ABSTRACT
As an innovative and convenient micro-mobility service, dockless bicycle-sharing systems (DBSSs) are essential to achieving green recovery of the transportation sector in post-COVID-19 world. DBSS green externalities on climate change have attracted the attention of scholars and have revealed different roles in carbon mitigation in different studies. In this study, Shanghai is employed as a case city to analyze DBSS green externalities. The direct carbon emissions reduced by DBSS cycling are calculated and the indirect carbon mitigation by a DBSS in promoting use of low-carbon public transport is estimated. The carbon consumption of DBSS from the perspective of life-cycle assessment is also valued. The results show that DBSSs have much greater carbon mitigation potential in promoting the use of low-carbon public transport than do cycling routes. The production, maintenance, and rebalance of DBSSs may produce a large number of carbon emissions and even offset their green benefits. The application of (electric) e-bikes and the integration of DBSSs and public transportation should be the key issue for policy makers to promote the green recovery of the transport sector. This study calls for further studies to demonstrate the green externality of DBSSs based on the detailed operation dataset.
ABSTRACT
ABSTRACT Background The surge of treatments for COVID-19 in the ongoing pandemic presents an exemplar scenario with low prevalence of a given treatment and high outcome risk. Motivated by that, we conducted a simulation study for treatment effect estimation in such scenarios. We compared the performance of two methods for addressing confounding during the process of estimating treatment effects, namely disease risk scores (DRS) and propensity scores (PS) using different machine learning algorithms. Methods Monte Carlo simulated data with 25 different scenarios of treatment prevalence, outcome risk, data complexity, and sample size were created. PS and DRS matching with 1: 1 ratio were applied with logistic regression with least absolute shrinkage and selection operator (LASSO) regularization, multilayer perceptron (MLP), and eXtreme Gradient Boosting (XgBoost). Estimation performance was evaluated using relative bias and corresponding confidence intervals. Results Bias in treatment effect estimation increased with decreasing treatment prevalence regardless of matching method. DRS resulted in lower bias compared to PS when treatment prevalence was less than 10%, under strong confounding and nonlinear nonadditive data setting. However, DRS did not outperform PS under linear data setting and small sample size, even when the treatment prevalence was less than 10%. PS had a comparable or lower bias to DRS when treatment prevalence was common or high (10% - 50%). All three machine learning methods had similar performance, with LASSO and XgBoost yielding the lowest bias in some scenarios. Decreasing sample size or adding nonlinearity and non-additivity in data improved the performance of both PS and DRS. Conclusions Under strong confounding with large sample size DRS reduced bias compared to PS in scenarios with low treatment prevalence (less than 10%), whilst PS was preferable for the study of treatments with prevalence greater than 10%, regardless of the outcome prevalence. Key Messages When handling nonlinear nonadditive data with strong confounding, DRS estimated by machine learning methods outperforms PS in scenarios with low treatment prevalence (less than 10%). However, if having linear data and small sample size data with strong confounding, we did not observe DRS outperformed PS even when treatment prevalence was less than 10%. Our results suggested that using PS performed better compared to DRS in tackling strong confounding problems with treatment prevalence greater than 10%. Small sample size increased bias for both DRS and PS methods, and it affected DRS more than PS.