Gini Coefficient Method Research Articles

The evolution trend, regional differences, and influencing factors of sustainable development efficiency in rural China under the constraint of water and energy

AbstractWater and energy are not only the foundation of high‐quality development in rural areas but also important constraints. Exploring the inner relationship between water, energy, and economic development has important significance for the high‐quality development in rural areas. This paper first constructs the evaluation index system of high‐quality development in rural areas, and quantifies high‐quality development levels in rural China from 1997 to 2021. Second, from the perspective of water footprint, the Super‐slack based measure model is used to calculate sustainable development efficiency under the constraint of water and energy. Finally, kernel density function, Gini coefficient method, and Tobit model are used to explore the evolution trend, regional differences, and driving factors of sustainable development efficiency in rural China. The results show that: (1) Under the constraint of water and energy, sustainable development efficiency in rural China is relatively low, showing a fluctuating trend of first rising and then declining. The efficiency in the western and eastern regions is higher than the national average, while the central region is the lowest. (2) The evolution trend of sustainable development efficiency in rural China, both the eastern and the western regions, shows the characteristics of “decreasing efficiency and weak polarization phenomenon,” while the central region shows the characteristics of “decreasing efficiency and no polarization phenomenon.” (3) The regional differences illustrate a fluctuating upward trend, which is mainly caused by the hypervariable density, followed by the differences among provinces in the eastern region. (4) Economic development, opening up, and rural marketization are the main factors.

Analysis of Regional Differences, Dynamic Evolution and Convergence Characteristics of The Level of Rural Revitalization in China

To help rural revitalization, it is extremely important to understand the inter-provincial characteristics. This paper uses the entropy value method to measure the comprehensive index of rural revitalization level, and reveals the regional differences and dynamic evolution trend of rural revitalization with the help of Dagum Gini coefficient and kernel density method, and finally uses the convergence model to test the spatial and temporal convergence. The study finds that the level of rural revitalization is low but tends to increase year by year. Regional differences are the main reason for the differences in rural revitalization, but they are decreasing. There is a polarization phenomenon of inter-provincial rural revitalization level, while there is a tendency for low-level provinces to catch up with high-level provinces. Finally, it is suggested that all kinds of factors should be fully activated to actively promote the coordinated development of regional rural revitalization level.

Regional unevenness in the construction of digital villages: A case study of China.

In regard to the comprehensive promotion of rural revitalization, the construction of digital villages is a crucial development. Because the construction of digital villages is considerably novel, the existing studies mainly focus on the theoretical aspects pertaining to the rational and practical robustness of digital villages, and with regard to regional unevenness, the number of studies that consider the current characteristics, absolute gaps, and impact mechanisms pertaining to the construction of digital villages is insufficient. Based on the regional unevenness that characterizes digital village construction, this study proposes a research framework for digital technology-enabled village construction, which integrates three major factors, namely technology, institutions, and human resources; thus, the comprehensive assessment pertaining to the level of digital village construction is enhanced. This study, which applies the aforementioned research framework, constructs an index system for evaluating the construction level of digital villages, and to reveal the characteristics pertaining to regional heterogeneity and the main influencing factors pertaining to the construction level of digital villages in China (study period; 2015-2020), it utilizes the Dagum Gini coefficient method and the spatial econometric model. Consequently, the researchers observe the following: First, the level of digital village construction in China exhibits a "W-shaped" recovery growth. Second, with respect to the regional level, the eastern region exhibits the highest level of digital village construction, followed by central and western regions; furthermore, we observe that the eastern and western regions account for the greatest intra-regional variation, and that with regard to the overall difference, the inter-regional gap represents the main causative factor. Finally, with regard to influencing factors, technology and innovation capabilities, occupational differentiation of farmers, economic development significantly contribute to the level of digital village construction, whereas fiscal autonomy exerts a significant inhibiting effect. In regard to the level of digital village construction, the research framework and results may provide a novel analytical framework for examining the main sources of regional unevenness, and it may also provide a reference for decision-making, which can influence the construction of digital villages in China as well as in other countries.

