VGTU Journals (Vilnius Gediminas Technical University - Vilnius Tech)
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Does income inequality affect green innovation? A non-linear evidence
No full textIt is crucial for the advancement of political economics and innovation economics to examine the relationship between income inequality and green innovation (GI). Using the panel fixed effect model, this study investigates the influence of income inequality on GI across 97 countries from 1991 to 2018 and demonstrates a significant non-linear association between the two. The empirical data exhibit an inverted U-shape relationship, suggesting that there is an optimal degree of income inequality that optimizes GI output, and the inflection point of our overall sample is at a Gini coefficient of 0.366. Additionally, we choose a set of robustness tests to validate the results by substituting explained variables, adding omitted variables, and employing the difference and system generalized method of moments (GMM) estimations. Moreover, heterogeneity analysis reveals that the non-linear patterns vary among samples, with the U-shape relationship being more significant in countries with lower income, higher corruption, and weaker government effectiveness. Our findings provide government decision-makers with a crucial reference for maximizing the importance of income distribution in fostering GI and achieving sustainable development.
First published online 24 August 202
Improving the strategies of the market players using an AI-powered price forecast for electricity market
No full textThis paper analyses the recent evolution of the electricity price of one of the East-European countries’ Balancing Markets (BM) – Romania, aiming to understand the prices trend and predict them in the current economic and geopolitical context. This is especially important as the electricity producers have to allocate their output between wholesale electricity market, ancillary services markets and BM targeting to maximize value and achieve a sustainable economic development. Therefore, in this paper, we propose an AI-powered electricity price forecast using several types of standout Machine Learning (ML) algorithms such as classifiers and regressors to predict the electricity price on BM. This approach, consisting of two steps, identifies the imbalance sign and significantly enhances the performance of the price forecast. The proposed method offers valuable insights into the market participants’ trading opportunities using two prediction solutions. The first prediction solution consists of averaging the results of five ensemble ML algorithms. The second one consists in weighting the results of the five forecasting ML algorithms using either a linear regression or a decision tree algorithm. Thus, we propose to combine supervised and unsupervised ML algorithms and find the fundamentals for creating optimal bidding strategies for electricity market players.
First published online 14 November 202
Procyclical economic policy and risks on economic growth sustainability in Romania
No full textThe current situation of the Romanian economy must be understood beginning from the analysis of the main measures of fiscal-budgetary policy applied over the last years by the public authority. In general, the Romanian fiscal policy (before and after accession) was procyclical. However, we continue by presenting some of its characteristics for the past years when we underwent the last ascending phase of the economic cycle. Actually, Romania’s GDP exceeded constantly the potential level, and the demand surplus became predominant, generating inflationary pressures. Maintaining the expansionist level of the fiscal policy, in the conditions of a positive deviation of GDP, as of 2017, and opting-out regarding the structural deficit target contributed to affecting the stability of public finances, on short- and medium-term. Romania entered into an extremely difficult economic context, generated by the pandemic, with an extremely narrow fiscal space which limited a lot the possibilities of combating the effects of the pandemic. In this paper we analyzed a period limited to the year 2020, because we consider this time as marking the end of an economic cycle in a period of peace and economic calm, as another is about to begin based on the new realities.
First published online 08 February 202
The perceived relationship between sustainable energy technologies, eco-innovation, economic growth and social sustainability: evidence from China
No full textSocial sustainability is a global necessity because of environmental and economic uncertainty. This issue needs the foremost solution, and for this purpose, researchers’ and policymakers’ emphasis is required. Thereby, the present paper investigates sustainable energy technologies such as solar and hydroelectric, eco-innovation and EG and their impact on social sustainability in China. The study also used industrialization and inflation as the control variables in the time span of 1981 to 2020. The present study also applied the Dynamic Auto-regressive Distributed Lags (DARDL) model to evaluate the association between the outlined variables. The results indicated that sustainable energy technologies such as solar and hydroelectric, eco-innovation, economic growth, industrialization and inflation are significantly associated with social sustainability in China. The present paper offers standard policies to regulators in making regulations related to maintaining social sustainability by using effective sustainable energy technologies and eco-innovation
Solving the puzzle of China’s low inflation: A new perspective from sectoral core inflation fluctuations
No full textChina’s constantly rapid economic growth accompanying by a low overall inflation has long been mysterious in macroeconomics. The core purpose of this paper is to solve this puzzle. Therefore, we integrate overdetermined set of equations into a MUCSVO model to explore the volatility mechanism of the overall inflation from a sectoral perspective. Our key findings include: 1) the hedging effect of sectoral inflation fluctuations principally accounts for China’s long-run stable overall inflation; 2) the main contradiction of China’s inflation has been shifting from high price levels in the traditional food and residence categories to rising prices in the health care category; 3) as the proportions of inflation in the food and residence categories fall steadily, sectoral inflation weights become more evenly distributed. In conclusion, China’s overall inflation and deflation will be much less likely to occur, while inflation is still of sectoral imbalance. Unusual price fluctuations in the food and health care categories, which are highly relevant to basic living standards of the low-income group, deserve close attention in particular. Overall, besides solving the puzzle of China’s low inflation, our model is applicable to economies that do not publish inflation weights, which is a useful extension of core inflation measurement.
