Understanding and assessing demographic (in)equity resulting from extreme heat and direct sunlight exposure due to lack of tree canopies in Norfolk, VA using agent-based modeling

Zamponi, Virginia; O’Brien, Kevin; Jensen, Erik Steen; Feldhaus, Brandon; Moore, Russell L.; Lynch, Christopher J.; Gore, Ross

doi:10.1016/j.ecolmodel.2023.110445

Cited by 2 publications

(1 citation statement)

References 52 publications

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“…In the realm of water resource management, Koop et al [8] applied the geometric aggregation method to enhance the lowest-scoring indicators in integrated water resources management. In studies pertaining to public health, Gore, Ross, et al [9] and Zamponi, Virginia et al [10] employed statistical modeling techniques to assess various aspects of public health. Typically, rating agencies determine the ESG or sustainability scores based on the information disclosed by the companies themselves [11].…”

Section: Introductionmentioning

confidence: 99%

Empirical Study of ESG Score Prediction through Machine Learning—A Case of Non-Financial Companies in Taiwan

Lin,

Hsu

2023

Sustainability

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In recent years, ESG (Environmental, Social, and Governance) has become a critical indicator for evaluating sustainable companies. However, the actual logic used for ESG score calculation remains exclusive to rating agencies. Therefore, with the advancement of AI, using machine learning to establish a reliable ESG score prediction model is a topic worth exploring. This study aims to build ESG score prediction models for the non-financial industry in Taiwan using random forest (RF), Extreme Learning Machines (ELM), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) and investigates whether the COVID-19 pandemic has affected the accuracy of these models. The dependent variable is the Taiwan ESG Sustainable Development Index, while the independent variables are 27 financial metrics and corporate governance indicators with three parts: pre-pandemic, pandemic, and the entire period (2018–2021). RMSE, MAE, MAPE, and r2 are conducted to evaluate these models. The results demonstrate the four supervised models perform well during all three periods. ELM, XGBoost, and SVM exhibit excellent performance, while RF demonstrates good accuracy but relatively lower than the others. XGBoost’s r2 shows inconsistency with RMSE, MAPE, and MAE. This study concludes the predictive performance of RF and XGBoost is inferior to that of other models.

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Section: Introductionmentioning

confidence: 99%

Empirical Study of ESG Score Prediction through Machine Learning—A Case of Non-Financial Companies in Taiwan

Lin,

Hsu

2023

Sustainability

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The state and future of extreme heat studies in Southeast Asian megacities: risk, impacts and adaptation strategies in a warming world

Macagba,

Delina

2024

Environ. Res. Commun.

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Southeast Asian cities are increasingly affected by heat-related phenomena and various climate-related disasters; however, research on urban heat in this region remains limited compared to other areas. This paper employs bibliometric and thematic analyses to investigate studies on extreme heat in mega-urban areas of Southeast Asia, with a particular focus on the risks and impacts faced by vulnerable populations, as well as their adaptation and mitigation strategies. The bibliometric analysis visualises the research landscape, identifying key clusters and highlighting prevalent themes and gaps. It reveals a predominant emphasis on characterising extreme heat and analysing urban temperature variations through satellite and meteorological data, which underscores a significant lack of research on the socio-economic factors affecting at-risk communities. The thematic analysis further examines how existing studies address these socio-economic vulnerabilities and evaluates the adaptation strategies employed, particularly concerning land use changes driven by population growth. Our findings indicate that, while there are studies addressing urban heat in Southeast Asia, their quantity is relatively small compared to the extensive body of research focused on other regions. Strategies to mitigate the effects of extreme heat on mental and social well-being emphasise the importance of green infrastructure and public spaces. There is also a pressing need to enhance urban planning and design to ensure that adaptation measures are inclusive of at-risk, lower-income communities. Understanding the complexities of the risks and impacts of extreme heat on urban populations is crucial for developing effective, context-specific adaptation strategies that prioritise the needs of vulnerable populations and promote equitable, sustainable urban development.

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Understanding and assessing demographic (in)equity resulting from extreme heat and direct sunlight exposure due to lack of tree canopies in Norfolk, VA using agent-based modeling

Cited by 2 publications

References 52 publications

Empirical Study of ESG Score Prediction through Machine Learning—A Case of Non-Financial Companies in Taiwan

Empirical Study of ESG Score Prediction through Machine Learning—A Case of Non-Financial Companies in Taiwan

The state and future of extreme heat studies in Southeast Asian megacities: risk, impacts and adaptation strategies in a warming world

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