Sustainable Digital Rural Development: Measurements, Dynamic Evolutions, and Regional Disparities—A Case Study of China
Abstract
1. Introduction
- A more comprehensive evaluation index system for digital rural development:
- 2.
- Multidimensional analysis of development dynamics:
- 3.
- Dual geographical perspectives:
2. Materials and Methods
2.1. Data Source
2.2. Methods
2.2.1. Construction and Evaluation Method of the Digital Rural Indicator System
2.2.2. Kernel Density Estimation
2.2.3. Decomposition of Dagum Gini Coefficient
2.2.4. Obstacle Degree Model
3. Results
3.1. Analysis of Measurement Results
3.1.1. National Level
3.1.2. Regional Level
3.1.3. Provincial Level
3.2. Analysis of the Characteristics of Dynamic Evolution of the Distribution of Levels of Digital Rural Development in China
3.2.1. National Level
3.2.2. Regional Level
3.3. Spatial Decomposition of Disparities in Digital Rural Development Levels in China
3.3.1. Analysis of General Disparity
3.3.2. Analysis of Intra-Regional Disparities
3.3.3. Analysis of Inter-Regional Disparities
3.3.4. Sources and Contributions of Differences
3.4. Diagnosis of Obstacle Factors Affecting the Level of Development of Digital Villages in China
3.4.1. Identification of Obstacles at the Subsystems Level of the Indicator System
3.4.2. Identification of Barrier Factors at the Secondary Index Level of the Indicator System
3.4.3. Analysis of Barrier Factors
4. Discussion
- The entropy weight method is first used to evaluate the level of digital village development in China. Our research findings show that between 2013 and 2022, our country’s overall development of digital rural areas had a swinging upward trajectory, with an average annual growth rate of 9.432%. The development trends of digital rural areas in the five regions are closely similar to the national level, with a significant fall in 2019, while the other years had an upward trajectory. Notably, notable discrepancies are evident at the province level, especially between Hainan (0.040) and Guangdong (0.330).
- Then, this research uses kernel density estimation to analyze the distributional dynamics of digital village growth in China. From the view of distribution dynamics, improving digital rural development levels across the country and the five major regions are accompanied by a trend of distribution becoming more dispersed. A decrease in the kernel density curve’s peak and an increase in its width serve as proof of this. In particular, extensibility is higher nationally and in the northern region, whereas multipolarity is still in the eastern region. On the other hand, there is a tendency toward bipolar to multipolarity nationally and in the other four areas. Regional development inequalities are chiefly ascribed to differences in resource endowments, governmental support, and infrastructure quality. This intensifies the Matthew effect, in which the privileged added more benefits while the underprivileged lagged further behind.
- This study utilizes the Dagum Gini coefficient and its decomposition method to thoroughly clarify the dynamics of spatial disparities and their origins in the emergence of digital villages in China. The research findings show that the growing imbalance in developing digital rural areas in our country is highlighted by the widening regional differences in the development of digital rural areas. The North–South discrepancies are mainly attributed to intra-regional variances, while the East–West differences are largely due to inter-regional disparities. Disparities are evident both intra-regional and inter-regional to differing extents.
- This study uses an obstacle degree model to identify the primary barriers to digital village development in China. The analysis of barriers suggests that the primary obstacle affecting the development of digital rural areas in China during the sample period is developing the rural digital industry within the subsystem layer. This obstacle mainly arises from four critical domains: uneven regional e-commerce advancement, inadequate infrastructure in central and western regions, subpar development of agricultural product quality and branding, and a homogeneous structure in e-commerce transactions. From a time series perspective, the swings in the barriers across various subsystem layers are relatively minor, yet their trends display significant differences: the barriers associated with the development of the rural digital industry and the digitalization of agricultural production show an overall upward trend, while the barriers on the rural digital development environment and rural digital infrastructure show a downward trajectory. With identifying barriers at the secondary index level, the number of Taobao villages emerges as the leading obstacle. For the eastern region, the top five causes hindering the development of digital rural areas are as follows: the number of Taobao villages, e-commerce procurement amount, e-commerce sales amount, rural postal and telecommunications service volume, and local fiscal science and technology expenditure. The barrier reasons in other regions align with those in the eastern region, although there is a reversal in the rank of the fourth and fifth causes compared to the eastern region.
