ABSTRACT
Oscillations in the global trade milieu have exacerbated the ambiguity experienced by Chinese enterprises, thereby influencing their ecological transition. The ongoing debate over whether trade uncertainty augments corporate emissions, exacerbating pollution, or attenuates emissions, encouraging sustainable production, has yet to reach a consensus. The current investigation employs a textual analysis methodology to explore the influence of trade policy uncertainty on pollution emissions, by sourcing indicators of trade policy uncertainty that echo firm-level uncertainty within the period 2008 to 2021. Utilizing the fixed effects model for our analysis, the findings substantiate that escalated uncertainty at the micro-level catalyzes an increase in pollution emissions originating from firms. Crucially, we find that risk diversification and innovation bolster firms' capacities to manage pollution under escalating uncertainty. Furthermore, our estimation reveals that enterprises with low market competitiveness, high financial constraints, and moderate overseas market share are most significantly impacted, whereas those with robust patent portfolios remain largely unaffected. This study carries considerable implications for firms striving to achieve an ecological transition and offers insights for fostering sustainable and high-quality global economic development.
Subject(s)
Commerce , Environmental Pollution , Policy , China , Consensus , Uncertainty , Internationality , Economic Development , Sustainable Development/economicsABSTRACT
Optimally efficient organic solar cells require not only a careful choice of new donor (D) and/or acceptor (A) molecules but also the fine-tuning of experimental fabrication conditions for organic solar cells (OSCs). Herein, a new framework for simultaneously optimizing D/A molecule pairs and device specifications of OSCs is proposed, through a quantitative structure-property relationship (QSPR) model built by machine learning. Combining the device bulk properties with structural and electronic properties, the built QSPR model achieved unprecedentedly high accuracy and consistency. Additionally, a large chemical space of 1â¯942â¯785 D/A pairs is explored to find potential synergistic ones. Favorable device bulk properties such as the root-mean-square of surfaces roughness for D/A blends and the D/A weight ratio are further screened by grid search methods. Overall, this study indicates that the simultaneous optimization of D/A molecule pairs and device specifications by theoretical calculations can accelerate the improvement of OSC efficiencies.
