Selected Determinants of Sustainable Transport in the Context of the Development of a Low-Carbon Economy in Poland

Economic Aspects of Low Carbon Development

Low-carbon development is a prerequisite and a fundamental element of sustainable development [...]

Driver Behavior and Intention Recognition Based on Wavelet Denoising and Bayesian Theory

Driver behavior and intention recognition affects traffic safety. Many scholars use the steering wheel angle, distance of the brake pedal, distance of the accelerator pedal, and turn signal as input data to identify driver behaviors and intentions. However, in terms of time, the acquisition of these parameters has a relative delay, which lengthens the identification time. Therefore, this study uses drivers’ EEG (electroencephalograph) data as input parameters to identify driver behaviors and intentions. The key to the driving intention recognition of EEG signals is to reduce their noise. Noise interference has a significant influence on EEG driving intention recognition. To substantially denoise EEG signals, this study selects wavelet transform theory and wavelet packet transform technology, collects the EEG signals during driving, uses the threshold noise reduction method on EEG signals to reduce noise, and achieves noise reduction through wavelet packet reconstruction. After the wavelet packet coefficients of EEG signals are obtained, the energy characteristics of the wavelet packet coefficients are extracted as input to the Bayesian theoretical model for driver behavior and intention recognition. Results show that the maximum recognition rate of the Bayesian theoretical model reaches 82.6%. Early driver behavior and intention recognition has important research significance for traffic safety and sustainable traffic development.

Bioethanol Production in Poland in the Context of Sustainable Development-Current Status and Future Prospects

The high dependence on imported fuels, the need to reduce greenhouse gas (GHG) emissions and the need to develop a low-carbon economy are reasons for the development of the renewable energy market in Poland. The wider use of biofuels can be a method for reducing oil dependence and reducing CO2 emission. Opportunities to reduce emissions and meet international requirements in the field of environmental protection are seen, among others, in the development of the production and greater use of biocomponents, including bioethanol. This article presents the current state of development in the area of bioethanol production in Poland. An outline of legal regulations in the examined area and statistical data, as well as the largest producers and their production capacity, are presented. The basic time range of analyses covered the years 2015–2019. According to the analyses, liquid biofuels in Poland are used on a small scale, although over 2015–2019, the production of bioethanol as a biocomponent in motor fuels increased by 43,537 tonnes. However, production potential is still underused. In recent years, there have been major changes in the structure of the use of raw materials for bioethanol production. The share of maize has significantly decreased (although it is still dominant in the consumption structure) in favour of waste raw materials.

The Role of Large Cities in the Development of Low-Carbon Economy—The Example of Poland

The main objective of the article is to evaluate the investment activity of large cities in Poland in the area of developing a low-carbon economy in 2014–2020, co-financed by European Union funds. This article poses several research questions, namely: Do large cities with environmental problems actively obtain EU funding to develop a low-carbon economy? What are the main socio-economic and environmental determinants of the level of the EU funding absorption among large cities in the research area? The empirical research was conducted on the basis of the data from the Ministry of Investment and Economic Development in Poland, which is responsible for the implementation of cohesion policy funds and from the Local Data Bank of Statistics Poland. Under the 2014–2020 perspective, 223 such projects have been implemented for a total of PLN 21 billion (EUR 4.74 billion). The projects focused on: transportation, electricity, gaseous fuels, steam, hot water and air for air conditioning systems, and environmental and climate change activities. In terms of both the number and the value of EU funds spent, great variation has been observed. Analysis of the correlation relationships showed a highly positive correlation between selected indicators of investment activity in the field of low-carbon economy co-financed by EU funds (especially taking into account the value of investments per area) and socio-economic indicators of Polish metropolises. Metropolises with high demographic, economic, and financial potential have proven to be more effective beneficiaries. Interestingly, no correlation was found between investment activity in the low-carbon economy and the level of environment pollution in large cities. This means that, unfortunately, pro-environmental activities depend on the state of finances of the cities, and not necessarily on the actual needs, even taking into account the fact that the EU covers a large proportion of the costs.

Selection of Electric Vehicles for the Needs of Sustainable Transport under Conditions of Uncertainty—A Comparative Study on Fuzzy MCDA Methods

All over the world, including Poland, authorities are taking steps to increase consumer interest in electric vehicles and sustainable transport as a way to reduce environmental pollution. For this reason, the electric vehicle market is dynamically and constantly developing, more and more modern vehicles are introduced to it, and purchases are often subsidized by the government. The aim of the article is to analyse the A–C segments of the Polish electric vehicle market and to recommend the most attractive vehicle from the perspective of sustainable transport. The aim of the research was achieved with the use of three multi-criteria decision aid (MCDA) methods, which deal well with the uncertainty and imprecision of data that occur in the case of many different parameters of electric vehicles. In particular, the following methods were used: the fuzzy technique for order of preference by similarity to ideal solution (TOPSIS), the fuzzy simple additive weighting (SAW) method, and the new easy approach to fuzzy preference ranking organization method for enrichment evaluation II (NEAT F-PROMETHEE II). Electric vehicle rankings obtained using each method were compared and verified by stochastic analysis. The conducted analyses and comparisons allowed us to identify the most interesting electric vehicles, which currently appear to be the Volkswagen ID.3 Pro S and Nissan LEAF e+.