Examining Spatial Disparities in Electric Vehicle Public Charging Infrastructure Distribution Using a Multidimensional Framework in Nanjing, China
Abstract
:1. Introduction
2. Literature Review
2.1. Demand for Public Charging Infrastructure
Citation | Category | Case Study | Methodology | Objective |
---|---|---|---|---|
Li et al. [9] | Equity analysis | The top ten cities in China by EVCS | Opportunity-based approach, Global and Local Moran’s I index | Spatial equity in the distribution of EVCSs |
Yu et al. [13] | Spatial analysis | Shenzhen, China | A cost-effective and high-efficient shared fast charging scheme | Difficult-to-charge issue in old residential communities |
Peng et al. [29] | Equity analysis | Hong Kong, China | 2SFCA, Global and Local Moran’s I, Gini coefficient, GWR | Assessing EV charging equity |
Al-Dahabreh et al. [30] | Demand analysis | Quebec, Canada | QoE performance metrics, Machine Learning model | EV charging demand forecasting |
Yang et al. [31] | Demand analysis | Shanxi, China | Data-driven analysis, cluster analysis | Differences in demand for charging facilities |
He et al. [32] | Spatial analysis | Hong Kong, China | Location-allocation model | Optimal deployment of public charging infrastructure |
Li et al. [33] | Spatial analysis | Chengdu, China | A two-layer genetic algorithm with a local search (TLGALS), simulated annealing (SA) | Public charging station localization and route planning of electric vehicles |
Carlton et al. [34] | Equity analysis | United States | Negative binomial regression, friction raster, Lorenz curves, Gini coefficient | EV charging equity and accessibility |
Loni et al. [35] | Equity analysis | San Francisco, USA | NSGA-II, TOPSIS | Equitable placement for electric vehicle charging stations |
Lin et al. [22] | Demand analysis | China | Independent samples t-test, ordered logit model, SEM | Consumer satisfaction with electric vehicle charging |
2.2. Spatial Equity of Public Charging Infrastructure
2.3. Evaluation of Equity in Charging Infrastructure Distribution
3. Study Area and Data Sources
3.1. Study Area
3.2. Data and Preprocessing
4. Methodology
4.1. Spatial Equity Analysis
4.1.1. Accessibility
4.1.2. Availability
4.1.3. Convenience
4.1.4. Affordability
4.1.5. Spatial Equity Score
4.2. Local Indicators of Spatial Autocorrelation (LISA)
5. Results
5.1. Equity Analysis of EVPCI Based on Multidimensional Spatial Indicators
5.1.1. Spatial Distribution of Multidimensional Indicators
5.1.2. Spatial Distribution of Equity in EVPCI Service
5.2. Spatial Disparity of Equity in EVPCI Service
5.3. Bivariate Local Spatial Autocorrelation Based on Social Factors
6. Discussion
6.1. Methodological Contribution
6.2. Implications for PCI Planning
6.3. Limitations and Future Research
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Vintages | New-Energy Vehicles | Electric Vehicles | Charging Piles |
---|---|---|---|
2014 | 22 | 8 | 2.8 |
2015 | 58 | 33 | 5.7 |
2016 | 91 | 73 | 15 |
2017 | 153 | 125 | 21.4 |
2018 | 261 | 211 | 30 |
2019 | 381 | 310 | 51.6 |
2020 | 492 | 400 | 55.8 |
2021 | 784 | 640 | 114.7 |
2022 | 1310 | 1045 | 179.7 |
2023 | 2041 | 1552 | 272.6 |
Citation | Research Subjects | Case Study | Methodology |
---|---|---|---|
Ghorbanzadeh et al. [42] | Healthcare Facilities | Florida, USA | 2SFCA, ARD |
Li et al. [9] | EV Charging Services | The top ten cities in China by EVCS | Opportunity-based approach, Global and Local Moran’s I index |
Zhang et al. [43] | Community-based Service Resources | Nanjing, China | G2SFCA, Local Moran’s I index |
Chang et al. [45] | Urban Public Facilities | Taiwan, China | Integrated Equity index, Local Moran’s I index |
Luo et al. [46] | High-speed Rail | China | Theil index |
Li et al. [44] | Urban Park | Yangzhou, China | 2SFCA, Lorenz Curve and Gini Coefficient |
Dadashpoor et al. [48] | Urban Facilities | Hamadan, Iranian | SIM model, Density index, Proximity coefficient, AHP |
Chen et al. [41] | Urban Bus Transit | Edmonton, Canada | DEA model, Lorenz Curves, Gini Coefficient |
Explanation | |||
---|---|---|---|
Dimension | Indicators | Description | |
Spatial level | Accessibility | Sum of the ratio of service capacity to community demand for charging piles | |
Availability | Opportunities for users to choose the number and type of charging facilities they use | ||