What drives the temporal dynamics and spatial differences of urban and rural household emissions in China?

Temporal dynamics and spatial differences of urban and rural household CO2 emissions are worth exploring in the face of climate change. This study combines decomposition analysis models and Dagum's Gini coefficient method to explore household emission characteristics and key drivers of 30 provinces in China. Income is still the major factor driving the growth of household emissions. Urbanization is accompanied by a population shift from rural areas to urban areas. Population has a positive effect on urban household emissions and a negative effect on rural household emissions. Although the rural population gradually decreases, their increasing income level and propensity to consume lead to emissions that are not negligible. Household CO2 emissions have significant spatial differences. For urban households in the eastern coastal region and rural households in the northern region, the population size effect drives the growth of household emissions. Rational population mobility is beneficial to reduce intra-regional differences in household emissions. Propensity to consume and emission intensity are important factors driving household emission growth in underdeveloped provinces and resource-based provinces, respectively. Thus improving household green awareness and promoting consumption upgrades are urgent. To enhance the coordination of regional development, cross-regional flows of technology and capital will help reduce regional emission disparities.

Research on Discharge Permit Allocation in Lushui River Based on Environmental GINI Coefficient

Water pollution is the main cause of global ecological degradation and seriously affects people’s water supply. In order to respond to the water environmental protection policy and provide management departments with a basis for refining water quality, this paper uses the environmental Gini coefficient (EGC) method based on four indicators, such as water environmental capacity, population, land area, and gross domestic production (GDP), to represent social, economic, and environmental factors, respectively. After the optimization, for COD, the EGC based on the land area was 0.30, EGC based on population was 0.21, EGC based on environment capacity was 0.02, and the EGC based on GDP was 0.45, and the sum of EGC was 0.962. From this result, we can observe that the change in the Gini coefficient of each indicator is not very considerable. Hence, the most significant change in the Gini coefficient was that of GDP, with a higher rate than the other criteria. Then, the COD, AND, and TP discharge allocation models were constructed to obtain the total allocated discharge permit for the Lushui Basin. The results show that the total discharge permit allocations of COD, AN, and TP for the Lushui Basin are 51,483.304, 843.119, and 340.926 tons/year, respectively. Based on GIS spatial analysis technology, the distribution of unfair factors that cause pollution inequity is investigated. Finally, reduction measures were proposed to implement environmental supervision and improve water environmental management.

Analysis of Inequality Levels of Industrial Development in Rural Areas through Inequality Indices and Spatial Autocorrelation

To achieve a balanced and sustainable rural development pattern, this study used the Theil index, Gini coefficient, and Spatial Autocorrelation method to analyze the inequality of industrial economic development between rural areas in South Korea. The results showed that considering the whole of South Korea, the inequality in industrial economic development level among rural areas was reasonably balanced, and the inequality level showed a trend of narrowing. Considering the first-level regions, the levels of inequality varied. The economic development level of the rural areas around the Capital Area and Metropolitan Cities was higher than that of other rural areas, and policies should be established to support the economic development of other rural areas.

