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spatial insights for sustainable transportation based on carbon emissions from multiple transport modes: A township-level case study in China

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CITIES 2024年 155卷

作者： Zhi, Danyue Song, Dongdong Chen, Yan Yang, Yitao Zhao, Hepeng Wang, Ting Wu, Hao Song, Weize Yang, Xiaobao Liu, Yongtao China Unicom Digital Technol Co Ltd Data Intelligence Div Dept Data Sci & Technol Beijing 100166 Peoples R China Beijing Jiaotong Univ Sch Syst Sci Beijing 100044 Peoples R China Changan Univ Sch Automobile Xian 710064 Peoples R China Univ Manchester Alliance Manchester Business Sch Manchester England Univ Leeds Sch Geog Leeds England China Mobile Grp Design Inst Co Ltd Beijing 100080 Peoples R China Tech Univ Munich Chair Transportat Syst Engn D-80333 Munich Germany Tsinghua Univ Lab Low Carbon Energy Beijing 100084 Peoples R China Tsinghua Univ Inst Climate Change & Sustainable Dev Beijing 100084 Peoples R China

Understanding the CO2 emissions and influencing factors of travelers' multiple modes can provide direction for energy conservation and emission reduction, which is of great significance for developing sustainable cities. Previous studies focused on the CO2 emissions of the transportation sector or individual modes. Which has overlooked the variations of emissions within the transport system. Hence, this study focuses on multiple modes (i.e., car, subway, bus, and bike) in the township in the Guangdong-Hong Kong-Macao Greater Bay Area. This study proposes a framework for exploring the spatial autocorrelation of urban transport emission structure based on ratios (i.e., CO2 emissions from each mode divided by total emissions) and key factors by combining spatial econometric model (i.e., Moran's I index and spatial error model) and machine learning model (i.e., Random Forest and SHAP model). In addition, the spatial autocorrelation of ratios at different spatial scales is investigated. The results indicate the high spatial dependence in the ratios from each transport mode and Moran's I indices for four ratios are 0.883, 0.886, 0.706, and 0.776, respectively. In addition, subway and car ratios exhibit a negative spatial correlation (-0.798), and subway and bike show a positive correlation (0.570). Population density, road length, and land use diversity are the key drivers of CO2 emission ratios and have different effects on various transport modes. Furthermore, as the spatial scales expand from townships to distinct and city, the spatial autocorrelation of the ratios decreases. This study could provide policy implications for optimizing urban transport strategies and reducing CO2 emissions.

关键词： CO 2 emissions Traffic emission structure Multiple transport modes spatial correlation spatial error model SHAP

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Testing for spatial correlation under a complete bipartite network

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ECONOMICS LETTERS 2024年 241卷

作者： Baltagi, Badi H. Liu, Long Syracuse Univ Ctr Policy Res 426 Eggers Hall Syracuse NY 13244 USA Florida Atlantic Univ Dept Econ 777 Glades Rd Boca Raton FL 33431 USA

This note shows that for a spatial regression with a weight matrix depicting a complete bipartite network, the Moran I test for zero spatial correlation is never rejected when the alternative is positive spatial correlation no matter how large the true value of the spatial correlation coefficient. In contrast, the null hypothesis of zero spatial correlation is always rejected (with probability one asymptotically) when the alternative is negative spatial correlation and the true value of the spatial correlation coefficient is near -1.

