Medium-sized Cities In China Research Articles

Are Medium-Sized Cities in China Shrinking from 2010 to 2020? An Empirical Analysis with a Multi-Dimensional Model

Urban shrinkage has emerged as a worldwide concern, which is increasingly prevalent in developing countries like China, particularly in medium-sized cities (MSCs). Compared to large cities and counties, MSCs find themselves in a national policy blind spot and have been neglected both in policy and research. Previous studies, based on population changes, have shown that urban shrinkage in China is not severe. However, urban shrinkage is not just about population decline, and it has not been adequately discussed from a multi-dimensional perspective. This paper adopts a multi-dimensional model, considering population, economy, and space dimensions to analyze shrinkage patterns in 164 MSCs in China from 2010 to 2020. Findings reveal that 6.1% of MSCs experienced population shrinkage, and 24.4% faced shrinkage in economic or spatial dimensions. Shrinking MSCs are spatially distributed in the Northeast as well as in the areas surrounding large cities. Industrial restructuring, job losses, and healthcare disparities contribute to urban shrinkage, and the impact of aging will be further felt. As large cities consistently attract population and resources and digitization has dramatically affected population mobility, more MSCs will shrink in the future. This paper contributes to the understanding of shrinkage of MSCs among scholars and policymakers, urging a shift towards more balanced and digital urban governance.

A Method for Evaluating Demand Response Potential of Industrial Loads Based on Fuzzy Control

Demand response (DR) can ensure electricity supply security by shifting or shedding loads, which plays an important role in a power system with a high proportion of renewable energy sources. Industrial loads are vital participants in DR, but it is difficult to assess DR potential because of many complex factors. In this paper, a new method based on fuzzy control is given to assess the DR potential of industrial loads. A complete assessment framework including four steps is presented. Firstly, the industrial load data are preprocessed to mitigate the influence of noisy and transmission losses, and then the K-means algorithm considering the optimal cluster number is used to calculate baseline load of industrial load. Subsequently, an open-loop fuzzy controller is designed to predict the response factor of different industrial loads. Three strongly correlated indicators, namely peak load rate, electricity intensity, and load flexibility, are selected as the input of fuzzy control, which represents response willingness. Finally, the baseline load of diverse clustering scenarios and the response factor are used to calculate the DR potential of different industrial loads. The proposed method takes into account both economic and technical factors comprehensively, and thus, the results better represent the available DR potential in real-world situations. To demonstrate the effectiveness of the proposed method, the case of a medium-sized city in China is studied. The simulation focuses on the top eight industrial types, and the results show they can contribute about 189 MW available DR potential.

The Impact of Park Green Space Areas on Urban Vitality: A Case Study of 35 Large and Medium-Sized Cities in China

The Impact of Park Green Space Areas on Urban Vitality: A Case Study of 35 Large and Medium-Sized Cities in China

System simulation and multi-objective optimization methodology for sustainable municipal solid waste classification management: A case study in China

Cities worldwide are actively implementing waste classification policies to develop efficient waste management systems that address the growing challenges of waste disposal and promote sustainable development. However, selecting tailored waste classification solutions is challenging due to the intricate interrelationships within the system and the complex trade-offs between different management objectives. This study presents a comprehensive methodology that simulates the entire waste classification management system by integrating the stages of waste generation, source classification, and final disposal, all linked by waste quantities and properties. A multi-objective optimization model is employed to identify optimal technical pathways that balance economic, environmental, and energy objectives. This methodology was applied in Zhangjiagang, a medium-sized city in China, revealing that economically optimal solutions focusing on landfill and composting as disposal pathways could exacerbate environmental impacts, with indicators deteriorating by 23.1 %–419.1 % except for freshwater eutrophication potential. In contrast, a multi-objective optimization solution prioritizing incineration and anaerobic digestion achieves environmental and energy benefits valued at 18.7 USD/t, with a modest increase in economic costs of 5.7 USD/t compared to the base year. The unconstrained optimal scenarios achieve net negative environmental costs and recover 2.0 × 106 MJ of energy. The study recommends moderate and dynamically adjusted kitchen waste separation rates depending on the stage of implementation. Additionally, the findings reveal that the “lock-in” effects of mismatched disposal facilities can significantly escalate costs. In conclusion, this study underscores the importance of systematic design and multi-objective optimization, offering a comprehensive methodology for cities aiming to enhance their waste classification strategies.

