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.

Economic decarbonization has gained attention as a viable way to achieve carbon neutrality without sacrificing high-quality economic development. Regional integration (RI), technological innovation (TI), and the digital economy (DIE) are important decarbonization driving forces. To analyze how they impact carbon reduction and the evolutionary patterns observed in urban development, this study focuses on the urban agglomeration of the Yangtze River Delta (YRD) and uses the spatial Durbin model (SDM) to decompose the carbon reduction effects into spatial spillover effects and short-term and long-term effects. The research findings include the following: (1) The decarbonization driving forces have promoted economic decarbonization in the YRD and exhibit distinct evolutionary patterns. (2) The decarbonization effects exhibit spatiotemporal heterogeneity depending on the economic situation, period, and energy status of the cities. Over time, TI and DIE demonstrate better decarbonization effects. Economically developed cities are more likely to expand their carbon reduction advantages, while cities with dense energy supplies struggle to achieve their full carbon reduction potential due to traditional energy structures. (3) Industrial structure upgrading (UIS) is a critical pathway through which TI and DIE reduce carbon emissions. These findings enrich existing research on decarbonization drivers and offer valuable insights for governments to formulate further policies for economic decarbonization.

As regional integration accelerates globally, green development has emerged as a pivotal imperative for reconciling economic growth with environmental sustainability. This study employs a Difference-in-Differences framework incorporating city and year fixed effects to examine the impact of regional integration on green development efficiency in China’s Yangtze River Delta. The empirical findings reveal that regional integration significantly undermines green development efficiency, a conclusion corroborated by rigorous robustness checks including parallel trends and placebo tests. Mechanism analysis demonstrates that trade openness and digital economy development function as partial mediating channels that modestly attenuate the direct adverse effect of regional integration, whereas the decline in secondary industry agglomeration amplifies the negative impact. Notably, innovation capability has yet to fully unlock its potential for green transformation, it intensifies the negative effects of regional integration across all three mediating mechanisms. Building on these findings, this study proposes policy recommendations including strengthening multi-level green governance frameworks, integrating ecological compensation and carbon trading systems, advancing low-carbon trade structures, promoting the synergistic development of digitalization and green transformation, facilitating the green transition of secondary industries, and reinforcing green technology innovation. These insights provide empirical evidence and policy references for achieving coherence between regional integration and sustainable development objectives.