Predicting the utilization factor of blasthole in rock roadways by random forest

The utilization factor of blasthole is a crucial indicator of the effectiveness of blasting in rock roadways. A significant value means that the explosive energy is fully utilized, the single-cycle advance is high, and the excavation rate is fast. A good blasting programme is a prerequisite for improving the utilization rate and predicting the utilization rate before blasting operations can verify the feasibility of the blasting programme. Firstly, a database of rock roadway blasting covering different geological and production conditions is established. Secondly, error analysis and the Gini coefficient method are used to weight the characteristic variables, quantify the importance of the variables and identify eight key indicators affecting the blasting hole utilization rate. Then, a random forest algorithm-based model for predicting utilization factor of blasthole is proposed, and the results of the model on the test set are: root mean square error (RMSE) is 0.0137, mean absolute error (MAE) is 0.0087, and coefficient of determination (R2) is 0.905. The performance of this method is compared with that of the neural network and support vector machine models on the test sets to verify the superiority of the random forest algorithm. Finally, to verify the generalization ability and practicality of the random forest prediction model, the model is applied to the rock roadway blasting construction of Gu Bei coal mine in Anhui Province. The results show that R2 is 0.913, so the model is reliable and accurate, which can meet the actual engineering requirements and lay the foundation for the promotion and application of this technology.

Spatial-Temporal Differentiation and Spatial Interaction Effect Analysis of Residents’ Consumption Capacity and Consumption Upgrading in China

To investigate Chinese residents’ consumption capacity and consumption upgrading, this study uses the Moran index, the Dagum Gini coefficient method, and the SPVAR model to analyze the spatial-temporal differentiation and interactions between the two; the entropy method is employed to measure the development level of both factors. As per the results, from 2008 to 2020, the levels of consumption capacity and consumption upgrading have increased rapidly and are distinctly unbalanced, but there is a clear trend of convergence between the developments in each province, and both show spatial agglomeration, mainly in the “high–high” and “low–low” clusters. The “high–high” clusters are primarily concentrated in the more economically developed eastern coastal and central fast-rising regions, with inter-regional differences being the main reason for the regional differences between the two. In terms of spatial interaction, the two are Granger causalities, with positive feedback effects that promote each other, and have a “center–periphery” spatial spillover effect, where the center generates spatial spillover and also suffers reverse shocks from the periphery, while the shock effect is limited by inter-provincial economic ties and geographical distance.

Variation and internal-external driving forces of grey water footprint efficiency in China's Yellow River Basin.

Grey water footprint (GWF) efficiency is a reflection of both water pollution and the economy. The assessment of GWF and its efficiency is conducive to improving water environment quality and achieving sustainable development. This study introduces a comprehensive approach to assessing and analyzing the GWF efficiency. Based on the measurement of the GWF efficiency, the kernel density estimation and the Dagum Gini coefficient method are introduced to investigate the spatial and temporal variation of the GWF efficiency. The Geodetector method is also innovatively used to investigate the internal and external driving forces of GWF efficiency, not only revealing the effects of individual factors, but also probing the interaction between different drivers. For demonstrating this assessment approach, nine provinces in China's Yellow River Basin from 2005 to 2020 are chosen for the study. The results show that: (1) the GWF efficiency of the basin increases from 23.92 yuan/m3 in 2005 to 164.87 yuan/m3 in 2020, showing a distribution pattern of "low in the western and high in the eastern". Agricultural GWF is the main contributor to the GWF. (2) The temporal variation of the GWF efficiency shows a rising trend, and the kernel density curve has noticeable left trailing and polarization characteristics. The spatial variation of the GWF efficiency fluctuates upwards, accompanied by a rise in the overall Gini coefficient from 0.25 to 0.28. Inter-regional variation of the GWF efficiency is the primary source of spatial variation, with an average contribution of 73.39%. (3) For internal driving forces, economic development is the main driver of the GWF efficiency, and the interaction of any two internal factors enhances the explanatory power. For external driving forces, capital stock reflects the greatest impact. The interaction combinations with the highest q statistics for upstream, midstream and downstream are capital stock and population density, technological innovation and population density, and industrial structure and population density, respectively.

