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Asia Pacific Academy of Science Pte. Ltd. (APACSCI) specializes in international journal publishing. APACSCI adopts the open access publishing model and provides an important communication bridge for academic groups whose interest fields include engineering, technology, medicine, computer, mathematics, agriculture and forestry, and environment.
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In the context of the global shift toward a low-carbon economy, carbon disclosure has emerged as a crucial tool for facilitating the low-carbon transition of firms and addressing climate change. As a result, it has become an increasingly prominent focus in academic research and policy making. This paper reviews the existing literature on carbon disclosure, examining the methods, standards, motivations, and impacts associated with current research in this area. Based on this analysis, the paper identifies key gaps in the existing literature and suggests directions for future research, aiming to contribute to the advancement of theoretical understanding and provide a valuable reference for future studies.

Understanding consumer purchasing behavior is crucial for businesses aiming to enhance customer engagement and optimize marketing strategies. In today’s digital economy, traditional marketing approaches are becoming less effective due to evolving consumer behaviors, the rise of online communities, and the widespread use of ad-blocking software. To remain competitive, businesses must adopt data-driven strategies to analyze consumer preferences and tailor their marketing efforts accordingly. Machine learning provides a powerful tool for predicting consumer purchasing behavior, enabling businesses to anticipate customer needs and implement targeted marketing campaigns. Previous studies have demonstrated the effectiveness of machine learning in consumer analysis, particularly in customer segmentation and purchase prediction. However, while much research focuses on technical model optimization, relatively few studies have applied machine learning specifically for marketing prediction and strategic decision-making. This study addresses that gap by leveraging machine learning to analyze consumer purchasing behavior and generate practical insights for marketing strategies and business applications. Using a dataset of 4680 transactions, we employ Generalized Linear Models (GLM), Logistic Regression, Random Forest, and XGBoost to predict repurchase behavior within a specified timeframe. Our objective is to provide practical implications for businesses, such as improving targeted promotions, refining customer segmentation, and enhancing demand forecasting.
Sustainability target setting and incentive design: A literature review
Article ID: 3134
Vol 2, Issue 2, 2025
DOI: https://doi.org/10.54517/bmtp3134
Vol 2, Issue 2, 2025
Received: 6 December 2024; Accepted: 10 April 2025; Available online: 11 April 2025; Issue release: 30 June 2025
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Abstract
The integration of sustainability targets and their connection to executive compensation is emerging as a new facet of corporate strategies in response to the low-carbon transition, ESG-driven demands from institutional investors, regulatory mandates, and commitments to corporate social responsibility (CSR). The purpose of this research is to explore issues related to sustainability target-setting and the associated methodologies. This paper summarizes the prior work on target setting and sustainability targets, and the results from previous studies on the reason and rationality of setting sustainability targets were discussed. It also pointed out the issues in sustainability targets and related incentive design. Based on this, several suggestions are offered to assist companies in developing and setting sustainability targets and goals.
Keywords
sustainability targets; executive compensation; management incentives
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