China’s high-quality economic development: a study of regional variations and spatial evolution

Government resource allocation practices for achieving carbon neutrality need to be guided by dynamic system theory to identify potential dynamics in China. In particular, dynamic system theory addresses the complex interrelationships and identifies the changes in dynamics to support the decision-making process in pursuing carbon neutrality among international trade, transportation, industrial demand and dynamic control. However, carbon neutrality involves various activities to better understand the complex activity interrelationships. The data are collected from four major databases including 30 Chinese provinces/cities covering a six-year period with the accumulated frequency in the Baidu Index. This study proposes a hybrid system approach to dynamic resource analysis. The results showed that carbon intensity control dynamics impose a greater influence on the other dynamics toward carbon neutrality. Many provinces can achieve carbon neutrality by regulating the electronics and telecommunications equipment industry. 

Government resource allocation practices toward carbon neutrality in China: A hybrid system approach

Abstract Based on China’s new development philosophy, and following the idea of “assessing the overall levels firstly, then anchoring the subsystems contributions and finally identifying the obstacle factors”, an evaluation system of high-quality development (HQD) levels is constructed from five subsystems of innovative development (ID), coordinated development (CD), green development (GD), open development (OD) and shared development (SD). In this paper, the entropy weight TOPSIS model is used to measure the HQD levels of 19 provincial units located in the Yellow River Basin (YRB) and the Yangtze River Economic Belt (YREB) in 2010–2019, and with the assistance of the Obstacle Degree Model, the obstacle factors affecting the HQD of both are depicted. The results show that: (1) between 2010 and 2019, the HQD indices in YRB and YREB have been on an increasing trend, and the subsystems indices all show a trend of “GD &gt; CD &gt; SD &gt; ID &gt; OD”, but the overall levels of HQD in both is not high, and there is still much room for improvement. (2) YREB has a significantly higher HQD level than YRB. From 2010 to 2019, the average values of the HQD indices in YRB and YREB were 0.2886 and 0.4073, respectively, but the index in YRB improves at a faster pace than YREB, and the gap of HQD levels between the two show a trend of continuous reduction towards regional balance and coordination. (3) The analysis of obstacle factors shows that while OD and ID are the main obstacles to HQD in both basins, the impact of both on the resistance to HQD in both basins is on a decreasing trend. GD is another bottleneck for the HQD of YRB, and the pressure on GD in the middle and upper reaches of the provincial units in the basin is increasing, and the obstacles are on a rising trend. 

Evaluation and obstacle analysis of high-quality development in Yellow River Basin and Yangtze River Economic Belt, China

Promoting high-quality development of logistics industry (LHQD) becomes imperative to decrease logistics costs in real economy and intensify endogenous power of economic development. In this paper, we construct an evaluation system consisting of 21 indicators in four dimensions: output scale, operation quality, social contribution and green development. Thereafter, we apply improved entropy weight method, kernel density estimation and Dagum Gini coefficient to carve out spatial and temporal distribution, dynamic evolution and regional differences of LHQD. Finally, we adopt geographical and temporal weighted regression (GTWR) model to reveal heterogeneous effect of each selected factor on LHQD. Outcome indicates: (1) The level of LHQD rose at average annual growth rate of 2.776% between 2004 and 2018, however with significant regional and inter-provincial differences. (2) The LHQD in the whole nation and the three major regions were bipolar or multipolar, with distinctive gradient characteristics. (3) The overall difference of LHQD primarily came from deviation in growth among three major regions, and this difference kept expanding. (4) The impact of various factors on LHQD was characterized by spatial and temporal heterogeneity. Specifically, the positive impact of innovation capacity, digitalization level, foreign direct investment, transportation infrastructure and environmental regulation on development of logistics industry gradually increased with time. It was stronger for eastern region than western region. In contrast, industrial agglomeration’s stimulus to LHQD had diminished, and the high-value region of its positive impact had shifted from east to center. 

Dynamic evolution, regional differences and influencing factors of high-quality development of China’s logistics industry

Tourism flows are an elementary part of mobility and an important topic in tourism research. Utilising Baidu migration data, we model tourism flows during China's May Day holiday from 2019 to 2023. Employing hotspot analysis and the differential index for tourism flow methods, we scrutinise the spatial and temporal dynamics of tourism flows in China before, during, and after the COVID-19 pandemic. The findings are as follows: (1) Regarding temporal variations, the COVID-19 pandemic has profoundly impacted China's tourism industry, albeit with a slow recovery currently underway. (2) Spatially, tourism flows primarily concentrated within four major city clusters, with emerging tourism destinations like Zibo gradually gaining prominence. (3) Analysing changes in tourism flow during holidays reveals that cities can be categorised into four major types and six minor types, with a predominant trend of continuous decrease in tourism flow. These findings shed light on the intricate dynamics of tourism flows in China, offering valuable insights for stakeholders in the tourism sector. 

