Environmental regulations (ERs) are effective in balancing industrial economic benefits with ecological protection. The study aimed to explore how ERs combine with R&D expenditure, human capital, industrial structure, and ownership structure to promote industrial green total factor productivity (GTFP) growth in China's industrial sector. Utilizing input-output data from the industrial sector covering 30 provinces and obtained between 2003 and 2021, the Meta-frontier Malmquist-Luenberger (MML) index is employed to measure GTFP. Then, a dynamic panel threshold model is constructed to empirically examine the nonlinear dynamic effect of different types of ERs on GTFP from the perspectives of R&D expenditure, human capital, industrial structure, and ownership structure, with the objective being to reveal the economic conditions under which ERs generate a “strong” Porter effect. The results indicate that: (1) GTFP is significantly enhanced during the observation period, yet it displays pronounced spatial disparities. Technological progress is the main source of GTFP growth. (2) The results of the benchmark model indicate a significant U-shaped relationship between ERs and GTFP. The stimulation effects driven by market-incentive environmental regulation (MIER) and public participation environmental regulation (PPER) are far stronger than those motivated by command-and-control environmental regulation (CCER) and performance-based environmental regulation (PBER). (3) The dynamic relationship between ERs and GTFP depends on R&D expenditure, human capital, industrial structure, and ownership structure. Specifically, ERs exert a stronger positive effect on GTFP in the region with lower degrees of nationalization and higher levels of R&D expenditure, human capital, and industrialization. MIER has the greatest positive effect. However, in the region with a higher degree of nationalization and lower levels of R&D expenditure, human capital, and industrialization, ERs exert a weaker positive effect or even a significant negative effect on GTFP.
New energy vehicles (NEVs) are increasingly regarded as a driver of energy transition. However, few scholars have conducted quantitative research on the impact of the large-scale adoption of NEVs on urban ecological resilience (UER). Based on the Pressure-State-Response framework adopted to assess UER, this study utilizes panel data from 294 cities in China from 2017 to 2023 and applies a double/debiased machine learning approach to analyze the impact of NEV adoption on UER and the mechanisms driving this effect. The findings yield four main conclusions. First, the adoption of NEVs significantly increases UER. A 1% rise in NEV stock leads to a 0.0132% increase in UER, and this outcome remains stable through multiple robustness tests. Second, the mechanism exploration demonstrates that the adoption of NEVs improves UER through three channels: industrial structure upgrading, boosting green technological innovation capabilities, and raising public environmental awareness. Third, heterogeneity analysis reveals that NEV adoption has a stronger effect on UER in western cities, non-resource-based cities, and cities with stricter environmental regulations. Fourth, sub-dimensional analysis indicates that the adoption of NEVs mainly improves the state criterion and response criterion of UER, while improvements in the pressure criterion occur only in non-resource-based cities and cities with stricter environmental regulations. This study offers empirical evidence demonstrating how NEV adoption contributes to UER and drives sustainable development, emphasizing that this process requires participation from multiple stakeholders including governments, enterprises, and the public.
Accurate energy consumption (EC) prediction for different road conditions is critical for battery management, regenerative braking control, and range estimation in battery electric vehicles (BEVs). However, conventional models trained on raw telemetry data often fail to capture complex driving dynamics due to noise and redundancy in high-dimensional inputs. To address these issues, this study proposes a novel hybrid interpretable framework that combines the sparse autoencoder, neural networks, and SHAP (Shapley additive explanations) (SAE-NN-SHAP), which effectively enables denoising, features extraction, reduces dimensionality, captures complex nonlinear characteristics and interpretable EC estimation, utilizing a real-world BEV dataset from a downloadable dynamometer database. Firstly, the enhanced SAE compresses the input space into 25 compact yet essential and meaningful information-dense latent features. Secondly, this representation is then fed into a Bayesian-optimized NN regressor. Both instantaneous (kW) and trip-level (Wh/km) EV-EC were estimated using the meaningful latent features. Thirdly, the proposed SAE-NN achieves the highest R 2 (0.991) and lowest root mean squared error (RMSE) (1.270) and mean absolute error (MAE) (0.930) among all evaluated models, reducing error by 96.67% relative to a conventional NN trained on raw features and by 14.19% relative to the strongest baseline; at the trip level, predicted and actual EC differ by less than 1% across unseen trips’ driving conditions. Finally, the innovative end-to-end SHAP-based interpretability method, combined with SAE-NN encoder weights and latent feature mapping, identifies the specific driving-dynamics factors influencing EC, including speed, regeneration, braking, torque, battery dynamics, etc. The findings provide physically meaningful explanations and actionable insights for an accurate EC and range estimation, proactive and intelligent energy management approach.
