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The sustainable and integrated management of resources is a crucial approach to achieving sustainable regional development in the face of limited land and increasingly scarce water resources. This paper proposes a multidimensional optimization model to jointly optimize land use patterns and water resource allocation under the conditions of maximizing economic benefits and minimizing social differences, ecological loads, environmental pollution, and carbon emissions. The optimization model is solved using the affiliation function method and then combined with random forest (RF) and cellular automata (CA) models to further obtain the optimal spatial layout of land use. This paper takes the Mid-Yangtze River City Cluster as an example and optimizes the land use pattern and water allocation in the region in 2020 and 2030. The results of the study show that the optimized water and land resource allocation scheme performs better than the status quo in all dimensions. The optimized economic benefits, social effects, ecological effects, environmental effects, and overall sustainability in 2020 are increased by 45.97%, 22.89%, 10.07%, 24.65%, and 42.73%, respectively. By 2030, overall sustainability reaches 0.69. The developed modeling framework helps to elucidate the internal linkages between land use and water allocation, and it provides decision-makers with diversified approaches to land and water resource management and adjustment for different cities in different periods, which promotes the sustainable development of urban agglomerations. [Display omitted] • The intrinsic relationship between different land use types and water demand is addressed. • A sustainable approach to integrated management of land and water resources is proposed. • The synergistic optimization of the economy, society, ecology and the environment is developed. • The spatial patterns of land use for the balanced development of urban agglomerations are promoted. [ABSTRACT FROM AUTHOR]

Addressing the global urgency for improved sustainable cities and communities, as per the United Nations Sustainable Development Goal (SDG) 11, requires innovative and disruptive approaches, which also include applying artificial intelligence (AI). While AI holds significant potential to address complex socio-economic and environmental challenges in cities, a comprehensive analysis of its applications and implications, particularly in urban contexts, is required to address the research gap in understanding how AI can be effectively deployed to meet the challenges. This paper reports on a study that evaluates how AI may facilitate achieving SDG 11. This assessment includes an expert-driven literature review, drawing insights from authoritative sources. In addition, a set of case studies illustrate practical applications of AI to improve urban sustainability. The combination of these approaches led to findings that underscore the pivotal role of AI in optimizing energy use, streamlining waste management, enhancing traffic flow, and contributing to environmental sustainability. However, according to the findings, AI implementation needs oversight to ensure it is ethical, inclusive, and privacy-respecting as an effective tool to aid decision-making. By fostering collaboration among planners, policymakers, and AI experts, the full potential of AI may be unlocked to shape sustainable urban environments and realize SDG 11. • Artificial Intelligence may assist in implementing sustainable development. • Cities and communities can become more sustainable with the use of AI. • There is a research gap on using AI to promote sustainable cities. • Some AI technologies may be used in urban contexts. • Various social, and environmental issues and obstacles need to be overcome. [ABSTRACT FROM AUTHOR]

The urgent decline of global progress towards achieving the Sustainable Development Goals (SDGs) in the post-COVID-19 era necessitates immediate action in the field of smart cities. This paper proposes a new framework to assess the performance of global smart cities, emphasizing the vitality and potential of sustainability-oriented smartness for global smart cities, as well as the role of benchmarking cities on a global scale. The framework centered around the principles of "humanity-technology-sustainability" tackles critical challenges related to feasibility, interoperability, and flexibility in smart city assessments. By integrating academic perceptions with quantitative performance results, the proposed framework aims to facilitate the advancement of sustainable smart city through urban comparisons and the expansion of global visions within the smart city ecosystem in a post-pandemic future. In each of the past three years, twenty globally renowned cities for smart city development are assessed under this framework and published on UNPAN (United Nations Public Administration Network) website. These assessments aim to draw the attention of more cities to the factors that contribute to advancing smart cities under the new world development goals, to identify benchmark cities across different dimensions, and to assist decision-makers in prioritizing future smart city development initiatives. • SDGs call for urgent responses in the field of smart cities in the Post-pandemic era • The multivocal in sustainability makes the goal of sustainable smart city ambiguous • Bridge the gap between sustainability vision and sustainability goal in smart city • The new smart city assessment framework based on "humanity-technology-sustainability" • 20 global metropolia as smart city benchmarks are assessed under the framework [ABSTRACT FROM AUTHOR]

Enhancing the sustainability of large-scale infrastructure construction is the foundation and trend of urban sustainable development and environmental protection. This paper proposes a perception and evaluation method based on Dynamic Fuzzy Cognitive Mapping (D-FCM) to address the dynamics, uncertainties, and implementation difficulties of sustainability evaluation in large-scale infrastructure construction. Using Hebbian rules to learn the experience and knowledge of experts, a dynamic evolution correlation matrix about evaluation indicators is obtained, and a D-FCM dynamic system model is established to obtain the sustainability evaluation results of large-scale infrastructure construction. The comprehensive sustainable evaluation value (K) of the selected subway project case is 0.934, which belongs to the V level in the sustainable level classification standard and has a good trend in sustainable development. Social public participation (S1), light pollution prevention (H1), wastewater discharge compliance rate (H2), soil pollution prevention (H3), and soil and water conservation and utilization (Z2) are the most significant and sensitive indicators for the sustainable development of large-scale infrastructure construction. Based on the interaction of influencing factors in the D-FCM model, an economically efficient control measures and decision-making plan can be proposed to improve the sustainability of large-scale infrastructure construction projects. [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]