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The article investigates factors associated with the relative success in adopting two specific alternative marine energies (liquefied natural gas [LNG] and electric batteries) in the Norwegian ferry market. This specific market segment is an interesting case study as its national-flagged fleet boasting the largest number of ships using alternative marine energies in comparison with the other countries of the region and the world.

A database tracking the yearly deployment of ships using a different combination of LNG and electric batteries was built from shipping lines’ online information and grey literature. The technological adoption approach was used to categorize different groups of users at each step of the adoption process and identify which factors separate the early adopters from the other groups of end-users. The compiled data allow tracing the changing distribution of Norwegian ferry operators along the conceptualized technology adoption curve.

Results indicated that the Norwegian ferry market matches required conditions to pass the “chasm” of uncertainties associated with transitioning to new technology. Some disparities between the adoption of LNG and the electric batteries in the Norwegian ferry markets are observed.

To the authors’ knowledge, no study has explored the adoption of new energies in the maritime industry based on the technology adoption process through a similar perspective. The analysis is helpful to shed light on the barriers associated with a high level of uncertainties when it comes to adopting new marine energies.

The paper aims to clarify how an incumbent retail organisation explores digitalisation for its existing business.

The paper draws from an in-depth case study of home-furnishing retail giant, IKEA conducted with semi-structured interviews, participant observations and document analyses.

In the exploration phase of digitalisation, three major activities – interpreting, interrelating and integrating – illuminate how the exploration process can be organised in practice.

Although digitalisation ranks amongst the most significant ongoing transformations in retail businesses, research on how incumbent retail organisations have engaged in exploring digitalisation in practice has remained scarce. The paper contributes insights into digitalisation processes in retail businesses that may also apply to other trends affecting the retail industry.

In the field of heritage science, especially applied to buildings and artefacts made by organic hygroscopic materials, analyzing the microclimate has always been of extreme importance. In particular, in many cases, the knowledge of the outdoor/indoor microclimate may support the decision process in conservation and preservation matters of historic buildings. This knowledge is often gained by implementing long and time-consuming monitoring campaigns that allow collecting atmospheric and climatic data.

Sometimes the collected time series may be corrupted, incomplete and/or subjected to the sensors' errors because of the remoteness of the historic building location, the natural aging of the sensor or the lack of a continuous check of the data downloading process. For this reason, in this work, an innovative approach about reconstructing the indoor microclimate into heritage buildings, just knowing the outdoor one, is proposed. This methodology is based on using machine learning tools known as variational auto encoders (VAEs), that are able to reconstruct time series and/or to fill data gaps.

The proposed approach is implemented using data collected in Ringebu Stave Church, a Norwegian medieval wooden heritage building. Reconstructing a realistic time series, for the vast majority of the year period, of the natural internal climate of the Church has been successfully implemented.

The novelty of this work is discussed in the framework of the existing literature. The work explores the potentials of machine learning tools compared to traditional ones, providing a method that is able to reliably fill missing data in time series.

The purpose of this study aims to analyze the dynamic behavior of the relationship between atmospheric carbon emissions and the Organisation for Economic Co-operation and Development (OECD) industrial production index (IPI) in the short and long term by applying multifractal techniques.

Multifractal de-trended cross-correlation technique is used for this analysis based on the relevant literature. In addition, it is the most widely used approach to estimate multifractality because it generates robust empirical results against non-stationarities in the time series.

It is revealed that industrial production causes long and short term environmental costs. The OECD IPI and atmospheric carbon emissions were found to have a strong correlation between the time domain. However, this relationship does not mostly take into account the frequency-based correlations with the tail effects caused by shocks that are effective on the economy. In this study, the long-term dependence of the relationship between the OECD IPI and atmospheric carbon emissions differs from the correlation obtained by linear methods, as the analysis is based on the frequency. The major finding is that the Hurst coefficient is in the range 0.40-0.75 indicating.

In this study, the local singular behavior of the time-series is analyzed to test for the multifractality characteristics of the series. In this context, the scaling exponents and the singularity spectrum are obtained to determine the origins of this multifractality. The multifractal time series are defined as the set of points with a given singularity exponent a where this exponent a is illustrated as a fractal with fractal dimension f(α). Therefore, the multifractality term indicates the existence of fluctuations, which are non-uniform and more importantly, their relative frequencies are also scale-dependent.

The results provide information based on the fluctuation in IPI, which determines the main conjuncture of the economy. An optimal strategy for shaping the consequences of climate change resulting from industrial production activities will not only need to be quite comprehensive and global in scale but also policies will need to be applicable to the national and local conditions of the given nation and adaptable to the needs of the country.

The results provide information for the analysis of the environmental cost of climate change depending on the magnitude of the impact on the total supply. In addition to environmental problems, climate change leads to economic problems, and hence, policy instruments are introduced to fight against the adverse effects of it.

This study may be of practical and technical importance in regional climate change forecasting, extreme carbon emission regulations and industrial production resource management in the world economy. Hence, the major contribution of this study is to introduce an approach to sustainability for the analysis of the environmental cost of growth in the supply side economy.