Energy is the main element for a modern lifestyle that must be considered in economically reliable and sustainable development dialogues. The financial performance of solar power projects has become the main issue, especially in developing countries such as Pakistan, where it has gained the special attention of government and regulatory authorities. The present study evaluates green technology strategies for the sustainable development of solar power projects in Pakistan. We examine the moderating role of cost and riskiness of the methods between the nexus of capital budgeting techniques and the financial performance of solar power projects. The analysis is performed on data collected from 44 respondents (chief financial officers and chief executive officers) by accompanying an inclusive questionnaire survey. Partial least squares structural equation modeling (PLS-SEM) is used to assess the formulated suppositions. The results reveal that green technology strategies positively impact the sustainable development of solar power projects. The profitability index is a good source of higher financial performance of the solar power projects. The results further demonstrate that the cost and riskiness of the methods significantly moderate the nexus of capital budgeting techniques and the financial performance of solar power projects. These findings provide a valuable manual for policymakers, government institutions, and regulators to select the appropriate green technology strategy to increase cleaner production and sustainable development of solar power projects.

To improve the economic benefits of power systems in the process of achieving multi-energy complementation and decarbonization, this paper proposes a dispatching optimization model for virtual power plants (VPP) that considers carbon trading and green certificates. Firstly, the structure of the VPP system integrating wind and solar generators (WP and PV), power-to-gas (P2G), carbon capture power plants (CCPP) and price-based demand response (PBDR) is established. Secondly, the two-way interactive trading models among the VPP, carbon trading and green certification market are constructed. Then, the dispatching optimization model of the VPP is constructed. Finally, the numerical example is solved and analyzed by the chaotic particle swarm optimization algorithm, which verifies the rationality and effectiveness of the new model. The results show that: (1) when the VPP considers the CCPP-P2G, the cost of the system is reduced by USD 2550.48, while the CO₂ emissions are reduced by nearly 50%; (2) the addition of PBDR reduces the CO₂ emissions of the thermal power unit, which has reduced the cost of carbon tax by nearly 27.8%, further reducing the cost of the VPP; (3) the introduction of the carbon trading and green certificate market has reduced the operating cost of the VPP by nearly 22.24%.

At present, electric cars are being developed rapidly in China as emerging carbon emission reduction vehicles, but their proportion in the Chinese automobile market is still small, and a large number of potential consumers are still holding a wait-and-see attitude. Therefore, for the sake of promoting the further development of electric cars in China, this paper based on the TPB (Theory of Planned Behavior) theoretical research framework, investigates potential consumers in typical areas of Beijing and collects a large amount of data through the design of paper and electronic questionnaires. SEM (Structural Equation Modeling) and MNL (Multinomial Logit Model) models are used to analyze key factors affecting consumers’ purchase intention and actual purchasing behavior. The results show that the positive and negative attributes of consumers’ attitude, subjective norm, and perceived behavior control will have different effects on consumers’ actual purchasing behavior. Finally, based on the analysis results, some reasonable suggestions are proposed for the government and EV (Electric Vehicles) enterprise service providers to increase electric vehicle diffusion.

Accurate wind power forecasting is essential to reduce the negative impact of wind power on the operation of the grid and the operation cost of the power system. Day-ahead wind power forecasting plays an important role in the day-ahead electricity spot trading market. However, the instability of the wind power series makes the forecast difficult. To improve forecast accuracy, a hybrid optimization algorithm is established in this study, which combines variational mode decomposition (VMD), maximum relevance & minimum redundancy algorithm (mRMR), long short-term memory neural network (LSTM), and firefly algorithm (FA) together. Firstly, the original historical wind power sequence is decomposed into several characteristic model functions with VMD. Then, mRMR is applied to obtain the best feature set by analyzing the correlation between each component. Finally, the FA is used to optimize the various parameters LSTM. Adding the forecasting results of all sub-sequences acquires the forecasting result. It turns out that the proposed hybrid algorithm is superior to the other six comparison algorithms. At the same time, an additional case is provided to further verify the adaptability and stability of the proposed hybrid model.

In order to protect the environment and reduce energy consumption, new energy vehicles have begun to be vigorously promoted in various countries. In recent years, the rise of intelligent technology has had a great impact on the supply chain of new energy vehicles, which, coupled with the complexity of the supply chain itself, puts it at great risk. Therefore, it is quite indispensable to evaluate the risk of the new energy vehicle supply chain. This paper assesses the risks faced by China’s new energy vehicle supply chain in this period of technological transformation. First of all, this paper establishes an evaluation criteria system of 16 sub-criterion related to three dimensions: the market risk, operational risk, and the environmental risk. Then, variable weight theory is proposed to modify the constant weight obtained by the fuzzy analytic hierarchy process (FAHP). Finally, a risk assessment of China’s new energy vehicle supply chain is carried out by combining the variable weight and the cloud model. This method can effectively explain the randomness of matters, and avoid the influence of value abnormality on the criteria system. The results show that China’s new energy vehicle supply chain is at a high level. Through the identification of risk factors, mainly referring to the low clustering risk, technical level risk and information transparency risk, this paper can provide a risk prevention reference for corresponding enterprises.

China’s electricity industry has been undergoing a process of regulatory reform. This study aims to analyse the impact of liberalization on the electricity market assuming different degrees of scope of the reforms by applying a computable general equilibrium (CGE) model. In this paper, we consider the three sub-sectors of the electricity industry, namely generation, transmission and distribution. We assume that the reform will phase out the entry barriers on the generation side and allow for competition on the distribution side, while keeping the transmission side under regulation. The results showed that the reform could enhance efficiency in the electricity sector and reduce energy prices for households. Introduction of a complete competition model would decrease welfare by 5.394 billion yuan, if contrasted to a limited competition model. The composite energy price would decline under both scenarios, whereas the quantity of energy consumed by the households would go up. This research, thus, contributes to literature on the economic effects of China’s electric power market reform, and can be used as a case study to support policy decisions for the decision-makers.