Spatial evolution pattern of tourism flow in China: case study of the May Day Holiday based on Baidu migration data

Soundscape is the production of sounds and the acoustic environment, and it emphasizes peoples’ perceiving and experiencing process in the context. To this end, this paper focuses on the Pearl River Delta in China, and implements an empirical study based on the soundscape evaluation data from the Participatory Soundscape Sensing (PSS) system, and the geospatial data from multiple sources. The optimal variable set with 24 features are successfully used to establish a random forest model to predict the soundscape comfort of a new site (F1 = 0.61). Results show that the acoustic factors are most important to successfully classify soundscape comfort (averaged relative importance of 17.45), subsequently ranking by built environment elements (11.28), temporal factors (9.59), and demographic factors (9.14), while landscape index (8.60) and land cover type (7.71) seem to have unclear importance. Furthermore, the partial dependence analysis provides the answers about the appropriate threshold or category of various variables to quantitatively or qualitatively specify the necessary management and control metrics for maintaining soundscape quality. These findings suggest that mainstreaming the soundscape in the coupled natural–human systems and clarifying the mechanisms between soundscape perception and geospatial factors can be beneficial to create a high-quality soundscape in human habitats.

/pdf/what-constitutes-the-high-quality-soundscape-in-human-19yao68m.pdf

What Constitutes the High-Quality Soundscape in Human Habitats? Utilizing a Random Forest Model to Explore Soundscape and Its Geospatial Factors Behind

Despite growing attention from researchers and governments, challenges persist in comprehensively assessing urban sound quality by integrating both quietness and naturalness aspects.This study aimed to develop an innovative soundscape quality index that concurrently evaluates quietness and naturalness in urban soundscapes. Our objectives included conducting urban soundscape quality mapping, analyzing influential mechanisms, and identifying priority zones for sound environment management.We collected sound pressure level (SPL) and raw audio data, from which we computed a normalized difference soundscape index (NDSI). With a dataset comprising 28 explanatory variables encompassing land use, built environment, vegetation characteristics, and temporal factors, we employed the random forest (RF) model to predict 10 indicators, including eight SPL-related indices, NDSI, and the QNS (quietness and naturalness soundscape) index. Crucially, we utilized SHAP (SHapley Additive exPlanations) values to interpret the RF model.Spatial variations in quietness and naturalness were evident, closely associated with road networks and vegetation, respectively, with discernible temporal variations. The top three variables influencing QNS were distance to major roads, normalized difference vegetation index (NDVI), and proportion of tree coverage. Moreover, interaction effects highlighted dual negative or synergistic promoting effects on QNS from factors such as road width, human disturbance, vegetation configurations, and land cover. Notably, these mechanisms were successfully applied to six typical tourist attractions in Xiamen city, where five types of management zones were mapped based on priority considerations of population density and soundscape quality. Interestingly, natural soundscape reserves were highly correlated with city parks, high-risk zones predominantly overlapped with road networks, and potential zones comprised inner communities between streets.The framework demonstrated effectiveness in mapping, exploring mechanisms, and guiding management strategies for the urban sound environment. 

An exploratory framework for mapping, mechanism, and management of urban soundscape quality: From quietness to naturalness.

Spatial-temporal differentiation pattern and influencing factors of high-quality development in counties: A case of Sichuan, China

Clustering algorithms are widely used to mine the heterogeneity between meteorological observations. However, traditional applications suffer from information loss due to data processing and pay little attention to the interaction between meteorological indicators. In this paper, we combine the ideas of functional data analysis and clustering regression, and propose a functional clustering regression heterogeneity learning model (FCR-HL), which respects the data generation process of meteorological data while incorporating the interaction between meteorological indicators into the analysis of meteorological data heterogeneity. In addition, we provide an algorithm for FCR-HL to automatically select the number of clusters, which has good statistical properties. In the later empirical study based on PM2.5 concentrations and PM10 concentrations in China, we found that the interaction between PM10 and PM2.5 varies significantly between regions, showing several types of significant patterns, which provide meteorologists with new perspectives to further study the effects between meteorological indicators.

/pdf/heterogeneous-learning-of-functional-clustering-regression-i1wehfal.pdf

Heterogeneous Learning of Functional Clustering Regression and Application to Chinese Air Pollution Data

Clusterwise Functional Linear Regression Modelling by Gibbs Sampler and Information Criterion

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