关键词： spatial error model Moran I test Complete bipartite network

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How does environmental regulation impact low-carbon transition? Evidence from China's iron and steel industry

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ANNALS OF OPERATIONS RESEARCH 2025年第2-3期345卷 637-663页

作者： Wang, Xiaoling Chen, Yu Dong, Yizhe Zhang, Tianyue Shi, Baofeng Univ Sci & Technol Beijing Sch Econ & Management Beijing 100083 Peoples R China Univ Edinburgh Business Sch Edinburgh EH8 9JS Scotland Northwest A&F Univ Coll Econ & Management Yangling 712199 Peoples R China

Comprehensive evaluation and identification of the critical regulatory determinants of carbon emission efficiency (CEE) are very important for China's low-carbon transition. Accordingly, this paper first employs an undesirable global super-hybrid measure approach to calculate the CEE of China's iron and steel industry (ISI). We then further use spatial error and threshold regression models to examine the spatial and non-linear effects of heterogeneous environmental regulations on CEE, respectively. Our empirical results show that (1) CEE varies significantly across China's regions, with the eastern region having the highest CEE score, followed by the western and central regions, with the northeast region ranking the lowest;(2) command-and-control and market-incentive regulations both promote CEE, whereas the public participation approach does not significantly contribute to performance gains;(3) all three types of environmental regulations exhibit a non-linear threshold effect on CEE;(4) openness level, technological progress, and industrial concentration enhance efficiency gains, while urbanization level exerts a negative impact on CEE. Our findings have important implications for the design of environmental regulations.

关键词： Environmental regulation Carbon emission efficiency low-carbon transition iron and steel industry spatial error model Panel threshold analysis

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Spatio-temporal effects and influence mechanism of digital technology on tourism efficiency in Chinese provinces

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SCIENTIFIC REPORTS 2024年第1期14卷 1-15页

作者： Zhang, Fan Cheng, Qian Zhejiang Gongshang Univ Sch Tourism & Urban Rural Planning Hangzhou Zhejiang Peoples R China

Digital technology serves as a new industrial driving force, playing a crucial role in promoting the efficiency of regional tourism. To explore the inherent logic between digital technology and tourism development, the study employed the DEA-BCC model and the entropy-weighted TOPSIS method to measure the tourism efficiency and digital technology development level of 31 provinces (municipalities and districts) in China from 2011 to 2022. The evolutionary characteristics of the relationship between digital technology and tourism efficiency were analyzed from the spatio-temporal dimension, and empirical tests were carried out using the spatial error model (SEM) and the spatial lag model (SLM) to explore the mechanism of the impact of digital technology on tourism efficiency. The results show that: during the study period, the overall trend of digital technology development and tourism efficiency is upward, but there is a certain degree of spatial mismatch between the two;digital technology is not only conducive to the improvement of tourism efficiency in the province, but also acts on neighboring provinces through spatial spillover effects, mechanism of action tests show that digital technology can positively moderate the effects of tourism economic growth and tourism industry structure on tourism efficiency;in western and northeast China, the positive effect of digital technology on tourism efficiency is more obvious. The conclusions provide a new perspective for understanding and analyzing the development of provincial tourism in China, as well as a reference for the rational use of digital technology to promote tourism development.

关键词： Digital technology Tourism efficiency spatial error model spatial lag model Spatio-temporal characteristics

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House Price modeling in DKI Jakarta: A Web-Based Comparative Study of spatial models

House Price Modeling in DKI Jakarta: A Web-Based Comparative...

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2024 International Conference on Aerospace Electronics and Remote Sensing Technology

作者： Sevien Gunawan, Alexander Agung Santoso Ohyver, Margaretha Bina Nusantara Univ Jakarta Sch Comp Sci Dept Stat Greater Jakarta Indonesia Bina Nusantara Univ Jakarta Sch Comp Sci Dept Comp Sci Greater Jakarta Indonesia

ISBN: (纸本)9798331542023;9798331542016

A house is a fundamental need for individuals. House sellers can easily determine the market price of their property using the NJOP formula, which is used in the calculation of Land and Building Tax (PBB). However, for buyers, determining a fair market price for a house can be challenging. As a result, it is common for buyers to purchase a house at a price higher than its fair market value. Therefore, this study aims to determine the attributes of a house that affect the price of a house but also considering the spatial value of the location of the district of the house by comparing spatial Autoregressive model with spatial error model. We concluded that the SEM model, characterized by the highest R-2 and the lowest AIC and BIC values, is identified as the best fit for the dataset. Furthermore, we embedded the house predicting models in a web application, making it easier for the public to determine the price of the house they want to purchase while also assisting property companies in planning future housing development strategies to generate maximum and sustainable profits.

