Transport Accessibility and Poverty Alleviation in Guizhou Province of China: Spatiotemporal Pattern and Impact Analysis
Abstract
:1. Introduction
2. Data and Methods
2.1. Study Area
2.2. Data Sources
2.3. Data Analysis
2.3.1. Measurement of Transport Accessibility
2.3.2. Measurement of Economic Potential
2.3.3. Measurement of the Poverty Index
2.3.4. Panel Data Regression Model
3. Results
3.1. Spatiotemporal Pattern of Transport Accessibility
3.2. Spatiotemporal Pattern of Economic Potential
3.3. Spatiotemporal Pattern of the Poverty Index
3.4. Impact of Transport Accessibility on Regional Poverty Alleviation
4. Discussion
4.1. Evolution of Transport Accessibility and Poverty Alleviation
4.2. Effect of Transport Accessibility on Poverty Alleviation
4.3. Policy Implications
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Authors | Methods | Samples | Findings |
---|---|---|---|
Aschauer [26] | Estimate the Cobb—Douglas production function using the OLS model. | Time series data of the U.S. in 1949–1985. | The elasticity coefficient of the core infrastructure, including highway and electricity supply, to economic growth was 0.39 [26]. |
Munnell [30] | Estimate the translog aggregate production function using the OLS model. | Panel data of 48 States in the U.S. in 1970–1986. | The output elasticity of highway capital was 0.04 [30]. |
Berndt et al. [33] | Estimate the cost function using the OLS model and GLS model. | Time series data of Sweden in 1960–1988. | The elasticity of public infrastructure investment to production cost and profit was 0.289 [33]. |
Bougheas et al. [31] | Estimate the aggregate production function using the OLS model. | Four-digit codes data of manufacture sectors in the U.S. in 1987 and 1997. | Core infrastructure, particularly transport, can reduce input costs and promote specialization [31]. |
Demurger [36] | Estimate the aggregate production function using the fixed-effects model, random-effects model and 2SLS model. | Panel data of 24 provinces in China in 1985–1998. | Transport facilities were key factors in promoting economic growth [36]. |
Fan et al. [38] | Based on the simultaneous equations model. | Provincial level data of China in 1970–1997. | Rural road investment can expand the non-agricultural employment opportunities of farmers, accelerate the pace of poverty alleviation and prosperity, and narrow the income gap between urban and rural residents. The output elasticity is 0.152 [38]. |
Balisacan et al. [39] | Based on the simultaneous equations model estimated by the three stage least square (3SLS) method and quantile regression. | Provincial data of Philippine in 1980s–1990s. | For every 1% increase in rural road investment, the per capita income of farmers increased by 0.32% on average [39]. |
Kwon [27] | Estimate the Cobb—Douglas production function. | Provincial level panel data on Indonesia in 1976–1996. | The investment of highway transport facilities can lead to poverty reduction by promoting regional economic growth and increasing farmers’ income [27]. |
Zhang [28] | Estimate the Cobb—Douglas production function using the error correction model and Granger test. | Provincial data of China in 1993–2004. | The output elasticity of transportation infrastructure to economic growth was between 0.0563 and 0.2058 [28]. |
Li [42] | Based on the p-score matching method. | Tracking survey data of China in 2001–2004. | Road construction has reduced the poverty rate in targeted rural areas by about 19 percent and increased the per capita net income of rural areas by 34 percent, benefiting the poor households more significantly [42]. |
