The scale effect of urbanization on improving carbon emission efficiency and achieving low-carbon targets is an important topic in urban research. Using dynamic panel data from 64 prefecture-level cities in four typical urban agglomerations in China from 2006 to 2016, this paper constructed a stochastic frontier analysis model to empirically measure the city-level total-factor carbon emission efficiency index (TCEI) at different stages of urbanization and to identify rules governing its spatiotemporal evolution. We quantitatively analyzed the influences and functional characteristics of TCEI in the four urban agglomerations of Pearl River Delta, Beijing-Tianjin-Hebei, the Yangtze River Delta, and Chengdu-Chongqing. Results show that the TCEI at different stages of urbanization in these urban agglomerations is increasing year by year. The overall city-level TCEI was ranked as follows: Pearl River Delta > Beijing-Tianjin-Hebei > Yangtze River Delta > Chengdu-Chongqing. Improvements in the level of economic development and urbanization will help achieve low-carbon development in a given urban agglomeration. The optimization of industrial structure and improvement of ecological environment will help curb carbon emissions. This paper provides decision-making references for regional carbon emission reduction from optimizing industrial and energy consumption structures and improving energy efficiency.

Ecological sustainability is treated as a main reflection of the synergy among social development, economic growth and environmental protection, while ecological efficiency is an index used to reflect the sustainable development of the ecological environment. The super efficiency model with undesirable outputs (SE-SBM) model was applied to measure the eco-efficiency of the 12 counties in the Minjiang-Source, China, in 2005–2017. The spatial and temporal evolution and spatial differentiation of the eco-efficiency were analyzed. The results showed that the eco-efficiency of 67.7% of the 12 counties remains at a low level but with an increasing trend. A typical spatial aggregation effect was found with the characteristics of “high in the east and low in the west”. The Malmquist-Luenberger index shows a trend of fluctuation with the same trend between scale efficiency and technical efficiency changes. The results proved the positive effect of technological progress on local eco-efficiency. Environmental regulation has a positive impact on eco-efficiency in the short term and an inhibition effect in the long run. Hence, technological innovation and industrial adjustment will be an effective way to improve the eco-efficiency of the Minjiang-Source and promote its sustainability.

This study adopts the contingent valuation method (CVM), Logit model, and quantile regression to evaluate the compensation level of public welfare forests and identify the influencing factors of compensation standards from the perspective of forest farmers’ willingness to accept compensation. The results show that the forest farmers’ expected compensation standard ranges from 1034 to 1128 CNY·ha⁻¹·year⁻¹, more than three times the current official compensation standard. Based on forest farmers’ willingness to accept, the current forest ecological compensation level only reaches 30.6%–33.4% of the expected standard, which is insufficient to mobilize the enthusiasm of forest farmers for forest management and protection. Therefore, it is necessary to further raise the public welfare forest compensation standard and implement diversified compensation measures that adapt to the differentiated demands of forest farmers. In addition, analysis of zero-willingness samples reveals that poor policy cognition is the main reason for zero compensation bids among partial respondents, rather than the overall sample. A better understanding of ecological compensation policies can significantly promote farmers’ willingness to accept compensation, suggesting that targeted policy publicity should be strengthened to improve forest protection awareness and policy satisfaction among forest farmers. Furthermore, household age, annual household income, forestland area, forest management cost, and policy understanding degree are core factors affecting compensation willingness. Quantile regression results indicate that these influencing factors show heterogeneous effects at different compensation levels, which provides evidence for the formulation of differentiated compensation schemes. The robustness test and heterogeneity analysis further confirm the reliability of the research conclusions. This study provides a practical reference for governments to optimize forest ecological compensation policies, balance household welfare, and promote sustainable forest resource protection.