Measurement and Evaluation of the Business Environment in Large and Medium-Sized Cities in China

The comprehensive development level of the business environment reflects the quality of a region’s economic development, and a good business environment will give a city a strong vitality. This paper uses the entropy method to measure and evaluate the business environment of 106 large and medium-sized cities in China from 2017 to 2021. The results show that: From the spatial point of view, the business environment index of China’s cities shows a gradually decreasing pattern from east to west and from south to north. The balance of the business environment of large and medium-sized cities in China is divided into four levels according to the standard deviation of the ranking of each sub-index. The greater the standard deviation, the more unbalanced the overall development of the business environment in the region. Finally, this paper put forward countermeasures and suggestions to further optimize the business environment.

Spatial quality measurement of commercial streets in old cities of medium-sized cities in China

ABSTRACT Chinese urban development has entered the stage of stock planning, and as an important part of urban public space, the construction of street space has a direct impact on urban development. High-quality street space contributes to sustainable urban development.High-quality street spaces are highly important for constructing vibrant and sustainable cities. Taking Jiaozuo city, a medium-sized city, as an example, this article establishes an evaluation index system for commercial street space quality from the aspects of external characterization and objective material space and uses the Pearson correlation coefficient to explore the influencing factors of urban commercial street space quality. This study finds that (1) crowd activities on commercial streets have different temporal and spatial patterns on weekdays and days off; (2) the external characterization of urban street space quality has a significant positive correlation with objective material spatial elements such as the density of public transportation stops, functional density, functional mixing, interface continuity, and spatial green visibility rate; (3) the enhancement of street accessibility and fine-tuning of functions are key to improving the spatial quality of commercial streets.This study provides theoretical basis and ideas for improving the spatial quality of commercial streets in medium-sized cities in China and promoting sustainable urban development.

Does income inequality restrain marriage? A longitudinal study from the 35 large and medium-sized cities of China

In recent years, the continuous decline of marriage rate in Chinese cities has attracted significant attention. The study examined the impact of income inequality on marriage rates, and explored the mechanisms through which this relationship operates. Panel data of 35 large and medium-sized cities of mainland China from 2004 to 2019 were used to empirically test the theoretical hypothesis, finding the following results: Firstly, widening income inequality emerges as a crucial factor contributing to the decline in marriage rates within Chinese cities. Secondly, the impact of the income inequality on the marriage rate shows significant spatial and temporal differences in urban areas. Lastly, factors such as Internet penetration, urbanization and housing price are important channels for income inequality to affect the marriage rate. The objective of this study is to explain the decline of marriage rate in China's cities from the perspective of income distribution, and subsequently unveil the macro-level correlation between income inequality and marital behavior.

Effects of the built environment on travel distance in bus-oriented, medium-sized cities in China

The impact of the built environment and weather conditions on travel behavior has been widely studied. However, limited studies have focused on better understanding such effects in medium-sized cities with bus-oriented transit systems, particularly from a separate perspective of travelers’ origins and destinations. We took Weinan, China, as a representative of second-tier cities in developing countries that concentrate on bus-oriented development strategies. New evidence of feature importance and nonlinear effects of crucial factors were revealed by an interpretable machine learning-based approach combining XGBoost and Shapley Additive Explanation (SHAP) with multi-source data. Most key factors were critical at both origins and destinations, such as the density of residential and commercial facilities. However, several important factors, such as road density and boarding time, had strong imbalanced effects on travel behavior. These findings provide novel insights and empirical implications to support urban planning strategies in medium-sized cities.

