A Receding Horizon Approach for Climate Change Control

Carnevale, Claudio; Sangiorgi, Lucia

doi:10.1109/codit58514.2023.10284190

2023 9th International Conference on Control, Decision and Information Technologies (CoDIT) 2023

DOI: 10.1109/codit58514.2023.10284190

|View full text |Cite

A Receding Horizon Approach for Climate Change Control

Claudio Carnevale,

Lucia Sangiorgi

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite1

Independent1

Authors

Journals

Cited by 2 publications

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

The impact of optimal climate change control on rice production in critical regions: the case of Southeast Asia

De Nardi,

Sangiorgi,

Raccagni

et al. 2024

IFAC-PapersOnLine

View full text Add to dashboard Cite

The impact of optimal climate change control on rice production in critical regions: the case of Southeast Asia

De Nardi,

Sangiorgi,

Raccagni

et al. 2024

IFAC-PapersOnLine

View full text Add to dashboard Cite

Data-Driven Models to Forecast the Impact of Temperature Anomalies on Rice Production in Southeast Asia

De Nardi,

Carnevale,

Raccagni

et al. 2024

Forecasting

Self Cite

View full text Add to dashboard Cite

Models are a core element in performing local estimation of the climate change input. In this work, a novel approach to perform a fast downscaling of global temperature anomalies on a regional level is presented. The approach is based on a set of data-driven models linking global temperature anomalies and regional and global emissions to regional temperature anomalies. In particular, due to the limited number of available data, a linear autoregressive structure with exogenous input (ARX) has been considered. To demonstrate their relevance to the existing literature and context, the proposed ARX models have been employed to evaluate the impact of temperature anomalies on rice production in a socially, economically, and climatologically fragile area like Southeast Asia. The results show a significant impact on this region, with estimations strongly in accordance with information presented in the literature from different sources and scientific fields. The work represents a first step towards the development of a fast, data-driven, holistic approach to the climate change impact evaluation problem. The proposed ARX data-driven models reveal a novel and feasible way to downscale global temperature anomalies to regional levels, showing their importance in comprehending global temperature anomalies, emissions, and regional climatic conditions.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A Receding Horizon Approach for Climate Change Control

Cited by 2 publications

References 13 publications

The impact of optimal climate change control on rice production in critical regions: the case of Southeast Asia

The impact of optimal climate change control on rice production in critical regions: the case of Southeast Asia

Data-Driven Models to Forecast the Impact of Temperature Anomalies on Rice Production in Southeast Asia

Contact Info

Product

Resources

About