Asia-Pacific Journal of Regional Science最新文献

Bangladesh is vulnerable to climate change because of its geographical position. Many extreme weather events such as floods, cyclones, drought, salinity, hailstorms, river, coastal erosion, etc. affect the country almost every year. Although the whole country is affected by extreme weather events to different degrees, it is important to categorize the zones according to the intensity of impacts from climate-induced losses to delineate mitigation and adaptation strategies. To do this in this study, we employed the global climate risk index (CRI) with the available data at district levels collected by the Bangladesh Bureau of Statistics through a national-level survey to estimate the district-wise CRI of Bangladesh. Moreover, we employed a time series regression model with long-term data collected by the Bangladesh Meteorological Department to estimate the variability of climate parameters (such as temperature, rainfall, humidity). The yearly increment in maximum temperature during 1980–2000 was 0.031 °C and it increased by 0.022 °C per year during 2001–2020. The annual rainfall over the whole period increased by 223 mm. In general, the overall relative humidity increased by 1.16% between the two periods. There were also periodic changes in climate events along with monthly fluctuations in temperatures, rainfall and humidity patterns. Among the 64 districts of Bangladesh, 4 districts were categorized as severe risk (CRI: > 50), 10 as high risk (CRI: 36–50), and 12 as medium risk (CRI: 26–35) based on climate change impacts. The severe climate risk districts are Sunamganj, Bhola, Kurigram and Patuakhali, where there were losses of 520–720 million USD. In general, 41.71% of the people were affected by climate extremes, and 5.55% suffered from hazard-induced sickness and injury. As a consequence of climate change, flooding showed the greatest contribution to household damages and losses in Bangladesh. These findings can help in prioritizing region-specific mitigation and adaptation actions against the impacts of climate change in Bangladesh.

The COVID-19 pandemic, a new type of coronavirus that the world faced, broke out at the end of 2019 causing numerous impacts on all countries. Particularly the European Union (EU) countries were notably affected by COVID-19 pandemic lockdowns and stringency policies. Furthermore, although Turkey was successful in combating many areas of the COVID-19 pandemic compared to the EU countries, its international trade was affected. This paper empirically investigates the impact of the COVID-19 pandemic on selected EU countries (Bulgaria, Estonia, France, Germany, Greece, Latvia, Lithuania, Luxembourg, Poland, Romania, Slovakia, Slovenia, Spain, Czechia) and Turkey’s exports with monthly panel data analyses over the March 2020–December 2021 period. The findings suggest that the COVID-19 pandemic had negative impacts on exports in the EU and Turkey. For example, a 1% increase in COVID-19 stringency reduced exports by 0.102%, and a 1% increase in total COVID-19 cases caused a decline in exports of 1.620%. Furthermore, the Dumitrescu–Hurlin panel causality test results show a two-way causality relationship between COVID-19 cases and export levels. In addition, unidirectional causality linkage exists from the COVID-19 stringency index. Therefore, causality test results confirm an impact of the COVID-19 pandemic on exports during the stringency measures and lockdowns.

One distinctive characteristic of an acceleration in the services sector in India is that only a few segments within the overall services sector have shown high and persistent growth. Moreover, these segments are concentrated in a few states leading to regional income disparity across states. Using data from 25 states for the period 1995–2020, we examined whether there is convergence or divergence in services net state domestic product (NSDP) across Indian states. We utilized parametric as well as non-parametric econometric techniques in the study. Non-parametric methods such as kernel density estimation and transition probability matrix allowed us to visualize the complete distribution dynamics of the convergence for the entire period considered in this study. Findings of our study suggest diverging trends in the per capita services NSDP across states with an indication of convergence in the first 10 years of the sample period.

The recent global economic crisis and pandemic have shown that housing markets are highly exposed to the prevailing economic and social circumstances. Relevant literature indicates that housing output and transaction volumes were negatively affected during the Covid-19 pandemic. In contrast, price levels displayed variations under different circumstances shaped by demand shifts. Housing affordability conditions also deteriorated and household budgets were squeezed by the high housing cost burden due to the loss of livelihood and increased time spent at home during lockdowns. In the Turkish case, a severe economic crisis in 2018/19 preceded the Covid-19 pandemic. Housing output, housing prices, and the unemployment rate were much more affected by the economic crisis than by the pandemic. Spatial variation in the changes in the supply level was not homogeneous throughout the country. Lower supply levels were observed in the southern and south-eastern parts of the country and the major employment centres. However, compared to the international tendencies, total demand for housing increased, raising the housing transaction volume to record-high levels. In addition, housing prices during the pandemic skyrocketed, accompanied by increasing inflation rates. Furthermore, a demand shift towards single-family houses was observed, and the price gap between single-family houses and flats has widened since the pandemic began. The investment function of housing became prominent once again for wealthier households during the high inflation periods observed during the pandemic. The major conclusion drawn from this study is that the housing market deepened the existing inequalities in society during the current pandemic.