Convenience | Inverse of distance to the nearest charging station for users within thresholds | ||
Affordability | Density of charging posts per neighborhood | ||
Social level | Community economic indicators | Population | Population density |
Housing prices | Average price per dwelling unit | ||
Dwelling age | Date of completion of dwelling | ||
Community environment indicators | Food and beverage | Restaurants, cafes, etc. | |
Company and enterprise | Companies, factories, etc. | ||
Shopping and consumption | Department stores, shopping streets, etc. | ||
Traffic facilities | Stations, parks, service areas, etc. | ||
Hotel accommodation | Hotels, guest houses, etc. | ||
Science and culture education | Schools, libraries, palaces of culture, etc. | ||
Tourist attraction | Parks, attractions, memorials, etc. | ||
Living service | Public toilets, baths and saunas, utilities, etc. | ||
Leisure and entertainment | Cinemas, playgrounds, bars, etc. | ||
Medical facilities | Hospitals, health centers, etc. | ||
Exercise and fitness | Ball fields, campgrounds, fitness centers, etc. |
Notation | Description |
---|---|
Indices | |
i | The demand point |
j | The supply point |
M | The type of charging post |
f | The direct current (DC) charging |
l | The alternating current (AC) charging |
Parameters | |
The demand scale at point i | |
The facility scale at point j | |
The time from demand point i to supply point j | |
The threshold time | |
N | The total number of all charging stations |
The index of charging stations | |
The total number of DC charging piles | |
The total number of AC charging piles | |
The average numbers of DC charging piles in each community | |
The average numbers of AC charging piles in each community in Nanjing Central Districts | |
The variety index of charging stations | |
The distance from the nearest charging station to i | |
The minimum distance from demand point I to supply point j | |
The total number of charging piles | |
The community population | |
Variables | |
The ratio of the number of charging piles in the charging station to the total population in the community | |
The accessibility of point i | |
The number of charging stations available at demand point i | |
The total availability of charging types in community i | |
The availability index at demand point i | |
The charging service to community i as the inverse distance of the nearest charging station | |
The number of piles per capita in community i | |
The community public charging infrastructure space equity index |
EVPCI Local Indicators of Spatial Autocorrelation | |||||
---|---|---|---|---|---|
Local Moran’s I | Z | p-Value | |||
Spatial level | Spatial equity | 0.661 | 27.023 | 0.001 | |
Social level | Community economic indicators | Population density | 0.315 | 16.819 | 0.001 |
Housing prices | 0.3 | 15.372 | 0.001 | ||
Dwelling age | 0.014 | 0.7474 | 0.218 | ||
Community environment indicators | Food and beverage | 0.355 | 17.691 | 0.001 | |
Company and enterprise | 0.372 | 18.803 | 0.001 | ||
Shopping and consumption | 0.309 | 15.909 | 0.001 | ||
Traffic facilities | 0.49 | 23.634 | 0.001 | ||
Hotel accommodation | 0.268 | 13.687 | 0.001 | ||
Science and culture education | 0.316 | 16.058 | 0.001 | ||
Tourist attraction | 0.25 | 13.056 | 0.001 | ||
Living service | 0.347 | 17.31 | 0.001 | ||
Leisure and entertainment | 0.3 | 15.152 | 0.001 | ||
Medical facilities | 0.359 | 18.437 | 0.001 | ||
Exercise and fitness | 0.247 | 12.746 | 0.001 |
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Wang, M.; Liang, Z.; Li, Z. Examining Spatial Disparities in Electric Vehicle Public Charging Infrastructure Distribution Using a Multidimensional Framework in Nanjing, China. ISPRS Int. J. Geo-Inf. 2024, 13, 296. https://doi.org/10.3390/ijgi13080296
Wang M, Liang Z, Li Z. Examining Spatial Disparities in Electric Vehicle Public Charging Infrastructure Distribution Using a Multidimensional Framework in Nanjing, China. ISPRS International Journal of Geo-Information. 2024; 13(8):296. https://doi.org/10.3390/ijgi13080296
Chicago/Turabian StyleWang, Moyan, Zhengyuan Liang, and Zhiming Li. 2024. "Examining Spatial Disparities in Electric Vehicle Public Charging Infrastructure Distribution Using a Multidimensional Framework in Nanjing, China" ISPRS International Journal of Geo-Information 13, no. 8: 296. https://doi.org/10.3390/ijgi13080296