Taofeng Wu

Amid growing pressure to balance economic growth and environmental sustainability, this study develops a comprehensive framework, ecological sustainability trajectory in dynamic evolution (ECO-STRIDE), to evaluate and forecast regional ecological–environmental transformation efficiency (EETE) in China. Drawing on panel data from 30 provinces (2010–2023), the framework integrates dynamic efficiency modeling, spatial–temporal analysis, deep learning prediction, and model interpretability. The findings reveal: (1) EETE remains low overall, with pronounced interregional disparities. Resource utilization efficiency generally exceeds environmental governance efficiency, with provinces like Guangdong showing marked imbalance between the two stages. (2) While both stages demonstrate an upward trend, regional gaps continue to widen. Resource utilization efficiency steadily improves, but with intensifying structural divergence, whereas environmental governance efficiency lags behind and displays clear polarization. The eastern region maintains a leading position across both stages due to its superior capacity in resource allocation and coordinated governance. (3) A hybrid CNN–LSTM–Attention model delivers high predictive accuracy, projecting continuous improvement in EETE through 2035, although spatial imbalances persist. (4) SHAP-based interpretation identifies urbanization, industrial upgrading, and digital infrastructure as key drivers, while inefficiencies in green finance allocation and underutilized ecological endowments remain critical constraints. By providing a full-period, integrated assessment from historical patterns to future trajectories, this study advances the methodological frontier of EETE research and informs region-specific strategies for long-term resource coordination and adaptive environmental governance under uncertainty.

Understanding the price dynamics of oil futures is crucial for advancing green finance strategies and supporting sustainable energy transitions. This study investigates the macroeconomic and energy market determinants of oil futures prices through Granger causality, cointegration analysis, and the error correction model, using daily data. It focuses on the influence of economic development levels, exchange rate fluctuations, and inter-energy price linkages. The empirical findings indicate that (1) oil futures prices exhibit strong correlations with other energy prices, macroeconomic factors, and exchange rate variables; (2) economic development significantly affects oil futures prices, while exchange rate impacts are statistically insignificant based on the daily data analyzed; (3) there exists a stable long-term equilibrium relationship between oil futures prices and variables representing economic activity, exchange rates, and energy market trends; (4) oil futures prices exhibit significant short-term dynamics while adjusting steadily toward a long-run equilibrium driven by macroeconomic and energy market fundamentals. By enhancing the accuracy of oil futures price forecasting, this study offers practical insights for managing financial risks associated with fossil energy markets and contributes to the formulation of low-carbon investment strategies. The findings provide a valuable reference for integrating energy pricing models into sustainable finance and climate-aligned portfolio decisions.

The development of the digital economy provides important opportunities and conditions for China to achieve the goal of carbon peak and carbon neutrality. Based on panel data from 30 provinces in mainland China from 2016 to 2022, this research investigates the spatial spillover effect and nonlinear impact of the digital economy on the energy optimization of the manufacturing industry using the spatial econometric and panel threshold model. It is found that both the digital economy and energy optimization of the manufacturing industry have a significant positive spatial correlation. The spatial econometric models under different weights all illustrate that the regional digital economy has not significantly promoted energy optimization of the manufacturing industry in a local region but produced a significant positive influence on the energy optimization of the manufacturing industry in neighboring regions. In addition, the impact of the digital economy on the energy optimization of the manufacturing industry presents a significant single threshold effect. With the improvement of digital economy, energy optimization of manufacturing industry has a U-shaped change trend. This study integrates the digital economy and manufacturing energy optimization into a cohesive analytical framework, elucidating the mechanisms through which the digital economy influences the restructuring of manufacturing energy and enhances energy efficiency while providing innovative pathways and theoretical support for advancing energy efficiency under carbon emission reduction constraints.

As new-energy vehicles (NEVs) gradually gain public attention, their carbon-reduction issues have become a focal point in academia. This study evaluates the carbon-reduction efficiency of NEVs in 21 Chinese provinces using an improved three-stage DEA model, analyzes spatial disparities with the Dagum Gini coefficient, and decomposes carbon-emission factors using the LMDI method. Results show that the overall carbon-reduction efficiency is low, with an average value of only 0.266. Significant differences exist in production- and consumption-stage efficiencies across regions. Shanxi Province performed the best, with efficiency scores of 1 in both stages, while the carbon-reduction stage showed the lowest efficiency, ranging between 0.2 and 0.3 in most regions. The central region exhibited the highest carbon-reduction efficiency, followed by the western and eastern regions, primarily influenced by intra-regional disparities. Energy intensity significantly suppresses carbon emissions, followed by energy structure, while economic development and population size positively contribute to carbon emissions. This study provides theoretical support for regional governments to formulate policies related to the NEV industry and offers practical guidance for its further development.