China has seen a transition from impulsive industrial development-driven urban expansion to urban planning and government control since the start of the 21st century. Urban planning attempts to ensure the sustainable development of cities and the rational use of land resources. The dual-track urbanization process does, however, often result in a significant gap between planning and actual development. This paper contends that a comprehensive dual-track urbanization research perspective is better suited for investigating urban sprawl in peri-urban areas in post-reform market economy-active regions. Taking Xiaoshan District, the peri-urban area of Hangzhou, China, as a case study, this research utilizes land use raster data from 1985, 2000, 2010, and 2020 to identify the dynamic changes in urban construction land and examines the driving elements through the logistic regression model. Based on the results, the mechanisms of market-incented and government-controlled impacts on urban expansion are explained. This study aims to (1) analyze the spatial and temporal patterns of Xiaoshan’s urban expansion during 1985–2020; (2) compare the compositional characteristics of three types of urban growth (edge growth, infilling, and leapfrog development) in each phase of Xiaoshan; (3) identify the natural environment, accessibility factors, and socioeconomic aspects that promote Xiaoshan’s urban expansion in 2000–2020; (4) discuss Xiaoshan’s dual-track urbanization from four perspectives: regional development, private business, government control, and major events. This study advanced our knowledge of the driving mechanisms of urban land expansion in small towns, from peri-urban areas to metropolitan sub-centers, and, thus, has important implications for Chinese urban planning and the management of metropolitan suburbs, especially those located in the Yangtze River Delta.

Chinese cities have made significant progress in fostering low-carbon societies and piloting a variety of low-carbon measures. Nonetheless, the effective implementation of these initiatives and the long-term upkeep of related amenities rely heavily on resident support. The existing studies provide limited insight into how local residents perceive and endorse different types of low-carbon measures, which often involve varying trade-offs. Addressing this gap, the present study surveyed the willingness to pay (WTP) of residents in Hangzhou—an early adopter of low-carbon practices in China—across five representative low-carbon measures. Survey data were collected from 13 distinct residential neighborhoods. The results indicate that Hangzhou residents are more inclined to financially support measures offering direct personal benefits compared to those benefiting the collective good, with this tendency being notably pronounced among highly educated individuals. Further findings include the following: (1) respondents aware of ongoing low-carbon measures were more willing to pay for them; (2) male respondents, recent migrants (within the past five years), high-income groups, and residents in aging communities tended to contribute higher amounts; (3) providing detailed information on carbon mitigation effects markedly increased both the likelihood and the magnitude of WTP; (4) the promotion of new energy vehicles (NEVs) remains contentious, particularly between NEV owners and gasoline vehicle owners. These findings highlight the need for targeted policies and educational programs to strengthen public awareness and support for low-carbon interventions, thereby advancing sustainability in fast-growing urban centers like Hangzhou. Overall, these findings provide key insights for the formulation of low-carbon city policies and sustainable urban planning, emphasizing the global importance of local socioeconomic dynamics and offering a valuable reference for cities worldwide seeking to advance sustainability transitions and meet international climate targets.

The journal retracts the article titled “Comparative Analysis of Residents’ Willingness to Pay for Diverse Low-Carbon Measures in Hangzhou, China: Implications for Urban Sustainability and Policy”

Traditional dwellings in southern Xinjiang, exemplified by the Suohema House, have evolved as adaptive responses to the region’s cold and arid climatic conditions, providing thermally comfortable living environments with relatively low energy consumption. Learning from these climate-responsive design strategies offers an effective approach to reconciling the conflict between energy efficiency and indoor comfort. Such exploration is of great significance for preserving regional architectural identity and promoting the development of low-carbon buildings. This study establishes a performance-driven morphological multi-objective optimization framework for traditional dwellings, taking building energy consumption, thermal comfort, and indoor temperature as the primary optimization objectives. The framework integrates parametric modeling, performance simulation, and multi-objective optimization within the Rhino & Grasshopper platform, employing a genetic algorithm to achieve performance-oriented design exploration. Key design variables were identified through data analysis, and the influence weights and prioritization of morphological parameters were quantified. The results reveal that the room depth in residential dwellings (4.57–4.73 m), room width (3.97–6.75 m), room clear height (2.33–2.42 m), wall thickness (lower wall thickness ranging from 1.14 to 1.22 m, upper wall thickness at 0.76 m), and building orientation (true south) have significant impacts on both energy consumption and indoor thermal performance. Based on these findings, adaptive optimization strategies were proposed from three perspectives: scale optimization, spatial hierarchy refinement, and enhancing the performance of building envelopes. The proposed framework provides methodological guidance for the conservation and adaptive renewal of traditional dwellings, as well as for the design of new, green, and low-carbon residential buildings suited to the climatic conditions of southern Xinjiang.