Decomposing main drivers of CO₂ emissions and predicting the trend of it are the key to promoting low-carbon development for coping with climate change based on controlling GHG emissions. Here, we decomposed six drivers of CO₂ emissions in Changxing County using the Logarithmic Mean Divisia Index (LMDI) method. We then constructed a model for CO₂ emissions prediction based on a revised version of the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model and used it to simulate energy-related CO₂ emissions in five scenarios. Results show that: (1) From 2010 to 2017, the economic output effect was a significant, direct, dominant, and long-term driver of increasing CO₂ emissions; (2) The STIRPAT model predicted that energy structure will be the decisive factor restricting total CO₂ emissions from 2018 to 2035; (3) Low-carbon development in the electric power sector is the best strategy for Changxing to achieve low-carbon development. Under the tested scenarios, Changxing will likely reach peak total CO₂ emissions (17.95 million tons) by 2030. Measures focused on optimizing the overall industrial structure, adjusting the internal industry sector, and optimizing the energy structure can help industry-oriented counties achieve targeted carbon reduction and control, while simultaneously achieving rapid economic development.

Freshwater is the lifeline of a city. Shortages in urban water supply and ecological losses occur when freshwater supply capacity and demand are imbalanced. Therefore, systematic research on the risk of freshwater consumption in urban areas is urgently demanded. A scientific understanding of the risk of urban water consumption will contribute to the efficient use of freshwater resources and ensure the stability and sustainable development of cities. Taking Xiamen City as the study area, we evaluated the ecological risk of freshwater consumption scenarios in the years 2020 and 2030 using a multilevel characterization method for urban ecological risk, stepwise regression analysis, and a gray prediction model. The results of our evaluation show that freshwater consumption in Xiamen is highly correlated with the total population, the crop acreage, the proportion of secondary industry, and the treatment rate of domestic sewage. In the 2020 and 2030 scenarios, freshwater consumption in Xiamen City is predicted to increase. Meanwhile, with the construction of water conservancy facilities, the supply capacity of freshwater in Xiamen City will be greatly improved. Therefore, the ecological impacts of freshwater consumption in the 2020 and 2030 scenarios were at the middle and low levels. In this study, the validity of the multilevel characterization method described herein for urban ecological risk has been confirmed. However, calculation of scenario probability is a difficult problem in the framework of this method, and future research should address this issue.

Context Ecological conservation and restoration (ECR) is a crucial pathway for protecting and rehabilitating terrestrial ecosystems, as highlighted in the United Nations Sustainable Development Goal 15 (SDG 15). However, existing studies often define restoration reference states based on historical states or adjacent natural areas, neglecting the upper limit of ecosystem development constrained by vegetation succession. In addition, restoration planning frequently overlooks cost–benefit trade-offs in ECR, which limits decision-making efficiency. Objectives This study proposed an ECR decision-making framework grounded in vegetation succession to identify ECR enhancement potential, determined the optimal ECR scale, and prioritized ECR areas. Methods We adopted Potential Natural Vegetation (PNV) as an ideal reference state. By comparing vegetation types and ecosystem services between the ideal and the current state, ECR potential areas were identified. We designed multi-level gradient scenarios across varying ECR proportions to identify the optimal implementation scale. Irreplaceability indicators were then introduced to spatially prioritize ECR areas. Results The empirical results for Fujian Triangle Urban Agglomeration indicate that approximately 60.45% of the study area has significant ecosystem service enhancement potential. When the ecological conservation and restoration target is set at 60%, ecological benefits can be maximized while effectively controlling restoration costs, with integrated ecosystem services increasing by 47.8%. Conclusions An ideal reference state defined by PNV facilitates a more objective identification of ecological conservation and restoration potential. Building on this, a unified analytical framework that integrates restoration scale optimization with spatial prioritization supports quantitative decision-making on both the spatial configuration and implementation scale of ECR.