The coordinated development of resource–environment–economy is the strategic choice to realize sustainable development. To explore the historical pattern of the coordinated development of resource–environment–economy, showing the logic of the spatiotemporal evolution of the system in China, this paper conducts a measurement study. Based on the actual data of 30 provinces in China from 2005 to 2019, the paper constructs an evaluation index system for the coordinated development of resource–environment–economy and establishes a coupling coordination degree (CCD) model and a spatial autocorrelation analysis model. The results show that the mean value of the coupled coordination of the three systems (resource–environment–economy) gradually increased from the stage of near dissonance (0.479) in 2005 to the stage of good coordination (0.853) in 2019. The global Moran’s I was 0.349, indicating that there is a certain spatial aggregation of resource–environment–economy at the province level. Coastal areas have a higher degree, while inland areas have a lower degree. In the spatial correlation analysis, the resource–environment–economy coupling coordination degree of 30 provinces in China is significantly positively correlated. Low–low clusters are found mainly in the Northwest (e.g., Xinjiang, Qinghai). Furthermore, the findings provide some targeted international recommendations. Relevant policies should encourage sustainable development and promote green transformation of industrial structure.

Risk is demonstrated as one of the most crucial drivers of value for money (VFM) in public–private partnerships (PPP), but in previous studies, the risk cost estimation of the quantitative evaluation of VFM was still a dilemma that strongly depended on specialist discretion or had low methodological operability. This paper establishes a prediction model for estimating the risk cost in the phase of VFM evaluation through a combination of case-based reasoning (CBR) and ontology technology. PPP information ontology was established to provide the technical basis of knowledge representation for the CBR cycle. Then, according to whether the information data were quantitative or qualitative, similarity calculation methods were used for the retrieval of similar cases. The conceptual semantic similarity algorithm based on the ontology tree structure was well implemented to compare abstract information. After the most similar cases were extracted, a revision mechanism was followed when there were deviations in the similar cases. Finally, the risk costs of the target case were obtained by weighting the extracted similar cases based on the similarity. An empirical analysis was performed with 18 historical projects from the China Public–Private Partnerships Center. The results showed that the relative errors between the estimated and actual costs of total risk and retained risk were 11.05% and 2.41%, respectively. This indicates that the estimation model could achieve a better risk cost prediction with small errors, which validates the availability of the model. Based on the proposed model, this research establishes an extensible PPP information ontology model. It promotes the integration and interoperability of information knowledge in the PPP domain, which can be further expanded according to the requirements. Coherent accuracy is provided by the whole CBR-based measurement process, which has offered a systematic and objective method for the risk costs measurement of PPP projects.

The successful implementation of the national carbon emissions reduction work necessitates the collaboration of various regions. Carbon emission reduction strategies need to be adjusted according to local circumstances due to the differences in regional development levels. From 2005 to 2020, carbon emissions were measured in Guizhou Province, and the contribution degree and action direction of various influencing factors were analyzed using the LMDI model. Using an SD model, we performed dynamic simulations of carbon emission trends under eight scenarios and calculated the Tapio decoupling relationship between economic growth and CO₂ emissions. According to the study, carbon emissions in Guizhou Province increased from 2005 to 2020, emphasizing the high pressure for carbon emission reduction. The industry sector ranked first in contribution, contributing 62.71% in 2020. Furthermore, this study found a weak decoupling relationship between economic growth and carbon emissions. The economic scale was the key driver driving the increase in carbon emissions, whereas the industrial fossil energy intensity was the main factor inhibiting the growth of carbon emissions. Additionally, it was predicted that carbon emissions would only peak at 277.71 million tons before 2030 if all three measures were implemented simultaneously, and a strong decoupling relationship with economic growth could be achieved as early as possible. These findings provided Guizhou Province with an effective path for reducing carbon emissions.