关键词： house price spatial regression spatial dependency spatial autoregressive model spatial error model

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Estimating medium-term regional monthly economic activity reductions during the COVID-19 pandemic using nighttime light data

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2024年 135卷

作者： Del Castillo, Ma. Flordeliza P. Fujimi, Toshio Tatano, Hirokazu Kyoto Univ Grad Sch Informat Dept Social Informat Kyoto Japan Ateneo Manila Univ Geomat Environm & Dev Manila Observ Quezon City Philippines Kyoto Univ Disaster Prevent Res Inst Kyoto Japan

Economic impact estimates of the initial lockdowns due to the COVID-19 pandemic showed a significant reduction in economic activities globally. However, the succeeding impacts and their spatiotemporal distribution within countries remain unknown. Studies showed that nighttime light data (NTL) has effectively revealed the spatiotemporal dimensions of the economic effects of COVID-19. Thus, this study used NTL data to determine the medium-term regional monthly economic impacts of the pandemic in the Philippines in terms of the Economic Activity Reduction (EAR) index. We generated a spatial error model, regressing pre-pandemic NTL on mean temperature, maximum rainfall, and built-up area. This model explained 81.6% of the pre-pandemic NTL and was used to estimate the counterfactual NTL. We subtracted the actual from the counterfactual to compute the EAR. Then, the EAR was regressed on regional factors to determine which ones influence the impacts. Results showed uneven distribution of EAR across space and time. The EAR was generally higher in urban regions than in rural ones. Overall, more regions in the south had higher EAR. Temporally, the EAR showed a dynamic pattern, increasing in less urban regions and decreasing in highly urbanized regions. Regional analysis showed that urbanization level, population density, and poverty incidence had a significant positive relationship with the EAR. Beyond the immediate impacts, NTL effectively revealed spatiotemporal dimensions of the economic effects of a long-term global hazard.

关键词： Economic impact assessment Regression Remote sensing Spatiotemporal spatial error model Philippines

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Differences in urban heat island and its driving factors between central and new urban areas of Wuhan, China

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第20期30卷 58362-58377页

作者： Chen, Xie Zhang, Shicong Tian, Zhiyong Luo, Yongqiang Deng, Jie Fan, Jianhua Huazhong Univ Sci & Technol Sch Environm Sci & Engn Wuhan 430074 Peoples R China China Acad Bldg Res Inst Bldg Environm & Energy Beijing 100013 Peoples R China Univ West London Sch Comp & Engn St Marys Rd London W5 5RF England Tech Univ Denmark Dept Civil & Mech Engn DK-2800 Lyngby Denmark

Urban heat island (UHI) is one of the important effects of urbanization on built environment. Land surface temperature data was taken from moderate-resolution imaging spectroradiometer (MODIS) to investigate the long-term spatiotemporal patterns of UHI in Wuhan during 2001 similar to 2018 and, the UHI intensity changes of built-up land in 13 administrative regions in Wuhan were analyzed. Furthermore, 34 spatial error models and 34 ordinary least squares models were established and compared. spatial error models showed good fitting effect, which were used to determine the influence of normalized difference vegetation index (NDVI), normalized difference building index (NDBI), and social-economic factors (population and nighttime light) on UHI intensity in central urban area and new urban area. The explanatory power changes of these four indicators during 2001 similar to 2018 were explored as well. The average UHI intensity in 2014 similar to 2018 has increased by about 0.45 degrees C compared to that in 2001 similar to 2005. NDBI is the most dominant factor contributing to the increase in temperature. The impact of NDVI on UHI intensity changes from negative to positive, and the impact of NDBI on UHI intensity in central urban area is weakened during 2001-2018. Social-economic factors have a greater impact on new urban area than on central urban area. These findings show the effects and the explanatory power changes of driving factors during 18 years, which can provide a better understanding of the formation and development of UHI and support for the future urban planning of Wuhan.

