Sustainable urban development is a critical challenge for China, necessitating efficient resource allocation and utilization. This study focuses on understanding the spatial and temporal variations in urban comprehensive efficiency across 38 Chinese cities between 2015 and 2019. The research question centers on identifying the factors contributing to varying levels of efficiency among these cities and exploring the spatial patterns of efficiency. This is particularly relevant given China's rapid urbanization and the government's push for sustainable urban development through initiatives like the 'Urbanization Plan' and the 'Belt and Road' initiative. Understanding the efficiency of urban development helps optimize resource allocation, improve policy effectiveness, and promote sustainable growth. This research contributes to the literature by providing a comprehensive analysis of urban efficiency in China, considering both spatial and temporal dimensions, and offering policy recommendations for enhancing sustainable urban development. The study utilizes a robust methodology to provide a nuanced understanding of the complex interplay of factors affecting urban efficiency. The implications extend beyond China, providing insights applicable to other rapidly urbanizing nations grappling with similar challenges.

The study draws upon extensive literature on sustainable urban development, including research on sustainability assessment, multilevel governance, data envelopment analysis (DEA), and smart city initiatives. It reviews existing studies on measuring the efficiency of decision-making units (DMUs), incorporating learning effects into DEA, and assessing sustainability using multi-criteria decision-making. The literature review also examines research on China's new-type urbanization, the challenges it faces, and the role of government spending and green finance in promoting sustainable economic performance. This review provides a solid foundation for the study's methodology and interpretation of results, situating the research within the broader context of sustainable urban development theory and practice. Existing research on city sustainability indices and indicators is also reviewed to provide context for the chosen methodology.

The research employs data envelopment analysis (DEA) to measure the comprehensive efficiency of 38 Chinese cities from 2015 to 2019. DEA is a non-parametric technique used to assess the relative efficiency of multiple decision-making units (DMUs) with multiple inputs and outputs. The study likely uses a specific DEA model, such as the super-SBM model, to account for undesirable outputs and potential slacks. Input and output variables are likely drawn from the 'China City Statistical Yearbook,' encompassing indicators across economic, social, and environmental dimensions. The selection of these indicators is crucial and should be justified based on existing literature and policy relevance. The study likely uses a panel data approach, analyzing data across multiple years to account for temporal variations. Geographic information system (GIS) techniques are probably integrated to visually display the spatial distribution of efficiency scores. The study likely addresses potential issues associated with DEA, such as sensitivity to input-output weights and the possibility of outliers influencing the results. The methodology section should clearly describe the selection of input and output indicators, the DEA model used, and how the data were processed and analyzed. The justification for specific choices in the methodology is crucial to ensuring the validity and reliability of the results.

The key findings of the study likely include: (1) a consistent pattern of higher comprehensive efficiency in coastal cities compared to inland cities from 2015 to 2019. This is visualized using maps illustrating the spatial distribution of efficiency scores, with a color gradient representing varying levels of efficiency. (2) The identification of Changzhou and Jiaxing as consistently high-performing benchmark cities over the study period. This suggests they are highly effective at resource allocation. (3) The identification of cities, such as Shenzhen, that experienced a decline in efficiency ranking despite previous high performance. (4) The detection of significant inefficiencies in governance and ICT, coupled with challenges in financial structures. This might be quantified through DEA efficiency scores and further elaborated upon through statistical analysis (e.g., regression analysis). (5) Evidence suggesting potential over-employment within urban government bodies, hinting at potential inefficiencies in public sector management. These findings are likely supported by specific data points and statistical analyses.

The findings highlight the importance of geographical location in influencing urban efficiency, with coastal cities possessing advantages potentially related to trade, access to resources, and economic opportunities. The consistently high performance of Changzhou and Jiaxing underscores the significance of strategic policy choices and efficient governance. The decline in efficiency observed in certain cities points to the dynamic nature of urban development and the challenges of maintaining competitiveness. The identified inefficiencies in governance, ICT, and financial structures suggest that streamlining bureaucracy, restructuring financial expenditures, and enhancing governance efficiency are crucial for improving urban development outcomes. This supports a call for government intervention and policy adjustments to address these systemic issues. These findings resonate with existing literature on sustainable urban development, suggesting the need for holistic approaches that integrate economic, social, and environmental factors.

The study demonstrates that achieving sustainable urban development requires a coordinated approach across various sectors, including ICT, the economy, society, and the environment. While acknowledging the progress made by the Chinese government, the research emphasizes the continued need to address inefficiencies and promote sustainability. Governance, ICT, and sustainability are identified as pivotal elements for future urban planning and policy. Future research could explore the specific policy interventions that drive efficiency improvements in high-performing cities and develop more detailed models to understand the complex interactions between different factors affecting urban efficiency.

The study's limitations may include: (1) the specific selection of indicators which could be expanded to include a broader range of factors. (2) the reliance on data from the 'China City Statistical Yearbook,' which may have its own limitations in terms of data quality and completeness. (3) the potential for omitted variable bias in the DEA analysis, even if adjustments were made. (4) the focus on 38 cities, which may not fully represent the diversity of Chinese urban contexts. These limitations should be acknowledged and discussed in the context of the study's findings and their broader implications.