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With the establishment of carbon peaking and carbon neutrality goals in China, developing low-carbon cities has become crucial. Previous studies primarily focus on the quantitative structure of land use to achieve low carbon optimization, while ignoring the spatial integration between urban form and carbon emissions. To fulfill this gap, this paper proposed a dual-scale low carbon oriented multi-objective land use allocation (DOLA) optimization model, utilizing idea-point multi-objective linear programming (IMLP) and non-dominated sorting genetic algorithm II (NSGA-II) to optimize the low-carbon-emission pattern of the city at both the structural and spatial scales respectively. By innovatively linking the urban shape index with carbon emissions, this model aims to minimize urban carbon emissions, while maximizing economic development, urban development suitability, and the compactness of city. The proposed method is applied to Wuhan's main urban area in China, the DOLA model shows significant improvements in target values across all scenarios. Among them, the scenario that prioritized low carbon emissions at both the structural and spatial scales achieved the most significant reduction of 6.43 % in carbon emissions. The DOLA effectively contributes to achieving multiple spatial targets, thereby aiding in reducing carbon emissions. It offers technical support for developing pathways towards China's "dual carbon" target. • A dual-scale low carbon oriented multi-objective land use allocation (DOLA) optimization model is proposed. • The DOLA model innovatively incorporates urban form indices to carbon emissions. • The DOLA model achieves spatial aggregation targets, thereby facilitating significant reductions in carbon emissions. • The DOLA model generates a series of land use optimization scenarios, offering a valuable reference for land use planning. [ABSTRACT FROM AUTHOR]

This study explores the impact of political uncertainty on sustainable urban development by examining carbon emission trading systems (ETS) in four major markets in China (Beijing, Shanghai, Guangdong, and Hubei) from 2014 to 2022. As an alternative to carbon taxes, carbon ETS markets have become increasingly popular due to their success in reducing greenhouse gas emissions. However, their effectiveness is often hindered by political instability and uncertainty. Utilising logistic regression and AR(1)-GARCH estimations, we identify a negative relationship between political uncertainty and carbon trading volume. Our study also reveals significant variations in the responses of these markets to political uncertainty. The paper contributes to the understanding of how ETS markets operate in a complex and constantly changing political environment. We suggest that policymakers need to consider the impact of political uncertainty on carbon trading when designing and implementing urban policies that promote sustainable development. Additionally, our research contributes to the development of urban policies that can be effectively implemented in both developed and developing regions. • Local government official turnovers are used as the measurement of political uncertainty. • Daily transaction data from four carbon ETS markets (Beijing, Shanghai, Hubei and Guangdong) in China • Considered the probability and proportion of zero transaction day • Logistic regression and AR(1)-GARCH estimations • A negative relationship between political uncertainty and carbon transaction volume [ABSTRACT FROM AUTHOR]