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This paper aims to estimate willingness to accept (WTA) hypothetical nuclear energy projects and the impact of net perceived benefits across three countries: Italy, a country without nuclear plants in operation; the UK, a country with nuclear plants in operation and the United Arab Emirates (UAE), which has more recently opted for the inclusion of nuclear energy in its energy mix. These valuations can support cost-benefit analyses by allowing policymakers to account for additional benefits and costs which would be otherwise neglected.

Data collection was conducted through online nationwide surveys, for a total of over 4,000 individuals sampled from Italy, the UK and the UAE. The surveys included choice experiments designed to elicit preferences towards nuclear energy in the form of WTA, indicating estimated compensations for welfare worsening changes and questions to measure perceived risks and benefits.

The average WTA/Km is the lowest for the case of the UAE. What is more, perceived net positive benefits tend to decrease the WTA required by the UAE respondents? Moreover, across the cases, albeit to a lesser extent with regard to Italy’s case, there is evidence that a more positive benefit perception seems to increase the valuation of environmental and public benefits offered as part of the experiment.

The contribution of this study is primarily twofold: first, it provides a comparison of WTA values in a context where the availability of choice experiment data is scant; second, it assesses whether and to what extent perceived net positive benefits of nuclear energy impact WTA of nuclear energy projects.

This paper aims to investigate the role of price-based information from major cryptocurrencies, foreign exchange, equity markets and key commodities in predicting the next-minute Bitcoin (BTC) price. This study answers the following research questions: What is the best sparse regression model to predict the next-minute price of BTC? What are the key drivers of the BTC price in high-frequency trading?

Least absolute shrinkage and selection operator and Ridge regressions are adopted using minute-based open-high-low-close prices, volume and trade count for eight major cryptos, global stock market indices, foreign currency pairs, crude oil and gold price information for February 2020–March 2021. This study also examines whether there was any significant break and how the accuracy of the selected models was impacted.

Findings suggest that Ridge regression is the most effective model for predicting next-minute BTC prices based on BTC-related covariates such as BTC-open, BTC-high and BTC-low, with a moderate amount of regularization. While BTC-based covariates BTC-open and BTC-low were most significant in predicting BTC closing prices during stable periods, BTC-open and BTC-high were most important during volatile periods. Overall findings suggest that BTC’s price information is the most helpful to predict its next-minute closing price after considering various other asset classes’ price information.

To the best of the authors’ knowledge, this is the first paper to identify the covariates of major cryptocurrencies and predict the next-minute BTC crypto price, with a focus on both crypto-asset and cross-market information.