关键词： Urban heat island Land surface temperature spatial autocorrelation spatial error model

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Autoregressive model With spatial Dependence and Missing Data

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JOURNAL OF BUSINESS & ECONOMIC STATISTICS 2022年第1期40卷 28-34页

作者： Zhou, Jing Liu, Jin Wang, Feifei Wang, Hansheng Renmin Univ China Ctr Appl Stat Beijing Peoples R China Renmin Univ China Sch Stat Beijing Peoples R China Nankai Univ Sch Stat & Data Sci LPMC Tianjin 300071 Peoples R China Nankai Univ KLMDASR Tianjin 300071 Peoples R China Peking Univ Guanghua Sch Management Beijing Peoples R China

We study herein an autoregressive model with spatially correlated error terms and missing data. A logistic regression model with completely observed covariates is used to model the missingness mechanism. An autoregressive model is used to accommodate time series dependence, and a spatial error model is used to capture spatial dependence. To estimate the model, a weighted least squares estimator is developed for the temporal component, and a weighted maximum likelihood estimator is developed for the spatial component. The asymptotic properties for both estimators are investigated. The finite sample performance is assessed through extensive simulation studies. A real data example about Beijing's PMlevel data is illustrated.

关键词： Missing data spatial error model spatial-temporal dependence Weighted least squares estimator Weighted maximum likelihood estimator

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Impact of human capital on the green economy: empirical evidence from 30 Chinese provinces

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第5期30卷 12785-12797页

作者： Wang, Xinyang Wang, Yaping Zheng, Ruijing Wang, Jingjing Cheng, Yu Shandong Normal Univ Coll Geog & Environm Jinan 250358 Shandong Peoples R China

In China, exploring the relationship between human capital and green economy in the context of economic transformation can promote the construction of an ecological civilization and high-quality economic development. This paper is dedicated to assessing the impact of human capital stock, human capital structure, and the difference between the two on China's green economy. Using the super-efficiency slack-based measure (SBM) model, this paper evaluated the green total factor productivity ( GTFP) of 30 Chinese provinces to assess the greenness of its economy and analyzed its spatiotemporal evolution from 2000 to 2017. A spatial error model (SEM) was established to explore the impact of human capital on green economy. The results show that (1) the GTFP of China increased continuously from 0.219 to 0.457, showing a regional spatial differentiation of "eastern region > central region > western region." The spatial gaps of the green economy in the east-west and north-south directions are narrowing. (2) Moran's I index and Moran's I scatter plot indicate a significant spatial correlation between human capital and China's green economy. The local spatial correlation between human capital and green economy is mainly characterized by "high-high" and "low-low" types of agglomeration. (3) The effects of human capital stock and human capital structure on green economy were both positive, with coefficients of 0.0005 and 0.1601, respectively, but the effects of human capital structure were not significant. (4) The results of regional regression show that the difference between the human capital impact coefficients on green economy in the eastern and midwestern regions is small. The impact of human capital stock and human capital structure on green economy is consistent with the national level results. It can be preliminarily concluded that the development of China's green economy relied more on improving the human capital stock than on the improvement of the human capital structur

关键词： Green economy Human capital stock Human capital structure Super-SBM model spatial error model China

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Explaining housing rents: A neural network approach to landscape image perceptions

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HABITAT INTERNATIONAL 2025年 155卷

作者： Wang, Xiaorui Yuan, Jihui Gu, Yangcheng Matsushita, Daisuke Osaka Metropolitan Univ Grad Sch Human Life & Ecol Dept Living Environm Design Osaka Japan

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