Abstract
The mobility of the rural population has become an important issue to address, which ensures proper connectivity for the rural population from their villages to market centers, urban towns, schools, and health facilities. The better connectivities can bring substantial economic and social changes among the masses that can be achieved through sustainable mobility planning. However, the sustainability of the mobility systems can only be expressed if the key indicators are identified appropriately to reflect its multiple aspects. This study proposes a novel mixed-method approach to exploring and ascertaining the socioeconomic indicators that the construction of rural roads has impacted. Rural road infrastructure often instigates direct or indirect socioeconomic impacts (SEIs) on the target population. Assessing and ascertaining SEIs pose a wide range of challenges considering their high number and qualitative and quantitative nature. Thus, a set of indicators have been identified in five different dimensions to deal with the sustainability in the mobility system for the rural population using a unique and novel way of geographical information system (GIS) and principal component analysis (PCA)-based multidimensional development framework. It proposes a mixed-method approach to identify the most significant criteria and subcriteria of the SEIs. The five dimensions of the rural development process considered in this study are transport, income, health, education, and neighborhood, which are analyzed and validated using geo-regression modeling. The applicability of this method has been demonstrated by employing a case study of rural roads. The findings of the study ascertain the status of impacted indicators to achieve sustainability. The study found relative impact/influence indicators, i.e., multidimensional development index (MDI) for various habitats of rural blocks of Jhunjhunu, Rajasthan. The eastern region of the Buhana block and region of the northern Khetri block are found to have very low and very high MDI value, respectively. The study found a strong and positive spatial correlation between rural block's proximity and the rural road network with its MDI value.
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The data that support the findings of this study are available from the corresponding author upon reasonable request.
References
Abur, C. C., Ademoyewa, G. R., & Damkor, M. (2015). Impact of rural roads infrastructure on the income and productivity of household’s farmers in North Central Nigeria. Research Journal of Agriculture and Environmental Management, 451–458.
Asomani-Boateng, R., Fricano, R. J., & Adarkwa, F. (2015). Assessing the socio-economic impacts of rural road improvements in Ghana: A case study of Transport Sector Program Support (II). Case Studies on Transport Policy. https://doi.org/10.1016/j.cstp.2015.04.006
Banister, D., & Berechman, J. (2003). Transport investment and economic development. University College London Press.
Baker, J. L. (2000). Evaluating the impact of development projects on poverty: A handbook for practitioners. World Bank Publications.
Caudill, S. B., Zanella, F. C., & Mixon, F. G. (2000). Is economic freedom one dimensional? A factor analysis of some common measures of economic freedom. Journal of Economic Development, 25, 17–40.
Chen, S. H., & Hsieh, C. H. (2000). Representation, ranking, distance, and similarity of LR type fuzzy number and application. Australian Journal of Intelligent Processing Systems, 6(4), 217–229.
Choi, I. (2002). Structural changes and seemingly unidentified structural equations. Econometric Theory, 18, 744–775. https://doi.org/10.1017/S0266466602183095
Drakos, K. (2002). Common factors in eurocurrency rates: A dynamic analysis. Journal of Economic Integration, 17, 164–184.
Filmer, D., & Pritchett, L. H. (2001). Estimating wealth effects without expenditure data-or tears: An application to educational enrollments in states of India. Demography, 38, 115–132. https://doi.org/10.1353/dem.2001.0003
Grootaert, C., & Calvo, C. M. (2002). Socioeconomic impact assessment of rural roads: methodology and questionnaires. Impact Evaluation report, Washington, DC: World Bank.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (1995). Multivariate data analysis (4th ed.). Prentice Hall.
Hajkowicz, S., & Collins, K. (2007). A review of multiple criteria analysis for water resource planning and management. Water Resources Management, 21(9), 1553–1566. https://doi.org/10.1007/s11269-006-9112-5
Harris, D. (1997). Principal components analysis of cointegrated time series. Econometric Theory, 13, 529–557. https://doi.org/10.1017/S0266466600005995
Hine, J., Sasidharan, M., Torbaghan, M. E., & Burrow, M. P. N. (2019). Evidence of the impact of rural road investment on poverty reduction and economic development. Knowledge, Evidence and Learning for Development., K4D, 1–24.
Hwang, C. L., Lai, Y. J., & Liu, T. Y. (1993). A new approach for multiple objective decision making. Computers & Operations Research, 20(8), 889–899. https://doi.org/10.1016/0305-0548(93)90109-V
Jolliffe, I. T. (2002). Principal component analysis. Springer.
Kanuganti, S., Sarkar, A. K., & Singh, A. P. (2016). Evaluation of access to health care in rural areas using enhanced two-step floating catchment area (E2SFCA) method. Journal of Transport Geography, 56, 45–52. https://doi.org/10.1016/j.jtrangeo.2016.08.011
Kanuganti, S., Dutta, B., Sarkar, A. K., & Singh, A. P. (2017). Development of a need-based approach for rural road network planning. Transportation in Developing Economies, 3, 14. https://doi.org/10.1007/s40890-017-0044-y
Leal, I., Engebretson, J., Cohen, L., Fernandez-Esquer, M. E., Lopez, G., Wangyal, T., & Chaoul, A. (2018). An exploration of the effects of Tibetan yoga on patients’ psychological well-being and experience of lymphoma: An experimental embedded mixed methods study. Journal of Mixed Methods Research, 12, 31–54. https://doi.org/10.1177/1558689816645005
Lebo, J., & Schelling, D. (2001). Design and appraisal of rural transport infrastructure: Ensuring basic access for rural communities. World Bank Publication.
