Performance assessment of low carbon cities (LCCs) attracts great interest and attention from academia, industry, and government as an effective measure to promote urban low-carbon development. However, the efforts of LCCs could be misinterpreted without consideration of the fact that cities with different endowments face different challenges and assume different responsibilities, thus it is important that this fact is considered in assessing LCCs’ performance. This study develops the previous study by the research team, “dual perspective diagnosis method for assessing LCC performance”, by introducing a correction factor to take into account the impacts of local endowments and mitigate the ‘one-size-fits-all’ phenomenon when comparing LCC assessments between cities. The empirical case study presented in the paper indicates that the adoption of a correction factor has improved the accuracy of the assessment results by demonstrating actual management efforts in developing LCC performance. It is proposed that applying the correction factor can help to achieve more accurate assessments of the status of low-carbon city practice, based on which more effective low-carbon policies can be designed and implemented towards achieving carbon peaking and carbon neutrality goals.

Mitigating methane (CH₄) emissions from paddy fields without compromising yield remains a critical challenge for sustainable agriculture, primarily due to trade-offs between emission reduction and nutrient availability. Biochar-based fertilizer (BF), which integrates biochar with mineral nutrients, may address this constraint by regulating carbon and nitrogen availability. This study evaluated the effects of biochar-based fertilization on CH₄ emissions and rice productivity. A one-season field experiment was conducted in Hydragric Anthrosols with four treatments: no fertilization (control), conventional chemical fertilization (CF), integrated application of biochar-based and chemical fertilizers (CBF), and biochar-based fertilizer alone (BF). Methane fluxes were monitored alongside soil physicochemical properties and the abundances of key functional microbial genes. Compared with CF, BF significantly reduced cumulative CH₄ emissions by 23.24% while maintaining comparable rice yield, resulting in a 28.67% reduction in yield-scaled CH₄ emissions. Biochar-based fertilization decreased dissolved organic carbon availability, buffered NH₄⁺-N concentrations, and increased soil pH. These changes shifted the microbial balance of CH₄ cycling, suppressing methanogenesis and enhancing methane oxidation, as reflected by a lower mcrA/pmoA ratio. Structural equation modeling indicated that CH₄ mitigation was jointly driven by reduced methanogenic activity and enhanced methane oxidation. Overall, biochar-based fertilization regulates soil chemical and microbial processes to mitigate CH₄ emissions without yield penalties, demonstrating strong potential as a scalable and field-applicable strategy for low-carbon rice production.

Urban green spaces are vital carbon sinks and play a central role in low-carbon city development. However, traditional evaluations often focus on outcome indicators such as green space area and greening rate, neglecting the governance performance within the construction process. To bridge this gap, we introduce a five-process PDCOA (Plan–Do–Check–Outcome–Act) framework, applying Python-based web scraping and automated text mining to conduct an integrated assessment of 296 prefecture-level cities in China. Our results reveal a national average score of 44.10, with scores ranging from 10.42 to 92.81 (Beijing). Significant regional disparities emerge, with East China (mean score: 50.08) leading the nation, while regions like Southern China (38.25) lag considerably. The process evaluation uncovers a systemic imbalance: while cities perform relatively well in the P (59.81) and D (57.60) processes, they falter in the C (38.94), A (36.10) and O (31.21) processes. This study demonstrates that effective governance, not resource endowment, determines success in low-carbon green space development. It underscores the urgent need to shift from outcome-only metrics to process-driven continuous improvement, prioritizing adaptive feedback mechanisms and differentiated spatial strategies to strengthen urban green space governance.

The transition to low-carbon urban environments is a cornerstone of global sustainability efforts. However, evaluating the diverse management efforts driving this transition is frequently hindered by fragmented and heterogeneous data. This study introduces a novel Retrieval-Augmented Generation (RAG) framework to systematically evaluate urban low-carbon governance. RAG represents a significant methodological innovation, integrating advanced information retrieval with generative artificial intelligence to provide a transparent and evidence-based assessment of policy implementation. The proposed framework is applied to evaluate the low-carbon energy practices of 296 Chinese cities. A critical finding is the systemic neglect of the “Check” phase across all urban tiers, posing a significant challenge to the long-term sustainability of carbon reduction initiatives. Conversely, high-performing cities are characterized by robust “Feedback” mechanisms. Policy priorities also vary significantly with city scale: larger cities emphasize strategic development and low-carbon transitions, while smaller cities focus on foundational planning and ecological preservation. This framework serves as a transparent, process-oriented tool for evidence-based low-carbon governance, thereby facilitating more resilient and sustainable urban pathways.

Improving urban land use efficiency is a critical pathway toward sustainable urban development, particularly in large countries undergoing rapid urbanization such as China. However, significant disparities in land use efficiency exist across cities, largely due to differences in economic development, resource endowments, and governance practices. These disparities highlight the necessity of conducting a systematic spatiotemporal assessment of land use mismatch at the city level to identify regional weaknesses and inform differentiated policy mechanisms. This study extends the land use mismatch (LUM) model, which introduces a supply–demand framework for analyzing the mismatch phenomenon of urban land use. Building on the LUM model, this study innovatively develops a classification system of five mismatch zones across eight construction land types, which provides a more systematic and comprehensive approach to identifying land use mismatch patterns. The empirical analysis is conducted using data from 283 prefecture-level cities in China. The results reveal substantial spatial heterogeneity in land use mismatch across Chinese cities. Most of the cities in East China generally fall within acceptable mismatch zones, where market mechanisms play a more effective role in land allocation. Cities in Western China exhibit more serious mismatch levels, where policy intervention seems more significant in land use planning. Cities in Central China demonstrate mixed patterns, ranging from acceptable to severe mismatch. The findings further indicate that these disparities are associated not only with economic and geographical differences but also with variations in governance practices, particularly the interaction between policy intervention and market mechanisms. This study introduces a new approach to examining the patterns of land use mismatch and provides evidence-based policy recommendations for cities in different regions to reduce land mismatch and promote more efficient use of urban land.

Global climate change poses escalating ecological challenges, with agriculture contributing approximately 30% of anthropogenic greenhouse gas emissions, primarily from nitrous oxide (N₂O) and methane (CH₄). The farmland carbon-nitrogen cycle represents a key nexus for coordinating pollution control and carbon mitigation. This study applies bibliometric methods, including co-occurrence analysis, clustering, and burst detection, to 1286 publications retrieved from the Web of Science Core Collection (1990–2025) and CiteSpace 6.2.R4. Results indicate that China (444 papers, centrality 0.42), the United States (211 papers), and Germany (151 papers) are leading contributors, with major institutions forming a multi-centered international collaboration network. Keyword analysis identified 11 core clusters (modularity Q = 0.82, silhouette S = 0.91), with nitrous oxide emerging as the central theme (frequency 670). The field has evolved through three stages: fundamental emission mechanism studies (1990–2005), agricultural management practices (2006–2015), and integrated mitigation strategies with microbial mechanism exploration (2016–2025). Current frontiers emphasize microbial-mediated carbon-nitrogen cycling and yield-scaled emission assessments bridging theory and practice. Future research should prioritize cross-scale coupling analysis, multi-objective management frameworks, smart agricultural technologies, and policy integration. This study provides a systematic bibliometric mapping of the evolution of synergistic carbon-nitrogen research in agricultural systems, offering a quantitative overview of development trends and research gaps.