/img/2026-workshop-li-urban-8.png?raw=true">
+    <img src="https://github.com/modelflows/modelflowsapp/blob/dev/assets/img/2026-workshop-li-urban-8.png?raw=true"
+         alt="vel_plot"
+         width="8500">
+  </a>
+</p>
+
+The figure should clearly illustrate (i) regional differences in urban energy efficiency and (ii) the impact of uncertainty levels (Γ) on efficiency scores. A China efficiency map combined with a regional comparison chart would communicate the results more effectively for a web audience than the full set of detailed regional plots
+
+## Scientific Contribution
+This work demonstrates that Robust Data Envelopment Analysis provides a reliable framework for evaluating urban energy efficiency under uncertainty.
+By combining DEA and Robust Optimization, the proposed methodology reduces sensitivity to data fluctuations and offers a more realistic assessment of urban sustainability performance.
+The framework can support policy evaluation, urban planning, energy management, and sustainable development strategies in complex socio-economic systems.
+
+## Scientific Contribution
+
+This work demonstrates that Robust Data Envelopment Analysis provides a reliable framework for evaluating urban energy efficiency under uncertainty.
+By combining DEA and Robust Optimization, the proposed methodology reduces sensitivity to data fluctuations and offers a more realistic assessment of urban sustainability performance.
+The framework can support policy evaluation, urban planning, energy management, and sustainable development strategies in complex socio-economic systems.
+
+## Relevance for ModelFLOWs
 
-### Other Relevant References
+This research illustrates the application of advanced data-driven methodologies to large-scale urban systems.
+Beyond traditional engineering applications, ModelFLOWs methodologies can be used to analyze complex socio-economic and environmental datasets, enabling robust decision-support tools for sustainability, smart cities, and energy planning.
 
-[*Yang, Z., & Wei, X., 2019. The measurement and influences of China's urban total factor energy efficiency under environmental pollution: Based on the game cross-efficiency DEA. Journal of cleaner production, 209, 439-450.*](https://doi.org/10.1016/j.jclepro.2018.10.271)
+## References
 
-[*Wang, R., Wang, Q., & Yao, S., 2021. Evaluation and difference analysis of regional energy efficiency in China under the carbon neutrality targets: Insights from DEA and Theil models. Journal of environmental management, 293, 112958.*](https://doi.org/10.1016/j.jenvman.2021.112958)
+Jiannan Li, Soledad Le Clainche, and collaborators.
+A Robust Cross-Efficiency DEA Model with Undesirable Outputs for Urban Energy Efficiency Evaluation.
+Manuscript in preparation.