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Nuclear power is a stable and reliable energy source that can improve energy structure while reducing carbon emissions, which is of great significance for environmental protection and combating climate change. As a unique industry, it is facing rare development opportunities in China and has broad market prospects. However, the characteristics of technical difficulty, loose organizational structure and uneven regional distribution limit the expansion of the nuclear power industry. This paper aims to a better understanding of the accumulation process for innovation capability from the perspective of network evolution and provides policy guidance for the market development of the nuclear power industry (NPI).

Methodologically, social network analysis is used to explore the co-evolution of multidimensional collaboration networks. First, the development and policy evolution of the NPI is introduced to divide the evolution periods. Then, the authors identify and analyze the core organizations, technologies and regions that promote nuclear power patent collaboration. Furthermore, three levels of collaboration networks based on organizations, technologies and regions are constructed to analyze the coevolution of patent networks in China’s NPI.

The results show that nuclear power enterprises always play the foremost role in the organizational collaboration network (OCN), and the dominance of foreign enterprises is replaced by Chinese state-owned enterprises in the third period. The technology hotspot has shifted from nuclear power plant construction to the control system. The regional collaboration network was initially formed in the coastal areas and gradually moved inland, with Guangdong and Beijing becoming the two cores of the network. The scale of three collaboration networks is still expanding but the speed has slowed down.

In response to the pain points of the NPI, this research focuses on multidimensional collaborative innovation, investigates the dynamic evolution process of collaborative innovation networks in China’s NPI and links policy evolution with network evolution creatively. The ultimate result not only helps nuclear power enterprises integrate innovative resources in complex environments but also promotes industrial upgrading and market development.

The purpose of this study is to explore the role of direct public engagement in shaping the sustainability image of nuclear energy in the UAE and the Arabian Gulf region. The study aims to measure the conflicting viewpoints of stakeholders, particularly the local community, regarding nuclear energy's dependability, cost-effectiveness, safety and environmental friendliness. The study also seeks to assess the effectiveness of direct stakeholder engagement strategies in enhancing public confidence in nuclear energy as a safe and sustainable source of electricity.

This study uses a quantitative-methods research design and used a sample of 318 participants. The SPSS AMOS application was used to conduct a structural equation model analysis. The purpose of this analysis is to examine the relationships among variables that constitute the key constructs of the study. In addition, confirmatory factor analysis was used to assess the reliability of the testing approach. Various fit indices and measurements, such as chi-square ratio, degrees of freedom, GFI, CFI and RMSEA, were used to evaluate the adequacy of the model.

The study finds that the construct “Direct Stakeholder Engagement (DSE)” has a positive effect on the dependent variables “Trust in Nuclear Sustainability (TNS)” and “Perception of Nuclear Energy as Safe (PNE)” with a probability value of (0.003, p < 0.05). Therefore, the hypothesis of the study is deemed acceptable. Hence, it can be concluded that each of the foregoing variables (DSE1, 2, 3, 4 and 5) and (TNS1, 2, 3, 4 and 5) with (PNE1, 2 and 3) have been observed and analysed in this study, and based on this analysis, it is plausible that the public's trust in nuclear sustainability and their acceptance of nuclear energy as a safe source of their nation's electricity can be positively affected by direct stakeholder engagement.

The study's findings have implications for policymakers and managers of nuclear power plants in the UAE and the Arabian Gulf region. The study provides insights into effective stakeholder engagement strategies that can enhance public participation and confidence in nuclear energy. The study's recommendations highlight the importance of incorporating public opinion in policymaking and management practices to address conflicting viewpoints and enhance public trust in nuclear sustainability. The study's findings also contribute to the ongoing discourse on nuclear sustainability and provide insights into the role of direct public engagement in shaping public perception of nuclear energy.