A cost-effective and scalable optoelectronic device based on an MXene-based Ni 2 O 3− X –P1HP (poly(1H-pyrrole))/TiO 2 nanocomposite was fabricated through a facile synthetic strategy involving the incorporation of this composite on additional P1HP seeding layer. The enhanced MXene-based Ni 2 O 3− X –P1HP/TiO 2 /P1HP optoelectronic performance originates from the synergistic integration of multiple semiconductor components with oxygen-deficient Ni 2 O 3− X , whose plasmonic characteristics and defect-rich electronic structure promote efficient light harvesting and charge transport. Under ultraviolet illumination, the fabricated device achieved a maximum responsivity of 2.0 mA W –1 and a specific detectivity of 0.18 × 10 10 Jones. Remarkably, the device maintained substantial photoactivity at lower photon energies (1.6 eV; ∼730 nm), delivering a responsivity of 1.5 mA W –1 and a detectivity of 0.13 × 10 10 Jones, thereby showing efficient operation within the visible–near-infrared spectral region. Morphological investigations revealed the formation of semispherical and rod-like nanostructures with characteristic dimensions of approximately 100 nm. Optical analysis further demonstrated a narrowed band gap of ∼1.48 eV, facilitating enhanced optical absorption across a broad spectral range. In addition, the oxygen-vacancy-rich Ni 2 O 3− X phase introduced localized defect states that promoted hot-carrier generation and suppressed charge recombination. Collectively, these structural and electronic features enable efficient photon harvesting, rapid charge-transfer kinetics, and improved carrier separation, highlighting the potential of this low-cost MXene-based architecture for next-generation optoelectronic sensing applications.
This study aims to evaluate and prioritize clean energy strategies to support Türkiye's national carbon neutrality goals. Achieving carbon neutrality requires adopting clean technologies and effective environmental policy tools. This study presents a novel multi-criteria decision-making context that combines the Picture Fuzzy Best-Worst Method (PiF-BWM) and Picture Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (PiF-TOPSIS) to evaluate and prioritize renewable energy strategies under uncertainty systematically. The empirical application focuses on Türkiye and is based on the evaluations of nine experts. Using Türkiye as the test bed, we assess alternatives such as climate-resilient low-carbon development plans, policy frameworks for low-carbon fuels, large-scale and distributed renewables, green jobs programs, and building electrification via heat pumps. Criteria span environmental impact, cost and financial viability, technological feasibility, socioeconomic effects, resilience, energy efficiency, energy security, and policy alignment. The methodology first applies PiF-BWM to determine the criteria weights, and then uses PiF-TOPSIS to rank the alternative strategies. Results show that environmentally effective and policy-enabled strategies deliver the highest urban decarbonization benefits, while building-focused electrification provides targeted gains when integrated with local grid constraints and building codes. The findings further indicate that climate-resilient low-carbon development plans and policy frameworks for low-carbon fuels are the most suitable strategic options, whereas heat pump-based building electrification receives the lowest score among the evaluated alternatives. This study positions energy as an instrument for sustainable development by foregrounding affordability, access, equity, and institutional capacity in strategy selection. From a policy perspective, the results suggest that governments should prioritize policy-supported and resilience-oriented clean energy pathways while aligning investment decisions with affordability, implementation capacity, and long-term sustainability goals. The policy-oriented framework supports sustainable development goal (SDG) 7 (Affordable and Clean Energy) and SDG 11 (Sustainable Cities and Communities), and is tailored to the realities of developing-country contexts.
