To promote the development of nuclear power project in inland China, a specialized multi-criteria decision support framework is constructed for the site selection of inland nuclear power plants in this work. The best worst method (BWM), decision-making trial and evaluation laboratory (DEMATEL), and technique for order performance by similarity to ideal solution (TOPSIS) are integrated with Z-numbers, namely Z-BWM, Z-DEMATEL, and Z-TOPSIS. Z-BWM and Z-DEMATEL are combined to produce the weights of criteria, and an extended Z-TOPSIS is utilized to determine the ranking of all alternatives. Finally, a case study is performed in Hunan province to demonstrate the practicability of the proposed decision support framework. The robustness and feasibility of the proposed method are shown by an in-depth analysis of the decision results.that are not prone to tsunamis or typhoons. China has a total of 48 nuclear power units in operation, but they are all in coastal areas. The specific research on the site selection of China's inland NPPs is still insufficient. In this work, a multi-criteria decision support framework is provided to identify the best site for inland NPPs in China, and the contributions are summarized as follows:(1) The existing researches on multi-criteria decision-making (MCDM) problems inevitably involve the uncertainly and unreliability of human preferences. Therefore, Z-number is introduced into the proposed MCDM framework in this work, and all evaluation information is given using Z-number linguistic variables.(2) Best worst method (BWM) and decision-making trial and evaluation laboratory (DEMATEL) are extended to Z-numbers, i.e., Z-BWM and Z-DEMATEL. The hybrid model not only considers the impact of the criterion relations on the criterion weights, but also provides a long-term improvement for inland NPP site selection.(3) The technique for order performance by similarity to ideal solution (TOPSIS) is combined with Z-numbers, namely Z-TOPSIS. It can effectively reduce the influence of outliers, and provide more reliable ranking results than the classic TOPSIS.(4) Currently, few researches focus on the site selection of China's inland NPPs. Based on the reactor siting criteria developed by IAEA, a specialized evaluation system considering the eco-environment, geographic features, and socio-economic factors in China's interior is constructed.The structure of the paper is organized as follows. Section 2 reviews some studies related to NPP site selection. Section 3 introduces some basic concepts of Z-numbers. Section 4 presents the proposed framework for inland NPP site selection. Section 5 provides a case study in Hunan province of China. Section 6 conducts an in-depth analysis concerning selection results. Finally, Section 7 draws conclusions.

Under the double pressure of global energy consumption and climate change, nuclear power has become a low-carbon alternative energy source that could transform the energy structure of the globe. In the nuclear power industry, selecting suitable suppliers plays a significant role in improving the overall performance of nuclear power projects. Along with this symmetrical impact, this paper aims to develop a multistage decision-support framework to determine the optimal nuclear power equipment supplier, which is constructed in the context of Z-number information. Concretely, the Analytic Network Process (ANP) and Tomada de Decisão Iterativa Multicritério (TODIM) are extended by Z-numbers symmetrically—namely, Z-ANP and Z-TODIM. Z-ANP is first applied to analyze the symmetrical interdependence of criteria, so as to accurately determine the criterion weights. Further, the ranking of alternatives is obtained by Z-TODIM, which sufficiently considers the risk preference and psychological states of decision-makers. Finally, a practical case of nuclear-grade cable procurement in the Karachi 2-3 international nuclear power project is performed to illustrate the practicality of the proposed method, and its robustness and superiority are proven by comparing it with current representative approaches.

The successful diagnosis of nuclear power equipment failures plays a vital role in guaranteeing the safe operation of nuclear power systems. Failure mode and effect analysis (FMEA) is one of the most commonly used methods for identifying potential failures. However, several shortcomings associated with the conventional FMEA method limit its further application. This paper develops an extended FMEA approach based on hesitant fuzzy linguistic Z-numbers (HFLZNs). Firstly, the concept of HFLZNs is proposed to describe the evaluation information, which inherits the prominent features of the hesitant fuzzy linguistic term set and linguistic Z-numbers (LZNs). Secondly, an HFLZN assessment method is developed to determine the weights of risk factors, and the weights of experts are measured based on hesitation degree. Subsequently, considering the psychological characteristics of decision makers, Tomada de Decisão Iterativa Multicritério and LZNs are integrated to obtain the risk ranking of failure modes. Finally, the practicability of the extended FMEA method is proven by an illustrative example concerning the risk evaluation of a nuclear main pump bearing, and its robustness is verified by indepth analysis.

