To achieve the energy transition and carbon neutrality targets, governments have implemented multiple policies to incentivize electricity suppliers to invest in renewable energy. Considering different government policies, we construct a renewable energy supply chain consisting of electricity suppliers and electricity retailers. We then explore the impact of four policies on electricity suppliers’ renewable energy investments, environmental impacts, and social welfare. We validated the results based on data from Wuxi, Jiangsu Province, China. The results show that government subsidy policies are more effective in promoting electricity suppliers to invest in renewable energy as consumer preferences increase, while no-government policies are the least effective. We also show that electricity suppliers are most profitable under the government subsidy policy and least profitable under the carbon cap-and-trade policy. Besides, our results indicate that social welfare is the worst under the carbon cap-and-trade policy. With the increase in carbon intensity and renewable energy quota, social welfare is the highest under the subsidy policy. However, the social welfare under the renewable energy portfolio standard is optimal when the renewable energy quota is low.

The aviation industry is experiencing over and over again a technological revolution, nowadays with airports at the forefront of embracing smart technologies to enhance operational efficiency, security and passenger experience. This article comprehensively analyzes the benefits, challenges, and legal implications of adopting smart technologies in airport facilitation and security control. It examines the regulatory framework established by the International Civil Aviation Organization (ICAO) on an international level and by sovereign states on a national level. It explores using smart solutions such as automated systems, data and biometric verification, artificial intelligence (AI), and the Internet of Things (IoT) devices in airport operations. The authors’ purpose is to highlight the improvements in airport facilities and security measures brought about by these technologies, while addressing concerns over privacy, cost, technological limitations and human factors. By emphasizing the importance of a balanced approach and considering innovation alongside legal and operational imperatives, the article underscores the transformative potential of smart and integrated technologies in shaping the future of air travel.

Using generative artificial intelligence systems in the classroom for law case analysis teaching can enhance the efficiency and accuracy of knowledge delivery. They can create interactive learning environments that are appropriate, immersive, integrated, and evocative, guiding students to conduct case analysis from interdisciplinary and cross-cultural perspectives. This teaching method not only increases students’ interest and participation in learning but also helps cultivate their interdisciplinary thinking and global vision. However, the application of generative artificial intelligence systems in legal education also faces some challenges and issues. If students excessively rely on these systems, their ability to think independently, make judgments, and innovate may be weakened, leading to over-trust in machines and reinforcement of value biases. To address these challenges and issues, legal education should focus more on cultivating students’ questioning skills, self-analysis abilities, critical thinking, basic legal literacy, digital skills, and humanistic spirit. This will enable students to respond to the challenges brought by generative artificial intelligence and ensure their comprehensive development in the new era.

Investors and company managements often rely on traditional performance evaluation indicators, such as return on equity, return on assets, and other financial ratios, to explain changes in a company’s market value added (MVA). However, the effectiveness of these traditional measures in explaining market value fluctuations remains uncertain. This research aims to investigate the impact of various profitability measures, namely return on equity, gross profit margin, operating profit margin, and return on assets, on explaining changes in the MVA of pharmaceutical and chemical companies listed on the Amman Stock Exchange. To achieve the study’s objectives, we analyzed the published financial statements of a sample consisting of 14 industrial companies out of a total of 53 companies listed on the Amman Stock Exchange during the period from 2008 to 2022. Relevant financial indicators were extracted from these statements to serve the purposes of the study. Correlation coefficients were employed to measure the extent to which the independent variables (profitability measures) could interpret changes in the dependent variable (MVA). One of the most significant findings of the study is that three dimensions of profitability measures have a statistically significant impact on explaining changes in the MVA of pharmaceutical and chemical companies listed on the Amman Stock Exchange, albeit to varying degrees. This suggests that traditional profitability measures still play a crucial role in influencing market perceptions of a company’s value, despite the potential limitations of these measures in capturing the full scope of a company’s performance and potential.

This study aimed to determine the socio-economic poverty status of those living in rural areas using data surveys obtained from household expenditure and income. Machine learning-based classification and clustering models were proven to provide an overview of efforts to determine similarities in poverty characteristics. Efforts to address poverty classification and clustering typically involve comprehensive strategies that aim to improve socio-economic conditions in the affected areas. This research focuses on the combined application of machine learning classification and clustering techniques to analyze poverty. It aims to investigate whether the integration of classification and clustering algorithms can enhance the accuracy of poverty analysis by identifying distinct poverty classes or clusters based on multidimensional indicators. The results showed the superiority of machine learning in mapping poverty in rural areas; therefore, it can be adopted in the private sector and government domains. It is important to have access to relevant and reliable data to apply these machine learning techniques effectively. Data sources may include household surveys, census data, administrative records, satellite imagery, and other socioeconomic indicators. Machine learning classification and clustering analyses are used as a decision support tool to gain an understanding of poverty data from each village. These strategies are also used to describe the profile of poverty clusters in the community in terms of significant socio-economic indicators present in the data. Village clusters based on an analysis of existing poverty indicators are grouped into high, moderate, and low poverty levels. Machine learning can be a valuable tool for analyzing and understanding poverty by classifying individuals or households into different poverty categories and identifying patterns and clusters of poverty. These insights can inform targeted interventions, policy decisions, and resource allocation for poverty reduction programs.

LEED (Leadership in Energy and Environmental Design) is a certification program for quantitatively assessing the qualifications of homes, non-residential buildings, or neighborhoods in terms of sustainability. LEED is supported by the U.S. Green Building Council (USGBC), a nonprofit membership-based organization. Worldwide, thousands of projects received one of the four levels of LEED certification. One of the five rating systems (or specialties) covered by LEED is the Building Design and Construction (BD + C), representing non-residential buildings. This rating system is further divided into eight adaptations. The adaptation (New Construction and Major Renovation) or NC applies to newly constructed projects as well as those going through a major renovation. The NC adaptation has six major credit categories, in addition to three minor ones. The nine credit categories together have a total of 110 attainable points. The Energy and Atmosphere (EA) credit category is the dominant one in the NC adaptation, with 33 attainable points under it. This important credit category addresses the topics of commissioning, energy consumption records, energy efficiency, use of refrigerants, utilization of onsite or offsite renewable energy, and real-time electric load management. This study aims to highlight some differences in the EA credit category for LEED BD + C:NC rating system as it evolved from version 4 (LEED v4, 2013) to version 4.1 (LEED v4.1, 2019). For example, the updated version 4.1 includes a metric for greenhouse gas reduction. Also, the updated version 4.1 no longer permits hydrochlorofluorocarbon (HFC) refrigerants in new heating, ventilating, air-conditioning, and refrigeration systems (HVAC & R). In addition, the updated version 4.1 classifies renewable energy into three tiers, differentiating between onsite, new-asset offsite, and old-asset offsite types.