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  • Methane reductions to moderate the global warming effects
    59-64
    Views:
    167

    The case-study overviews the possible reduction for the methane gas emission in order to avoid of the more global warming effects and climate change caused by the human activity at latest decades. To collect international data base is for analysing and valuing methane gas emission based on the different country-groups, emphasizing responsibility of developing countries and highly developed countries for gas emission, also the methane emission based is on the economic sectors. China and India have share 8% of China and 2% of India respectively of cumulative CO2 emissions over the period 1900-2005, the US and the EU are responsible for more than half of emissions. Based on the estimation the global gas emissions of methane in the whole world has increased by 37% for period of 1990- 2030, as four decades, and this was 0,92% annual rate growth, while the OECD has increased the methane emission by 8,5% for this period, which means 0,21% growth rate annually. Scenario in developing countries for 2013-2020 the methane gas emission reduction could have been 8200 Mt of CO2e (Equivalent) and less than 10 US dollar per ton in more cost financing. Highly developed and developing economies (last one their methane emission share 56% in 1990, estimated 66,8% in 2030) increase their economic growth by mostly fossil energy resulted in increasing also methane gas emissions. The methane gas emission can be solved by those results-based-finance forms relevant to Kyoto Protocol, which can extend in the world by financial institutions.

  • The analysis of agro-economic effects of household food wastage through the example of bread
    9-18
    Views:
    234

    In our busy world, where numerous people starve and where the resources are restricted, it is a key issue to pay particular attention to the topic of prevention and decrease of food loss as well as food wastage.Wastage of food produced and delivered to the end user (customer) is an issue arising globally and nationally as well, which results in efficiency loss at economic level in any case. While the FAO study mentions food waste of the order of 1.3 billion tonnes on a world scale, then the annual quantity of food waste in Hungary is estimated at about 1.8 million tonnes, which contains the waste of every member of the chain from production to consumption. On the basis of the data published by the Hungarian Food Bank (2015), the amount of food waste caused by the population is 400 000 tonnes. In compliance with our objectives, inputs – expressed by non-financial and financial indicators – emerge during production are assigned to the quantity of wasted food. Applying the aforementioned method we would like to make customers realize how many resources (land, water, artificial fertilizer, pesticide, seed and gasoil) are utilized needlessly in food verticum by the end products – at present by different breads they throw out. As our calculations prove by 10% waste of breads the utilization of 5 300 hectares of wheat land and 660 hectares of rye land can be considered unnecessary. By 10% waste of breads the financial value of the utilized resources is altogether 3.25 million EUR. Out of this the financial value of utilized artificial fertilizer is 1.10 million EUR (34%), of utilized pesticide is 1.15 million EUR (35%), of utilized gasoil is 0.70 million EUR (22%) and of utilized seed is 0.30 million EUR (9%). Among different breads, white bread is purchased in the greatest volume by the Hungarian households, from which 121 900 tonnes are bought annually on an average. This quantity is equal to almost the 40% of the annual bread sell. If 10% of purchased white bread is thrown out, it results in useless utilization of 2 676 hectares of wheat land in food verticum. The quantity of utilized water arising form wastage is 15.8 million m3. Further losses emerge as regards material inputs: artificial fertilizer- to the value of 0.50 million EUR, pesticide- to the value of 0.58 million EUR, seed to the value of 0.15 million EUR and gasoil-loss to the value of circa 0.35 million EUR. Totally, material input to the value of 1.58 million EUR is owing to the Hungarian households in case of 10% white bread wastage.

    JEL code: Q53

  • Determinants of Mongolian Economic Growth
    61-66
    Views:
    305

    Mongolia is the second largest landlocked country, which has unique economic condition. This paper aims to examine Mongolian economic growth from 2000 until 2016 and identify its determinants. The growth was studied based on the growth rate of National Domestic Product. Initially, 20 macroeconomic variables are chosen and tested for the economic growth determinators such as; unemployment rate, human capital index, import growth, inflation rate, export growth, and interest rate, etc. The results showed that the growth rate of dollar exchange, inflation rate, and the growth rate of export were the main factors (81.4%). Mongolian GDP per capita and poverty rate were compared with other Asian lower-middle-economies, which are classified in the same classification as Mongolia. An increment of average salary was adjusted by the inflation rate, which showed the purchasing power declined in 2015. Statistics of Central Bank of Mongolia, Central Intelligence Agency, World Bank’s statistics, and the statistics from National Statistics Office of Mongolia are used for the research.

