Scientists predict higher global temperatures over this century. While this may benefit some countries, most will face varying degrees of damage. This has motivated research on solar geoengineering , a technology that allows countries to unilaterally and temporarily lower global temperatures. To better understand the security implications of this technology, we develop a simple theory that incorporates solar geoengineering, countergeoengineering to reverse its effects, and the use of military force to prevent others from modifying temperatures. We find that when countries’ temperature preferences diverge, applications of geoengineering and countergeoengineering can be highly wasteful due to deployment in opposite directions. Under certain conditions, countries may prefer military interventions over peaceful ones. Cooperation that avoids costs or waste of resources can emerge in repeated settings, but difficulties in monitoring or attributing interventions make such arrangements less attractive.
How beneficial is basic energy access – typically lighting and mobile charging – for rural households? Despite research on the economic impacts of basic energy access, few studies have investigated how it changes household behavior. Here we report results from a randomized controlled trial in rural Uttar Pradesh, India, which identifies the behavioral impacts of providing solar lanterns to households that normally rely on kerosene as their primary source of lighting. Eighty-nine of the 184 households partici- pating in the study were given a free, high-quality solar lantern. Comparing changes in responses from the baseline questionnaire and an endline questionnaires administered six months later, we find that the lanterns reduced energy expenditures, improved lighting, improved satisfaction with lighting, more use of lighting for domestic activities (e.g., reading), and improved satisfaction with lighting for domestic activities. Overall, our results show that basic energy access can offer substantial benefits within the households, even if broader rural economic transformation is not plausible.
To reach the United Nations Sustainable Development Goal of universal household electrification by 2030, developing countries are spending billions of dollars to expand access. India, for example, recently undertook an audacious expansion plan which aimed to electrify every household by December 2018. However, there is little academic study of strategies to increase electrification rates. We argue that significant transaction costs inhibit household applications for connections. As evidence, we report the results of a randomized controlled trial (in 200 communities and 2000 households) in the Indian state of Uttar Pradesh, with a treatment consisting of an informational campaign about the costs and procedure of applying. We found that households exposed to the campaign were three times as likely to apply for a connection. Yet actual connection rates remained unchanged. The results suggest that transaction costs are an important barrier to electrification, but limited capacity and incentive to expand connections are equally important.
How can demand for electricity be estimated without fine-grained usage data? Employing an original and large dataset, we develop a novel method for determining drivers of demand without electricity meter data. We first segment Indian consumers by their willingness to pay for electricity service, their level of usage, and their satisfaction with lighting, and then use cluster membership as a dependent variable in order to determine which household-level factors predict electricity usage. Our approach employs machine-learning and more traditional regression techniques to determine the optimal number of segments, generate the segments, and determine the predictors of segment membership. The dataset consists of more than 10,000 households in more than 200 villages in the states of Bihar, Odisha, Rajasthan, and Uttar Pradesh. We find that the rural Indian electricity market can be segmented into three clusters based on households' willingness to pay, satisfaction with lighting, and appliance wattage. The clusters consist of potential customers, low-demand customers, and high-use customers. We then determine the predictors of membership in these clusters. We show that different types of consumers can be identified along easily observable measures. Moreover, we show that there are clear groups of consumers that vary along their satisfaction, willingness to pay, and existing appliance usage.