The Malaysian government’s efforts to promote solar photovoltaic (PV) usage among households face a challenge due to its low adoption rate. This study delves into the factors influencing the exponential adoption of solar PV electricity generation among landed residential property owners in Malaysia. The research aims to comprehensively examine the predictors influencing the adoption of solar PV systems among Malaysian households. Hence, the study employs an enhanced Theory of Planned Behavior framework, integrating sustainable energy security dimensions such as availability, affordability, efficiency, acceptability, regulation, and governance. The sample comprised 556 Malaysian residents who owned and resided in the landed properties. The home locations where at least one solar PV installation existed within a residential street. Snowball sampling was employed through referrals, leveraging social and community networks. Collected data was analyzed using the partial least squares structural equation modeling. Attitude, affordability, and acceptability emerged as pivotal factors significantly impacting the intention to use solar PV systems among Malaysian households. This research not only enriches academic discourse but also offers practical implications for policymakers, guiding the formulation of targeted strategies to promote sustainable energy practices and facilitate the widespread adoption of solar PV systems in Malaysia.

In the third national communication submitted by Ecuador, the total greenhouse gases (GHG) emission was calculated at 80,627 GgCO_2-eq, considering the country’s commitment to the Framework on Climate Change. In 2018, Ecuador ratified its nationally determined contribution (NDC) to reduce its GHG emissions by 11.87% from the business-as-usual (BAU) scenario by 2025. The macroeconomic impacts of NDC implementation in the energy sector are discussed. A Computable Equilibrium Model applied to Ecuador (CGE_EC) is used by developing scenarios to analyze partial and entry implementation, as well as an alternative scenario. Shocks in exogenous variables are linked to NDC energy initiatives. So, the NDC’s feasibility depends on guaranteeing the consumption of hydropower supply, either through local exports or domestic demand. In the last case, the government’s Energy Efficiency Program (PEC) and electricity transport have important roles, but the high levels of investment required and poor social conditions would impair its implementation. NDC implementation implies a GDP increase and price index decrease due to electricity cost reductions in the productive sector. These conditions depend on demand-supply guarantees, and the opposite case entails negative impacts on the economy. The alternative scenario considers less dependence on the external market, achieving higher GDP, but with only partial fulfillment of the NDC goals.

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.

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.

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.

Global CO₂ emissions pose a serious threat of climate change for high-growth countries, requiring increased efforts to preserve the environment and meet growing economic needs through the use of renewable energies. This research significantly enhances the current literature by filling a void and differentiating between short-term and long-term impacts across economic growth, renewable energy consumption, energy intensity, and CO₂ emissions in BRIC countries from 2002 to 2019. In contrast to approaches that analyze global effects, this study’s focus on short and long-term effects offers a more dependable insight into energy and environmental research. The empirical results confirmed that the effect of economic growth on CO₂ emissions is positive both in the short and long term. Moreover, the effect of energy consumption is negative in the short term and positive in the long term. The effect of energy intensity is positive in the short term and negative in the long term. Accordingly, policy recommendations must be adopted to ensure that these economies respond to the notion of sustainable development and the relationship with the environment. BRIC countries must strengthen their industries in the long term in favor of the use of renewable energies by introducing innovation and technology. These economies face the challenge of a transition to renewable energy sources by creating a new energy and industrial sector environment that is more environmentally friendly atmosphere.