The management and disposal of sewage sludge is becoming a growing concern at the global level. In the past, the main goal was to completely eliminate sewage sludge since it was deemed a threat to ...humans and the environment, but recently different possibilities for energy generation and material recovery are emerging. Existing technologies such as incineration or direct application in agriculture contribute to quantity reduction and nutrient recovery but are unable to fully exploit the potential of sewage sludge within the frameworks of circular economy and bioeconomy. This paper developed a model within the PROMETHEE method, which analyses technologies for the sustainable management of sewage sludge, which could make the most from it. For the empirical part of the study, the Republic of Croatia was used as a country in which sewage sludge is increasing in quantity as a result of recent upgrades and expansions in the wastewater system. Incineration, gasification, anaerobic digestion, and nutrient recovery were analyzed as treatment concepts for the increased amounts of sewage sludge. The model reveals that the best solution is the material recovery of sewage sludge, using the struvite production pathway through analysis of selected criteria.
Even if creating jobs and fostering economic growth are two of the tourist industry's most important roles, preserving its sustainability is a big issue on a global scale. Our research aims to ...provide a bibliometric analysis of the relevant literature used in the VOSviewer software's examination of the sustainability of the industry. The goal is to evaluate the current status of research on environmental aspects of sustainable tourism development and to pinpoint the key areas, journals, articles, and authors from 1996 to 2021. A total of 337 relevant papers on subjects like the utilization of renewable energy sources, energy efficiency, energy indicators, and sustainability in the tourism industry were analysed in the Scopus database, based on their authors, publication year, geographic location, and number of citations. This study provides helpful information to promote theoretical and practical research in this area by adopting a systematic and visual review of all studies pertinent to the development of sustainable tourism.
The waste management sector is transitioning from a dirty and undesirable industry towards a green and sustainable future where energy and materials are recycled. Recycling has potential in sewage ...sludge treatment, where energy and highly valuable nutrients can be recovered through innovative and sustainable sludge management. Although there are many technologies and techniques already used for sewage sludge, the indicators for their sustainability are not developed. In terms of sustainable and innovative sewage sludge treatment, usually, only techno-economic aspects of existing and current technologies are considered. We explore the existing indicators and propose new indicators for sustainable and innovative sewage sludge treatment technologies. The indicators are differentiated into four main categories: technical, social, environmental and economic, where specific indicators are explained, followed by a description of their impact on sustainability. We also consider a case study using the LEAP tool, which considered GHG emissions when utilizing sewage sludge as an energy feedstock to replace existing fossil fuels in the energy mix in several scenarios. The results showed a significant emissions reduction when sludge is used—37.6% and 90.9% in 2030 and 2050, respectively.
The latest regulatory framework, which has been introduced globally in the form of Basel III, and its implementation in the legislation of the member states of the European Union has generated much ...interest in the impact of regulation on the efficiency and profitability of banks. This study aims to examine the impact of the introduction of two major regulatory changes (Basel II and Basel III) on bank performance, in terms of bank size and bank-specific and macroeconomic variables. A two-stage empirical analysis was conducted on a sample of 433 European commercial banks over the 2006–2015 period. In the first stage, relative efficiency was calculated using non-parametric data envelopment analysis. In the second stage, the generalized method of moments was used to examine the impact of bank-specific and macroeconomic variables as well as regulation on bank performance, that is, profitability and efficiency. Considering bank size, the results show a diverse impact of regulation on bank performance. Regarding large- and medium-sized banks, regulation positively affects both efficiency and profitability, whereas, for small banks, it negatively affects performance. The results suggest that larger banks have skillfully adapted to the new regulatory environment. In contrast, small banks have problems with profitability and efficiency because the new regulatory framework has imposed additional administrative and regulatory burdens. This could result in future failure or mergers with larger banks, resulting in a higher concentration in the banking sector and increased systemic risk. Our results strongly suggest that regulation should not be implemented equally for all banks; that is, on a one size fits all terms. A distinction between small and large banks when introducing new regulatory frameworks should be made if a reasonable level of competition is to be preserved.
We explore the relationship between economic growth and energy consumption using regional level data for 2004–2014 period. Data on electricity consumption for regional distributional areas cannot be ...used with the economic data for county/regional level since the borders of regional electricity distributional areas do not correspond to administrative NUTS-3 borders. We use a proprietary dataset of actual electricity consumption for Croatian counties which allows us to revisit the energy-growth nexus dilemma from a disaggregated viewpoint. We find significant policy implications stemming from comovement between counties and long term positive influence of energy, capital and labour on industrial output.
•Electricity distribution areas do not fully correspond with NUTS-3 borders.•Energy consumption and economic activity affect each other simultaneously.•Electricity consumption, capital and labour positively influence industrial output.•Economic growth should be decoupled from energy consumption to avoid negative impact.
The choice of an adequate risk measure in portfolio optimization depends to a large extent on the characteristics and dynamics of the underlying assets. For investors and asset managers, a range of ...potential market risks provides much- needed insights into the optimization of their portfolio of assets. Since this paper focuses on multiple risk measures, it presents the investors with a better insight into the potential magnitude of the risk they are faced with. Since the risk-reward optimization target can be adjusted for a broad choice of risk measures in this paper we will test the performance of the classical risk measure i.e. standard deviation versus a tail risk measure such as expected tail loss (ETL). Our goal is to find which of the two offers the better performance for a portfolio of cryptocurrencies and if the differences are statistically significant. The setup for our analysis is testing two optimization targets (MinVar and MinETL) on 10 portfolios of cryptocurrencies randomly chosen from a sample of 70 cryptocurrencies with the highest market capitalization.
