The present study investigates electricity consumption, carbon dioxide (CO2) emission, and economic growth decoupling using data from 1971 to 2020 for the economy of China. The study uses decoupling ...analysis (DA) as the prime methodology for analysis. Furthermore, the findings put forward a significant contribution to an economic picture of the economy of China and a sizeable addition to related research and findings under the assigned issues discussed in the study. The study’s main contribution is to decouple electricity consumption from the gross domestic product (GDP), which is rare in the existing literature in the context of China. Moreover, the study shows the decoupling of environment affects electricity consumption, and GDP growth. The DA model shows that electricity consumption is the main driving force enhancing economic growth. However, industrialization has increased greenhouse gases, global warming, and climate change due to production and consumption. China’s economy uses coal for energy resources, which indicates that China produces a large proportion of electricity with coal, which causes high CO2 emissions. Finally, further analysis with the Granger causality test confirms the main findings.
The article focuses on the study of performance factors of Slovenian hotel companies through Data Envelopment Analysis between 2001 and 2018. For this purpose, we introduced a balanced panel data of ...20 hotel companies which differ by their size, type, the number of hotels within the company, and location. To determine efficiency factors, we used the Malmquist index, which can be broken down into a change in efficiency and technological change. The change in efficiency was further broken down into a change in pure technical efficiency and a change in scalar efficiency. Between 2001 and 2018, hotel companies recorded decline in total factor productivity index which was mainly due to the inability to implement new production technologies. One of the key reasons for deterioration in technological change was the 2008 economic crisis.
A cross-sectional-based study was conducted in Torghar Pakistan to analyze the association between impacts of poor governance and household food security through sociological lens. A sample size of ...379 household heads was chosen randomly for data collection through structured questionnaire. The collected data was then analyzed in terms of bivariate and multivariate analyses, and binary logit model. At bivariate analysis, the study found that inadequate governance, political instability in terms of shortage of food supply chain, smuggling of food commodities had open new vistas toward starvation and household food insecurity. At multivariate analysis, the family composition has vivid association between household food security and poor governance. Although religious education and lower level of education deteriorate the existing food security at household level were also explored. Lastly, at binary logistic regression model depicted that increased in poor governance influence household food security negatively. Thus, the government should collaborate with local political leaders to identify those lacunas and institutional weakness that affect the good governance patterns in terms of smuggling and nepotism which deteriorate the existing channel of food supply chain during militancy were put forwarded some of the recommendations in light of the present study.
A comparative analysis of the spatial transformation of two different farm-size cattle systems, in Hungary and Slovenia, is presented in this paper. Concentration, mobility, and spatial ...autocorrelation measures are used to study spatial cattlestock distribution and their changes over time, as well as spatial cattle-stock clustering using data from two agricultural censuses. Results confirm the decline in cattle stock on large-size farms in Hungary and on small-size farms in Slovenia, with a relative increase in the importance of medium-size farms in both countries. The decline and spatial changes in cattle stock are greater in Hungary than in Slovenia. Hungarian cattle clusters are concentrated in flat areas with medium- and large-size largely commercial farms, whilst in Slovenia they predominate in mainly hilly grassland and partly cornsilage areas on small and some medium-size family farms. Such specific cattle clustering is linked to geographical and farm-size structural characteristics that can also be linked to agricultural-policy-measure-related support for cattle and dairy, associated with less-favoured or disadvantaged-area status linked to geographical and structural land and farm characteristics typical of Slovenian mountain and particularly hilly areas. These spatial changes in the cattle sector have socioeconomic, land use, and environmental implications in terms of ecological sustainability and rural livelihoods.
