The mechanism of environmental regulation on energy conservation and carbon reduction in the petrochemical industry through directed technological progress remains uncertain due to the directional characteristics of technology. This paper develops a mechanism framework and employs a panel two-way fixed-effects model to clarify the impact of environmental regulation on directed technological progress and energy conservation, while uncovering its underlying mechanisms. Subsequently, a dynamic Kaya model is constructed, using the Monte Carlo method to determine the required intensity of environmental regulation for China's petrochemical industry to actualize the SSP1-CHN, SSP1, and SSP2 scenarios. The model also simulates the future bias of technological progress, energy utilization, and potential carbon emissions under each scenario. The findings indicate that increasing the intensity of environmental regulation drives technological progress toward energy conservation, thereby enhancing energy-saving biased technological progress, improving energy productivity, and optimizing the energy structure. Furthermore, to actualize the carbon peak by 2030 and carbon neutrality by 2060 under the SSP1-CHN scenario, the annual growth rate of environmental regulation intensity in China's petrochemical industry should be no less than 8 % before 2030 and should be strengthened to 20 % after 2030.This study not only extends the application of directed technological progress theory in the energy field but also provides innovative and practical environmental policy recommendations for the low-carbon development of the global petrochemical industry.

The mechanisms and effects of technological investment on energy productivity and energy structure in the petrochemical industry remain unclear due to the directional nature of technological progress. This study proposes a unified theoretical framework for the impact of directed technological investment on energy productivity and energy structure by incorporating energy factors into the theory of technological progress bias. The aim is to elucidate the impact of technological progress on energy productivity and energy structure, and to unravel the underlying effect mechanisms. A fixed effects model that includes moderating effects is also developed to support the assessment. The study found that the petrochemical industry's technological investment in China was initially biased towards enhancing labour-augmenting technological progress. The mechanism analysis revealed that technological investment, under the moderating effects of price and environmental governance, preferred a capital-energy bias, leading to insignificant improvements in energy productivity but a substantial increase in labour productivity. In addition, the technological investment, influenced by the moderating effect of environmental governance, led to some improvement in the energy structure during the sample period. This study integrates the mechanisms of directed technological investment on energy productivity and energy structure into a unified analytical framework, systematically investigating the reasons, effect mechanisms, and consequences of bias, while providing empirical evidence that supports low-carbon development in the petrochemical industry.

Data has become an increasingly important production factor in the green transition, yet its independent contribution to green total factor productivity (GTFP) remains insufficiently understood. Using panel data for 30 Chinese provinces from 2011 to 2023, this study constructs a composite index of data factors and measures GTFP using the SBM-GML approach. A two-way fixed effects framework is combined with mediation, threshold, and spatial econometric models to examine the effect of data factors on green productivity. Four main conclusions are obtained: (1) Data factors significantly promote GTFP. Economically, a one-standard-deviation increase in the index of data factors raises GTFP by 0.311, equivalent to 25.3% of the sample mean of GTFP. This result remains robust after a series of robustness and endogeneity tests, with the instrumental-variable estimate reaching 6.203. (2) Industrial structure upgrading, green technological innovation, and environmental regulation are important transmission channels. (3) The effect is nonlinear, with estimated thresholds of 0.238 and 0.239, and the promoting effect is strongest in the middle regime. (4) Data factors also generate positive spatial spillovers, part of which are transmitted through financial agglomeration. These findings underscore the important role of data factors development in advancing energy-efficient green transformation.