Zou et al. [37] | Based on the fixed-effects model. | Panel data of 1994–2022 as well as time series data of 1978–2002. | Highway construction was more conducive to promoting economic growth and reducing poverty than railway construction [37]. |
Mao et al. [29] | Estimate the Cobb—Douglas production function using the individual fixed-effects model. | Panel data of 30 provinces in China in 1999—2008. | The contribution rate of rural transport infrastructure investment to farmers’ income in China was between 0.02 and 0.09 [29]. |
Banerjee et al. [32] | Estimate the aggregate production function by the Granger test. | County data of China in 1986–2006. | Transport infrastructure was the Granger reason for economic growth [32]. |
Juan et al. [34] | Estimate the cost function using regression method. | 2005 OD Travel Survey of Bogota. | Strong correlation existed between transport accessibility and regional poverty alleviation [34]. |
Chatterjee et al. [35] | Estimate the CES production function using regression method. | Cross-section data of government including infrastructure investment and labor income. | Infrastructure investment had an impact on productivity and income distribution, which, in turn, affected poverty alleviation [35]. |
Kang et al. [23] | Based on the dynamic panel model estimated by the generalized method of moments (GMM) in two steps. | Provincial data of China in 1998–2012. | Highway transport infrastructure and highway transport industry can significantly reduce the income gap between urban and rural areas, and the role of railway in promoting the economy had a lag [23]. |
Li et al. [40] | Based on the VAR model. | Panel data of 30 provinces in China in 1988–2014. | The long-balanced one-way relationship existed between transport infrastructure construction and poverty reduction effect was long-term, and transport investment had a strong positive impact on farmers’ income [40]. |
Marinho et al. [41] | Based on the dynamic panel model estimated by the GMM method. | Panel data of 26 States in Brazil in 19795–2011. | Significant negative relationship existed between public infrastructure investment and poverty [41]. |
Xie et al. [43] | Based on the 2SLS model. | China family tracking survey data of 25 provinces in 2010 and 2014. | The availability of rural road infrastructure had a positive impact on rural poverty reduction and helped to reduce rural poverty incidence [43]. |
Chen et al. [24] | Based on the threshold effect model. | Panel data of 30 provinces in China in 2010–2019. | Transport infrastructure had three threshold effects on rural poverty alleviation, and it showed an obvious “inverted U” trend overall [24]. |
Elevation (m) | Slope (°) | Speed (km/h) |
---|---|---|
0–1000 | 0–15 | 20 |
15–25 | 10 | |
>25 | 1 | |
>1000 | 0–15 | 20 |
>15 | 1 |
Variable | Description | Descriptive Statistics | |||
---|---|---|---|---|---|
Min. | Max. | Mean | SD | ||
Poverty index (POV) | Ratio of the poverty-stricken population to the total residential population in each county. | 0.000 | 0.989 | 0.540 | 0.308 |
Transport accessibility (ASTT) | ASTT of each county, (an inverse index). | 2.135 | 8.939 | 4.177 | 1.163 |
Urbanization development (BUI) | Ratio of the urban construction land area to the total land area of the county. | 0.039 | 29.278 | 1.051 | 3.122 |
Industrial structure (INS) | Ratio of the tertiary industry in the county GDP. | 0.000 | 83.123 | 38.765 | 12.747 |
Social development (RAT) | Annual growth rate of per-capita GDP in each county. | −8.000 | 45.400 | 11.783 | 4.429 |
Infrastructural investment (INV) | Fixed-asset investment in each county. | 1649 | 7,754,823 | 802,502.5 | 1,282,272 |