Liang, G. S. (1999). Fuzzy MCDM based on ideal and anti-ideal concepts. European Journal of Operational Research, 112(3), 682–691. https://doi.org/10.1016/S0377-2217(97)00410-4
Llanto, M. G. (2012). The impact of infrastructure on agricultural productivity (Report No. 2012-12). Makati City, Philippines: Philippine Institute for Development Studies.
Mardia, K. V., Kent, J. T., & Bibby, J. M. (2006). Multivariate analysis (probability and mathematical statistics). Elsevier.
Mustafa, N. A., Munikanan, V., Zakaria, R., & Aminudin, E. (2021). A review on rural roads in Malaysia: Green practice toward socio-economics. International Journal of Modern Social Sciences, 1(1), 12–16.
Nakamura, S., Bundervoet, T., & Nuru, M. M. (2020). Rural roads, poverty, and resilience: Evidence from Ethiopia. Journal of Development Studies, 56(2), 1–18. https://doi.org/10.1080/00220388.2020.1736282
Nirban V. S., Metri, B. A., Singh, A. P., Sandra, A. K., & Sarkar, A. K. (2003). Socio-economic benefits of PMGSY Projects: Perceptions of rural community. In Proceedings of a seminar on integrated development of rural and arterial road network for socio-economic growth (pp. 166–173). New Delhi.
O’Cathain, A., Murphy, E., & Nicholl, J. (2007). Integration and publications as indicators of ‘“yield”’ from mixed methods studies. Journal of Mixed Methods Research. https://doi.org/10.1177/1558689806299094
Padash, A. (2017). Modeling of environmental impact assessment based on RIAM and TOPSIS for desalination and operating units. Environmental Energy and Economic Research, 75–88. https://doi.org/10.22097/EEER.2017.46458.
Porter, G. (2012). Reflections on a century of road transport developments in West Africa and their (gendered) impacts on the rural poor. EchoGeo, 20, 1–14. https://doi.org/10.4000/echogeo.13116
Reichlin, L. (2003). Factor models in large cross sections of time series. In M. Dewatripont, L. Hansen, & S. Turnovsky (Eds.), Advances in economics and econometrics: Theory and applications (pp. 47–86). Cambridge University Press.
Riverson, J., Gaviria, J., & Thriscutt, S. (1991). Rural roads in sub-Saharan Africa: lessons from World Bank experience (Report No. WTP 141). Washington, D.C: World Bank.
Rogers, P. J. (2009). Matching impact evaluation design to the nature of the intervention and the purpose of the evaluation. Journal of Development Effectiveness. https://doi.org/10.1080/19439340903114636
Singh, A. P., Chakrabarti, S., Kumar, S., & Singh, A. (2017). Assessment of air quality in Haora River basin using fuzzy multiple-attribute decision making techniques. Environmental Monitoring and Assessment, 189(8), 373. https://doi.org/10.1007/s10661-017-6075-3
Tunde, A. M., & Adeniyi, E. E. (2012). Impact of road transport on agricultural development: A Nigerian example. Ethiopian Journal of Environmental Studies and Management, 5, 232–238. https://doi.org/10.4314/ejesm.v5i3.3
Van de Walle, D. (2009). Impact evaluation of rural road projects. Journal of Development Effectiveness. https://doi.org/10.1080/19439340902727701
Vyas, S., & Kumaranayake, L. (2006). Constructing socio-economic status indices: How to use principal components analysis. Health Policy and Planning, 21(6), 459–468. https://doi.org/10.1093/heapol/czl029
Wagale, M., & Singh, A. P. (2019). The application of adaptive neuro-fuzzy inference system and fuzzy Delphi technique to assess socio-economic impacts of construction of rural roads. Transport and Telecommunication Journal, 20(4), 325–345. https://doi.org/10.2478/ttj-2019-0027
Wagale, M., Singh, A. P., & Sarkar, A. K. (2019). Impact of rural road construction on the local livelihood diversification: Evidence from Pradhan Mantri Gram Sadak Yojana in Jhunjhunu district, India. GeoJournal, Springer. https://doi.org/10.1007/s10708-019-10007-3
Wagale, M., Singh, A. P., & Sarkar, A. K. (2021). Socio-economic impacts of low-volume roads using a mixed-method approach of PCA and Fuzzy-TOPSIS. International Review for Spatial Planning and Sustainable Development, 9(2), 112–133. https://doi.org/10.14246/irspsda.9.2_112
Yoon, K., & Hwang, C. L. (1981). Multiple attribute decision making: Methods and applications. Springer.
Zeleny, M. (2011). Multiple criteria decision making (MCDM): From paradigm lost to paradigm regained? Journal of Multi-Criteria Decision Analysis, 18, 77–89. https://doi.org/10.1002/mcda.473
Zhu, X., Chen, X., Cai, J., & Balezentis, A. (2021). Rural financial development, spatial spillover, and poverty reduction: Evidence from China. Economic Research, 2(1), 12–31. https://doi.org/10.1080/1331677X.2021.1875859
Acknowledgments
The authors acknowledge the support provided by the National Rural Infrastructure Development Agency (NRIDA), Ministry of Rural Development, Government of India, New Delhi, and Birla Institute of Technology and Science Pilani, Rajasthan (India) to carry out the research work.
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The authors declare an equal contribution to the manuscript. All authors contributed equally in analyzing results obtained through PCA and fuzzy-TOPSIS methods by developing an appropriate model. The second author also collected the data from the field. The first, third, and fourth authors edited the manuscript with a proper conclusion.
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Singh, A.P., Wagale, M., Dhadse, K. et al. Socioeconomic impacts of low-volume roads using a GIS-based multidimensional impact assessment approach. Environ Dev Sustain 24, 6676–6701 (2022). https://doi.org/10.1007/s10668-021-01723-3
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DOI: https://doi.org/10.1007/s10668-021-01723-3