5. Recommendations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Subsystems | Primary Index | Secondary Index | Unit | Data Source or Calculation Formula |
---|---|---|---|---|
The level of digital infrastructure in rural areas | The level of Internet development | The number of broadband access users in rural areas (X1) | Ten thousand people | China’s Macroeconomic Database |
The average number of computers per 100 households among rural residents (X2) | Size | China’s Macroeconomic Database | ||
The number of mobile Internet users in rural areas (X3) | Ten thousand people | The number of mobile Internet users × rural population/total population | ||
Digital communication capabilities | The average number of mobile phones per 100 households among rural residents (X4) | Size | China’s Macroeconomic Database | |
Fiber-optic cable length (X5) | Kilometer | China’s Macroeconomic Database | ||
Level of information infrastructure development | Agricultural meteorological observation workload (X6) | Size | China’s Macroeconomic Database | |
Volume of rural postal and telecommunications services (X7) | Hundred million yuan | Total volume of postal and telecommunications services × rural population/total population | ||
The level of the digital development environment in rural areas | Consumption situations of rural residents | Per capita disposable income of rural residents (X8) | Yuan | China’s Macroeconomic Database |
Per capita expenditure on transportation and communication among rural residents (X9) | Yuan | China’s Macroeconomic Database | ||
Per capita healthcare expenditure among rural residents (X10) | Yuan | China’s Macroeconomic Database | ||
Living conditions of rural residents | Rural radio program coverage rate (X11) | % | China’s Macroeconomic Database | |
Rural TV program coverage rate (X12) | % | China’s Macroeconomic Database | ||
Number of village health clinics (X13) | Size | China’s Macroeconomic Database | ||
Local government fiscal expenditures | Local government science and technology expenditures (X14) | Hundred million yuan | National Bureau of Statistics | |
Local government transportation expenditures (X15) | Hundred million yuan | National Bureau of Statistics | ||
Local government education expenditures (X16) | Hundred million yuan | National Bureau of Statistics | ||
Local government healthcare expenditure (X17) | Hundred million yuan | National Bureau of Statistics | ||
The level of development of the digital industry in rural areas | Rural digital basis | Number of Taobao villages (X18) | Size | List of Taobao Villages |
Proportion of administrative villages with postal services (X19) | % | China’s Macroeconomic Database | ||
Level of digital transactions | E-commerce procurement amount (X20) | Hundred million yuan | China’s Macroeconomic Database | |
E-commerce sales amount (X21) | Hundred million yuan | China’s Macroeconomic Database | ||