The northeastern part of Ethiopia, particularly Raya area is a pilgrimage site famed for its antique civilization, archaeological sites, and rural landscapes. Despite existing ecotourism potentials, the area has not been utilized for tourism for millennia. While previous work looked at the availability of natural resources, it did not identify and prioritize the resources, so knowledge gaps continue to exist in prioritization of potential ecotourism sites. This study attempted to identify various ecotourism indicators, evaluate and produce maps of suitable ecotourism sites, and prioritize optimum protected areas that are best suited for sustainable ecotourism development of Raya areas. For this analysis, 13 spatial indicators from physical, environmental, archaeological, socio-cultural, and socioeconomic sectors were considered. Analytic Hierarchical Process (AHP) was used to calculate the details of the spatial indicators and class weights. The suitability maps were classified into four classes as Highly Suitable (S1), Moderately Suitable (S2), Marginally Suitable (N1), and Not Suitable (N2). The results revealed 114.37 Km² (10.33%), 13.36 Km² (1.91%), and 10.39 Km² (1.62%) fall under the highly suitable class in Blocks B, A and C, respectively. AHP weights with ultimate criterion and field observations also ranked lake Ashenge, Hugumburda, and Gratkhassu national forest priority areas as 1st, 2nd, and 3rd optimal zones, respectively. The outcomes of this study are crucial for conservation pioneer work in ecological development, and should be used as an ideal blueprint by ecotourism planners and decision-makers for sustainable ecotourism development strategies.

Several changes in urban mobility patterns have been observed in response to COVID-19 since March 2020 when the World Health Organization (WHO) declared COVID-19 a pandemic. However, whether mobility changes are entirely pandemic related has not been investigated for each urban function. This paper evaluates the potential determinants of urban mobility changes for six different urban functions during the COVID-19 pandemic via community mobility data collected by Google. Using artificial neural networks (ANN), the dynamics that affect mobility changes in the following urban functions; grocery/pharmacy, residential, parks, retail/recreation, transport stations, and workplaces, were analysed for Çankaya, one of the densest districts in the capital of Turkey. Results of the prediction model show that responses to the pandemic differed considerably by urban function. Before the new normal era, changes in urban mobility trends were strongly dependent on the pandemic as a public health threat and represented by restrictive government measures. However, impacts of the pandemic on intracity mobility decreased in the new normal era when rules were relaxed. These results are useful for developing proactive policies to ensure rapid post-pandemic recovery in urban economics and planning.

An n-firm oligopoly is introduced in which, in addition to production levels, the pollution emissions are also included. A regulator cannot monitor individual emission volumes of firms, so uniform incentives are introduced to firms to reduce pollution concentrations. The regulator cannot observe the exact concentrations, so the incentives are also uncertain. Therefore, each firm considers random profit with expectations that it is maximized by minimizing variances or standard deviations. This idea leads to a multi-objective optimization problem for each firm, so two different concepts are applied as a solution. The unique positive Nash equilibrium is proven in all cases examined, and the effects of the environmental tax rate on industry output, prices, and pollution emission levels are analyzed.

This paper assesses the variability in financial inclusion and its determinants for Indian states categorized by income groups and geographical regions for two periods: United Progressive Alliance (UPA) (2011–2012) and National Democratic Alliance (NDA) (2018–2019)-led political regimes. We found that the coefficient of variation (CV) for financial inclusion index increased from 0.893 to 0.910 during the two periods. In addition, across six major geographical regions of India, the southern region had the highest rank in the financial inclusion index, while the north-eastern region was the lowest in terms of financial inclusiveness during both periods. Interestingly, Gujarat (western) and Jammu–Kashmir (northern) showed substantial improvement in terms of the level of financial inclusion from 2011–2012 to 2018–2019. The estimated Tobit model regression results suggest that factors such as per capita GSDP, number of factories, and ease of doing business have a significant positive impact in accelerating financial inclusion in 2011–2012. While in 2018–2019, we found that along with these factors, per capita power consumption, length of national highways and length of railway routes are also positively significant in determining financial inclusion. This may arise because of the emphasis on the development of infrastructure and energy sectors during the NDA-led political regime. Therefore, the relevance of such policies to accelerate financial inclusiveness in the most deprived regions is reflected.