Yiying Chao

Highway accessibility plays a vital role in supporting local economic development, particularly in regions lacking access to sea or river ports. Recognizing the functional transformation of road infrastructure, the Chinese government has made substantial investments in its expansion. Nevertheless, a theoretical gap remains in justifying whether such investments yield significant economic returns. Drawing on the theory of economic externalities, this study investigates the causal relationship between highway development and regional economic growth, and assesses whether highway construction leads to an acceleration in growth rates. Utilizing panel data from 14 Chinese cities spanning 2000 to 2014, the synthetic control method (SCM) is employed to evaluate the economic externalities of highway investment. The results indicate a positive impact on surrounding industries. Furthermore, a growth rate forecasting analysis based on Back-Propagation Neural Networks (BPNNs) is conducted using industrial enterprise data from 2005 to 2014. The growth rate in the treated city is 1.144%, which is close to the real number 1.117%, higher than the number for the weighted control group, which is 1.000%. The findings suggest that the growth rate of total industrial output improved significantly, confirming the existence of positive spillover effects. This not only enriches the empirical literature on transport infrastructure but also provides targeted enlightenment for the sustainable development of urban economy in terms of policy guidance.

This study addresses the Plug-in Hybrid Electric Vehicle Routing Problem (PHEVRP), an extension of the classical VRP that incorporates energy mode switching and partial charging strategies. We propose a novel routing model that integrates three energy modes—fuel-only, electric-only, and hybrid—along with partial recharging decisions to enhance energy flexibility and reduce operational costs. To overcome the computational challenges of large-scale instances, a Dantzig–Wolfe decomposition algorithm is designed to efficiently reduce the solution space via column generation. Experimental results demonstrate that the hybrid-mode with partial charging strategy consistently outperforms full-charging and single-mode approaches, especially in clustered customer scenarios. To further evaluate algorithmic performance, an Ant Colony Optimization (ACO) heuristic is introduced for comparison. While the full model fails to solve instances with more than 30 customers, the DW algorithm achieves high-quality solutions with optimality gaps typically below 3%. Compared to ACO, DW consistently provides better solution quality and is faster in most cases, though its computation time may vary due to pricing complexity.

Economic growth can bring material prosperity and improvements in public services to a country yet can simultaneously lead to environmental pollution that threatens population health, which has important implications for shaping macro-level policies. Here, we investigate the relationship between economic growth and mortality attributable to air pollution in China from 2002 to 2021, using data analyzed with Stata 18 software. We identify a counterintuitive U-shaped relationship between the two, with the coefficient of the primary term for economic growth at −1.222 and the quadratic term at −0.053, both statistically significant at the 1% level. The inflection point, with accounting for control variables, is calculated to be 99,708 yuan (CNY) per person. These results withstand different empirical testing. We then perform heterogeneity analyses at the city level and find that disparities in social, economic, and physical geographical conditions lead to an unequal mortality burden that persists. Economic growth may have negative impacts on population health after crossing a certain threshold, although the effects vary across different regions. Our findings reveal that the benefits of economic growth may not “trickle down” to improve population health. Policymakers cannot take economic growth as an intrinsic good that would inevitably lead to better population health. Greater emphasis should be placed on egalitarian welfare systems, investments in environmental improvements, and other life-supporting priorities, as these measures could mitigate the negative health impacts of economic growth and promote a virtuous cycle between the economy and population health.

This study investigates a variant of the shortest path problem (SPP) tailored for plug-in hybrid electric vehicles (PHEVs), incorporating two practical features: hybrid energy mode switching and partial charging. A novel modeling framework is proposed that enables PHEVs to dynamically switch between electricity and fuel along each edge and to recharge partially at charging stations. Unlike most prior studies that rely on more complex modeling approaches, this paper introduces a compact mixed-integer linear programming (MILP) model that remains directly solvable using commercial solvers such as Gurobi. To address large-scale networks, a customized labeling algorithm is developed for an efficient solution. Numerical results on benchmark networks show that the hybrid mode and partial charging can reduce total cost by up to 29.76% and significantly affect route choices. The proposed algorithm demonstrates strong scalability, solving instances with up to 33,000 nodes while maintaining near-optimal performance, with less than 5% deviation in smaller cases.