The level of digitalization in agricultural production | Degree of agricultural mechanization | Total agricultural machinery power/crop planting area (X22) | Kilowatt hours/hectare | China’s Macroeconomic Database |
Effective irrigation rate of farmland | Effective irrigated area/crop planting area (X23) | Thousand hectares | China Agricultural and Forestry Database | |
Degree of electrification in agricultural production | Total output value of agriculture, forestry, animal husbandry, and fishing/rural electricity consumption (X24) | Yuan/kilowatt hours | China Agricultural and Forestry Database |
Province | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 |
---|---|---|---|---|---|---|---|---|---|---|
Beijing | 0.087 | 0.095 | 0.104 | 0.109 | 0.125 | 0.134 | 0.142 | 0.136 | 0.154 | 0.159 |
Tianjin | 0.052 | 0.058 | 0.063 | 0.074 | 0.076 | 0.077 | 0.082 | 0.081 | 0.088 | 0.085 |
Hebei | 0.139 | 0.15 | 0.162 | 0.178 | 0.205 | 0.236 | 0.272 | 0.264 | 0.29 | 0.305 |
Shanxi | 0.075 | 0.079 | 0.087 | 0.092 | 0.102 | 0.111 | 0.122 | 0.11 | 0.116 | 0.119 |
Inner Mongolia | 0.061 | 0.068 | 0.077 | 0.082 | 0.093 | 0.098 | 0.111 | 0.1 | 0.107 | 0.107 |
Liaoning | 0.077 | 0.085 | 0.092 | 0.101 | 0.108 | 0.114 | 0.119 | 0.107 | 0.114 | 0.116 |
Jilin | 0.061 | 0.064 | 0.071 | 0.075 | 0.083 | 0.092 | 0.099 | 0.088 | 0.088 | 0.087 |
Heilong jiang | 0.066 | 0.07 | 0.078 | 0.085 | 0.099 | 0.107 | 0.117 | 0.1 | 0.104 | 0.107 |
Shanghai | 0.079 | 0.079 | 0.087 | 0.102 | 0.108 | 0.114 | 0.121 | 0.121 | 0.143 | 0.151 |
Jiangsu | 0.153 | 0.167 | 0.205 | 0.226 | 0.258 | 0.297 | 0.34 | 0.321 | 0.35 | 0.357 |
Zhejiang | 0.131 | 0.146 | 0.189 | 0.218 | 0.265 | 0.333 | 0.402 | 0.398 | 0.451 | 0.503 |
Anhui | 0.073 | 0.086 | 0.105 | 0.122 | 0.139 | 0.169 | 0.198 | 0.179 | 0.195 | 0.207 |
Fujian | 0.088 | 0.1 | 0.114 | 0.121 | 0.14 | 0.161 | 0.19 | 0.181 | 0.201 | 0.218 |
Jiangxi | 0.071 | 0.081 | 0.092 | 0.098 | 0.118 | 0.139 | 0.158 | 0.149 | 0.161 | 0.169 |
Shandong | 0.15 | 0.16 | 0.184 | 0.202 | 0.235 | 0.272 | 0.307 | 0.296 | 0.329 | 0.34 |
Henan | 0.135 | 0.148 | 0.166 | 0.182 | 0.203 | 0.234 | 0.264 | 0.244 | 0.264 | 0.285 |
Hubei | 0.088 | 0.102 | 0.116 | 0.126 | 0.14 | 0.166 | 0.19 | 0.179 | 0.187 | 0.201 |
Hunan | 0.098 | 0.107 | 0.119 | 0.13 | 0.152 | 0.178 | 0.203 | 0.184 | 0.194 | 0.208 |
Guangdong | 0.173 | 0.187 | 0.243 | 0.264 | 0.307 | 0.364 | 0.433 | 0.41 | 0.445 | 0.469 |
Guangxi | 0.072 | 0.078 | 0.089 | 0.097 | 0.114 | 0.139 | 0.163 | 0.148 | 0.162 | 0.169 |
Hainan | 0.018 | 0.021 | 0.027 | 0.03 | 0.038 | 0.043 | 0.052 | 0.051 | 0.058 | 0.063 |
Chongqing | 0.048 | 0.056 | 0.065 | 0.072 | 0.081 | 0.092 | 0.104 | 0.098 | 0.109 | 0.116 |
Sichuan | 0.135 | 0.149 | 0.172 | 0.184 | 0.206 | 0.238 | 0.269 | 0.245 | 0.262 | 0.279 |
Guizhou | 0.05 | 0.06 | 0.071 | 0.078 | 0.092 | 0.11 | 0.13 | 0.106 | 0.117 | 0.128 |
