Growth and Change最新文献

Although the growth in developing countries have always been accompanied by large-scale land expropriation, whether there is a standard relationship between the scale of land expropriation and the stage of economic development remains unclear. This paper aims to explore the practice of land expropriation in China, and while there are polarized views around its role in economic growth, inter-provincial differences also mean that it is an excellent sample for clarifying the relationship. By exploring the growth coalition consisting of local bureaucracies, central government and peasants within the land-centered development model, the possible inverted U-shaped trend of land expropriation scale with economic development is theoretically analyzed, from which hypotheses on the Land-expropriation Kuznets Curve (LKC) are proposed. Further, using Chinese provincial panel data, the results not only show the invert-U shaped relationship between economic development and land expropriation and its regional heterogeneity, but also identify four determinants of dependence on land expropriation: internal demands of local bureaucracies to advance urban infrastructure, real estate and manufacturing sectors, horizontal competition among local bureaucracies, vertical supervision by the central government, and external forces from peasants. These conditions combine to expand the scale of land expropriation in the less developed period, leading to the phenomenon of land expropriation dependence. However, with the transformation of China's economic growth and development model, changes in above-mentioned various conditions have made it possible to reduce land expropriation dependence, and the reduction typically occurs when GDP per capita exceeds 50,000 RMB. This paper provides a new perspective for understanding China's land-centered development story based on the Growth Machine Theory and generates policy implications for the land management practices in other developing countries.

This research explores the impact of subjective well-being (SWB) on CO2 emissions in Scandinavian countries based on the need to analyze the effects of social indicators shaping environmental sustainability. The study aims to introduce the Welfare-Based SWB approach to the literature by expanding the classical Environmental Kuznets Curve (EKC) hypothesis with the social welfare dimension. SWB is considered in this study to be a social reflection and indirect measure of economic growth, which is the core determinant in the classical EKC hypothesis. The second-generation cointegration test, the Durbin-Hausman, and AMG long-term forecasting methods were used in this context. According to the empirical findings, SWB and digitalization increase carbon emissions, while tourism revenues and renewable energy consumption reduce emissions. The findings indicate that the classical EKC hypothesis is not confirmed and, similarly, an increase in social welfare (SWB) does not automatically lead to environmental improvement. Therefore, policymakers should develop social policies that will increase environmental sensitivity along with an increase in welfare.

As ICT has emerged as a general-purpose technology, interest in fostering the industry has also grown in non-capital areas. Consequently, the growth rate of the regional ICT service industry has become an important indicator for regional industrial policy. However, when comparing industrial growth across regions, conventional indicators are affected by initial size, making it difficult to assess whether a region's growth is relatively higher than that of others. To address this limitation, this study proposes a size-free shift-share analysis (SSA) and applies the method to Korean ICT service data. The analysis confirms that, while the ICT service industry is indeed quantitatively concentrated in the capital region, such as Seoul, adjusting for initial size reveals that many non-capital regions have achieved ICT service growth surpassing that of the capital region.

China exhibits notable regional disparities, with inland cities generally trailing behind their coastal counterparts—a gap strongly linked to their distance to core ports. Drawing on data from 284 prefecture-level and higher cities, this study provides evidence that the digital economy significantly alleviates the geographical disadvantage faced by inland cities remote from core ports. Inland cities boasting a robust digital economy have narrowed the per capita GDP gap with coastal cities. Mechanistically, the digital economy weakens the correlation between trade costs and geographical distance, expands the import-export volumes of inland cities, attracts more foreign direct investment, and enables them to reap the benefits of agglomeration effects. This research provides a novel perspective for coordinating regional development in the digital era.

This article analyses regional economic resilience from different perspectives and examines its relationship with regional dynamics and disparities. The labor markets of the regions of Brazil, Chile and Colombia were analyzed in the context of one of the most significant global recessionary shocks for South America, the Asian crisis. The study uses cycle dating methodology and a dynamic algorithm for the endogenous identification of structural breaks. The least resilient regions, according to different approaches, were concentrated in Chile and Colombia. These regions experienced prolonged recovery phases, which in some cases led to structural breaks and amplified regional disparities.

This study analyzes two decades of land use changes in Cache County, Utah, focusing on the shift from agricultural land to urban areas using publicly available National Land Cover database, demographic and parcel datasets. Through GIS-based change detection, raster image analysis and integration of population and employment statistics, we reveal varying urbanization insights across selected municipalities. We introduced a development index, combining land transition, population growth and employment prospects, to assess the desirability of cities. Results highlight that housing demand from population increase does not hold a direct linear relationship to farmland conversion and supports urban densification. Smithfield was identified as the regional development hotspot in Cache County. The study highlights the need for proactive land use planning to balance growth with farmland preservation and demonstrates a replicable GIS-based approach for monitoring urbanization. Our findings offer valuable insights for regional urban planners and policymakers aiming for sustainable growth and informed land use decisions.

The study aims to forecast future urban growth and comprehend potential risks, with a particular focus on the dynamic nature of urban growth in medium-sized cities. By identifying factors affecting growth in urban areas, the SLEUTH model was utilized to analyze patterns of urban growth and associated changes in land use. Four scenarios were developed to anticipate urban development in Osmaniye, a medium-sized Turkish city, using the SLEUTH model for the year 2039. Scenarios S.1 and S.4 focus on the impacts of public investment on urban growth, while S.2 and S.3 examine the effects of urbanization on rural areas. Scenario S.3 also explores diverting urban development from high-risk seismic zones. S.1 poses the highest risk to agriculture (51% urbanization), while S.3 is the least threatening (37%). For forests, S.2 presents the highest risk (31%), but S.3 is the safest (25%). Overall, Scenario 3 provides the most effective approach for urbanization strategy, particularly for rural areas, protecting them from urbanization pressures and preserving geologically hazardous locations in Osmaniye. The study highlights how the SLEUTH model demonstrates the interaction between urban growth and spatial limitations, emphasizing the importance of understanding the consequences of urban growth for implementing effective zoning regulations.

As artificial intelligence (AI) becomes increasingly integrated into economic and societal domains, it emerges as a pivotal force driving the shift toward low-carbon energy systems. This study examines how AI impacts the transformation of urban energy systems by utilizing a panel dataset comprising 278 Chinese spanning the years 2010–2019. The findings confirm that AI significantly enhances energy transition performance in urban settings. By precisely optimizing the integration and consumption of renewable energy, driving the energy efficiency revolution, and breaking the dependence on high-carbon energy development models, as well as enhancing grid resilience and ensuring energy supply security to overcome the vulnerabilities of the energy transition, AI also strengthens the innovation capacity of energy transition through accelerating technological breakthroughs and incubating new business models. Heterogeneity analysis reveals that AI better facilitates energy transition in those cities that are small and medium in size, cities with a solid industrial base, cities with a high level of economic clustering, and cities located in central and eastern China. Mechanism tests show that during AI-enabled transition processes, green technology innovation, human–machine compatibility, and energy efficiency play significant roles. Further analysis using a threshold model reveals that as electronic commerce, human capital, and business growth increase, AI's marginal effects on energy transition exhibit an incremental trend. This implies that improving digital infrastructure, raising human capital levels, and boosting economic growth are pathways to realizing the transformative effects of AI. This study assesses AI technology's effectiveness in promoting energy sustainability and high-quality development goals.