THE DIFFERENTIAL EFFECTS OF CREDIT POLICY ON REAL ESTATE PRICES FROM THE PERSPECTIVE OF CLUB CONVERGENCE

This study investigates the differential effects of credit policy in the context of real estate price convergence in China. First, we analyze the real estate price convergence of 35 large and medium-sized cities in China using [Formula: see text] convergence and club convergence methods. Subsequently, based on the convergence situation, we use panel models to compare the heterogeneous effects of the credit policy regulation of different clubs. The findings are as follows. (1) In terms of overall convergence, the findings confirm that there is no [Formula: see text] convergence in China’s real estate prices. Prices in each city do not show a trend of convergence to the total mean. (2) There are currently four convergence clubs and one divergence group in terms of regional convergence. Cities with similar initial price levels have similar structural characteristics and tend to converge to their respective local steady states. GDP per capita and population density are the main driving forces for the formation of a real estate price convergence club. (3) We show that the credit scale has a statistically positive impact on the real estate price, and the credit scales of various convergence clubs have different effects on real estate prices. Clubs with more developed real estate and financial markets are more sensitive to changes in credit policy.

Managing Uncertainty in Urban Road Traffic Emissions Associated with Vehicle Fleet Composition: From the Perspective of Spatiotemporal Sampling Coverage

With pronounced differences in emission factors among vehicle types and marked spatiotemporal heterogeneity of vehicle fleet composition, extrapolating fleet composition from insufficient sample hour periods and road segments will introduce significant uncertainty in calculating regional daily road traffic emissions. We proposed a framework to manage uncertainty in urban road traffic emissions associated with vehicle fleet composition from the perspective of spatiotemporal sampling coverage. Initially, the respective relationships of the temporal and spatial sampling coverages of fleet composition with the resulting regional daily road traffic emission uncertainties were determined, using the core area of a typical small and medium-sized city in China with the widely-used International Vehicle Emissions (IVE) model as example. Subsequently, function models were developed to explore the determination of the spatiotemporal sampling coverage of fleet composition. These results of emission uncertainties and function models implied that gases with larger emission factor discrepancies between vehicle types, such as NOx, required greater spatiotemporal sampling coverage than gases with smaller discrepancies, such as CO2, under the same uncertainties target. Therefore, sampling efforts should be prioritized for gases with larger emission factor discrepancies. Additionally, increasing sampling coverage in one dimension (either spatial or temporal) can reduce the minimum required coverage in the other dimension. To further reduce uncertainty, enhancing both spatial and temporal sampling coverage of the fleet composition is more effective than enhancing one type of coverage alone. The framework and results proposed in this work can reduce the uncertainty of emissions calculations caused by insufficient sampling coverage and contribute to more accurate transport emission reduction policy formulation.

Factor Analysis of EV Purchase Behavior in Medium-sized Cities in China:

Factor Analysis of EV Purchase Behavior in Medium-sized Cities in China:

Exploring the combined cooling effect of street canyon geometry and the surrounding built environment.

Exploring the impact of complex urban morphology on the urban heat island (UHI) effect is essential for sustainable environmental management and enhancing human well-being. This study explored the combined cooling effect of street canyon geometry and the surrounding built environment using a CatBoost model and the Shapley method. The findings indicated that in streets with low building height and density, a high proportion of sky and vegetation and a flatter skyline are conductive to mitigate UHI effect. In streets with high building height and density, a lower proportion of sky and vegetation, and a well-proportioned skyline, can effectively mitigate UHI effect. Regardless of the building density and height around the street, street trees are the optimal choice for greening construction and improvement of large and medium-sized cities in China, given their high controllability and the current urban stock background. Therefore, reasonable control and allocation of street trees can effectively adjust the street canyon geometry, providing suitable cooling strategies for streets with different surrounding built environments. This study proposed a method to mitigate the UHI effect through street canyon geometry, which can be extended to other high-density urban thermal environment studies and guide policymakers on street construction and urban design.