The Belt and Road Initiative (BRI) has promoted the deployment of renewable energy to achieve sustainability. It is essential to reveal the influence of renewable energy on low-carbon economic development. The share of renewable energy consumption (SREC) is taken as the core explanatory variable in this paper, and its impacts on carbon emission intensity (CEI) and economic growth are investigated from the spatialtemporal perspective. First, the panel Granger causality test is applied for revealing the causal links among SREC, CEI, and economic growth during 1999-2017. Then, this paper investigates the impacts of SREC on economic growth and CEI through rigorous econometric techniques. Based on the regression results, Shapley value decomposition is utilized to account for the cross-country inequalities of economic growth and CEI.The main findings are as follows: (1) There exist bidirectional Granger causalities between SREC, economic growth, and CEI, which shows there is a systematic link between the three variables. (2) All models demonstrate the inverted U-shaped nexus between SREC and economic growth, indicating renewable energy deployment costs are urgent to be decreased with SREG increasing. Besides, capital investment and openness positively affect economic growth, but energy intensity has an opposite impact. (3) From the spatial heterogeneity perspective, the cross-country inequality in economic growth is primarily due to the regional inequality of capital investment, followed by energy intensity and SREC. By contrast, the impacts of labor and openness are negligible. (4) SREC has a negative effect on CEI. In addition, an inverted U-shaped 2 nexus between economic growth and CEI is observed. Energy intensity positively affects CEI, while the impacts of urbanization and openness are insignificant. (5) From the spatial heterogeneity perspective, the cross-country CEI inequality is mostly caused by the inequality of energy intensity, followed by SREC, urbanization and economic growth, while the contribution of the openness gap is little. This article provides important implications for low-carbon development in the BRI countries.

The Belt and Road Initiative (BRI) has promoted the deployment of renewable energy to achieve sustainability. It is essential to reveal the influence of renewable energy on low-carbon economic development. The share of renewable energy consumption (SREC) is taken as the core explanatory variable in this paper, and its impacts on carbon emission intensity (CEI) and economic growth are investigated from the spatial-temporal perspective. First, the panel Granger causality test is applied for revealing the causal links among SREC, CEI, and economic growth during 1999-2017. Then, this paper investigates the impacts of SREC on economic growth and CEI through rigorous econometric techniques. Based on the regression results, Shapley value decomposition is utilized to account for the cross-country inequalities of economic growth and CEI. The main findings are as follows: (1) There exist bidirectional Granger causalities between SREC, economic growth, and CEI, which shows there is a systematic link between the three variables. (2) All models demonstrate the inverted U-shaped nexus between SREC and economic growth, indicating renewable energy deployment costs are urgent to be decreased with SREG increasing. Besides, capital investment and openness positively affect economic growth, but energy intensity has an opposite impact. (3) From the spatial heterogeneity perspective, the cross-country inequality in economic growth is primarily due to the regional inequality of capital investment, followed by energy intensity and SREC. By contrast, the impacts of labor and openness are negligible. (4) SREC has a negative effect on CEI. In addition, an inverted U-shaped nexus between economic growth and CEI is observed. Energy intensity positively affects CEI, while the impacts of urbanization and openness are insignificant. (5) From the spatial heterogeneity perspective, the cross-country CEI inequality is mostly caused by the inequality of energy intensity, followed by SREC, urbanization and economic growth, while the contribution of the openness gap is little. This article provides important implications for low-carbon development in the BRI countries.