ABSTRACT IN ENGLISH: The purpose of this paper is to investigate the performance of VaR models at measuring risk for WTI oil one-month futures returns. Risk models, ranging from industry standards ...such as RiskMetrics and historical simulation to conditional extreme value model, are used to calculate commodity market risk at extreme quantiles: 0.95, 0.99, 0.995 and 0.999 for both long and short trading positions. Our results show that out of the tested fat tailed distributions, generalised Pareto distribution provides the best fit to both tails of oil returns although tails differ significantly, with the right tail having a higher tail index, indicative of more extreme events. The main conclusion is that, in the analysed period, only extreme value theory based models provide a reasonable degree of safety while widespread VaR models do not provide adequate risk coverage and their performance is especially weak for short position in oil. // ABSTRACT IN CROTIAN: Predmet ovog rada je istraziti uspjesnost VaR modela u mjerenju trzisnog rizika jednomjesecnih futures ugovora na WTI naftu. Modeli mjerenja rizika, u rasponu od industrijskih standarda, kao sto su RiskMetrics sustav i povijesna simulacija do kondicionalnog modela ekstremnih vrijednosti koristeni su u izracunu trzisnog rizika nafte pri ekstremnim kvantilima distribucije: 0,95, 0,99, 0,995 i 0,999 za duge i kratke trgovinske pozicije. Dobiveni rezultati pokazuju da od testiranih leptokurti%Dcnih distribucija jedino generalizirana Pareto distribucija najbolje opisuje oba repa distribucije prinosa na naftu iako se oni sami medusobno znacajno razlikuju, s time da desni rep distribucije ima znatno visi indeks repa sto ukazuje na prisutnost ekstremnijih dogadaja. Nas glavni zakljucak je da, u promatranom razdoblju, samo modeli temeljeni na teoriji ekstremnih vrijednosti uspjesno predvidaju stvarnu razinu rizika pri kratkim i dugim trgovinskim pozicijama, dok rasireni modeli mjerenja pokazuju iznimno slabe rezultate, posebice kod mjerenja rizika kratkih trgovinskih pozicija. Reprinted by permission of the University of Rijeka Faculty of Economics, Ivana Filipovica 4, 51000 Rijeka, Croatia
The authors analyze the relationship between GDP, imports-coverage ratio (NEX), FDI and gross fixed capital formation (GFC) in selected CEE countries by using an error correction model. The empirical ...results confirm a positive long-run influence of the imports-coverage ratio, FDI and GFC on GDP growth for all of the countries, except Croatia. In the case of Croatia, there is a significant negative feedback between FDI and GDP growth in the long run and a positive one in the short run. By using B. Horvat's research on this subject, a logical explanation of this sort of paradoxical behavior is suggested. The second uncommon result is the long-run positive relationship between GDP and the imports-coverage ratio. The obtained result speaks in favor of a conservative approach to running a national economy, where the current account and the imports-coverage ratio are taken into account and the economic growth is achieved through slower but stable, internally driven growth.
We investigate the relative performance of a wide array of Value at Risk (VaR) models with the daily returns of Turkish (XU100) and Croatian (CROBEX) stock index prior to and during the ongoing ...financial crisis. In addition to widely used VaR models, we also study the behaviour of conditional and unconditional extreme value theory (EVT) and hybrid historical simulation (HHS) models to generate 95, 99 and 99.5% confidence level estimates. Results indicate that during the crisis period all tested VaR model except EVT and HHS models seriously underpredict the true level of risk, with EVT models doing so at a higher cost of capital compared to HHS model. PUBLICATION ABSTRACT Reprinted by permission of the University of Rijeka Faculty of Economics, Ivana Filipovica 4, 51000 Rijeka, Croatia
We present the results of short-term forecasting of Henry Hub spot natural gas prices based on the performance of classical time series models and machine learning methods, specifically; neural ...networks (NN) and strategic seasonality-adjusted support vector regression machines(SSA-SVR). We introduce several improvements to the forecasting method based on SVR. A procedure for generation of model inputs and model input selection using feature selection (FS) algorithms is suggested. The use of FS algorithms for automatic selection of model input and the use of advanced global optimization technique PSwarm for the optimization of SVR hyper parameters reduce the subjective inputs. Our results show that the machine learning results reported in the literature often over exaggerate the successfulness of these models since, in some cases, we record only slight improvements over the time series approaches. We have to emphasize that our findings apply to Henry Hub, a market which is known among traders as the “widow maker”. We find definite advantages of using FS algorithms to preselect the variables both in NN and SVR. Machine learning models without the preselection of variables are often inferior to time-series models in forecasting spot prices and in this case FS algorithms show their usefulness and strength.
•Forecasting of Henry Hub gas prices by time-series and machine learning models.•Improvements to SVR forecasting introduced: FS algorithms and PSwarm optimization.•Machine learning results in literature often over exaggerate their successfulness.•FS algorithms highly successful in improving machine learning results.
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