중국의 토지 공급 정책이 부동산 시장에 미치는 영향

Purpose - This study aims to explore the relationship between housing and land prices, with a specific emphasis on the impact of government policies on these factors such as land supply quantity and the ratio of residential land to total land supplied. The goal is to identify the most effective government intervention strategies for controlling both housing and land prices. Design/methodology/approach - Data from 70 primary and medium-sized cities in China spanning from 2003 to 2017 are utilized in this research. The analysis employs a panel vector autoregressive (PVAR) model, with a primary focus on examining the relationships among housing prices, land prices, and government intervention policies. Findings - Housing and land prices are influenced by various factors. Through impulse response analysis and variance decomposition, it is observed that both housing and land prices are predominantly influenced by their internal dynamics, with comparatively weaker effects attributed to policy interventions. Research implications or Originality - By investigating the impact of government policies on housing and land prices, This study establishes a foundation for effective price control measures. Our study advocates for a comprehensive examination of China's land supply mechanism to enhance understanding of the pathways through which government policies influence the markets.

HOUSE PRICE VOLATILITY IN CHINA: A PERVASIVE PATTERN WITH GEOGRAPHIC DISPARITY

The booming real estate sector has been regarded as the “gray rhino” risk emerging in China over the past decade. Yet, the house price volatility per se has not been thoroughly examined. Filling the gap in the literature, this paper explores the house price volatility and its determinants for 70 large and medium-sized cities in China, using an extensive monthly data set from 2005 to 2019. We find evidence of significant geographical disparities in both the GARCH effects and the best-fitted volatility specification. Significant GARCH effects are found in 57 cities, among which 40% of cities show a persistent volatility pattern. We also find that both the house price volatility pattern and the associated volatility value are affected significantly by education and healthcare amenities.

The Construction of Sponge Public Space in Child-Friendly Cities - Takes the Waterfront of Macao as the Prospect

As countries protect the rights of children and safeguard the rights and interests of children through laws, regulations, and policies, for the concept of children from "demand" to "respect rights", the understanding of "child-friendly" urban design planning concept is also deepening and strengthening, many large and medium-sized cities in China are active efforts to explore the construction of "child-friendly" city and community. This paper first analyzes the current situation of the waterfront area of Macao, then analyzes the concept of child-friendly areas through other cases, mainly focusing on the technical and ecological means of functional wetland, and builds the public space of the waterfront area of Macao, to provide suggestions for the construction of the waterfront area of the inner port of Macao.

Urban park green space use analysis based on trajectory big data: Experience from a medium–sized city in China

Regular visits to park green space offer remarkable benefits for the physical and mental health of urban residents. Achieving a comprehensive understanding of the utilization across the entire city is a prerequisite for improving the overall utilization rate of park green spaces. Traditional social survey methods are limited by their sample size and time-consuming nature, while methods based on geographic location big data are gaining momentum. This study focuses on Xuchang, a medium-sized city in China, and systematically analyzes the current state and influencing factors of park green space utilization by mining GPS trajectory big data from April 3 to 12, 2022. Results indicate that residents' choices of park green spaces are highly diverse. Approximately 20% of visitors on holidays and weekends, and about 25% of visitors on weekdays, prefer the park green space closest to their homes. Notably, the distance threshold for park green space visits on weekdays, weekends, and holidays is 3633, 3824, and 4127 m, respectively. These distances are significantly higher than the several hundred meters specified in planning documents or commonly used in accessibility analyses. For individuals who frequently visit park green spaces, distance is the most critical influencing factor. Conversely, for those who occasionally visit, distance is not the primary consideration. For individuals who rarely or never visit park green spaces, personal attitudes play an essential role. In comparison to weekdays, the number of visitors on holidays and weekends is larger, the travel distance is longer, and they are more inclined to choose larger parks. Visits are concentrated in the afternoon and evening, and weather changes remarkably affect park green space utilization. Importantly, no compensatory effect is observed between the frequency and duration of park green space visits. These findings hold important implications for urban planning, management, and the promotion of park green space utilization.