This study's originality lies in its focus on the UAE and the Arabian Gulf region, where nuclear energy is a critical source of electricity. The study contributes to the limited research on stakeholder engagement and public perception of nuclear energy in the region. The study's novel framework of stakeholder engagement, tailored to cultural dimensions, provides insights into effective engagement strategies that can enhance public participation and confidence in nuclear energy. The study's quantitative-methods research design also provides a comprehensive understanding of the conflicting viewpoints of stakeholders, enhancing the understanding of the role of direct public engagement in shaping public perception of nuclear energy.

This research aims to update the literature about the importance of reliability in supply chain (SC) and to find out the SC determinants.

This research surveys while contributing to the academic grasp of supply chain reliability (SCR) concepts. The study found 45 peer-reviewed publications using a structured survey technique with a four-step filtering process. The filtering process includes data reduction processes such as an evaluation of abstract and conclusion. The filtered study focuses on SCR and its determinants.

One of the major findings is that most of the study has focused on mathematical and conceptual studies. Also, this study provides the answer to a question like how can reliability be better accepted and evolved within the SC after finding the determinants of SCR.

The observed methodological gap in understanding and development of SCR was identified and classified into three categories: mathematical, conceptual and empirical studies (case studies and survey’s mainly). This research will aid academics in developing and understanding the determinants of SCR.

Understanding China's carbon dioxide (CO2) emission status is crucial for getting Carbon Neutrality status. The purpose of the paper is to calculate two possible scenarios for CO2 emission distribution and calculated input-output flows of CO2 emissions for every 31 China provinces for 2012, 2015 and 2017 years.

In this study using the input and output (IO) table's data for the selected years, the authors found the volume of CO2 emissions per one Yuan of revenue for the industry in 2012 and the coefficient of emission reduction compared to 2012.

Results show that in the industries with a huge volume of CO2 emissions, such as “Mining and washing of coal”, the authors cannot observe the reduction processes for years. Industries where emissions are being reduced are “Processing of petroleum, coking, nuclear fuel”, “Production and distribution of electric power and heat power”, “Agriculture, Forestry, Animal Husbandry and Fishery”. For the “construction” industry the situation with emissions did not change.

“Transport, storage, and postal services” and “Smelting and processing of metals” industries in China has the second place concerning emissions, but over the past period, emissions have been sufficiently reduced. “Construction” industry produces a lot of emissions, but this industry does not carry products characterized by large emissions from other industries. Authors can observe that Jiangsu produces a lot of CO2 emissions, but they do not take products characterized by significant emissions from other provinces. Shandong produces a lot of emissions and consumes many of products characterized by large emissions from other provinces. However, Shandong showed a reduction in CO2 emissions from 2012 to 2017.

In the process of building the “Belt and Road” and “Bright Road” community of interests between China and Kazakhstan, this paper proposes the construction of an inland nuclear power plant in Kazakhstan. Considering the uncertainty of investment in nuclear power generation, the authors propose the MGT (Monte-Carlo and Gaussian Radial Basis with Tensor factorization) utility evaluation model to evaluate the risk of investment in nuclear power in Kazakhstan and provide a relevant reference for decision making on inland nuclear investment in Kazakhstan.

Based on real options portfolio combined with a weighted utility function, this study takes into account the uncertainties associated with nuclear power investments through a minimum variance Monte Carlo approach, proposes a noise-enhancing process combined with geometric Brownian motion in solving complex conditions, and incorporates a measure of investment flexibility and strategic value in the investment, and then uses a deep noise reduction encoder to learn the initial values for potential features of cost and investment effectiveness. A Gaussian radial basis function used to construct a weighted utility function for each uncertainty, generate a minimization of the objective function for the tensor decomposition, and then optimize the objective loss function for the tensor decomposition, find the corresponding weights, and perform noise reduction to generalize the nonlinear problem to evaluate the effectiveness of nuclear power investment. Finally, the two dimensions of cost and risk (estimation of investment value and measurement of investment risk) are applied and simulated through actual data in Kazakhstan.