Counties | Transport Accessibility | Poverty Index Values | Counties | Transport Accessibility | Poverty Index Values | ||||
---|---|---|---|---|---|---|---|---|---|
2000 | 2018 | 2000 | 2018 | 2000 | 2018 | 2000 | 2018 | ||
Yunyan | 2.97 | 2.13 | 0.01 | 0 | Huishui | 4.87 | 3.49 | 0.99 | 0.50 |
Nanming | 3.04 | 2.14 | 0.90 | 0.18 | Pingtang | 4.95 | 3.89 | 0.83 | 0.20 |
Baiyun | 3.05 | 2.16 | 0.82 | 0.34 | Tongxin | 4.96 | 4.00 | 0.76 | 0.07 |
Huaxi | 3.20 | 2.24 | 0.96 | 0.61 | Huichuan | 5.00 | 2.88 | 0.99 | 0.79 |
Longli | 3.25 | 2.32 | 0.91 | 0.50 | Qianxi | 5.04 | 2.72 | 0.84 | 0.36 |
Pingba | 3.26 | 2.39 | 0.91 | 0.50 | Luodian | 5.05 | 3.15 | 0.74 | 0.18 |
Fuquan | 3.29 | 2.36 | 0.86 | 0.27 | Xingren | 5.11 | 3.80 | 0.94 | 0.29 |
Majiang | 3.41 | 2.48 | 0.95 | 0.42 | Puan | 5.17 | 4.16 | 0.85 | 0.40 |
Guanshanhu | 3.41 | 2.24 | 0.84 | 0.22 | Sandu | 5.20 | 3.21 | 0.98 | 0.66 |
Bozhou | 3.41 | 2.54 | 0.49 | 0.13 | Tianzhu | 5.24 | 3.62 | 0.96 | 0.30 |
Wengan | 3.44 | 2.40 | 0.98 | 0.59 | Cengong | 5.26 | 3.63 | 0.96 | 0.34 |
Kaili | 3.47 | 2.49 | 0.02 | 0 | Congjiang | 5.29 | 3.60 | 0.95 | 0.31 |
Xifeng | 3.47 | 2.48 | 0.97 | 0.29 | Panzhou | 5.31 | 4.22 | 0.96 | 0.44 |
Meitan | 3.65 | 2.65 | 0.95 | 0.43 | Zhenning | 5.37 | 3.26 | 0.95 | 0.60 |
Duyun | 3.66 | 2.45 | 0.89 | 0.32 | Zhenfeng | 5.46 | 3.39 | 0.92 | 0.45 |
Puding | 3.68 | 2.72 | 0.60 | 0.26 | Wangmo | 5.51 | 4.17 | 0.93 | 0.39 |
Huangping | 3.70 | 2.69 | 0.87 | 0.15 | Suiyang | 5.53 | 4.51 | 0.86 | 0.29 |
Yuqing | 3.70 | 2.74 | 0.81 | 0.26 | Sinan | 5.61 | 3.36 | 0.46 | 0.09 |
Xiuwen | 3.74 | 2.38 | 0.95 | 0.63 | Wuchuan | 5.65 | 4.03 | 0.40 | 0.04 |
Shibing | 3.83 | 2.83 | 0.98 | 0.48 | Zhengan | 5.71 | 3.96 | 0.61 | 0.17 |
Taijiang | 3.85 | 2.79 | 0.03 | 0 | Weining | 5.77 | 4.21 | 0.79 | 0.13 |
Liuzhi | 3.86 | 3.01 | 0.35 | 0.03 | Xingyi | 5.80 | 4.31 | 0.95 | 0.43 |
Qingzhen | 3.91 | 2.85 | 0.30 | 0.03 | Anlong | 5.86 | 4.17 | 0.51 | 0.16 |
Wudang | 3.91 | 2.16 | 0.98 | 0.48 | Ziyun | 5.89 | 4.61 | 0.90 | 0.27 |
Kaiyang | 3.94 | 2.40 | 0.96 | 0.25 | Hezhang | 5.91 | 3.82 | 0.26 | 0.05 |
Xixiu | 3.97 | 2.58 | 0.68 | 0.03 | Wanshan | 5.91 | 3.47 | 0.35 | 0.12 |
Dushan | 4.03 | 2.97 | 0.80 | 0.27 | Yanhe | 5.92 | 4.34 | 0.90 | 0.24 |
Honghuagang | 4.05 | 2.64 | 0.92 | 0.45 | Yinjiang | 5.92 | 3.58 | 0.76 | 0.26 |
Guiding | 4.06 | 2.89 | 0.96 | 0.46 | Bijiang | 5.95 | 3.61 | 0.92 | 0.45 |
Zhenyuan | 4.15 | 3.10 | 0.10 | 0 | Guanling | 6.06 | 4.07 | 0.95 | 0.33 |
Qinglong | 4.21 | 3.27 | 0.26 | 0.07 | Liping | 6.07 | 4.01 | 0.74 | 0.34 |
Fenggang | 4.24 | 3.01 | 0 | 0 | Leishan | 6.08 | 4.63 | 0.91 | 0.24 |
Zhijin | 4.29 | 2.73 | 0.91 | 0.25 | Jiangkou | 6.08 | 3.97 | 0.73 | 0.27 |
Sansui | 4.30 | 3.04 | 0.93 | 0.31 | Jianhe | 6.10 | 3.73 | 0.55 | 0.25 |
Danzhai | 4.42 | 2.79 | 0.83 | 0.17 | Renhuai | 6.16 | 3.27 | 0.95 | 0.41 |
Shiqian | 4.51 | 3.03 | 0.98 | 0.72 | Jinping | 6.17 | 4.19 | 0.90 | 0.43 |
Jinsha | 4.60 | 3.41 | 0.89 | 0.28 | Nayong | 6.17 | 3.71 | 0.85 | 0.34 |
Zhongshan | 4.65 | 3.55 | 0.95 | 0.30 | Ceheng | 6.53 | 4.23 | 0.99 | 0.34 |
Dejiang | 4.66 | 3.25 | 0 | 0 | Daozhen | 6.65 | 4.15 | 0.84 | 0.30 |
Changshun | 4.76 | 3.12 | 0.96 | 0.53 | Shuicheng | 6.66 | 3.67 | 0.72 | 0.18 |
Rongjiang | 4.77 | 3.25 | 0.65 | 0.12 | Qixinguan | 6.79 | 3.97 | 0.96 | 0.59 |
Dafang | 4.78 | 2.88 | 0.58 | 0.02 | Xishui | 7.98 | 4.39 | 0.49 | 0.02 |
Yuping | 4.80 | 3.43 | 0.47 | 0.03 | Songtao | 8.38 | 4.20 | 0.54 | 0.09 |
Libo | 4.82 | 3.74 | 0.95 | 0.37 | Chishui | 8.94 | 4.34 | 0.95 | 0.36 |
Mean value | 4.90 | 3.30 | 0.75 | 0.29 | Standard deviation | 1.21 | 0.70 | 0.27 | 0.19 |
Category | Variable | Collinearity Test | Unit Root Test | |
---|---|---|---|---|
VIF Value | LLC Test | ADF Test | ||
