Riding towards a sustainable future:an evaluation of bike sharing’s environmental benefits in Xiamen Island,China

In the pursuit of sustainable urbanization,Bike-Sharing Services(BSS)emerge as a pivotal instrument for promoting green,low-carbon transit.While BSS is often commended for its environmental benefits,we offer a more nuanced analysis that elucidates previously neglected aspects.Through the Dominant Travel Distance Model(DTDM),we evaluate the potential of BSS to replace other transportation modes for specific journey based on travel distance.Utilizing multiscale geographically weighted regression(MGWR),we illuminate the relationship between BSS’s environmental benefits and built-environment attributes.The life cycle analysis(LCA)quantifies greenhouse gas(GHG)emissions from production to operation,providing a deeper understanding of BSS’s environmental benefits.Notably,our study focuses on Xiamen Island,a Chinese“Type Ⅱ large-sized city”(1–3 million population),contrasting with the predominantly studied“super large-sized cities”(over 10 million population).Our findings highlight:(1)A single BSS trip in Xiamen Island reduces GHG emissions by an average of 19.97 g CO_(2)-eq,accumulating monthly savings of 144.477 t CO_(2)-eq.(2)Areas in the southwest,northeast,and southeast of Xiamen Island,characterized by high population densities,register significant BSS environmental benefits.(3)At a global level,the stepwise regression model identifies five key built environment factors influencing BSS’s GHG mitigation.(4)Regionally,MGWR enhances model precision,indicating that these five factors function at diverse spatial scales,affecting BSS’s environmental benefits variably.

城市群水污染物排放的驱动因素及尺度效应——基于长三角305个县域的实证分析

Revealing the drivers and scale effects of water pollutant discharges is an important issue in the study of the environmental consequences during urban agglomeration evolution.It is also a prerequisite for realizing collaborative water pollutant reduction and environmental governance in urban agglomerations.This paper takes 305 counties in the Yangtze River Delta(YRD)as an example and selects chemical oxygen demand(COD)and ammonia nitrogen(NH_(3)–N)as two distinctive pollutant indicators,using the Spatial Lag Model(SLM)and Spatial Error Model(SEM)to estimate the drivers of water pollutant discharges in 2011 and 2016.Then the Multiscale Geographically Weighted Regression(MGWR)model is constructed to diagnose the scale effect and spatial heterogeneity of the drivers.The findings show that the size of population,the level of urbanization,and the economic development level show global-level increase impacts on water pollutant discharges,while the level of industrialization,social fixed assets investment,foreign direct investment,and local fiscal decentralization are local-level impacts.The spatial heterogeneity of local drivers presents the following characteristics:Social fixed assets investment has a strong promoting effect on both COD and NH_(3)–N discharges in the Hangzhou–Jiaxing–Huzhou region and the coastal area of the YRD;industrialization has a promoting effect on COD discharges in the Taihu Lake basin and Zhejiang province;foreign direct investment has a local inhibitory effect on NH3–N discharge,and the pollution halo effect is more prominent in the marginal areas of the YRD such as northern Jiangsu,northern Anhui,and southern Zhejiang;local fiscal decentralization has a noticeable inhibitory effect on COD discharge in the central areas of the YRD,reflecting the positive impacts on improved local environmental awareness and stronger constraints of multilevel environmental regulations in the urban agglomeration.Therefore,it is recommended to guide greener development to reduce the water pollutant discharge;to embed an environmental push-back mechanism in the fields of industrial production,capital investment,and financial income and expenditure;and to establish a high-quality development pattern of urban agglomerations systematically compatible with the carrying capacity of the water environment.

Impact of neighborhood features on housing resale prices in Zhuhai (China) based on an (M)GWR model

The paper aims at exploring the relationship between housing resale prices and neighborhood features in Zhuhai,as well as structure and location characteristics.Thirteen neighborhood features are collected to analyze their influence on average community-level apartment resale prices in 2018.Six neighbor-hood features,structural and location characteristics,are selected according to their statistical significance and multi-collinearity test results from an OLS model.Regression analysis is performed by OLS,GWR,and MGWR to compare their per-formance in housing price research at community level.The comparison of the three models also demonstrates that the GWR(66%)and MGWR(68%)models perform much better than OLS model(52%).MGWR is not significantly different from GWR in areas with few sample points,and the optimal bandwidth at different spatial scales is hard to be captured in a city-level study area.The regression parameter indicates that building age is the most important factor among all influen-cing factors.Proximity to schools and factories have positive and negative significant effects on housing resale prices,respectively.The spatial pattern of neighborhood features is also detected at town level.GWR and MGWR models accurately demonstrate local spatial heterogeneity of the housing resale market,which provides better results than the traditional OLS model in the goodness of fit and parameter estimates when spatial dependency is present.The results provide references for local planning departments,helping to reveal the compli-cated relationship and spatial patterns between housing price and determinants over space.