Territorial Distribution of EU Funds Allocation for Developments of Rural Romania during 2014–2020

This study uses cross-section regressions and spatial econometrics techniques to identify determinants of rural development project implementation based on the Common Agriculture Policy (CAP) of the European Union. For this, we use 40 Romanian counties. Results show that agricultural land abundancy and land concentration degree are significant positive factors. On the contrary, the local human development level is a negative determinant, low values for this factor being an incentive to compensate the lack of own resources through European funding. No significant effects of the average salary or population density were depicted. Spatial analysis indicates contagion and diffusion processes for fund accession through projects. This behavior is like that in other financial sectors, in which human behavior is a decisive factor, such as the insurance one. A West–East clusterization process is identified for the total project value, conditioned by the identified factors.

Spatial machine learning: new opportunities for regional science

This paper is a methodological guide to using machine learning in the spatial context. It provides an overview of the existing spatial toolbox proposed in the literature: unsupervised learning, which deals with clustering of spatial data, and supervised learning, which displaces classical spatial econometrics. It shows the potential of using this developing methodology, as well as its pitfalls. It catalogues and comments on the usage of spatial clustering methods (for locations and values, both separately and jointly) for mapping, bootstrapping, cross-validation, GWR modelling and density indicators. It provides details of spatial machine learning models, which are combined with spatial data integration, modelling, model fine-tuning and predictions to deal with spatial autocorrelation and big data. The paper delineates “already available” and “forthcoming” methods and gives inspiration for transplanting modern quantitative methods from other thematic areas to research in regional science.

Parameterless Pruning Algorithms for Similarity-Weight Network and Its Application in Extracting the Backbone of Global Value Chain

Purpose With the availability and utilization of Inter-Country Input-Output (ICIO) tables, it is possible to construct quantitative indices to assess its impact on the Global Value Chain (GVC). For the sake of visualization, ICIO networks with tremendous low- weight edges are too dense to show the substantial structure. These redundant edges, inevitably make the network data full of noise and eventually exert negative effects on Social Network Analysis (SNA). In this case, we need a method to filter such edges and obtain a sparser network with only the meaningful connections. Design/methodology/approach In this paper, we propose two parameterless pruning algorithms from the global and local perspectives respectively, then the performance of them is examined using the ICIO table from different databases. Findings The Searching Paths (SP) method extracts the strongest association paths from the global perspective, while Filtering Edges (FE) method captures the key links according to the local weight ratio. The results show that the FE method can basically include the SP method and become the best solution for the ICIO networks. Research limitations There are still two limitations in this research. One is that the computational complexity may increase rapidly while processing the large-scale networks, so the proposed method should be further improved. The other is that much more empirical networks should be introduced to testify the scientificity and practicability of our methodology. Practical implications The network pruning methods we proposed will promote the analysis of the ICIO network, in terms of community detection, link prediction, and spatial econometrics, etc. Also, they can be applied to many other complex networks with similar characteristics. Originality/value This paper improves the existing research from two aspects, namely, considering the heterogeneity of weights and avoiding the interference of parameters. Therefore, it provides a new idea for the research of network backbone extraction.

The Correlation of SDG 1 and 8 and Spatial Effect of Human Development Index in Central Java

Human Development Index (HDI) is the approach to measure socio-economics since 1990. Furthermore, integrating socio-economics with the environment brings the development of Sustainability Development Goals (SDGs). The relations between human development and sustainable development are complementary to build a better society. However, by 2020, the growth of HDI in Indonesia starts slowing down during Covid-19 to only 0,03% from the previous year. Central Java is one of the provinces that can still manage the HDI growth higher than Indonesia. This study aims to find the SDG 1 and 8 factors that affect HDI in Central Java with the spatial econometrics method to analyze the spatial dependency in variables. The variables of SDG 1 and 8 in this study are Ln Poverty Line, Ln GDRP per capita, unemployment, and poverty rate. This study shows that the SDG 1 and 8 variables have significant results and implicates spatial effects through Spatial Lag in the HDI of Central Java. The implication of this study is to encourage collaborative action in strengthening the implementation of SDGs and improving the HDI of the regions and cities in Central Java.

