Modelling and Management of Environment, Energy and Resources: Methods, Applications, and Challenges

Topic Information

Dear Colleagues,

Climate change has emerged as one of the main challenges of our generation. Tragic events, such as geopolitical conflicts including COVID-19, the Russia–Ukraine war or geopolitical tensions such as those in the Asia-Pacific region, not only pose significant threats to world peace and the global economy, but they also generate considerable uncertainty regarding efforts to combat climate change and ensure effective global environmental management. Soil and groundwater supplies have been contaminated, posing a grave threat to the survival of wild creatures and human health, and the ecological environment's worth has also diminished. We require scientific data to comprehend the effects on the global environmental, energy, and resource management and efforts to combat climate change. This Special Issue is devoted to researching the intriguing subject of statistical methods, econometric methods, qualitative methods, machine learning, artificial intelligence, and blockchain applications for diverse environmental, energy, and resource issues. Here, we investigate the convergence of cutting-edge technology with environmental, energy, and resource issues to address critical problems and produce novel solutions that can be implemented practically in decision making and management. This Special Issue, titled “Management of Environment, Energy and Resources: Methods, Applications and Challenges”, is a timely addition to this area of research.

This Special Issue's objectives include the modelling and management of the environment, energy and resources, as well as solutions available to policymakers for addressing environmental challenges. This SI covers topics including but not limited to the following:

• Environmental monitoring and assessment;
• Air quality and pollution control;
• Climate change modelling and prediction;
• Blockchain and sustainable development;
• Environmental risk assessment and mitigation;
• Urban planning and smart cities;
• Natural resource management;
• Land use and land cover change analysis;
• Environmental impact assessment;
• Environmental policy and decision support systems;
• Social and behavioral aspects of environmental management;
• Carbon emission and energy transition;
• Sustainable development of economies;
• ESG and corporate performance;
• Climate policy and sustainable development;
• Energy market and sovereign debt risk;
• Environmental governance and sovereign debt risk;
• Energy market and financial risks;
• Carbon market risk and energy price shocks;
• Economic policy uncertainty and energy and carbon management.

Dr. Yi-Shuai Ren
Dr. Yong Jiang
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Atmosphere atmosphere	2.9	4.1	2010	17.7 Days	CHF 2400	Submit
Energies energies	3.2	5.5	2008	16.1 Days	CHF 2600	Submit
Environments environments	3.7	5.9	2014	23.7 Days	CHF 1800	Submit
Land land	3.9	3.7	2012	14.8 Days	CHF 2600	Submit
Economies economies	2.6	3.2	2013	21.4 Days	CHF 1800	Submit

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Published Papers (1 paper)

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All Atmosphere Energies Environments Land Economies

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17 pages, 4829 KiB  

Open AccessArticle

MTS Decomposition and Recombining Significantly Improves Training Efficiency in Deep Learning: A Case Study in Air Quality Prediction over Sub-Tropical Area

by Benedito Chi Man Tam, Su-Kit Tang and Alberto Cardoso

Atmosphere 2024, 15(5), 521; https://doi.org/10.3390/atmos15050521 - 25 Apr 2024

Viewed by 187

Abstract

It is crucial to speed up the training process of multivariate deep learning models for forecasting time series data in a real-time adaptive computing service with automated feature engineering. Multivariate time series decomposition and recombining (MTS-DR) is proposed for this purpose with better [...] Read more.

It is crucial to speed up the training process of multivariate deep learning models for forecasting time series data in a real-time adaptive computing service with automated feature engineering. Multivariate time series decomposition and recombining (MTS-DR) is proposed for this purpose with better accuracy. A proposed MTS-DR model was built to prove that not only the training time is shortened but also the error loss is slightly reduced. A case study is for demonstrating air quality forecasting in sub-tropical urban cities. Since MTS decomposition reduces complexity and makes the features to be explored easier, the speed of deep learning models as well as their accuracy are improved. The experiments show it is easier to train the trend component, and there is no need to train the seasonal component with zero MSE. All forecast results are visualized to show that the total training time has been shortened greatly and that the forecast is ideal for changing trends. The proposed method is also suitable for other time series MTS with seasonal oscillations since it was applied to the datasets of six different kinds of air pollutants individually. Thus, this proposed method has some commonality and could be applied to other datasets with obvious seasonality. Full article

(This article belongs to the Topic Modelling and Management of Environment, Energy and Resources: Methods, Applications, and Challenges)

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