Asia-Pacific Journal of Regional Science最新文献

This Special Feature investigates the social, economic, and spatial impacts of the COVID-19 pandemic in Turkey and highlights the factors differentiating Turkey from the other countries. The articles contributing to this Special Feature are classified into three main parts. The first group of articles addresses spatial implications of the COVID-19 pandemic in Turkey with a specific focus on the place-based factors affecting the spread of the pandemic, the determinants of pandemic-induced changes in intracity mobility, and the use of social media to forecast commercial real estate figures during COVID-19. The second group of articles investigates the social and economic implications of the COVID-19 pandemic and diversely affected economic sectors in Turkey. These articles analyze the vulnerability and resilience of regions and diversely affected economic sectors with a specific focus on the housing market that displays an opposite trend to international tendencies regarding transaction volumes and private rental housing prices. The third group of articles considers the economic impact of the COVID-19 pandemic from an international perspective. These articles analyze the impact of the COVID-19 pandemic on international trade with a specific focus on exports and the fragility of the global trade structure and network in the framework of global value chains. Analyzing the impacts of COVID-19 from different perspectives, the articles in this Special Feature reveal the factors differentiating Turkey from the other countries and highlight the challenges.

This study examined how Sri Lankan cooperative banks performed in changing markets and environmental conditions, including the COVID-19 pandemic. We analyzed quarterly financial data for 103 cooperative rural banks (CRBs) between 2016 and 2020 to estimate technical efficiency and total factor productivity (TFP) using the input distance function with multiple outputs. The technical efficiency (TE) of CRBs declined from 99 to 85% over the period and differences in TE between banks increased substantially. TFP decreased substantially, by 38%, so for further analysis, TFP change was separated into a three component-scale change, technical change, and technical efficiency change. According to TFP decomposition, the dominant factor contributing to this decline was the scale change. The loan relief program enacted during the COVID-19 crisis, as well as increased competition in the market, may have reduced the size of operations, thus possibly contributing to this decline. The second component, technical change was overall positive, but minute likely due to the reluctance of cooperative banks’ to adopt new technologies. The third component technical efficiency change was negative throughout the period, likely due to increased operating expenses and non-performing loans. These findings suggest the need for a more market-sensitive government intervention, adaptation of modern technology, and comprehensive human resource development to enhance the performance of CRB operations.

Rent prices have a strong relationship with economic factors in addition to the structural and environmental characteristics of housing stocks. Previous research demonstrated that impacts of unexpected and sudden circumstances such as war and epidemics on urban housing markets relate to their effects on the economy. Following the first COVID-19 case in Turkey, which was officially announced on 11 March 2020, changes in both housing preferences and economic structure have significantly affected the rental housing market due to the pandemic conditions. To highlight challenges in the rental housing market, this study addressed how the COVID-19 pandemic has influenced rental housing prices in 81 provinces of Turkey using the big data set of Endeksa, a private real-estate platform in Turkey. The data set was descriptively analyzed through four main periods identified on the basis of changing COVID-19 pandemic regulations and implementations in Turkey. Average rent prices of Turkish provinces during the identified periods were compared using ArcGIS 10.6. to show how private rent prices changed during the pandemic. The findings demonstrated that the unit rent prices generally increased from March 2020 to December 2021 throughout the whole country. Furthermore, the findings highlighted that while metropolitan cities have the highest unit rent price, the highest rent price rise occurred in provinces located in Central and Eastern Anatolia. This study contributes to the literature on how sudden shocks such as pandemics affect rent prices in free rental markets. In addition, it shows how the impacts of the COVID-19 pandemic on the rental housing market differ from country to country by revealing the increasing trends in Turkey.

This study investigated pollution haven hypothesis (PHH) validation and existence of the environmental Kuznets curve (EKC) hypothesis in Bangladesh. The study used CO₂ emissions as the key indicator of environmental pollution. Moreover, we considered relevant explanatory variables such as foreign direct investments, trade openness, financial development, gross fixed capital formation, energy consumption and urbanization to achieve our goals. Covering the time series data from 1986 to 2018, the autoregressive distributed lag (ARDL) approach was applied. The findings revealed a long-run cointegration between the considered variables, and the ARDL results cannot validate the PHH in Bangladesh. These results contribute to the existing literature by concentrating on the EKC hypothesis for financial development purposes. Furthermore, we found that urbanization, gross fixed capital formation and trade openness positively influence CO₂ emissions, while energy use reduces CO₂ emissions. These findings suggest that Bangladesh should take advantage of the invalidity of the PHH and introduce eco-friendly urbanization planning to mitigate detrimental effects of environmental pollution.

