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• Climate change impact on crop production in Central Asian Countries

  75-82

  Anna Viter

  Sándor J. Zsarnóczai

  László Vasa

  Views:

  210

  Increased risk due to global warming has already become embedded in agricultural decision making in Central Asia and uncertainties are projected to increase even further. Agro-ecology and economies of Central Asia are heterogeneous and very little is known about the impact of climate change at the subnational levels. The bio-economic farm model is used for ex-ante assessment of climate change impacts at sub-national levels in Central Asia. The bio-economic farm model is calibrated to ten farming systems in Central Asia based on the household survey and crop growth experiment data. The production uncertainties and the adaptation options of agricultural producers to changing environments are considered paramount in the simulations. Very large differences in climate change impacts across the studied farming systems are found. The positive income gains in large-scale commercial farms in the northern regions of Kazakhstan and negative impact in small-scale farms in arid zones of Tajikistan are likely to happen. Producers in Kyrgyzstan may expect higher revenues but also higher income volatilities in the future. Agricultural producers in Uzbekistan may benefit in the near future but may lose their income in the distant future. The negative impacts could be further aggravated in arid zones of Central Asia if irrigation water availability decline due to climate change and water demand increase in upstream regions. The scenario simulations show that market liberalization and improved commodity exchange between the countries have very good potential to cope with the negative consequences of climate change.

  JEL classification: Q11, Q18

• The connenction between global innovation index and economic well-being indexes

  87-92

  Szlobodan Vukoszavlyev

  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