The paper analyzes the corporate carbon emissions and GDP contributions of the top ten companies by turnover for 2020–2023 in Germany, South Korea, China and the United Kingdom. Focusing on Scope 1, 2, and 3, the study explores the contribution of these companies to carbon intensity across different sectors and economies. The analysis shows that there are significant gaps in carbon efficiency, with the UK’s and Germany’s firms emitting the lowest emissions per unit of GDP contribution, followed by China and South Korea. Additionally, the study further examines the impact of Economic Policy Uncertainty on both firm carbon intensity and economic productivity. While EPU is positively associated with GDP contributions, its impact on emissions is nuanced. Firms apparently respond to policy uncertainty by increasing energy efficiency in direct (Scope 1) and energy-related (Scope 2) emissions but find it more difficult to manage supply chain emissions (Scope 3) in that case. The results point out the critical role of comprehensive ESG reporting frameworks in enhancing transparency and addressing Scope 3 emissions, which remain the largest and most volatile component of corporate carbon footprints. The paper then emphasizes the importance of standardized ESG reporting and bespoke policy intervention for promoting sustainability, especially in carbon-intensive industries. This research contributes to the understanding of how industrial and policy frameworks affect carbon efficiency and economic growth in different national contexts.

The need for global energy conservation has become more urgent because of the negative effects of excessive energy use, such as higher fuel consumption, greater environmental pollution, and depletion of the ozone layer. There has been a significant increase in the demand for central and high-capacity household air conditioning systems in Muscat in recent years. The need for this is influenced by factors such as arid climate, increasing temperatures, air pollution, and population increase. As a result, there has been a significant increase in electricity use, putting a strain on power resources. To tackle this difficulty, the incorporation of solar collectors as supplementary thermal compressors in air conditioning systems offers a chance to utilise renewable energy sources. The objective of this hybrid technique is to enhance the effectiveness of cooling systems, hence minimising the need for electricity and lowering the release of environmental pollutants.

Financial inclusion and social protection have been recognised as the primary essential stimuli from the potential they carry as avenues for economic development, especially with respect to reduction in poverty and inequalities, the creation of employment and the enhancement overall welfare and livelihood. However, inclusive access to financial resources and equitable access to social protection interventions have remained a significant concern in Nigeria. In addition, the emergence of the COVID-19 pandemic exposed the weakness of Nigeria in all sectors of the economy such as energy, health, education and food systems and low-level inclusive access to financial resources and social protection coverage. On the other hand, this study argues that financial inclusion and social protection has the potential to mitigation shocks orchestrated by the COVID-19 pandemic. This study empirically examines how social protection interventions and access to financial resources responded to COVID-19 pandemic. The study made use of data sourced from the World Bank’s COVID-19 national longitudinal phone survey 2020 and applied the logit regression. The findings show that social protection and access to financial resources significantly associated with the likelihood of shock mitigation during the COVID-19 pandemic. The results show that social protection intervention reduces the probability of being severely affected by shocks by 0.431. Given this result, the study recommends that the government should put more effort into proper social protection intervention to mitigate the effect of the COVID-19 pandemic.

With the advancement of the green economy, the labor market is experiencing the emergence of new employment forms, positions, and competencies. This arises from the special relationship between the green job market and the transforming energy sector. On the other hand, the energy sector’s influence on the green labor market and the creation of green jobs is particularly significant. It is because, the energy sector is one of the fundamental foundations of any country’s economy and impacts its other sectors. Key components of this influence include green employment and green self-employment. The purpose of this study is to identify elements of the green labor market within the context of the green economy and the energy sector. The methodology employs a hybrid literature review, combining a systematic literature review facilitated by the use of VOSviewer software. Exploring the Scopus database enabled the identification of keywords directly related to the green economy and the energy sector. Within these identified keywords, elements of the green labor market were searched. The main result is the empirical identification of the crucial term ‘green skills,’ which links elements of the green labor market, as presented in bibliometric maps. The research results indicate a gap in the form of insufficient discussion on green self-employment within the energy sector. Aspects of green jobs and elements of the green labor market are prominently featured in current research. However, there is a notable gap in the literature regarding green self-employment, presenting promising avenues for further research.

Credit policies for clean and renewable energy businesses play a crucial role in supporting carbon neutrality efforts to combat climate change. Clustering the credit capacity of these companies to prioritize lending is essential given the limited capital available. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are two robust machine learning algorithms for addressing complex clustering problems. Additionally, hyperparameter selection within these models is effectively enhanced through the support of a robust heuristic optimization algorithm, Particle Swarm Optimization (PSO). To leverage the strength of these advanced machine learning techniques, this paper aims to develop SVM and ANN models, optimized with the PSO, for the clustering problem of green credit capacity in the renewable energy industry. The results show low Mean Square Error (MSE) values for both models, indicating high clustering accuracy. The credit capabilities of wind energy, clean fuel, and biomass pellet companies are illustrated in quadrant charts, providing stakeholders with a clear view to adjust their credit strategies. This helps ensure the efficient operation of banking green credit policies.

With the increasing demand for sustainable energy, advanced characterization methods are becoming more and more important in the field of energy materials research. With the help of X-ray imaging technology, we can obtain the morphology, structure and stress change information of energy materials in real time from two-dimensional and three-dimensional perspectives. In addition, with the help of high penetration X-ray and high brightness synchrotron radiation source, in-situ experiments are designed to obtain the qualitative and quantitative change information of samples during the charge and discharge process. In this paper, X-ray imaging technology based on synchrotron and its related applications are reviewed. The applications of several main X-ray imaging technologies in the field of energy materials, including X-ray projection imaging, transmission X-ray microscopy, scanning transmission X-ray microscopy, X-ray fluorescence microscopy and coherent diffraction imaging, are discussed. The application prospects and development directions of X-ray imaging in the future are prospected.