The environmental Kuznets curve for Turkish provinces: a spatial panel data approach

This paper aims to test the existence of the environmental Kuznets curve (EKC) hypothesis using SO2 measurements in Turkish provinces between 2004 and 2019. The existing studies concerning the EKC hypothesis for Turkey either use a country-level analysis or panel data techniques covering provincial data that do not account for the spatial dimension. To account for the spatial dependence and overcome the biases resulting from the existence of such spatial spillovers, this paper combines the traditional panel data methodology with the recent advances in spatial econometrics. Our findings confirm the presence of a non-linear link between regional economic prospects and environmental degradation. However, unlike the core expectations of the EKC hypothesis, our results demonstrate a U-shaped relationship between economic development and SO2 levels. Moreover, these findings are robust to the inclusion of a spatial battery which highlights the existence of regional spillovers. Overall, our results show that the post-2000 epoch calls for a different action plan to mitigate the rising impact of environmental degradation in Turkey.

Spatial spillover effects of capital factor agglomeration on the urban industrial structure upgrading in China: Based on panel data of 284 prefecture-level cities

The spatial agglomeration of capital factors has become an important force affecting regional economic development and industrial structure. Investigating the spatial relationship of capital factor agglomeration is a key way to accelerate the upgrading of urban industrial structure and realize sustainable development. Based on the panel data of 284 cities in China from 2008 to 2017, we use the theoretical framework of spatial econometrics and estimate the spatial effects of capital factor agglomeration on the upgrading of urban industrial structure. Both the global Moran index and the local Moran scatter chart present that the agglomeration of capital factors and the upgrading index of urban industrial structure shows the characteristics of spatial agglomeration. The results reveal that the agglomeration of capital factors can significantly promote the upgrading of the industrial structure of local and surrounding cities. Still, the spatial spillover effect is not significant. We then explore the possible factors that limit the spatial spillover effects of capital agglomeration. Using the results of the paper, we provide policy suggestions for strengthening urban industrial construction and optimizing the urban governance model.

A REVIEW OF SPATIAL ECONOMETRICS IN EXPLICIT LOCATION MODELLING OF COMMERCIAL PROPERTY MARKET

‘Location, location, location’ is a real property parlance mostly used to describe the influence of location in the property market. Location is mainly considered as the most significant influencer of commercial property prices. Location is modelled traditionally using hedonic pricing model by either proxy location dummies or distances relative to other neighbourhood features. This was shown to be inadequate due to spatial autocorrelation and heterogeneity inherent in spatial data, which jeopardises the estimates' consistency. Consequently, spatial econometrics is used to explicitly model location into property pricing by controlling spatial effects of autocorrelation and heterogeneity. Housing studies dominate the use of this approach with limited application in the commercial property market. This paper reviewed spatial econometrics and found that the commercial property market exhibits significant spatial dependence and heterogeneity. Accounting for such effects improves model accuracy significantly. It, therefore, recommends increase use of spatial econometrics in commercial property market modelling.

Identifying the Determinants of Nongrain Farming in China and Its Implications for Agricultural Development

Promoted by rapid industrialization and urbanization, the structure and spatial pattern of farming in China has changed greatly, and nongrain farming (NGF) has become more common. However, excessive NGF in some areas is not conducive to sustainable agricultural development and threatens China’s food security. In this study, we briefly analyze the stage characteristics of NGF in China and investigate the spatial agglomeration of NGF and its influencing factors from the perspective of spatial econometrics. The results showed that the average annual growth rate of NGF in China from 1985 to 2019 was 0.64%, and there was a growing positive spatial correlation between NGF in each province. Spatial Durbin model (SDM) estimation showed that both the per capita disposable income of local rural residents and the local urbanization rate promoted the development of NGF, while local per capita farmland, road density, and the functional orientation of the main grain-producing areas had a negative impact on NGF. The per capita disposable income of rural households and urbanization rate in neighboring areas had a promoting effect on the development of NGF, while road density in neighboring areas was negatively correlated with NGF. Ultimately, some targeted measures are proposed to promote China’s agricultural development in the new era.