The plastic industry is an economic sector that plays an important role in promoting a circular economy in Vietnam. This study used the updated 2018 input-output (IO) table to identify and analyse the production and consumption of seven different plastics including: HDPE, PS, PE, PET, PVC, PP, and others. The study also integrated the IO model to unveil the environmental burden of these plastics through the plastic demand of 40 economic sectors and households. As a result, in 2018, the amount of direct solid waste from the plastic industry was 58,147 tons and the amount of indirect solid waste from the plastic industry to other economic sectors and the household sector were 214,258 tons and 6,262 tons, respectively. Agriculture and its services, food processing, fashion manufacturing, basic chemical production, electrical and electronic equipment production, and transport production embodied the highest indirect burdens due to their use of plastic products. This study contributes to MFA research and developing strategies for sustainable production and consumption of plastics and the management of plastic waste in Vietnam.

Renewable energy has been actively researched and developed in many countries to replace the conventional energy resources that come from fossil fuels. As social and environmental awareness of the renewable energy has grown, it is essential to address both quantitative factors and qualitative factors in determination of the optimal renewable energy portfolio. This paper proposes a novel approach to integrate a financial model and a fuzzy model to analyze both quantitative and qualitative factors. The financial model is utilized to calculate the quantitative factors, thereby assisting experts make judgments more accurately in the fuzzy model. The fuzzy model is utilized to evaluate the qualitative factors based on the expert judgements. Moreover, this paper proposes multi-segment judgment model that analyzes the evaluation of different groups, including government, investor and user groups. The results show that each group has different priority order. For example, the highest-priority factor of Government, Investor and User is environmental (with a score of 0.665), economic (with a score of 0.854), and technological criteria (with a score of 0.771), respectively. The results also indicated that small-scale onshore wind energy, large-scale onshore wind energy and solar energy is the best option for Government, Investor and User, respectively.

Maintenance cost contributes a large portion in total life-cycle cost of offshore wind systems and improves the efficiency of maintenance activities that has emerged as an effective solution in enhancing cost-effectiveness of offshore wind energy. This study proposes a mathematical model to determine the optimal maintenance schedules, by highlighting the deterioration process of offshore wind systems and accounting the optimal vessel routing of maintenance activities, maintenance cost therefore could be shifted to an optimal level. Firstly, reliability of each component is precisely analyzed with respect to the effect of previous maintenance activities. Then, an optimal individual maintenance schedule and a grouping maintenance schedule in daily manner are established based on reliability analysis and opportunity cost consideration. Finally, an optimal maintenance schedule approach accounting vessel routing is apparently presented to optimize the transportation cost of maintenance activities. Numerous influential factors such as system reliability, weather condition, maintenance resource, maintenance duration, production loss cost, and vessel related issues are deliberately examined to enhance the robustness and efficiency of the proposed approach. The experimental results investigating four different scenarios demonstrate that by applying the proposed approach, number of maintenance activity is lowered down, maintenance duration and vessel travelling distance are also significantly reduced in the range of 43.86% - 52.77%, and 52.90% - 58.35%, respectively. Therefore, the economic benefit on maintenance cost could be achieved in an interval of 15.15% - 23.25% by selecting the optimal maintenance schedule.

Enhancing the efficacy of financial subsidies holds paramount significance in fostering sustainable development due to their influence on energy utilization, installation scalability, and the crucial pursuit of decarbonization. Despite a surge in scholarly interest surrounding financial subsidies for renewable energy, the literature studies fall short in comprehensively addressing the factors that impact on the accuracy and reliability of the model for identifying optimal financial subsidies. This paper introduces a robust approach to optimize financial subsidies to achieve the predefined target for renewable energy installation, considering the equilibrium between investor remuneration and government subsidy outlay. Center to this approach is the integration of a forward-looking price forecasting model and the establishment of an economic evaluation approach based on empirical data encompassing weather patterns, technical factors, investor expectations, subsidy disbursements, market risk assessments, and other pertinent parameters. The case study of Taiwan is conducted, and the results indicate that, in the baseline scenario, the financial subsidy is only effective in the risk-neutral market with a small solar system; in the mix scenario, the benefit of energy system increased at least 4.5%, however, the mix scenario is still ineffective to encourage the renewable energy development; and in the optimal scenario, by providing accurate recommendations for optimal financial subsidies, government can achieve the predefined target, even with reduced subsidies due to declining installation costs.

The scarcity of energy resources and global warming over the past few decades have prompted the widespread adoption of renewable energy sources. Among the potential renewable energy sources, solar energy has emerged as one of the most promising renewable energy sources. However, the uncertainty and fluctuations of solar power generation create negative impacts on the stability and reliability of the electric grid, planning of operation, economic feasibility, and investment. Therefore, accurate prediction of solar power generation is crucial to ensure the stability of the power grid and promote a large-scale investment in a solar energy system. A large number of research studies have been conducted on predicting solar power generation under different perspectives. However, no existing study analyses and predicts power generation of multi-solar energy sites by only one prediction model. The integration of multiple sites into one predictive model will reduce the number of required models for each site, thereby saving the computing resources and required calculation time. This paper proposes a novel methodology to group multiple solar sites and develop an integrated model by using a machine learning algorithm to predict power generation of each group. Firstly, the K-means clustering algorithm is utilized to cluster multiple solar sites which have similar power generation properties into one group. Then, a machine learning algorithm has been developed to predict power generation in a computationally fast and reliable manner. The proposed approach is verified by the real data of 223 solar sites in Taiwan. The experimental results show that the training time can be reduced by 93.2% without reducing the accuracy of the prediction model. Therefore, the cluster-based prediction approach gives better performance as compared with existing models.

The Voluntary Carbon Market (VCM) is emerging as a complementary governance mechanism supporting global net-zero transitions. While small and medium-sized enterprises (SMEs) represent the backbone of most emerging economies, their organizational readiness to engage in carbon markets remains underexplored. This study advances the organizational readiness literature by contextualizing SME preparedness within emerging carbon market governance and sustainability transition frameworks. Integrating the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), this study identifies and prioritizes key determinants of SME readiness and compares readiness levels across three major sectors in Vietnam: Industry and Construction (IC), Services (SV), and Agriculture, Forestry, and Fishing (AFF). The analysis assesses five dimensions of readiness: knowledge, financial, institutional, managerial, and technological readiness. The findings indicate that knowledge readiness received the highest priority weight, followed by financial and institutional readiness. Sectoral analysis reveals that IC SMEs exhibit the highest readiness and remain the top-ranked sector across all leave-one-expert-out and criterion-weight sensitivity analyses, whereas the relative ordering of SV and AFF is more sensitive to panel composition. By revealing the relative importance of readiness dimensions and sectoral differences in an early-stage carbon governance context, this study provides policy-relevant insights for fostering inclusive low-carbon development pathways.