Based on the Chinese county-level panel data from 2014–2018, in the study reported in this paper, we empirically tested the “complementarity and substitution” effects of internet finance and rural finance on rural economic development using the feasible generalized least squares (FGLS) estimation method. The research data were obtained from the China County Statistical Yearbook. Similarly, the data of agricultural credit societies were obtained from the unique database of the agricultural credit societies of Shaanxi Rural Finance Research Center, while the internet finance development index was obtained from the Digital Finance Research Center of Peking University. In this research, we found that rural finance and internet finance contribute to rural economic growth. In the context of rural economic growth, internet finance and rural finance development show a substitution effect. The scale and efficiency of rural finance contribute to rural economic growth, while the structure of rural finance has the opposite effect. The development of internet finance reduces the marginal contribution of the rural financial scale and efficiency to rural economic growth. It weakens the negative effect of the rustic financial structure on rural economic growth. Furthermore, the development of internet finance contributes to the rural economic growth of counties of high economic levels but hinders the development of counties of low and medium economic levels. In counties with a high economic status, the development of internet finance and rural finance have a complementary effect on rural economic growth, while in counties of low and medium economic levels, we can observe a substitution effect. These assessments provide guidance, a source for policy recommendations and a reference for researchers and policy makers seeking to optimize the structure and break the monopoly pattern of agricultural credit cooperation in the rural financial market and to strengthen innovation and significantly improve the operational level of rural financial institutions. Moreover, the development of internet financial business and technology is necessary to overcome the demerits of traditional financial institutions.

Trackers installed in vehicles gives insights into many useful information and predict future mobility patterns and other aspects related to vehicles movement which can be used for smart and sustainable cities planning. A novel approach is used with the COPERT model to estimate fuel consumption on a huge dataset collected over a period of one year. Since the data size is enormous, Apache Spark, a big data analytical framework is used for performance gains while estimating vehicle fuel consumption with the lowest latency possible. The research presents peak and off-peak hours fuel consumption’s in three major cities, i.e., Karachi, Lahore and Islamabad. The results can assist smart city professionals to plan alternative trip routes, avoid traffic congestion in order to save fuel and time, and protect against urban pollution for effective smart city planning. The research will be a step towards Industry 5.0 by combining sustainable disruptive technologies.