Nowadays, the development of a metropolitan area has become one of the milestones for regions to move towards a high level of urbanization and has been elevated to a national strategy. In this paper, the economic and social development level of the four major metropolitan areas in the Yangtze River Delta region from 2005 to 2020 was dynamically evaluated by constructing an evaluation index system, which is based on a new development concept of China, and applying the “vertical and horizontal” evaluation method and model. Meanwhile, in order to reflect the core development concept of “leading small cities with large ones and coordinating development”, this study incorporates the spatial and temporal economic connectivity between the central city and the surrounding small and medium sized member cities, as well as the industrial structure rationality and synergy into the index system, and applies the gravity model, the Thiel index and the industrial structure similarity coefficient to measure them, respectively. The study shows that the economic and social development of the four metropolitan areas is extremely uneven, with each area having certain shortcomings, and the development level of each area fluctuates significantly in terms of economic connectivity and industrial structure. Finally, relevant suggestions are put forward according to the shortcomings to provide reference for the future development direction of the metropolitan area, which is of great practical significance to promote the high-quality development of the regional economy.

In this paper, we propose a speaker recognition system that leverages multi-task learning and features integration (MTFI), to improve the performance of x-vector based speaker recognition models. It is important to integrate complementary information from different features such as MFCC, Fbank, spectrogram and LPCC, as often a single feature usually cannot cover all information about a speaker and generalization is insufficient. Since the x-vector model outputs affine transformation values with the penultimate hidden layer in the trained model, the parameter distribution of this layer should be stable and should not be affected by tasks that are not current branches when switching tasks. Therefore, we propose a shared unit (SU) in multi-task learning, which is useful for sharing common representations and other auxiliary tasks. Then, an attention mechanism is designed to calculate the frame weight in the statistical pooling layer, so as to enhance the key frame information. The proposed system had an EER of 0.98% in voxceleb1 and the average score fusion obtained the EER of 0.65%.

Against the dual strategic backdrop of carbon peaking and carbon neutrality goals and high-quality urbanization development, extant literature exhibits four prominent research gaps: oversimplified evaluation indicator systems, exclusive exploration of the unidirectional carbon impacts exerted by urbanization, a scarcity of long-time-series coupling analyses targeting the Yangtze River Economic Belt (YEB), and functional fragmentation between coupling coordination assessment and predictive simulation tools. Drawing on panel data covering 11 provinces and municipalities within the YEB spanning 2000 to 2021, this study constructs a comprehensive urbanization evaluation framework encompassing four dimensions: population, economy, society, and spatial layout. Meanwhile, an integrated carbon emission assessment system is established from the perspectives of population, economy, energy consumption, and carbon sinks. The entropy-weight method is adopted to assign indicator weights, and a combination of the coupling coordination degree model and system dynamics (SD) model is employed to analyze spatiotemporal evolutionary characteristics and simulate development trends from 2022 to 2032. By organically integrating the coupling coordination model and the SD model, this study establishes an integrated analytical framework that unifies static comprehensive evaluation and driving-mechanism decomposition, thereby compensating for the limitations of time-series forecasting models such as the grey prediction model and ARIMA, which only fit trends from historical data. Empirical results reveal that regional urbanization levels witnessed sustained growth across 2000–2021, with spatial urbanization acting as the core driving pillar. The overall coupling coordination degree maintained a steady upward trajectory, while the east–west regional disparity gradually narrowed. The simulation projections for 2022–2032 demonstrate continuous improvements in coordinated development across the entire basin: the coupling coordination degree ranges from 0.788 to 0.954 for the eastern region, 0.810 to 0.859 for the central region, and 0.752 to 0.865 for the western region. Such spatial differentiation corresponds to distinct practical development pathways: low-carbon stock optimization in the east, low-carbon industrial undertaking in the central zone, and clean energy transition acceleration in the west. All provincial-level administrative regions are projected to achieve an upgrade in their coupling coordination grades by 2032. This study acknowledges several limitations: missing raw data are supplemented via interpolation, only a single baseline scenario is simulated, predictive uncertainty is not quantitatively measured, and subjectivity persists in the weight assignment of coupling subsystems. Ultimately, differentiated low-carbon urbanization governance strategies are proposed for the three sub-regions, offering empirical references for the coordinated realization of dual carbon targets throughout the Yangtze River basin.