Leveraging recent advances in deep learning and an increasing availability of high resolution satellite images, the paper demonstrates a new approach to track economic and environmental outcomes at a fine-grained spatial level. To pilot this approach, we apply the method to the Zambian context where we observe rich variation in road quality due to its tropical/sub-tropical climate combined with a vulnerable road network and substantial government-led efforts to improve transportation linkages. We trained an Artificial Intelligence (AI) algorithm to predict road surface conditions for the country's core road network over the last decade. The AI algorithm was also used to predict built-up and its changes over the same period. Using the connectivity between locations and the spatial distribution of economic mass, we further built an innovative spatial graph network with edges reflecting travel time based on road quality and with nodes containing information on AI-predicted built-up, nighttime light, air pollution and green space. Using changes in market access due to road improvement far-away as a source of exogenous variation, the paper finds that market access increases the size of urbanized area as measured by built-up and income as approximated by nighttime light, at a cost of increased air pollution and loss of green space. Conditional on the initial level of nighttime light, improvements in market access is found to slow down growth to a modest level, in line with the "urbanization without growth" phenomenon commonly observed in the African context.

Although plenty of evidence has shown that vans that are servicing e-commerce are a growing contributor to traffic and congestion, consumers are also making fewer shopping trips using vehicles. I provide the first available empirical estimate on the effect of e-commerce on urban traffic congestion, using the exogenous shock of an influential online shopping retail discount event in China (similar to Cyber Monday). Comparing the weeks before and after the event, I find that urban traffic congestion dropped by 1.7\% during peak hours while online shopping increased by about 1.6 times during the event. An instrumental variable (IV) estimation suggests that a 10\% increase in online shopping causes a 2.3\% reduction in traffic congestion. A welfare analysis conducted for Beijing suggests that the congestion-relief effect has a monetary value of around 419 million dollars annually. The finding suggests that online shopping is more traffic-efficient than in-store shopping, along with generating sizable knock-on welfare gains.