To achieve carbon peak and neutrality goals and promote sustainable development, the power sector, as China’s largest source of carbon emissions, is the first industry to implement the national carbon emission trading scheme (ETS). A differences-in-differences model is employed on firm-level data to assess the causal impact of China’s national ETS, launched in 2017, on the sustainable technology progress of power generation enterprises. This study employs green patents and total factor productivity as measures for sustainable technology progress and then explores mechanisms and heterogeneity of the impact. Results show that: (1) The national ETS has a positive effect on green innovation capability and efficiency in the power industry, and the increasing causal effect is mainly achieved through research and development expenditure. (2) The national ETS exerts a more significant positive effect on power generation enterprises that are non-state-owned, have smaller asset scale, demonstrate superior environmental performance, and are located in the eastern region. However, there is no significant difference in total factor productivity across power enterprises. (3) Green innovations are predominantly concentrated in new energy and hybrid power generation enterprises. This study contributes to the literature by providing novel empirical evidence from China’s national ETS, highlighting its dual impact on innovation and productivity within a unified framework. The findings not only offer targeted recommendations for China’s power sector but also serve as an important reference for other high-emitting industries and other regions worldwide facing the same challenges in their pursuit of sustainable development.

Evaluating the industrial eco-system is essential for promoting resource efficiency, environmental development, and public health protection. However, traditional Data Envelopment Analysis (DEA) methods are prone to biases and rely on precise numerical values, which pose significant challenges when evaluating the industrial ecosystem in China, particularly given the prevalent data uncertainties and environmental complexities. Additionally, DEA models are typically static, fail to capture the long-term trends and dynamic characteristics of the industrial ecosystem. To address these issues, this study proposes a dynamic three-stage model with an endogenous fuzzy directional distance function. The model integrates the generalized smooth bootstrap method and fuzzy comparison techniques to correct errors and account for data uncertainties, improving the accuracy and scientific validity of eco-efficiency assessments. Chinese enterprises face challenges such as excessive resource consumption, environmental pollution, and health risks, necessitating a more comprehensive and flexible evaluation system to adapt to the complex and dynamic nature of the industrial ecosystem. By focusing on industrial production, environmental governance, and health threats (IPEGHT) in China, the research aims to provide a robust framework for enhancing industrial eco-efficiency in a dynamic and uncertain environment. The results show: (1) The proposed method for selecting endogenous direction vectors is applied to the industrial production stage, thereby identifying the subsequent improvement directions for industrial production in various provinces. (2) The idea of integration is adopted to introduce the generalized smooth bootstrap method and fuzzy mathematics, to conduct a precise and comprehensive evaluation of the IPEGHT industrial eco-system. (3) From 2011 to 2021, China’s IPEGHT industrial eco-system efficiency showed a step-like distribution, and attention needs to be paid to environmental governance. The proposed method’ application can facilitate the integration of DEA and fuzzy mathematics, and fosters a more sustainable industrial eco-system.

Global resource depletion has intensified scrutiny on Sustainable Development Goal 12 (SDG12), where consumption-based material footprint serves as a critical sustainability metric. Despite its transformative potential, the paradoxical role of Information and Communication Technology (ICT) in resource conservation remains underexplored. This study adopts an extended STIRPAT model as the analytical framework. It employs the Method of Moments Quantile Regression to evaluate the non-linear effects of digitalization-related indicators and other influencing factors on material footprint. The analysis is conducted across different quantiles for G20 countries from 2000 to 2020. The results show that (1) ICT exhibits a substantial positive effect on consumption-based material footprint under all quantiles. This leads to an increase in the material footprint, hindering the G20’s progress toward achieving SDG12. (2) The impact of ICT varies notably, with a more pronounced adverse effect on SDG12 in countries with higher resource consumption. (3) ICT goods export trade, technological innovation, and globalization significantly mitigate ICT’s adverse impact on resource consumption. This study provides targeted recommendations for G20 countries on how to leverage ICT to achieve SDG12 more effectively.