High-quality guided development and environmental forcing are two routes for China’s green development, and each has different focuses and outcomes. The aim of this study is to clarify coordinated mechanisms to reveal the reasons and determine paths for China’s green development. A research framework for synergetic evolution is established, and the Haken model is applied to analyze the different effects of high-quality development that guide resource- and environmental-forcing mechanisms. This research showed that: (1) the preferred route and key factor in China’s green development is high-quality developmental guidance; (2) high-quality development and environmental-forcing mechanisms are non-coordinated, and while the former might coordinate with the latter, the latter does not; (3) mutual promotion of high-quality developmental guidance and the resource mechanism was not observed; (4) the critical point coordinated value is 0.5686 for China’s green development. Eastern China possesses a relatively high level of green development, while the north, east, west, and central areas of the country still have much progress to make.

Purpose: China’s national carbon market will be officially launched in 2020, when it will become the world’s largest carbon market. However, China’s carbon market is faced with various major challenges. One of the most important challenges is its impact on the social and economic development of arid and semi-arid regions. By simulating the carbon price trends under different economic development and energy consumption levels, this study aims to help the government can plan ahead to formulate various countermeasures to promote the integration of arid and semi-arid regions into the national carbon market. Design/methodology/approach: To achieve this goal, this paper builds a back propagation neural network model, takes the third phase of the European Union Emissions Trading System (EU ETS) as the research object and uses the mean impact value method to screen out the important driving variables of European Union Allowance (EUA) price, including economic development (Stoxx600, Stoxx50, FTSE, CAC40 and DAX), black energy (coal and Brent), clean energy (gas, PV Crystalox Solar and Nordex) and carbon price alternatives Certification Emission Reduction (CER). Finally, this paper sets up six scenarios by combining the above variables to simulate the impact of different economic development and energy consumption levels on carbon price trends. Findings: Under the control of the unchanged CER price level, economic development, black energy and clean energy development will all have a certain impact on the EUA price trends. When economic development, black energy consumption and clean energy development are on the rise, the EUA price level will increase. When the three types of variables show a downward trend, except for the sluggish development of clean energy, which will cause the EUA price to rise sharply, the EUA price trend will also decline accordingly in the remaining scenarios. Originality/value: On the one hand, this paper incorporates driving factors of carbon price into the construction of carbon price prediction system, which not only has higher prediction accuracy but also can simulate the long-term price trend. On the other hand, this paper uses scenario simulation to show the size, direction and duration of the impact of economic development, black energy consumption and clean energy development on carbon prices in a more intuitive way.

Unveiling the spatial and temporal distribution of urban ecological resilience and analyzing the configuration paths for enhancing its levels are crucial for promoting sustainable development in China. Our study integrates the DPSIR and EES models, considering the causal relationships between systems affecting urban ecological resilience while also examining their internal structures. Based on this, we construct an evaluation system for urban ecological resilience indicators. Utilizing the entropy-TOPSIS method, we assess the ecological resilience index (ERI) across 280 Chinese cities from 2011 to 2021, and the kernel density estimation and Markov chain are used to study the evolution process while the magnitude and source of spatial–regional differences are examined by the Dagum Gini coefficient decomposition method. Additionally, we empirically investigate the driving mechanisms toward high ERI with the focused stepwise quantitative case analysis (fsQCA) method based on the technology–organization–environment (TOE) framework. The results find that the ERI in China shows a tendency of moderate growth in variability, with an obvious gradient distribution: higher levels in the eastern and southern and lower levels in the western and northern regions. Also, ERI exhibits evolutionary features of increasing polarization and inter-regional differentiation. Spatial disparities gradually increase with fluctuations, driven primarily by transvariation density and intra-regional differences, contributing to a dual non-equilibrium state of east–west and north–south directions. Achieving a high ERI is influenced by various antecedent variables interacting with each other, and there are three predominant driving paths among these variables, with the level of informatization playing a central role in each pathway.

This study reports the results of farmers’ production via irrigation resources utilization and efficiency parameters of technical efficiency and allocative efficiency by way of sustainable environmental economics. The hypothesis is that factors of farmers’ production affect technical efficiency and allocative efficiency in the irrigation scheme as sustainable environmental economics. Data from cross section and panel data were used and then the productivity parameters measurement of the production function are outlined in two scenarios: first, the data report that the parameters such as output elasticity determine factors of inefficiency and technical efficiency. Second, it presents the scores for the allocative efficiency to explain whether production factors (resources) are optimally, under- or over-allocated by farmers in the irrigation systems under environmental sustainability. This paper presents the productivity and efficiency parameters estimated using stochastic frontier analysis for the translog production function, which was estimated by the MLE method, and the allocative efficiency for the factor inputs allocation in the irrigation systems estimated by ordinary least square for the Cobb-Douglas production function. This study concludes that collective farmers lead into technical inefficiency and over use of factors of production.