This paper aims to develop the concept and the definition of the maritime common good, its sub components and sub layers and to classify and analytically systematize it in the framework of modern theories addressing economic goods. Possible theoretical advancements and extensions in classification criteria are provided. International formal institutional framework is presented and elaborated. The accent is given to the development of theoretical concept and classification of economic goods as well as development of the Institutional Analysis and Development framework – IAD framework that is used to provide analytical understanding of the maritime good classification as well as allocation problems arising. This is performed in the light of ICZM protocol addressing coastal zones as of special concern particularly considering the intensive interrelations between humans and coastal zones. According to the developed classification criteria and analysis performed, the maritime good, as a complex good, can be classified dominantly as common good with limited renewability. The importance of further advancements of maritime common good governing mechanisms based on stakeholders’ inclusion into decision making process is emphasized in order to strengthen the potential of the mechanisms itself and the information background necessary for a successful management of the complex maritime common good.
Demand forecasting is one of the key activities in planning the freight flows in supply chains, and accordingly it is essential for planning and scheduling of logistic activities within observed supply chain. Accurate demand forecasting models directly influence the decrease of logistics costs, since they provide an assessment of customer demand. Customer demand is a key component for planning all logistic processes in supply chain, and therefore determining levels of customer demand is of great interest for supply chain managers. In this paper we deal with exactly this kind of problem, and we develop the seasonal Autoregressive Integrated Moving Average (S-ARIMA) model for forecasting demand patterns of a major product of an observed beverage company. The model is easy to understand, flexible to use and appropriate for assisting the expert in decision making process about consumer demand in particular periods.
The research problem this paper is concerned with is the effect of tourism on solid waste generation in Croatia’s coastal area. We are aware of the fact that this has not been thoroughly addressed, especially considering tourism’s share in the Croatian economy and the pressure it generates on sustainable coastal management. This is of particular importance considering the governing complexity of coastal areas. Thus, we ask a simple question regarding the role of the tourism industry in the solid waste generation in the Croatian coastal area: Do tourists generate relatively more solid waste than the domestic population? The falsifiable hypothesis is stated in terms of the difference in the production of communal waste between domestic population and tourists, taking into account local idiosyncratic factors, when such a difference is recoverable through statistical analysis of measurable tourist presence in panel data. The first hypothesis is thus: The amount of solid waste produced by local residents in Croatian coastal municipalities diverges significantly in statistical terms from the amount of solid waste produced by tourists. The second hypothesis is: The amount of waste-streams is influenced by local idiosyncrasies of coastal settlements, their economic structure, per capita GDP and/or cultural background of local people. Our dataset is a panel of 160 municipalities in the Croatian coastal area spanned across a time period of 12 months during 2019, giving us a total of 1920 panel observations. We performed a Panel Estimated Generalized Least Squares cross-section fixed effects analysis with Panel Corrected Standard Errors on domestic population and tourist overnight stays and their solid waste generation. We used the above mentioned method to achieve better results with higher significance, and lower Standard Errors than comparable methods. We complemented the analysis with a dynamic Panel Generalized Method of Moments First Differences test. Results show a relatively larger relative impact of tourist overnight stays on municipal solid waste generation than what is to be expected from the locals only. Our different methods of analysis ended with non-contradicting results. The number of tourist overnight stays in some municipalities shadows the overnight stays of the local population as an indicator of solid waste generation, exacerbating the problem of sustainability of waste disposal. We conclude that the relative waste disposal impact of the tourists is at least 22% greater and possibly up to 55% greater than the one of local inhabitants, contradicting some other research. We also found evidence of possible Environmental Kuznets Curve behavior.
The paper tests for statistical association between employment and value added of freight transport industry and its component activities against overall economy in a ten-year panel ranging from 2008 to 2017 of the thirteen newest European Union member countries. In this paper, the nature of correlation between economic growth as the independent variable and freight transport industry as a dependent variable is examined. To achieve stationarity, and to lose autocorrelation and the idiosyncratic effects, the variables are first differenced. The results of the “Granger causality” tests show the null hypothesis of no-causation may be rejected for most conjectures with high F-Statistics as well as high statistical significance. The results of the Panel EGLS cross-section fixed effects do not reject the results gained by the Granger test, and the same may be said for the Panel Generalised Method of Moments First Differences test. The result of the Arellano-Bond test shows no serial correlation in the residuals. It has been concluded that changes in overall economy (value added and employment) have a significant and measurably strong impact on freight transportation and warehousing sector. This conclusion is useful in assessing future impacts on freight transport industry, especially as a consequence of contingent events.
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