Yunnan | 0.063 | 0.071 | 0.082 | 0.086 | 0.101 | 0.128 | 0.152 | 0.128 | 0.138 | 0.145 |
Shaanxi | 0.076 | 0.083 | 0.091 | 0.098 | 0.111 | 0.12 | 0.133 | 0.118 | 0.13 | 0.143 |
Gansu | 0.053 | 0.056 | 0.065 | 0.071 | 0.081 | 0.099 | 0.109 | 0.095 | 0.103 | 0.106 |
Qinghai | 0.026 | 0.034 | 0.039 | 0.044 | 0.046 | 0.052 | 0.058 | 0.058 | 0.06 | 0.061 |
Ningxia | 0.025 | 0.029 | 0.035 | 0.039 | 0.045 | 0.052 | 0.057 | 0.056 | 0.059 | 0.057 |
Xinjiang | 0.055 | 0.06 | 0.069 | 0.075 | 0.082 | 0.093 | 0.104 | 0.095 | 0.107 | 0.116 |
Eastern region | 0.104 | 0.113 | 0.134 | 0.148 | 0.17 | 0.195 | 0.224 | 0.215 | 0.239 | 0.252 |
Central region | 0.083 | 0.092 | 0.104 | 0.114 | 0.129 | 0.149 | 0.169 | 0.154 | 0.164 | 0.173 |
Western region | 0.06 | 0.068 | 0.078 | 0.084 | 0.096 | 0.111 | 0.126 | 0.113 | 0.123 | 0.13 |
Northern region | 0.076 | 0.083 | 0.092 | 0.1 | 0.113 | 0.126 | 0.14 | 0.13 | 0.141 | 0.146 |
Southern region | 0.089 | 0.099 | 0.118 | 0.13 | 0.151 | 0.178 | 0.207 | 0.193 | 0.212 | 0.226 |
National average | 0.083 | 0.091 | 0.105 | 0.115 | 0.132 | 0.152 | 0.173 | 0.162 | 0.176 | 0.186 |
Region | Category | Indicator Ranking | ||||
---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | ||
Eastern region | Barrier factors | X18 | X20 | X21 | X7 | X14 |
Barrier degree | 26.55% | 9.31% | 9.08% | 7.49% | 7.30% | |
Central region | Barrier factors | X18 | X20 | X21 | X14 | X7 |
Barrier degree | 27.11% | 10.48% | 9.86% | 7.70% | 6.87% | |
Western region | Barrier factors | X18 | X20 | X21 | X14 | X7 |
Barrier degree | 26.02% | 10.18% | 9.61% | 7.97% | 6.77% | |
Northern region | Barrier factors | X18 | X20 | X21 | X14 | X7 |
Barrier degree | 26.38% | 9.96% | 9.52% | 7.97% | 7.06% | |
Southern region | Barrier factors | X18 | X20 | X21 | X14 | X7 |
Barrier degree | 26.64% | 9.93% | 9.45% | 7.33% | 7.06% |
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Lei, M.; Yang, X.; Hong, S.; Wang, D.; Zhang, W.; Chen, H. Sustainable Digital Rural Development: Measurements, Dynamic Evolutions, and Regional Disparities—A Case Study of China. Sustainability 2025, 17, 4250. https://doi.org/10.3390/su17094250
Lei M, Yang X, Hong S, Wang D, Zhang W, Chen H. Sustainable Digital Rural Development: Measurements, Dynamic Evolutions, and Regional Disparities—A Case Study of China. Sustainability. 2025; 17(9):4250. https://doi.org/10.3390/su17094250
Chicago/Turabian StyleLei, Ming, Xinyu Yang, Shuifeng Hong, Dandan Wang, Wei Zhang, and Hui Chen. 2025. "Sustainable Digital Rural Development: Measurements, Dynamic Evolutions, and Regional Disparities—A Case Study of China" Sustainability 17, no. 9: 4250. https://doi.org/10.3390/su17094250
APA StyleLei, M., Yang, X., Hong, S., Wang, D., Zhang, W., & Chen, H. (2025). Sustainable Digital Rural Development: Measurements, Dynamic Evolutions, and Regional Disparities—A Case Study of China. Sustainability, 17(9), 4250. https://doi.org/10.3390/su17094250