This study evaluates the sustainability and ethical practices of Kerry Logistics Network Limited (KLN), a prominent logistics service provider headquartered in Hong Kong. Using normative ethical theories, stakeholder analysis, and the Circle of Sustainability framework, this research examines KLN’s alignment with global sustainability standards, particularly the United Nations Sustainable Development Goals (SDGs). The findings reveal that KLN has achieved significant milestones in environmental management, such as reducing greenhouse gas emissions by 11% from 2021 to 2022 through the deployment of electric trucks and incorporating renewable energy in warehouse operations. KLN has also enhanced social responsibility and governance practices by implementing fair labor policies and establishing a rigorous code of conduct, ensuring compliance with ethical guidelines across its supply chain. However, the study identifies areas for improvement, including biodiversity actions, battery recycling processes, and transparency in stakeholder engagement. Emphasizing the importance of third-party validation, this paper underscores KLN’s leadership in the logistics industry and provides insights for other companies aiming to improve sustainability performance through comprehensive, verifiable practices.

The conversion of the energy supply to renewable sources (wind, photovoltaics) will increase the volatility in electricity generation in the future. In order to ensure a balanced power balance in the power grid, storage is required - not only for a short time, but also seasonally. The bidirectional coupling of existing energy infrastructure with the power grid can help here by using the electricity in electrolysis systems to produce hydrogen. The hydrogen can be mixed with natural gas in the existing infrastructure (gas storage, pipelines) to a limited extent or converted directly to methane in a gas-catalytic reaction, methanation, with carbon dioxide and/or carbon monoxide. By using the natural gas infrastructure, the electricity grids are relieved and renewable energies can also be stored over long periods of time. Another advantage of this technology, known as “Power-to-Gas”, is that the methane produced in this way represents a sink for CO₂ emissions, as it replaces fossil sources and CO₂ is thus fed into a closed cycle.

Research in the field of Power-to-Gas technology is currently addressing technological advances both in the field of electrolysis and for the subsequent methanation, in particular to reduce investment costs. In the field of methanation, load-flexible processes are to be developed that are adapted to the fluctuating supply of hydrogen. The profitability of the Power-to-Gas process chain can be increased through synergistic integration into existing industrial processes. For example, an integrated smelting works offers a promising infrastructural environment, since, on the one hand, process gases containing carbon are produced in large quantities and, on the other hand, the oxygen as a by-product from the water electrolysis can be used directly. Such concepts suggest an economic application of Power-to-Gas technology in the near future.

In green construction, sustainable resources are essential. One such material is copper, which is widely utilized in electronics, transportation, manufacturing, and residential buildings. As a very useful material, it has many beneficial impacts on human life. Observed from the recent demand spike is in line with the overall trend and the current growing smelter construction in Indonesia. Researchers intend to adapt the existing Copper Smelting Plant Building into an environmentally friendly building as a part of the production chain, in addition to reducing public and environmental concerns about the consequences of this development. We have identified a disparity in cost, where the high cost of green buildings is an obstacle to its implementation to enhance the cost performance with increased renewable energy of the Smelter Construction Building, this study investigates the application of LEED parameters to evaluate green retrofit approaches through system dynamics. The most relevant features of the participant assessments were identified using the SEM-PLS approach, which is used to build and test statistical models of causal models. We have results for this Green Retrofitting study following significant variables according to the following guidelines: innovation, low-emission materials, renewable energy, daylighting, reducing indoor water usage, rainwater management, and access to quality transit.

This research analyzes the relationship between political stability, renewable energy utilization, economic progress, and tourism in Indonesia from 1990 to 2020. We employ advanced econometric techniques, including the Fourier Bootstrap Autoregressive Distributed Lag (ARDL) approach and Fourier Toda-Yamamoto causality testing, to ensure the robustness of our results while accounting for smooth structural changes in the data. The analysis uncovers a long-term equilibrium relationship between tourism and its fundamental determinants. Our research reveals significant positive impacts of political stability and renewable energy consumption on tourism in Indonesia. A stable political environment creates a favorable climate for tourism development, instilling confidence in both domestic and international tourists. Promoting renewable energy usage aligns with sustainable tourism practices, attracting environmentally conscious travelers. Furthermore, our findings demonstrate a bi-directional causal relationship between these variables over time. Changes in political stability, renewable energy consumption, and economic growth profoundly influence the tourism sector, while the growth of tourism itself can also stimulate economic development and foster political stability. Our findings underscore the need for environmentally sustainable and politically stable tourism policies. Indonesia’s tourism sector can grow sustainably with renewable energy and stability. Policymakers can develop strategies with tourism, political stability, renewable energy, and economic prosperity in mind.

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.

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.