This paper aims to explore China's contributions to global green energy and low-carbon (GELC) development based on the Belt and Road (B&R) Initiative. Basic situations of B&R countries reveal an urgent requirement for developing green energy. Carbon intensity is an efficient indicator reflecting the degree of GELC development, which is affected by many factors. By analyzing the spatial distribution of carbon intensities in 29 B&R countries excluding China, the spatial agglomeration and positive radiation effects are discovered, while the negative radiation effects are disappearing, indicating that the studied B&R countries lack an effective driving mechanism to promote GELC development. Besides, the spatial convergence results support significant absolute and conditional convergences in the 29 B&R countries, and a faster convergence speed when considering control variables. Therefore, B&R countries trend to converge to a steady stable carbon intensity to achieve the GELC development. Furthermore, the investment rate and openness play a driving role in pushing the decrease of carbon intensity growth rate, revealing that the B&R Initiative can promote reducing the global carbon emissions and developing global green energy. Moreover, the carbon intensity of the country will be positively affected by those of the surrounding areas, indicating that reducing carbon emission is a global governance issue requiring the participation of all countries. Finally, several policy suggestions are proposed to promote the global GELC development under B&R framework, according to the empirical findings.

This study constructed a hybrid model for assessing the environmental impact caused by power grid projects (PGP) in high altitude area (HAA). Firstly, this study analyzed the characteristics of the environment in HAA and the possible environmental impacts caused by the PGP in HAA. On this basis, an evaluation indicator system reflecting the particularity of HAA was established, including three perspectives named natural, social and ecological environment. Next, considering the availability of evaluation index data and the scarcity of evaluation samples, the best and worst method (BWM) was employed to obtain the objective and credible indicator weights. Furthermore, the Vague set theory was introduced into the comprehensive evaluation model, overcoming the shortcomings of comprehensive evaluation model based on fuzzy sets. Finally, the practicability and effectiveness of the proposed hybrid model was validated via a practical PGP in Qinghai-Tibet Plateau. Overall, the results of this paper can play an important supporting role in promoting green construction and sustainable development of PGP. Besides, the proposed hybrid evaluation framework requires fewer index values and evaluation samples, having good applicability and promotion value in handling the evaluation issues with uncertain and incomplete information.

In recent years, new energy sources have ushered in tremendous opportunities for development. The difficulties to finance new energy enterprises (NEEs) can be estimated through issuing corporate bonds. However, there are few scientific and reasonable methods to assess the credit risk of NEE bonds, which is not conducive to the healthy development of NEEs. Based on this, this paper analyzes the advantages and risks of NEEs issuing bonds and the main factors affecting the credit risk of NEE bonds, constructs a hybrid model for assessing the credit risk of NEE bonds based on factor analysis and logistic regress analysis techniques, and verifies the applicability and effectiveness of the model employing relevant data from 46 Chinese NEEs. The results show that the main factors affecting the credit risk of NEE bonds are internal factors involving the company's profitability, solvency, operational ability, growth potential, asset structure and viability, and external factors including macroeconomic environment and energy policy support. Based on the empirical results and the exact situation of China's NEE bonds, this article finally puts forward several targeted recommendations.