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The steady rise in carbon emissions has significantly exacerbated the global climate crisis, posing a severe threat to ecosystems due to the greenhouse gas effect. As one of the most pressing challenges of our time, the need for an immediate transition to renewable energy is imperative to meet the carbon reduction targets set by the Paris Agreement. Buildings, as major contributors to global energy consumption, play a pivotal role in climate change. This study diverges from previous research by employing multi-task deep learning techniques to develop a predictive model for electricity load in commercial buildings, incorporating auxiliary tasks such as temperature and cloud coverage. Using real data from a commercial building in Taiwan, this study explores the effects of varying batch sizes (100, 125, 150, and 200) on the model's performance. The findings reveal that the multi-task deep learning model consistently surpasses single-task models in predicting electricity load, demonstrating superior accuracy and stability. These insights are crucial for companies aiming to enhance energy efficiency and formulate effective renewable energy procurement strategies, contributing to broader sustainability efforts and aligning with global climate action goals. [ABSTRACT FROM AUTHOR]

This study seeks to identify the impact of sustainable development goals (SDGs) in predicting corporate financial performance (CFP) in the information communications technology (ICT) industry. Data over the period of 2016–2020 that are relevant to financial reporting and corporate social responsibility (CSR) reporting have been extracted for 208 firms in the ICT industry. Important variables have been identified to help predict the financial performance in the following years upon the publication of CSR reports. Drawing on resource‐based view and stakeholder theory, the purpose of this study is to find the quintessential variables that influence the prediction accuracy of financial performance. To better forecast earnings per share (EPS), machine learning feature selection methods have been implemented. The findings suggest that certain variables such as return on total assets, SDGs adoption and whether the firm has established KPI for SDGs achievements can help enhance EPS prediction. With the various predictive models, the artificial neural network model is the most effective in predicting CFPs. Most importantly, the adoption of SDGs can be utilized to sharpen the forecast on financial performance as it enables firms to bolster stakeholder engagement and evaluate environmental, social, and corporate governance efforts. [ABSTRACT FROM AUTHOR]

The European Union intends to adopt a more aggressive unilateral policy-carbon tariffs to promote the reduction of global carbon emissions. In our research, a global supply chain model is developed to determine the effects of carbon tariffs along with domestic carbon policies. Domestic carbon policies in which the retailer country has a (1) carbon tax or (2) cap-and-trade, while the manufacturing country has (1) a carbon tax or (2) no carbon policy are considered. The proposed production - inventory model for a three-stage supply chain comprises the supply of raw materials, a single export manufacturer's productionand delivery, and a single import retailer's orders and sales for deteriorating items. The exchange rate for transactions among supply chain members is also taken into consideration. Mathematical programming analysis is used to determine the manufacturer's optimal material supply, production, and delivery strategies, as well as the retailer's optimal pricing and replenishment strategies. The main goal is to maximize joint total profit in both settings, and this is achieved through numerical and sensitivity analysis. This study found that carbon tariffs have a positive impact on the emission reduction effect of cross-border supply chain systems. Additionally, it revealed that within a multinational three-stage supply chain, considering the deterioration of raw materials and finished products, the increase in the carbon tariff rates and exchange rates enhance the emission reduction effect of the supply chain and have a positive impact on the joint total profits of the supply chain. Finally, unlike previous studies, this study also explored that the adjustment of carbon tariff relief rate have a complex impact on the profits and emissions reduction of the supply chain members. These results can help enterprises understand the impact of carbon tariffs on supply chain inventory and pricing decisions. [ABSTRACT FROM AUTHOR]