Spatial and temporal evolution and driving factors of county solid waste harmless disposal capacity in China

Currently, China mainly adopts the waste treatment model of “household sorting, village collection, town transfer and county disposal.” Determining the spatial and temporal distribution of China’s county solid waste harmless disposal capacity and formulating strategies according to local conditions are of great significance in promoting the construction of beautiful villages in China and realizing the Beautiful China strategy. This paper explores the spatial and temporal evolution characteristics of county solid waste harmless disposal capacity by selecting relevant data from 27 provinces in China from 2006 to 2020, and adopts the Dagum Gini coefficient method to measure the spatial gap of it. In addition, this paper empirically analyses the drivers affecting county solid waste harmless disposal capacity using the spatial Durbin model (SDM). The main conclusions are as follows: 1) In terms of time, county solid waste harmless disposal capacity in China as a whole shows a year-by-year increasing trend, especially after 2018 when the growth rate is faster. 2) In terms of spatial patterns, the solid waste harmless disposal capacity of coastal areas is generally higher than that of inland areas, and the distribution of provinces with low and middle levels of solid waste harmless disposal capacity is characterized by concentrated contiguity. From the perspective of spatial agglomeration, the characteristics of spatial agglomeration in the north are gradually becoming more pronounced, while those in the south are not significant. From the trajectory of the evolution of the spatial center of gravity, the center of gravity of county solid waste harmless disposal capacity as a whole shows a northeast, then northwest, then northeast movement, and the speed of “northward expansion” is greater than the speed of “eastward expansion”. 3) The results of the Dagum Gini coefficient and its decomposition show that the northeast has the smallest average annual rate of change in the Gini coefficient. The reduction of the within-group gap is an important driver towards equilibrium. The contribution of hypervariable density is decreasing year by year. 4) The number of harmless disposal plants, GDP per person, population urbanization, the number of township waste transfer stations and county waste disposal fixed asset investment are important drivers of county waste harmless disposal capacity. Findings provide helpful insights into optimizing rural habitat and promoting the comprehensive transformation of China’s county development.

Study on the Dynamic Evolution and Regional Differences of the Level of High-Quality Economic and Social Development in China

Analyzing the dynamic evolution and regional differences of the level of high-quality economic and social development based on the perspective of long time can be beneficial for informing the effect of the economy in China. This study builds a development evaluation index system oriented to high-quality economic and social development in five directions. To deepen the analysis, TOPSIS entropy is used to measure the level of economic and social development of 31 provinces in China from 2000 to 2019, considering the temporal characteristics and evolution laws of four regions. Dagum’s Gini coefficient method is also applied to measure regional differences and discover driving factors. There are three key results. First, the comprehensive development index runs well; however, the index of economic momentum, innovationability, and safety assurance differ significantly. There is obvious path dependence, demonstrating a spatial gradient, with development that is high in the east, moderate in the central part, and low in the west. Second, the trend of the comprehensive development index of each province is gratifying, but there are obvious differences in the three dimensions of economic momentum, innovation ability, and security index. Third, the overall development coordination of the four regions is weak—that is, the levels of economic and social development oriented to high-quality intensified differentiation; it will be difficult to close the regional gap in the short term, given the development heterogeneity among regions. To promote the formation of a new, balanced pattern of regional development of high quality with complementary advantages, the four regions should be targeted in a double cycle of implementing regional development strategies and enhancing development coordination. Attention should also be paid to the growth pole function of advanced regions, complementing the advantages of other regions, and ultimately promoting high-quality development jointly.

Coupling coordination development of energy-economy-carbon emissions in China under the background of "double carbon".

Based on the panel data of 30 provinces in China from 2010 to 2019, this paper measured the coupling coordination development of energy-economy-carbon emissions and investigated its regional differences and spatial convergence. The research methods in this paper include entropy weight technique method for order preference by similarity to an ideal solution, coupling coordination degree model, Dagum Gini coefficient and decomposition method, Moran's I index, σ convergence model and β convergence model. The study found that the coupling coordination degree of energy-economy-carbon emissions in China has been continuously improved and has obvious regional and stage characteristics, but it is still on the verge of imminent disorder; the overall difference in the coupling coordination degree of energy-economy-carbon emissions shows a decreasing and then increasing trend, the main source of which is inter-regional differences; the coupling coordination degree of energy-economy-carbon emissions has a positive spatial correlation; except for the Southern Coastal Economic Zone and the Middle Yangtze River Economic Zone, there is no significant σ-convergence and β-convergence in the coupling coordination degree of energy-economy-carbon emissions system in other economic zones; the coupling coordination degree of energy-economy-carbon emissions changes fastest in the Middle Yangtze River Economic Zone. The innovation of this paper is to measure the coupling coordination degree of energy-economy-carbon emissions and to analyse its regional differences and spatial effects. It is of great practical significance to promote the coupling coordination development and regional balanced development of energy-economy-carbon emissions in China under the background of "dual carbon".