In December 2019, COVID-19 infections first occurred in Wuhan City, China, after which it rapidly spread throughout the world. Today, COVID-19 has become a major disaster affecting countries physically, socially, and especially economically. However, reasons behind the spread of COVID-19 are still unclear. Therefore, many scholars from different disciplines try to understand the various leading indicators. Our study aimed to reveal place-based factors affecting COVID-19 incidences in Turkey while addressing and analyzing a set of indicators (physical, natural, economic, demographic, and mobility based) within the scope of the recent research findings in the literature on the COVID-19 Pandemic. Following this purpose, we addressed 81 provinces of Turkey using city-level data obtained from the Ministry of Health, and employed global and local regression methods through ArcGIS and GeoDa: Ordinary Least Square, Spatial Lag Model, Spatial Error Model, and Geographically Affected Weighted Regression to highlight place-based factors affecting the spread of the Pandemic. The results of our analyses demonstrated that three factors: (1) population density, (2) annual temperature, and (3) health capacity; are related to the COVID-19 incidences in Turkey. Our results also demonstrated that the impact of these factors causes varying spatial effects within the country, especially in the West–East direction. Although these results provide a base for future studies, COVID-19 is still spreading with several mutations. Therefore, the reliability of produced models and the effectiveness of factors should be retested using new and updated data for cities and at other geographical scales.

The COVID-19 outbreak has deeply affected the global economy of most countries and Turkey is no exception. However, the impacts of the outbreak differ on a regional basis, and both scientists and policymakers have neglected this regional differentiation. In an attempt to redress this situation, our study aimed to reveal the regional disparities related to the economic impacts of the outbreak and the dynamics that created this differentiation in Turkey. Our statistical analyses were carried out based on two different periods. The first period covered the first trimester when the outbreak began, and several shutdowns were implemented. The second period covered the second trimester when society began to open up again. The first trimester was termed the shock period, and the second trimester the recovery period. We developed a resilient–vulnerability index based on selected variables such as employment, energy consumption, exports, and the number of companies established and closed using a location quotient (LQ) analysis. While our index offers a picture of resilient and vulnerable regions, we also used this index as a dependent variable in our study. In the second stage, we focused on what kinds of dynamics gave rise to the resilience or vulnerability of a region. Our findings revealed that regions are economically affected by the outbreak at different levels. The regression analysis results showed that the innovation capacity and export levels of the regions predict regional resilience negatively, while firm size predicts positively. The recovery of regions also differed regionally. Our analyses show that Turkish regions with relatively larger economies recover more slowly, while regions with smaller economies recover more quickly.

As global value chains have taken shape, the geographic concentration of production in specific centers or hubs to minimize production costs is an issue that has been raised following the onset of the Covid-19 pandemic. The criticism typically highlights how the production capacity within these global value chains is insufficient to meet the global needs for medical equipment and devices in this type of crisis. This study uses complex network analysis to examine the global trade structure of surgical masks and medical ventilators and its general patterns. The findings of this study conducted between 2019 and 2020 show that this trade structure has complex network properties and a core-periphery structure. A comparative evaluation of the results from these 2 years also reveals the economic fragility of the ventilator trade network even if it is easier to adapt urgent conditions in mask trade. In addition, according to the network analysis and the authority centrality values for 2020 the fact that the highest-ranked countries for ventilator imports are almost exclusively developed countries suggests that the trade structure might also indicate a moral deterioration. In sum, the empirical findings confirm that the structure of the current global value chains will not be immune to supply shocks during emergencies such as a pandemic.

Dependence of the real estate sector on human activity has been unveiled during the COVID-19 pandemic. In addition, it is assumed that trends emitted from the location-based social networks (LBSNs) successfully reflect human activities, hence commercial property trends. This study examined the use of social media to forecast commercial real estate figures during COVID-19 in Istanbul and determined the potential of social media data for forecasting the future rent/price levels of retail properties. Instagram and Twitter, two major LBSN platforms, were selected as social media data sources. First, 17 million geo-tagged Instagram posts and 230 thousand geo-referenced tweets were collected. Then, the data sets were superposed on COVID-19 key points in Turkey and the relationships observed. Finally, the data sets were combined with the commercial real estate data to monitor increases in the accuracy of rent and price predictions. Beşiktaş District of Istanbul was chosen as the pilot region to test the methodology. The results showed that the LBSN-supported models outperformed baseline models most of the time for price predictions and occasionally for rent predictions. Also, both Instagram and Twitter were found essential to the study and could not be omitted. This study demonstrates the significance and leveraging potential of applying human activities to the decision-making processes of the commercial real estate sector under COVID-19 conditions. This is the first study to adopt LBSN data to forecast commercial property prices.