Spatial Empirics for Economic Growth and Convergence

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PySAL: A Python Library of Spatial Analytical Methods (2010, methodology): “In PySAL, the spatial dynamic module implements spatialized versions of these three mobility indicators, including a spatial-τ statistic, spatial-Θ (Rey 2004), and spatial-Markov model (Rey 2001).”
Spatiotemporal dynamics of carbon intensity from energy consumption in China (2014, positive): “China has surpassed the United States and become the leading country in CO2 emissions in the world (IEA, 2009). Moreover, due to the long-term durative influence of economic growth and industrial transition, China’s CO2 emissions will increase unceasingly, leading to increasing diplomatic pressure i”
Dynamics, Space, and Regional Inequality in Provincial China: A Case Study of Guangdong Province (2012, methodology): “On the one hand, following a distribution dynamics model proposed by Quah (1993a, 1993b, 1996) and the spatial Markov chains developed by Rey (2001), we move beyond the traditional s- or b-convergence analysis to recognize the temporal and spatial dimensions of regional inequality in Guangdong.”
Spatial econometric analysis of China’s province-level industrial carbon productivity and its influencing factors (2016, methodology): “In order to further reveal the dynamic characteristics of the spatial distribution of industrial carbon productivity, we use the space–time transition measures proposed by Rey [32] to depict the evolution of the Moran’s I scatter plots.”