Dependent | POV | / | −13.255 * (0.000) | 459.548 * (0.000) |
Independent | ASTT | 1.371 | −28.994 * (0.000) | 759.597 * (0.000) |
BUI | 1.275 | −18.731 * (0.000) | 796.275 * (0.000) | |
INS | 1.280 | −81.920 * (0.000) | 473.494 * (0.000) | |
RAT | 1.044 | −66.974 * (0.000) | 417.245 * (0.000) | |
INV | 1.331 | −43.424 * (0.000) | 259.472 * (0.000) |
Independent Variable | Coefficient | Standard Error |
---|---|---|
Transport accessibility (ACC) | 0.839 *** | 0.314 |
Urbanization (BUI) | −0.223 * | 0.120 |
Industrial structure (INS) | −0.178 | 0.129 |
Social development (RAT) | 0.230 *** | 0.055 |
Infrastructural investment (INV) | −0.120 *** | 0.030 |
Constant | 3.778 | / |
R2 | 0.917 | / |
Year | Document | Policy |
---|---|---|
2001 | 10th Five-years Plan for Transportation Development | Support the transport development in the western region. |
2003 | Implementation Opinions on Reconstruction Project of Intercounty and Rural Highways | Solve the problem of township-to-village, county-to-country and intercounty access to asphalt or cement roads. |
2012 | Poverty Alleviation Plan Outline of Transportation Construction in Concentrated Contiguous Destitute Areas (2011–2020) | Focus on 11 concentrated contiguous destitute areas as key targets for transport poverty alleviation. |
2013 | Construction Plan of ‘Changing Rope to Bridge’ (2013–2015) | Convert ziplines in remote mountainous areas of Yunnan, Sichuan, Guizhou, Shaanxi, Gansu, Qinghai, and Xinjiang provinces into pedestrian or vehicular high bridges. |
2015 | Opinions of the Ministry of Transport on Promoting the Construction of ‘Four Good Rural Roads’ | Focus on the construction of rural roads in poverty alleviation areas for poverty alleviation. |
2016 | 13th Five-years Plan for Transport Poverty Alleviation | Expand the scope of transport poverty alleviation, including 1177 counties in contiguous poverty-stricken areas, old revolutionary areas, ethnic minority areas and border counties. |
2018 | Three-year Action Plan for Transport Poverty Alleviation (2018–2020) | Propose 12 key tasks, including trunk highway construction, rural highway safety life protection project construction, and “transport + industry” poverty alleviation. |
2020 | China’s Sustainable Development of Transportation | Promote high-quality development of transport in poor areas. |
2021 | Implementation Opinions of the Ministry of Transportation on Consolidating and Expanding the Achievements of Transport Poverty Alleviation and Promoting Rural Revitalization | Propose 13 specific implementation measures about the high-quality development of rural transportation and rural revitalization. |
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Cai, J.; Huang, C.; Deng, Z.; Li, L. Transport Accessibility and Poverty Alleviation in Guizhou Province of China: Spatiotemporal Pattern and Impact Analysis. Sustainability 2023, 15, 3143. https://doi.org/10.3390/su15043143
Cai J, Huang C, Deng Z, Li L. Transport Accessibility and Poverty Alleviation in Guizhou Province of China: Spatiotemporal Pattern and Impact Analysis. Sustainability. 2023; 15(4):3143. https://doi.org/10.3390/su15043143
Chicago/Turabian StyleCai, Jiayuan, Chunchun Huang, Zilin Deng, and Linna Li. 2023. "Transport Accessibility and Poverty Alleviation in Guizhou Province of China: Spatiotemporal Pattern and Impact Analysis" Sustainability 15, no. 4: 3143. https://doi.org/10.3390/su15043143
APA StyleCai, J., Huang, C., Deng, Z., & Li, L. (2023). Transport Accessibility and Poverty Alleviation in Guizhou Province of China: Spatiotemporal Pattern and Impact Analysis. Sustainability, 15(4), 3143. https://doi.org/10.3390/su15043143