The authors assess the core indicators of Kazakhstan's nuclear power plants throughout their construction and operating cycles, based on data relating to a cluster of nuclear power plants of 10 different technologies. The authors compared it with several popular methods for evaluating the benefits of nuclear power generation and conducted subsequent sensitivity analyses of key indicators. Experimental results on the dataset show that the MGT method outperforms the other four methods and that changes in nuclear investment returns are more sensitive to changes in costs while operating cash flows from nuclear power are certainly an effective way to drive investment reform in inland nuclear power generation in Kazakhstan at current levels of investment costs.

Future research could consider exploring other excellent methods to improve the accuracy of the investment prediction further using sparseness and noise interference. Also consider collecting some expert advice and providing more appropriate specific suggestions, which will facilitate the application in practice.

The Novel Coronavirus epidemic has plunged the global economy into a deep recession, the tension between China and the US has made the energy cooperation road unusually tortuous, Kazakhstan in Central Asia has natural geographical and resource advantages, so China–Kazakhstan energy cooperation as a new era of opportunity, providing a strong guarantee for China's political and economic stability. The basic idea of building large-scale nuclear power plants in Balkhash and Aktau is put forward, considering the development strategy of building Kazakhstan into a regional international energy base. This work will be a good inspiration for the investment of nuclear generation.

This study solves the problem of increasing noise by combining Monte Carlo simulation with geometric Brownian motion under complex conditions, adds the measure of investment flexibility and strategic value, constructs the utility function of noise reduction weight based on Gaussian radial basis function and extends the nonlinear problem to the evaluation of nuclear power investment benefit.

This research assesses the economic impact of biomass plant installations on Brazilian municipalities, focusing on (1) labor income, (2) sectoral labor income and (3) income inequality.

Municipal data from the Annual Social Information Report, the National Electric Energy Agency and the National Institute of Meteorology spanning 2002 to 2020 are utilized. The Synthetic Difference-in-Differences methodology is employed for empirical analysis, and robustness checks are conducted using the Doubly Robust Difference in Differences and the Double/Debiased Machine Learning methods.

The findings reveal that biomass plant installations lead to an average annual increase of approximately R$688.00 in formal workers' wages and reduce formal income inequality, with notable benefits observed for workers in the industry and agriculture sectors. The robustness tests support and validate the primary results, highlighting the positive implications of renewable energy integration on economic development in the studied municipalities.

This article represents a groundbreaking contribution to the existing literature as it pioneers the identification of the impact of biomass plant installation on formal employment income and local economic development in Brazil. To the best of our knowledge, this study is the first to uncover such effects. Moreover, the authors comprehensively examine sectoral implications and formal income inequality.

The purpose of this study is to explore the genocidal impacts of uranium mining for Native Americans in the Northwest and Northern Plains, as well as their resistance to historical and contemporary acts of colonialism.

Using a case study approach, this study gathered qualitative data from various government, tribal and news sources to investigate the extent of ecological violence experienced by Native Americans specific to uranium mining processes on Spokane Indian Reservation, Pine Ridge Reservation and Wind River Reservation.

Native Americans in the Northwest and Northern Plains are victimized by the capitalism-genocide involved in uranium production. The consequences of the uranium industry boom in the 1950s–1980s has left Native Americans with degraded lands, polluted water sources and a legacy of adverse health effects, including some of the highest rates of cancer.

The work discussed in this paper offers possibilities for collaborating with Native Americans to develop more sustainable energy options for the USA to make the necessary shift away from fossil fuels and nuclear energy.

Prior research has addressed the genocidal impacts of uranium mining for Native Americans in the Southwest USA and claimed these actions were direct consequences of toxic colonialism, capitalistic agendas and the treadmill of production (Fegadel, 2023). Most uranium was recovered from ore deposits within the Colorado Plateau, and most abandoned uranium mines (AUMs) are located within the same region. Tribes residing in the Northwest and Northern Plains have, however, experienced similar plights as those in the Southwest, but these issues have not been widely examined.

This paper aims to critically evaluate the impact of empowering leadership on safety behavior and safety climate during safety monitoring at a nuclear power plant (NPP) in the United Arab Emirates (UAE).