Calculation of inter-provincial differences in the appropriate proportioning of land transfer income to support rural revitalization

This research examined the suitability and regional differences of the distribution ratio of land transfer income aimed at supporting rural revitalization by calculating the distribution ratio of 31 provincial-level administrative divisions (abbreviated as 31PLAD), while providing a reference template for land transfer incomes. Herein, we collated and calculated the fiscal expenditure of five dimensions of each province (city, district) in 2005, 2010, 2015, and 2020, and then predicted the situation of the next period (that is, 2025) using GM (1, 1). Further, we used a time series to calculate the land transfer income of each province in 2025. After combining these two, the possible support of land transfer income in all dimensions of rural revitalization across the 31PLAD in 2025 was calculated. Then, according to the Dagum Gini coefficient method, the regional differences of each dimension across the 31PLAD were observed. The financial expenditure of the 31PLAD across the five dimensions studied revealed that the expenditure of industrial prosperity and its proportion is higher, followed by the dimensions of ecological livability and rural civilization. The expenditure of the other dimensions, except for that of affluence in Regions I and II, is better than that of the same dimension in the region. The overall differences among the 31PLAD represent low industrial prosperity, high affluent life, similar overall differences between rural civilization and effective governance, and relatively large differences in ecological livability. Our findings provide relevant suggestions that would help support rural revitalization effectively. After focusing on the first four dimensions of rural revitalization, we suggest measures to promote the development of other dimensions within industrial prosperity and the linkage and cross-development of all dimensions so as to achieve complete rural revitalization. Further, we specify local policies and regulations for using land transfer income to make overall plans and proper arrangements. According to the industrial development and changes found among the 31PLAD, the necessary support path ahead is clear. According to the forecast trend and changes in the income difference found in this study, the PLAD could use these to plan objectives, clarify fund management, establish relevant supervision systems, and develop policy communication methods, among other measures.

Dynamic Evaluation and Regional Differences Analysis of the NEV Industry Development in China

In the transportation sector, new energy vehicles (NEVs) are critical to reduce CO2 emissions in the context of carbon neutralization. The study of dynamic evaluation and regional difference analysis is helpful to the NEV industry development in policy design and industrial planning. In this study, based on the provincial data in China from 2016 to 2020, the grey target model and Dagum Gini coefficient method are employed for the dynamic evaluation and regional differences of the NEV industry development. The results were as follows: (1) The overall and provincial level of the NEV industry development showed an increasing pattern. The bull’s eye degrees of Guangdong, which had the best development, were 0.4884, 0.5361, 0.6067, 0.6787, and 0.7047 during the study period. (2) The regional differences in the NEV industry development were significant. The east region had the best development, followed by the middle, the west, and the northeast regions. The intra-regional differences were expanding with different annual growth rates. (3) The inter-regional differences between the east and the other three regions were the largest. The regional differences in the NEV development are mainly derived from inter-regional dereference. (4) The D1, D2, and D3 dimensions all contributed significantly to provinces with higher levels of development, while the D4 dimension contributed significantly to provinces with lower levels of development. Based on these results, different provinces should take differentiated development strategies and enhancement paths to promote their NEV industry development.

