Asia-Pacific Journal of Regional Science最新文献

India is a developing market economy that comprised over 18% of the global population in 2020 and showed a 1.29% share of world GDP (Gross Domestic Product) in 1990. In addition, 3.20% of global energy consumption belonged to India in 1990. By 2020, India’s share of the world GDP was 3.08%, increasing its GDP by almost 3 times. However, energy usage increased by less than 2 times with a share of 6.25% in the world’s total energy consumption. Therefore, India managed to decrease its energy intensity per capita level by 64.35% in 2020 compared to 1990 by using less energy even with an increased income. In this context, this study investigated the question of how the Indian economy reduced its energy intensity for the period between 1990 and 2020. The impacts of GDP per capita, economic integration, foreign direct investments (FDI) and industrialization on energy intensity were analyzed using annual data from 1990 to 2020. First, the standard Augmented Dickey–Fuller Test (ADF) and Fourier ADF test methods were used to determine stationarity of the series. Then Fourier Autoregressive Distributed Lag (ADL) and Fourier Engle–Granger tests, recently introduced in the literature, were used to examine the cointegration relationships because all of the series were stable after subtracting the first differences. The results indicated a cointegration link between the variables. According to the empirical evidence obtained from FMOLS/CCR (DOLS) analysis, an increase of 1% in economic growth and foreign direct investment over the long run led to a decrease in energy intensity of approximately 1.08%/1.12% (1.14%) and 0.01%/0.001% (0.05%), respectively. Additionally, the results from FMOLS/CCR (DOLS) analysis indicated that a 1% rise in industrialization and trade openness in the long term resulted in an increase in energy intensity of approximately 0.25%/0.13% (0.39%) and 0.15%/0.18% (0.21%), respectively. Finally, fully modified ordinary least squares (FMOLS), Charnes, Cooper and Rhodes Model (CCR), and Stock-Watson Dynamic Ordinary Least Squares (DOLS) estimators were used for short and long-term coefficient estimations. Therefore, we conclude based on these findings that economic growth and foreign capital decrease energy intensity over the long term, while industrialization and economic integration increase energy intensity.

Climate change constitutes one of the most critical challenges of the contemporary period and can affect various sectors of economies across the globe, the agricultural sector is not an exception. This study aimed to assess the impact of climate change on India’s agricultural sector from 1990 to 2020. The autoregressive distributed lag (ARDL) approach was utilized to determine the short-run and long-run relationships between variables such as carbon dioxide emissions, temperature, energy utilization, and fertilizer consumption. The ARDL method and the Johansen and Juselius cointegration test both supported the existence of a significant and long relationship among the selected variables. The estimated short- and long-run findings showed that carbon dioxide emissions (CO₂), temperature, and energy consumption affect agricultural yield positively and significantly. These findings have several implications for the Indian economy. With a large population dependent on agriculture, improved productivity can directly impact food security and rural income, consequently leading to the country’s overall economic development. Enhanced agricultural output due to these factors may potentially lead to surplus production, allowing India to export more agricultural produce. This can positively impact the country’s trade balance and generate revenue through exports.

Literature on New Economic Geography (NEG) highlights the importance of spatial concentration and spillover effects in economic growth or decline. Northeast China, as an old industrial base, is experiencing a regional decline since its transition to the post-industrial stage. Therefore, what is the main sectoral composition in Northeast China and how does this influence regional decline? To what extent do spatial spillovers play a role before and during the regional decline of Northeast China? Based on these questions, we investigated the spatial connections between regional decline and structural changes in Northeast China over three development periods: Rust Belt (1995–2002), revival (2002–2015), and decline (2015–2019). The recent exploratory space–time data analysis was employed on prefecture-level income and its structural change components (sectoral output and employment ratio). We found that the possible reason for the regional decline in Northeast China is premature deindustrialisation. Spatial co-decline in the employment of industry and construction, the primary source of regional decline, facilitates most of the space–time patterns of the regional income. Agglomeration of the agricultural sector has shifted to the north, while industry and construction have gravitated towards the middle and south, with no clear spatial patterns in the service sector. Dependence on natural resources has a "lock-in effect" that inhibits the transition from industry to services, so industry and construction remain the most efficient in Northeast China. Strengthening spatial connections is essential for local governments to develop service sectors and overcome declining conditions.

Air pollution mitigation strategies are either global or national. In this study, we highlight the need for regional/sector-wise mitigation strategies. We first explored the relationship between economic activity (measured using Nighttime Lights) and air quality for India (and Norway as a comparative reference) employing the Environmental Kuznets Curve (EKC). Second, we focused on the need to shift from global/national-level air pollution mitigation policies to geoclimatic region-wise policy implementation in developing nations with significant economic and physical geography heterogeneity. We considered regional/sector-wise differences in the economic activity–air quality relationship, thereby aiding future policy action in these regions/sectors. We used a panel econometric research design on geospatial variables extracted from the Google Earth Engine. We investigated causality using an Instrument Variable strategy. Economic activity in Norway led to improved air quality, while India is still far from an inverted U-shaped EKC. An inverted U-shaped EKC existed for BIMARU (Bihar, Madhya Pradesh, Rajasthan, and Uttar Pradesh) states after accounting for transboundary pollution. Transport and industrial sectors were significant contributors to air pollution in India, with Nitrogen Dioxide concentrations highest in the Central, Western, and Eastern regions. At the same time, West India was no longer a Sulfur Dioxide polluting hub. Export hubs were sources of Nitrogen Dioxide pollution, while the primary, secondary, and tertiary sectors were characterized by Carbon Monoxide and Sulfur Dioxide emissions. Given the geographical heterogeneities, global/national intervention policies may not solve the underlying problem anymore. Alternatively, shifting to a decentralized approach involving source-level interventions is the need of the hour.

