The use of air quality monitoring networks to inform urban policies is critical especially where urban populations are exposed to unprecedented levels of air pollution. High costs, however, limit city governments’ ability to deploy reference grade air quality monitors at scale; for instance, only 33 reference grade monitors are available for the entire territory of Delhi, India, spanning 1500 sq km with 15 million residents. In this paper, we describe a high-precision spatio-temporal prediction model that can be used to derive fine-grained pollution maps. We utilize two years of data from a low-cost monitoring network of 28 custom-designed low-cost portable air quality sensors covering a dense region of Delhi. The model uses a combination of message-passing recurrent neural networks combined with conventional spatio-temporal geostatistics models to achieve high predictive accuracy in the face of high data variability and intermittent data availability from low-cost sensors (due to sensor faults, network, and power issues). Using data from reference grade monitors for validation, our spatio-temporal pollution model can make predictions within 1-hour time-windows at 9.4, 10.5, and 9.6% Mean Absolute Percentage Error (MAPE) over our low-cost monitors, reference grade monitors, and the combined monitoring network respectively. These accurate fine-grained pollution sensing maps provide a way forward to build citizen-driven low-cost monitoring systems that detect hazardous urban air quality at fine-grained granularities.
The economic impacts of climate change are highly uncertain. Two of the most important uncertainties are the sensitivity of the climate system and the so-called damage functions, which relate climate change to economic costs and benefits. Despite broad awareness of these uncertainties, it is unclear which of them is most important, especially at the regional level. Here we construct regional damage functions, based on two different global damage functions, and apply them to two climate models with vastly different climate sensitivities. We find that uncertainty in both climate sensitivity and aggregate economic damages per degree of warming are of similar importance for the global economic impact of climate change, with the decrease in global economic productivity ranging between 4% and 24% by the end of the century under a high-emission scenario. At the regional level, however, the effects of climate change can vary even more substantially, depending both on a region's initial temperature and the amount of warming it experiences, with some regions gaining in productivity and others losing. The ranges of uncertainty are therefore potentially much larger at a regional level. For example, at the end of the century, under a high-emission scenario, we find that India's productivity decreases between 13% and 57% and Russia's increases between 24% and 74%, while Germany's change in productivity ranges from an increase of 8% to a decrease of 4%. Our findings emphasize the importance of including these uncertainties in estimates of future economic impacts, as they are vital for the resulting impacts and thus policy implications.
Common resources may be managed with inefficient policies for the sake of equity. We study how rationing the commons shapes the efficiency and equity of resource use in the context of agricultural groundwater use in Rajasthan, India. We find that rationing binds on input use, such that farmers, despite trivial prices for water extraction, use roughly the socially optimal amount of water on average. The rationing regime is still grossly inefficient, because it misallocates water across farmers, lowering productivity. Pigouvian reform would increase agricultural surplus by 12% of household income yet fall well short of a Pareto improvement over rationing.
Rural isolation can limit access to basic services and income-generating opportunities. Among some communities, rainfall induced flooding can cause increased uncertainty where first-mile transportation infrastructure is limited. In Rwanda, this challenge is apparent, where 90% of the population below the poverty line live in rural areas that are typically mountainous with frequent flooding - events that may be increasing in frequency and severity as the climate changes. To reduce these transportation barriers, the non-profit organization Bridges to Prosperity (B2P) plans to construct hundreds of trailbridges in Rwanda between 2018 and 2023. This scale of rural infrastructure services presents an opportunity for experimental investigation of the effects of these new trailbridges on economic, health, agricultural and education outcomes in rural communities. In this paper, we present a cohort study evaluating the potential community benefits of rural trailbridges - including economic, health and social outcomes for Rwandan communities experiencing environmental change. We examined households living near 12 trailbridge sites and 12 comparison sites over February 2019–March 2020. We found that labor market income increased by 25% attributable to the trailbridges. We did not observe any significant effects on agricultural income, education or health outcomes, however given the small sample and short duration of this study we anticipate observing additional outcomes within the recently started 200 site, 4 year trial.
This paper seeks to explain why billions of people in developing countries either have no access to electricity or lack a reliable supply. We present evidence that these shortfalls are a consequence of electricity being treated as a right and that this sets off a vicious four-step circle. In step 1, because a social norm has developed that all deserve power independent of payment, subsidies, theft, and nonpayment are widely tolerated. In step 2, electricity distribution companies lose money with each unit of electricity sold and in total lose large sums of money. In step 3, government-owned distribution companies ration supply to limit losses by restricting access and hours of supply. In step 4, power supply is no longer governed by market forces and the link between payment and supply is severed, thus reducing customers' incentives to pay. The equilibrium outcome is uneven and sporadic access that undermines growth.
Weak contract enforcement may reduce the efficiency of production in developing countries. I study how contract enforcement affects efficiency in procurement auctions for the largest power projects in India. I gather data on bidding and ex post contract renegotiation and find that the renegotiation of contracts in response to cost shocks is widespread, despite that bidders are allowed to index their bids to future costs like the price of coal. To study heterogeneity in bidding strategies, I construct a new measure of firm connectedness, based on whether a firm has been awarded coal concessions by the Government. Connected firms choose to index less of the value of their bids to coal prices and, through this strategy, expose themselves to cost shocks to induce renegotiation. I use a structural model of bidding in a scoring auction to characterize equilibrium bidding when bidders are heterogeneous both in cost and in the payments they expect after renegotiation. The model estimates show that bidders offer power below cost due to the expected value of later renegotiation. The model is used to simulate bidding and efficiency with strict contract enforcement. Contract enforcement is found to be pro‐competitive. With no renegotiation, equilibrium bids would rise to cover cost, but markups relative to total contract value fall sharply. Production costs decline, due to projects being allocated to lower‐cost bidders over those who expect larger payments in renegotiation.