Enhancing agricultural carbon emission efficiency (ACEE) is a pivotal pathway for advancing environmentally sustainable transformation in agriculture and achieving the ‘dual carbon’ targets. However, empirical evidence on whether and how new-quality productive forces in agriculture contribute to improvements in ACEE remains limited. Using a super-efficiency slack-based measure (SBM) model, this study estimates the performance of carbon emissions from agricultural activities for 30 Chinese provinces from 2011 to 2022. Based on provincial panel data, fixed-effects, mediation, and threshold-effect models are combined within a panel data framework to investigate the impact of agricultural new-quality productive forces (ANQPF) on ACEE and its underlying mechanisms. The results indicate that: (1) ANQPF exerts a significant positive effect on ACEE. (2) Land transfer and the level of agricultural socialized services serve as important transmission channels through which ANQPF improves ACEE. (3) The relationship between ANQPF and ACEE exhibits a pronounced threshold effect. Below the threshold, the positive impact is significantly strengthened; however, as population density increases, the marginal effect of ANQPF on ACEE gradually declines. (4) Heterogeneity analysis indicates that the enhancing effect of ANQPF on ACEE is more pronounced in non-resource-based provinces and major grain-producing regions. In light of these findings, the results suggest that ANQPF should be positioned as a core policy lever. Simultaneously, land transfer should be optimized, the development of agricultural socialized services should be strengthened, and region-specific policies should be formulated to achieve synergistic outcomes in agricultural carbon reduction, efficiency improvement, and green growth.

Estimating the impacts of climate change on streamflow in the Xiaoxingkai Lake Basin is vital for ensuring sustainable water resource management and transboundary cooperation across the entire Xingkai Lake Basin, a transboundary lake system shared between China and Russia. In this study, 11 Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under two Shared Socioeconomic Pathways (SSP245 and SSP585) were used to drive the Soil and Water Assessment Tool Plus (SWAT+) model. Streamflow projections were made for two future periods: the 2040s (2021–2060) and the 2080s (2061–2100). To correct for systematic biases in the GCM outputs, we applied the Delta Change method, which significantly reduced root mean square error (RMSE) in both precipitation and temperature by 3–35%, thereby improving the accuracy of SWAT+ simulations. To better capture inter-model variability and enhance the robustness of streamflow projections, we used the Bayesian Model Averaging (BMA) technique to generate a weighted ensemble, which outperformed the simple arithmetic mean by reducing uncertainty across models. Our results indicated that under SSP245, greater increases were projected in annual streamflow as well as in wet and normal-flow seasons (e.g., streamflow in normal-flow season in the 2080s increased by 13.0% under SSP245, compared to 7.0% under SSP585). However, SSP585 produced a much larger relative amplification in the dry season, with percentage changes relative to the historical baseline reaching up to +171.7% in the 2080s, although the corresponding absolute increases remained limited due to the low baseline flow. These findings quantify climate-driven hydrological changes in a cool temperate lake basin by integrating climate projections, hydrological modeling, and ensemble techniques, and highlight their implications for understanding hydrological sustainability under future climate scenarios, providing a critical scientific foundation for developing adaptive, cross-border water management strategies, and for further studies on water resource resilience in transboundary basins.

Digital technologies are increasingly integrated into agricultural production, rural services, finance, and information infrastructure, creating opportunities to improve low-carbon agricultural performance. This study develops an applied evaluation framework linking rural digital technology systems with agricultural carbon emission efficiency. Using panel data from 30 Chinese provinces during 2011–2023, we construct a digital village technology index covering digital infrastructure, digital financial infrastructure, digital service platforms, and digital living scenarios. Agricultural carbon emission efficiency is measured using a super-efficiency slacks-based measure (SBM) model incorporating inputs, desirable output, and undesirable carbon emissions. Two-way fixed-effects models, instrumental-variable estimation, mediation tests, moderation analysis, and heterogeneity tests are then employed. The results show that rural digital technology systems are positively associated with agricultural carbon emission efficiency. Agricultural technological innovation and agricultural socialized services are examined as potential channels, and fiscal support for agriculture is considered as an institutional condition that may shape the DVC–ACEE relationship. The subgroup results suggest that this association is more evident in flat-terrain and large-/medium-urban-scale regions. This study provides an applied framework for evaluating how digital infrastructure and service systems are linked to low-carbon agricultural transformation.