Economic Effect and Disparity of Airport Economic Zones Utilizing the Synthetic Control Method

This study investigates the economic impact of an airport economic zone (AEZ) on a city using the synthetic control method (SCM). It is an important issue for small-sized and medium-sized cities to make effective strategic plans for AEZs according to their own characteristics. Cities have begun to imitate the successful experiences of AEZs in cities with larger economies, but they often fail to replicate the same path. Moreover, the current methods are likely to reduce the reliability of the causality argument because of the lack of random sampling and random allocation of treatment. The SCM can solve this problem. In this research, the SCM was used to investigate panel data for 37 small- and medium-sized cities in China for the period 2000–2016. The results showed that the presence of an airport had a significant positive economic effect on cities with a higher economic strength and a weak positive effect on cities with a lower economic strength in our treatment group sample. In investigating the causal mechanism for this disparity, we found that the higher the match between the types of industries laid out in the AEZ and the city’s dominant industries, the better the economic development from a long-term development perspective. In addition, since AEZs are embedded in specific socio-economic contexts, it is sometimes difficult to apply successful experiences to AEZs in less developed cities. Our research utilized a new policy evaluation method and has significant implications for decision-makers.

Research on Subsidy Policies to Promote China’s Low-Carbon Development: Taking the Promotion of Electric Vehicles as an Example

The large-scale promotion of electric vehicles is China’s strategic measure to alleviate the energy crisis, deal with climate change and promote green development. Taking 19 large and medium-sized cities in China as samples, this thesis constructs a quantitative model of policy subsidies and a gray correlation model to evaluate the subsidies of the main demand-side policies of electric vehicles and their impact on the demand for electric vehicles. The study found that the subsidy intensity (varies from 34,000 to 129,000 yuan per year) of the “direct” policy is stronger than that of the “indirect” policy (ranges from 5,000-104,000 yuan per year). The subsidy intensity of the “indirect” policy varies greatly among cities. Thanks to the convenience of the use link, the gray correlations between the intensity of the “direct” and “indirect” policies and the promotion of electric vehicles are 0.61 and 0.92, respectively, indicating that the influence of the “indirect” policy on consumer behaviour is greater than that of the “direct” policy significantly. Based on the research conclusions, in order to further expand the electric vehicle market, this thesis intends to put forward relevant policy suggestions.

The measurement of financial support for real estate and house price bubbles and their dynamic relationship: An empirical study based on 31 major cities in China

<p>In recent years, China's real estate prices have continued to rise, preventing the bursting of the house price bubble, and the various risks it triggers has become an important issue that governments at all levels have to face. In this paper, the backward sup ADF (BSADF) method was used to dynamically portray the evolution of real estate price bubbles in 31 large and medium-sized cities in China over a period of 11 years, from 2013 to 2021, and the dynamic relationship between the degree of financial support for real estate (hereinafter referred to as financial support) and house price bubbles in cities was investigated by employing a panel vector autoregressive (PVAR) model with panel data. The results showed that: Multiple cyclical bubbles significantly existed in the real estate markets of the cities during the study period, and in general, house price bubbles appeared earlier in the more economically developed regions than in the less economically developed regions and have spread to the less economically developed regions. Financial support contributed to house price bubbles, supporting the theory of excessive financial support. Furthermore, the results of the sub-sample showed that financial support contributed most strongly to house price bubbles in cities in the northern region.</p>

A Study on the Factors Influencing Household Consumption from a Money Demand Perspective: Evidence from Chinese Urban Residents

Based on the classic Keynesian theory of money demand and city panel data, in this study, we investigate the impacts of different money demand motives on urban household consumption in China, and provide ideas for promoting sustainable growth in household consumption in China. The results of this study show the following: First, in general, the theory of money demand motivation can adequately explain household consumption in large and medium-sized cities in China. Second, the CPI time series has a significant adverse effect on the real money demand of most households. Third, residents significantly reduce food consumption to satisfy speculative money demand for financial instruments, and the lower the income level, the keener households are to invest in high-risk stocks. Fourth, even for high-income households, the precautionary money demand generated via the purchase of commercial insurance still has a significant crowding-out effect on their total consumption. Social security spending, which reduces the precautionary money demand of households, has a much more positive impact on high-income households.