Evaluation of the Results of China’s Fiscal Medical and Health Expenditure

PurposeTo evaluate the resulting level of fiscal medical and health expenditure in China, and to provide the scientific basis for further improving fiscal medical and health service capacity in China.Patients and MethodsThe data envelopment method and Gini coefficient method were used to analyze the efficiency and regional fairness of fiscal medical and health expenditure results by using the relevant provincial and municipal data of China from 2007 to 2019.Results1.Overall, from 2007 to 2019, the total expenditure continued to increase, the expenditure efficiency increased first and then decreased, and the expenditure fairness continued to improve. 2. From the perspective of subregions, there are apparent differences between regions in terms of total expenditure, expenditure efficiency, and expenditure fairness, showing a better situation in the central, western, northeast and a lower situation in the east.ConclusionThe overall level of fiscal medical and health expenditure in China shows an upward trend, but there is still much room for improvement. At the same time, there are pronounced regional differences, and the problems of efficiency and fairness coexist among regions. Therefore, in the future, we should increase medical and health investment and enhance the government’s close attention; Improve the expenditure performance appraisal system; Formulate policies according to local conditions and avoid “one size fits all.”.

Fuzzy-theory-based pedestrian dynamics models for studying the waiting passenger distribution at the subway platform

The behavior of waiting area choice of passengers at the subway platform can directly affect the travel efficiency, thereby affecting the service level of the station. Combining fuzzy logic theory and cellular automata model/social force model, a waiting area choice model for passengers at the subway platform is proposed, which can be used to predict the distribution of passengers. The distance from passengers to waiting areas, the number of passengers in the waiting area, and the length of the remaining waiting area are selected as the main influence factors after the analysis of field investigation, which are defined as the input variables of the fuzzy logic system. The choice probability of passengers to each waiting area is defined as the output variable. The waiting area with the highest probability is taken as the destination of passengers, thereby the static field value in the cellular automata model and the desired direction in the social force model can be determined. The total error of the established method can be as low as 10.13%, which shows a relatively good prediction effect. The effects of passenger attributes, stair attributes, and passable area size on the waiting area choice behavior are simulated. The results show that the influence degree of passenger companion behavior on passenger distribution depends much on the proportion of passengers in the company. The body radius and the desired speed of passengers have a great impact on the distribution balance of waiting passengers based on the analysis of the Gini coefficient and random forest method. The interval of multiple stairs should be the same as far as possible. The setting of a passable area is conducive to the flow of passengers during peak hours. The number of passengers is most pronounced in the distribution balance of passengers by comparing five different influence factors. Guidance strategies can be released through the LED display screen on the basis of the predicted distribution information. This paper provides a new idea for modeling the waiting area choice behavior of passengers, which lays a theoretical basis for the design and transformation of platform facilities.

Multi-Objective Decision Method for Airport Landside Rapid Transit Network Design.

To better deploy the landside rapid transit network for large airports, this study proposes a multi-objective transit network design model to maximize passenger demand coverage, reduce passenger travel time and minimize operational cost simultaneously. This model is formulated as an equivalent integer programming problem by predefining the transportation corridors and passengers’ OD pairs. A branch-and-cut algorithm is proposed to find a non-inferior solution set. We also conduct trade-off analysis between efficiency, effectiveness and equity under each deployment strategy using the modified Gini coefficient method. The effectiveness of the proposed model and solution algorithm are tested with rapid transit network of the Beijing Capital International Airport. Results show that among the three common network topologies, including star, tree and finger, the passenger demand coverage and travel time reduction per unit cost under star topology outperform the other two topologies. As for finger topology, the performances of the passenger demand coverage and travel time reduction are the best among the three, but the cost is the poorest. In addition, the trade-off analysis shows that the solution whose objective is to maximize passenger demand coverage has a higher efficiency and a lower unit cost than the solution whose objective is to reduce travel time. However, the latter has a higher level of equity, especially for the medium and low-cost solutions. The proposed method in this study can help the decision makers to design effective landside rapid transit networks for large airports to improve the service level.