This paper presents a comparative analysis of the spatial transformation in the Hungarian and Slovenian pig sectors at the level of local administrative units (LAU). Concentration and inequality ...measures were applied in the empirical analyses, along with Markov transition probability matrices, to examine the stability and/or mobility over time and the presence of clustering effects. Both countries experienced a rapid decline in pig population. This profound structural change has led to a smaller number of more concentrated pig farms and increased territorial concentration. The degree of farm and territorial concentration and inequality in Hungary has been much higher than in Slovenia, and the concentration gap between the countries has increased. Between 2000 and 2010, the degree of concentration was much higher in Hungary than in Slovenia; average herd size per holding increased by 68 percent in Hungary, and only seven percent in Slovenia. In Hungary, clustering effects were particularly significant, with the pig sector moving towards large-scale concentration. The former effect was also confirmed in the Slovenian pig sector, but significantly weakened during the period under investigation. The exploitation and policy management of spatial externalities justifies these agricultural, economic, and agri-environmental practices.
Purpose
This paper aims to provide a reliable statistical model for time-series prices of short-stay accommodation and overnight stays in a eurozone country.
Design/methodology/approach
Exploiting ...the unit root feature, the cointegrated vector autoregressive model solves the problem of misspecification. Subsequently, variables are modelled for a long-run equilibrium with included deterministic variables.
Findings
The empirical results confirmed that overnight stays for foreign tourists were positively associated with the prices of short-stay accommodation.
Research limitations/implications
The major limitation lies in the data vector and its time horizon; its extension could provide a more specific view.
Practical implications
Findings can assist practitioners and hotel executives by providing the information and rationale for adopting seasonal volatility pricing. Structural breaks in price time-series have practical implications for setting seasonal-pricing schemes. Tourists could benefit either from greater price stability or from differentiated seasonal prices, which are important in the promotion of the price attractiveness of the tourist destination.
Originality/value
The originality of the paper lies in the applied unit root econometrics for tourism price time-series modelling and the prediction of short-stay accommodation prices.
This paper analyses the determinants of farmer participation in agri-environmental measures (AEMs) using the Slovenian Farm Accountancy Data Network (FADN) during the 2004–2010 period. Previous ...papers have not shown a straightforward relationship between farm size and decisions to participate in AEM. Considering explicitly the farm size, the controversial subject of the role of farm size is investigated by conducting logit regression analyses. We examine the influence of farm-specific characteristics on participation in AEMs using three different farm sizes: small, medium, and large. The findings strongly suggest that there are differences between the determinant factors of AEM participation based on farms’ utilised agricultural area, particularly between small and large farms. This conclusion is supported by those variables that describe farm capital per land intensity, off-farm income and type of farming as significant determinants for large farm models but not for small farm models. Furthermore, variables that describe land productivity negatively influence participation in AEMs for large farms, whereas these variables positively influence the participation of small farms. The results highlight the importance of how these previously confirmed factors influencing AEM participation differ according to the three different farm sizes.
The paper investigates the impact of different sources of income on wine farm total income inequality in Hungary using Farm Accountancy Data Network data for the period 2013-2019. The decomposition ...of the Gini coefficient is applied to focus on the impact of the Common Agricultural Policy (CAP) shift from market to government budgetary support on wine farm total income inequality. Off-farm income has a rather stable impact on wine farm total income inequality. CAP Pillar 1 subsidies have remained more important than CAP Pillar 2 subsidies, both in the structure of wine farm total income and in the reduction of wine farm total income inequality. The most striking finding is regarding a shift in wine farm market income from a negative (losses) to a positive (profit) value and its increasing role in wine farm total income inequalities. The 20% of the largest wine farms created from almost 90% to less than 80% of wine farm total incomes between 2013 and 2019, but during the same period their participation in CAP subsidy payments was reduced much more from more than 80% to around 60%. Subsidies from Pillars 1 and 2 were reduced, and wine market income increased wine farm total income inequality, while it remained constant for off-farm income. The wine farm market income has driven wine farm total income inequalities. This might strengthen because of the ongoing market selection process with the exit of less efficient and loss-making wine farms and the increasing role of surviving profitable wine farms. This market selection process can be related to managerial, entrepreneurial, and innovation activities based on the differentiation and segmentation of wine farm products and their market incomes.
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