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To examine the effects of historical land-use change and static land-use state on the modeling, we established three cellular automata (CA) models using the spatial autoregressive model (SAR). The models are CASAR-Cha based on the change data, CASAR-Sta based on the start-state data, and CASAR-End based on the end-state data. The models that considered five different neighborhood sizes (from 3 × 3 to 11 × 11) were applied to simulate the urban growth of Jiaxing, China from 2008 to 2018, and predict the urban scenario to the year 2048. All three models can accurately reproduce the urban growth from 2008 to 2018, and the CASAR-End model performed best in calibration and validation. The differences in historical land data did affect the spatial distribution of the simulated urban patterns. The neighborhood size has a significant impact on the model's allocation ability, yet the appropriate size depends on the unique landscape context being studied. [ABSTRACT FROM AUTHOR]

The central question this paper asks is: How can we understand the evolution of markets for water pollution allowances? To answer, we have conducted an in-depth study of the development of water pollution allowance markets in Jiaxing, China, which was one of the first cities to experiment with these markets. We have completed a longitudinal analysis and conclude that market design is a continual learning process, with adjustments to solve issues and to respond to external changes. [ABSTRACT FROM AUTHOR]