Model-based temporal evolution and spatial equilibrium analysis of green development in China's Yangtze River Economic Belt from 2009 to 2018

Green development is one of the important guidance and core contents of high-quality development of regional economy and society, and its spatial–temporal pattern evolution analysis is helpful to the scientific formulation of relevant policies. However, the green development evaluation is faced with multi attribute decision making difficulties. In view of this, firstly, based on discussing the connotation of green development from ontology, epistemology, axiology and methodology, a new evaluation indicator system from the four dimensions of energy conservation, emission reduction, efficiency increase and harmony was explored. Then, coupling symmetric cross entropy multi attribute decision making and Gini coefficient method, a comprehensive evaluation framework of temporal evolution and spatial equilibrium of green development was constructed and applied in 11 provinces (cities) of China's Yangtze River Economic Belt (YEB) from 2009 to 2018. The results show that during the study period, the green development level of YEB has improved year by year, but there was still a large gap. Among the 120 research samples, only 41.7% had a ranking score of more than 6, and only Chongqing in 2017 had a ranking score of more than 8. The Gini coefficient of green development in 11 provinces (cities) is about 0.2, which is balanced on the whole, but there is a significant difference in the indicators, such as proportion of new energy power generation, sulfur dioxide emission per 104 RMB yuan GDP and proportion of investment in environmental protection; The effect of energy conservation and emission reduction is obvious, while efficiency increase and harmony need to be improved. The complementary role of 11 provinces (cities) in YEB's green development has become increasingly prominent, which made that YEB (as a whole) ranked second in comparison with 11 provinces (cities) in 2018.

The trend and factors affecting renewable energy distribution and disparity across countries

Renewable energy plays a crucial role in clean energy transformation and emission mitigation. It's, therefore, necessary to understand the trend and factors affecting renewable energy distribution and disparity across countries. This paper investigates the development pattern of renewable energy worldwide. It depicted a distributional picture of the world renewable energy using a club convergence algorithm, to form 5 clubs with 78 economies based on the renewable energy generation during 2000–2018. China, the United Kingdom, and the United States are the top economies in renewable energy generation. And most of the economies are in the clubs with less renewable energy generation. With the Dagum Gini coefficient and decomposition method, it is found that the disparities of renewable energy between clubs are increasing, while the disparity within each club is decreasing. Affluence, demand for energy, technology level, industry and population structure are the factors determining the club according to the result of the ordered probit model and the fixed-effects panel regression model. Target recommendations are proposed for international policy formulations on renewable energy based on the results.

THE REGIONAL DIFFERENCES AND RANDOM CONVERGENCE OF URBAN RESILIENCE IN CHINA

This paper focuses on calculating resilience index of 282 cities in China from 2012 to 2019, to analysis the regional differences and random convergence. We use the entropy method to calculate the urban resilience index, adopt the Dagum Gini coefficient method to analyze the regional differences and the sources, explore the variation coefficients method and beta convergence model to diagnose the convergence mechanism. The conclusions are: (1) The urban resilience in China is at a medium and low level with a stable growth tendency, with a significant regional unbalance of “higher in east, and lower in other regions”. As the sub-resilience, there is a big gap in the regional difference of the resilience structure with good performance in social resilience and economic resilience, poor in ecological resilience and infrastructure resilience. (2) The Gini coefficient of urban resilience continuously decreases with the regional unbalance narrowing accordingly. The Gini coefficients in different regions have a phased convergence tendency, and the hypervariable density contribution and intra-regional differences contribution are the main sources of differences in urban resilience. (3) The urban resilience in China and eastern region has σ convergence, while China and all regions have significant absolute β and conditional β convergence. Therefore, this paper proposes to continuously accelerate the urban resilient construction, make up for the shortcomings, and narrow the regional development gap, to promote the healthy and orderly development of cities.