Amidst the transformation of logistics dynamics due to the global pandemic, the attention directed toward the effects of the Arctic shipping route on Shanghai Port has intensified. This study thoroughly investigates the incorporation of the Arctic shipping route into Shanghai Port logistics, with a dedicated focus on sustainability implications in the post-pandemic era. Leveraging both gravity models and stochastic frontier gravity models, we meticulously analyze the multifaceted opportunities and challenges stemming from this integration, highlighting the pivotal roles of economic growth and geographical proximity. The empirical analysis, spanning the years 2010 to 2021, provides compelling evidence of the Arctic shipping route’s positive effects on the logistical operations of Shanghai Port. The analysis highlights its potential to substantially enhance trade volumes, streamline logistical efficiency, and broaden transit options. Additionally, our assessment of the post-pandemic challenges and opportunities faced by Shanghai Port underscores its adaptability and resilience in the constantly shifting trade milieu. Overall, this study makes significant contributions by offering a comprehensive perspective on the complex interplay between economic, geographical, and external factors. The insights provided here serve as invaluable guidance for policymakers, trade analysts, and businesses that are navigating the intricacies of the contemporary global trade environment. Our findings aim to foster sustainable and resilient trade relationships, facilitating the development of port logistics in the post-pandemic era.

This paper investigates whether green credit policy can mitigate firms’ financialization. Using data from Chinese non-financial public listed firms during 2008 to 2019, we take the green credit policy promulgated in 2012 as a quasi-natural experiment and find that: (1) the green credit policy can reduce firms’ entrusted loans, especially affiliated entrusted loans. Moreover, the governance effect exerted by banks’ green credit policy reduces free cash flow and excess cash in heavily polluting firms, as shown in mechanism analysis. (2) The profit-seeking incentive dominates shadow banking activities in heavily polluting enterprises. (3) The 2012 green credit guidelines reduce entrusted loans maturity but increase the interest rate. The findings of this paper provide market-oriented insights into the regulation of shadow banking activities. This research also contributes to the literature on the effects of green credit policy by exploring their impact on firm shadow banking activities, and takes one step further to investigate the effect of green credit policy on firms’ entrusted loans. This paper also sheds light on how green credit policy can alleviate the imperfections and distortions of financial markets in the emerging market.

Any result from regression analysis may be subject to omitted variable bias of unknown magnitude and direction as, in practice, no dataset contains all the variables of the population model. At the same time, many variables are irrelevant and don’t contribute to the analysis. This paper explores which combination of data sources or structures will produce the best results and should be made available to the research community. We present a unified statistical framework that nests and comparable sets of constraints that characterize the effectiveness of these approaches in reducing omitted variable bias. We demonstrate our framework by estimating a wage and labor market transition model using German administrative data with a large set of linked survey variables. Overall, we find that unobserved effects panel data models with a restricted set of regressors are preferable to cross-sectional analysis with an extended set of variables. Consequently, we recommend that data providers supply administrative panel data for key variables instead of conducting extensive cross-sectional surveys.

Maritime decarbonisation requires fuel-consumption models that are accurate, physically plausible, and suitable for operational decision support. We develop a three-layer interpretable machine-learning framework combining predictive validation, physical-plausibility auditing, and operational aggregation. Using 489,620 speed-stable cruising observations from 21 vessels, the predictive layer separates record-level interpolation from temporal and cross-vessel transfer; RMSE increases by about threefold under stricter validation, showing that random splits overstate transferability. The explanatory layer audits SHAP-derived responses against pre-specified physical expectations and empirical support. Speed responses are strongly monotonic and nonlinear, the primary wave perturbation is not confirmed under the locked C4 rule, and the rudder response is empirically unidentifiable. The operational layer evaluates speed-reduction sensitivity using an observation-period main-engine CII proxy. Fixed-time responses are heterogeneous across vessel groups and reproduced across XGBoost, LightGBM, and Random Forest models; deeper perturbations progressively leave multivariable training support. Paired ERA5 controls retain the principal transfer and wave-audit conclusions but do not remove pathway–vessel-type confounding. These findings are local, model-based sensitivities in the studied 21-vessel sample and do not estimate a statutory annual CII or establish causal vessel-type mechanisms. The framework provides an auditable validation protocol for maritime ITS; applicability beyond shipping requires independent validation.