The COVID-19 pandemic has accelerated the work-from-home trend, with significant variations in location, industry and firm size. These shifts have significantly affected economies and societies but traditional data cannot be easily tracked. This paper presents a method for measuring office attendance and examines its trends and characteristics. To this end, we first introduced the working-at-office ratio, which is the percentage of people going to an office compared to the pre-COVID-19 period, considering 74 office submarkets in six major Japanese cities. We captured mobility trends in office buildings using rich Global Positioning System-based mobile location data combined with specific office location data. Subsequently, we demonstrated the effectiveness of the proposed indicators by comparing them with other mobility data and office attendance indicators. Finally, we examined the relationships between the working-at-office ratio and the characteristics of the office buildings and tenants in each submarket. The findings indicated that factors, such as the proportion of large buildings, concentration of specific sectors, and tenant size, were significantly related to office attendance, with these relationships evolving over the duration of the pandemic. Our approach provides real-time, granular insights into office attendance trends, which are crucial for anticipating future work paradigms.

Smart specialization (SS) strategy emerged as a policy framework within the European Union’s (EU) regional policies, particularly after the 2008 financial crisis. Although a wide range of literature on smart specialization exists, there is limited empirical evidence on how the SS framework correlates with regional entrance and exit of technologies. Moreover, little is known about the connection between SS variables and technological growth paths, particularly in the context of a developing country. This study aimed to investigate the correlation between two building blocks of SS (relatedness and complexity) on technological trajectories in a developing country. Using regional patent data for Turkey’s regions at the NUTS3 level between 1978 and 2017, the study investigated patterns of regional trajectories by exploring how entry and exit are correlated within the SS framework. GDP per capita, population, the number of universities and techno-parks, Herfindahl–Hirschman Index, and complexity of regions were included in the study as control variables. The results showed that technological complexity is negatively correlated with the probability of entry, and positively correlated with the probability of exit. The results also revealed the role of technological relatedness as a tool to attract new and complementary technologies to a region. However, correlations between relatedness and probability of entry are weakened as the number of universities and techno-parks increases in a region, while the correlations between complexity and exit are enhanced, particularly with the presence of universities.

Innovation and technological progress are crucial to the sustainability of regional economies. Assessing the role of the spatial dimension in the innovation process and evaluating the convergence of regional disparities in technological levels encompasses a vital research endeavor. The significance of this research effort lies in the fact that the convergence of economic disparities can be accelerated or impeded depending on the innovation process, that is, whether regional disparities in technological levels decrease or increase over time. This study provides new insights into regional disparities of total factor productivity (TFP) in Japan using a new method, the stochastic convergence model. The stochastic convergence model allows determination of the long-term stationarity in the convergence of disparities, which has been challenging in conventional analyses. In this study, we first evaluated the technological level of regional industries by measuring industry-specific TFP. Then, we verified the convergence of disparities in TFP among regions using a stochastic convergence model. The analysis results showed that TFP is rising with narrowing of regional disparities, particularly in the manufacturing sector. Furthermore, TFP converged to steady-state levels in each region, revealing the importance of the regional production environment in innovation. The results of this study provide important implications for developing strategies to enhance the sustainability of regional economies.

Education plays a vital role in the development of any country or region, making it imperative to address obstacles that hinder educational quality such as school bullying. This is an under-researched topic in the social sciences. Bullying is a form of social mistreatment that may have detrimental effects, because students who experience frequent bullying tend to perform poorer compared to their peers who do not report such incidents. Given this evidence, it is crucial to assess the educational performance of countries by considering the overall well-being, including mental well-being, of students. In this context, data envelopment analysis (DEA) is a valuable method for evaluating the performance of decision-making units in education. In our paper, we discuss various approaches to apply DEA when dealing with "undesirable" outputs like bullying. Common techniques involve transforming undesirable outputs using reciprocal transformations and employing a "bad-output" model. However, these methods have several drawbacks, such as altering the nature of the selected output items, loss of linearity, reduced robustness of the efficiency frontier, and limited versatility. To address these concerns, our paper proposes and tests a value inversion–DEA model that can consistently transform "undesirable" data into a reverse measurement scale, while preserving linearity. We demonstrate the high versatility of this model across different types of DEA models with undesirable outputs. Furthermore, we apply this proposed method to assess the educational efficiency of OECD countries, focusing on bullying as an undesirable output. Our findings show that significant improvements in performance are possible in many countries by addressing school bullying.