First published online 15 March 202
Exploring factors influencing the digital economy: uncovering the relationship structure to improve sustainability in China
No full textDigital economy is a great route to promote the efficient utilization of natural resources and promote sustainability due to its high-tech, rapid growth, extensive penetration, deep integration and other characteristics. Existing study on the influencing factors of the digital economy is not deep enough and lacks the analysis on the relationship structure of factors influencing the digital economy, which is not conducive for an overall grasp of the digital economy. To correctly understand how to better develop the digital economy, this paper studies its influencing factors and the relationships between them. Based on the time-series data of China from 2002 to 2018, grey correlation analysis was applied to calculate the correlation between these influencing factors and the digital economy, and determine the major influencing factors of digital economy development in China. The Granger causality test and a review of existing research were used to judge the interrelationship of various factors. The interpretative structure model was utilized to determine the relationship structure of the main factors affecting the development of China’s digital economy. The results show that the number of digital talents, state of the technology market, and degree of digitalization are direct influencing factors of the digital economy. The results help to better understand the development of the digital economy and will enable the implementation of policies to improve towards more sustainable cities
Quantifying the economic survive across the EU using Markov probability chains
No full textThe multiple global crisis has made the economies of the world’s countries, including EU’s economy, vulnerable through the downgrading of the pandemic and the subsequent outbreak of geo-political conflict. These two events had the effect of decelerating the European economy and increasing the poverty level of the population, even that these developments are weaker than in rest of the world. The main objective of the present scientific approach is to identify a risk function based on Markov probability chains and to assess the possibilities of economic recovery through a package of policies structured over different time horizons. The used methods consist of meta-analysis, statistical analysis and geo-spatial and temporal modelling. The results of the study capture the integrated developments of risk-generating macroeconomic elements such as inflation, unemployment, public debt growth in a regionally segregated manner. These elements are useful for supranational decision-makers to increase the economic survival rate after multiple shocks through our proposed policy package.
First published online 14 March 202
Impact of heterogeneous local government competition and green technology innovation on economic low-carbon transition: new insights from China
No full textLow-carbon transformation of the economy is the inevitable orientation of socialism with Chinese characteristics to high-quality development in the new era, while the Chinese decentralized development model determines that the competition of local governments in China is an important factor influencing the green technological innovation on low-carbon transformation of the economy. How to achieve coordinated economic growth and ecological environment has become a prob-lem for local governments. Data from a Chinese provincial panel covering the years 2007–2019 is used to investigate the effects of heterogeneous local government competition (Comp), namely, economic, ecological and service competitions on economic low-carbon transition, and moderating effects of heterogeneous government competition and green technology innovation (GTECH) on the low-carbon economic transition (LCT). The results reveal that there are substantial disparities in the consequences of heterogeneous government competition on low-carbon economic transition (LCT). Among them, economic competition significantly dampens economic low-carbon transition (LCT), and ecological competition and service competition significantly boost economic low-carbon transi-tion (LCT). After performing robustness checks, these results continue to be strongly convincing. The study of moderating effects shows that economic competition can dampen the positive influence of green technology innovation (GTECH) to the economic low-carbon transition (LCT). However, ecological competition and service competition facilitate the promoting effect of green technology innovation on economic low-carbon transition (LCT).
First published online 14 March 202
Robot adoption and urban total factor productivity: evidence from China
No full textIndustrial robots are having a profound and lasting impact on China’s economy. This research examines the deployment of industrial robots and their effects on urban total factor production from theoretical and empirical angles. It is created using panel data from 286 cities at the prefecture level between 2003 and 2017. It is found that: First, robot adoption promotes urban total factor productivity. Second, adopting robots has a more positive influence on urban total factor productivity development in western, underdeveloped, and less market-oriented areas compared to the developed and market-oriented areas in the east. Third, adopting robots could enhance urban innovation vitality, increase total factor productivity, boost industrial agglomeration, and improve technological progress or technical efficiency. Policy enlightenment provided by these findings can guide future technological advancements and promote high-quality city development.
First published online 07 June 202
On which socioeconomic groups do reverse mortgages have the greatest impact? Evidence from Spain
No full textReverse mortgage is one of the products (perhaps the main one) that is good to obtain additional income by using the habitual residence as collateral. The main objective of this paper is to analyse the effects that reverse mortgage contracting has on household finances over the lifetime of a family according to the socioeconomic group to which it belongs in Spain. Four indicators are employed to measure the immediate and long-term effects. We use a stochastic model with a double source of randomness, survival and entry into dependency, and apply it to the three socioeconomic groups obtained with cluster methodology from the 2017 Spanish Household Financial Survey data. We conclude that the effects are very different depending on the group: regarding only the effects of hiring a reverse mortgage on the income of the family, widowed women aged between 81 and 85 years, with low income and expenses as well as little net wealth, and a habitual residence that represents half of her net wealth (Cluster 1) are the most benefited; considering that the highest impact indicators are on the probability of illiquidity and on the value of lack of liquidity, the use of reverse mortgages benefits more the families in Cluster 3 (high income and expenses and really high net wealth, head of household aged between 76 and 80 years) and less the families in Cluster 2 (medium income, net wealth and expenses, head of household aged between 65 and 75 years).
First published online 17 April 202