Data were collected using questionnaires filled out by 500 participants from the UAE nuclear sector. The relationships among the variables were analyzed using structural equation modeling.

The results indicated that empowering leadership has a positive impact on safety behavior, and a positive safety climate leads to increased levels of safety behavior (compliance and participation). The results also showed that safety climate partially mediates the relationship between empowering leadership and safety behavior.

This study contributes to the existing knowledge regarding empowering leadership, safety monitoring, behavior and climate. Because limited information is available on this topic, this study extends the research on the relationship between empowering leadership and safety research at an NPP. Specifically, it outlines that safety monitoring partially mediates the relationship between empowering leadership and safety behavior. This research enables NPPs worldwide to incorporate empowering leadership to enhance safety monitoring and ensure better safety behavior and climate.

The purpose of this paper is to investigate the social acceptance of renewable and non-renewable energies in Iran using the social acceptance pyramid.

Today, social acceptance is considered a very important phenomenon in the development, implementation and achievement of energy policy goals. Low acceptance will make it difficult to achieve energy development goals; therefore, social acceptance must be taken into account when making policy. Firstly, the model criteria, using data obtained from questionnaires, are weighted by the Shannon entropy method and, finally, four sources of fossil, nuclear, wind and solar energy were ranked by means of VIKOR, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).

The results show that, in Iran, the social acceptance criterion and trust sub-criterion are the most important criteria for energy acceptance. The results of the ranking of options based on multiple-criteria decision-making (MCDM) techniques show that, given Iran's specific energy requirements, social acceptance of fossil energy is higher than wind, solar and nuclear, and wind, solar and nuclear energy come later in the rankings.

This research contributes to the literature in two ways: Firstly, social acceptance is considered a very important phenomenon in the development, implementation and achievement of energy policy goals; thus social acceptance must be taken into account when making policy. The results of the ranking of options based on MCDM techniques show that, given Iran's specific energy requirements, social acceptance of fossil energy is higher than wind, solar and nuclear, and wind, solar and nuclear energy come later in the rankings. Also, the social acceptance criterion and trust sub-criterion are the most important criteria for energy acceptance in Iran.

In this paper, we aim to provide an extensive analysis to understand how various factors influence electricity prices in competitive markets, focusing on the day-ahead electricity market in Romania.

Our study period began in January 2019, before the COVID-19 pandemic, and continued for several months after the onset of the war in Ukraine. During this time, we also consider other challenges like reduced market competitiveness, droughts and water scarcity. Our initial dataset comprises diverse variables: prices of essential energy sources (like gas and oil), Danube River water levels (indicating hydrological conditions), economic indicators (such as inflation and interest rates), total energy consumption and production in Romania and a breakdown of energy generation by source (coal, gas, hydro, oil, nuclear and renewable energy sources) from various data sources. Additionally, we included carbon certificate prices and data on electricity import, export and other related variables. This dataset was collected via application programming interface (API) and web scraping, and then synchronized by date and hour.

We discover that the competitiveness significantly affected electricity prices in Romania. Furthermore, our study of electricity price trends and their determinants revealed indicators of economic health in 2019 and 2020. However, from 2021 onwards, signs of a potential economic crisis began to emerge, characterized by changes in the normal relationships between prices and quantities, among other factors. Thus, our analysis suggests that electricity prices could serve as a predictive index for economic crises. Overall, the Granger causality findings from 2019 to 2022 offer valuable insights into the factors driving energy market dynamics in Romania, highlighting the importance of economic policies, fuel costs and environmental regulations in shaping these dynamics.

We combine principal component analysis (PCA) to reduce the dataset’s dimensionality. Following this, we use continuous wavelet transform (CWT) to explore frequency-domain relationships between electricity price and quantity in the day-ahead market (DAM) and the components derived from PCA. Our research also delves into the competitiveness level in the DAM from January 2019 to August 2022, analyzing the Herfindahl-Hirschman index (HHI).