    JEL Classification: H0, H30, H6, H70

  • The new strategic directions of rural development in Hungary
    143-150
    Views:
    115

    The notion of sustainability is the basis for our future possibilities. Local sustainability, in the centre of which can be found the livable settlement, is especially important in rural areas.Without developing rural areas, there is no developing society. The growth of the Earth’s population and the world economy has already surpassed the carrying capacity of this planet which may result in an “overshoot and collapse”. This can still be prevented today. The population of towns and cities is rapidly increasing. Urbanization is a very fast process, even in Hungary. In large cities with millions of inhabitants crime and lumpen lifestyle pose huge problems. However, the bases of a successful economy are morals and a puritan lifestyle, which so far have characterized rural villages. 70% of the poor and needy live in rural areas in the developing countries and agriculture provides livelihood for 40% of the world’s population. The International Assessment of Agricultural Knowledge, Science and Technology for Development (IAASTD) was established in 2002 by FAO and theWorld Bank to learn more about the role of agricultural science and technology. After the positive decision some comprehensive summaries were made on all the related topics with the participation of 400 scientists. The assessment provided many lessons to learn and at the 2008 closing sessions in Johannesburg, the reports were accepted and it was proved that rural areas have a significant role in providing adequate means of earning a livelihood. The Ministry of Rural Development composed a domestic-level study with the title of the National Rural Strategy. The objectives stated in the study can be seen as the main directions of the Hungarian rural strategy. The land policy aims to support the 50–70 hectare family farms and have the agricultural lands under national authority. The population must be provided with ample and safe food. The priority of local economy, local sale, and local markets is important. The positive exploitation of our natural resources may result in the strengthening of rural areas. The deterioration of rural areas must be stopped. In order to halt these processes swiftly fundamental, patriotic economic and social policy changes, a strong people’s party, a short-run crisis treating and a medium-long-run strategic development and action plan are needed which is based on the respect of work and moral norms, national cooperation, solidarity, and the defense of our mutual interests rather than on speculation (ÁNGYÁN, 2010). The greatest problem of Hungary is low employment.Workplaces may be created in the least expensive and the fastest manner in irrigational agriculture. In order to achieve this, the role of the state must be reconsidered and EU rules on state intervention must be reviewed.

  • Changes in the Relationship Between ICT Use and Economic Development in EU Member States 2010-2016
    91-100
    Views:
    230

    In this study, we examined some ICT indicators of the EU Member States between 2010 and 2016 based on data of the World Bank and Eurostat. We wanted to know, how can the EU Member States be grouped according to these indicators, and which group can Hungary belong to. With the help of international literature reviews, three indicators were chosen. According to these we created three groups (underdeveloped, developing, developed) with the K-Mean cluster method that is classified by their level of development. Interesting changes took place during the period under review. By the end of the analyzed period, six countries lost their “developed” rating among others some founding members. There were also interesting changes in the clusters. The value of some indicators increased more than 40% in some cases, surprisingly, only in one case measured reduction. The proportion of ICT specialists decreased in developing countries (by 1%). The highest growth rate was observed in the developed countries in e-commerce. Because of the high proportion of ICT professionals and the share of e-commerce in the developed cluster we assumed that service would be the dominant sector. The two-sample t-test did not confirm our hypothesis. We supposed the focus in developing countries will be on the industry, due we think the developed countries started to outsource their SSCs (shared service centers) to less developed countries. With the help of a statistical indicator, we confirmed our assumption, but the result not so convincing since the significant level is only 11%. Although we thought that the underdeveloped group of countries was based on agriculture, statistical studies did not support our hypothesis.

    JEL Classification: O13, O14, O52

  • The connenction between global innovation index and economic well-being indexes
    87-92
    Views:
    582

    We study the connection of innovation in 126 countries by different well-being indicators and whether there are differences among geographical regions with respect to innovation index score. We approach and define innovation based on Global Innovation Index (GII). The following well-being indicators were emphasized in the research: GDP per capita measured at purchasing power parity, unemployment rate, life expectancy, crude mortality rate, human development index (HDI). Innovation index score was downloaded from the joint publication of 2018 of Cornell University, INSEAD and WIPO, HDI from the website of the UN while we obtained other well-being indicators from the database of the World Bank. Non-parametric hypothesis testing, post-hoc tests and linear regression were used in the study.
    We concluded that there are differences among regions/continents based on GII. It is scarcely surprising that North America is the best performer followed by Europe (with significant differences among countries). Central and South Asia scored the next places with high standard deviation. The following regions with significant backwardness include North Africa, West Asia, Latin America, the Caribbean Area, Central and South Asia, and sub-Saharan Africa. Regions lagging behind have lower standard deviation, that is, they are more homogeneous therefore there are no significant differences among countries in the particular region.
    In the regression modelling of the Global Innovation Index, it was concluded that GDP per capita, life expectancy and human development index are significant explanatory indicators. In the multivariable regression analysis, HDI remained the only explanatory variable in the final model. It is due to the fact that there was significant multicollinearity among the explanatory variables and the HDI aggregates several non-economic indicators like GII.

    JEL Classification: B41, I31, O31, Q55

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