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Abstract Greater adoption of renewable energy technologies by households is a key component of decarbonization and energy transition goals. Although existing literature has examined how sociodemographic characteristics, “green” preferences, and peer effects impact adoption of new energy technology, the role of behavioral preferences has not been adequately studied. In this paper, we examine the effect of two types of behavioral preferences, namely the degree of risk tolerance (risk preference) and attitude toward delayed reward (time preference) on the contract decision to lease or own a solar photovoltaic (PV) system. We develop a theoretical framework to show that the effect of risk and time preferences on the relative utilities from the two contracts is monotonic: Lower risk aversion and lower discount rate (more patience) imply a higher chance of solar PV ownership. To test these predictions empirically, we first estimate preference parameters (risk aversion and discount rate) from laboratory data collected from solar PV adopters. We then combine the parameter estimates with data on actual solar PV contract choice to examine the relationship between solar PV adopters' time and risk preferences and their lease‐versus‐own choice. Our regression results confirm that less risk averse individuals have a higher tendency to choose the ownership option, whereas more patient individuals are (weakly) more likely to own their solar PV systems. These findings contribute to a greater understanding of the role of behavioral factors in household decisions related to energy technologies. 

Abstract As the global energy sector transitions towards a cleaner and more sustainable future, observational evidence suggests that many new energy technologies share a close relationship with well-established technologies. Yet, the topic of how closely technologies are related has not been addressed rigorously, rather it has been the purview of practitioner know-how and informal expert opinion. In this study, we propose a quantitative method to supplement practitioners’ subjective understanding of the relatedness between technology domains. The method uses patents to represent the position of a technology in knowledge space and calculates the Hausdorff distance between patent domains to proxy the relatedness between technologies. We apply this method to investigate the relatedness of offshore wind energy technology to two more mature domains: onshore wind energy technology and offshore oil and gas technology. We examine the technological relatedness of individual offshore wind components to these two technologies, and represent the changes in relatedness through time. The results confirm that offshore wind components such as foundations, installation, and maintenance are more related to the offshore oil and gas industry; while other components, such as rotors and nacelles, are more related to onshore wind energy. The results also suggest that many offshore wind energy components are becoming less related through time to both of these domains, possibly indicating increasing innovation. This method can provide quantitative parameters to improve the modeling of technological change and guide practitioners in strategic decision-making regarding the positioning of industries and firms within those industries. 

CPU affinity reduces data copies and improves data locality and has become a prevalent technique for high-performance programs in datacenters. This paper explores the tension between CPU affinity and sustainability. In particular, affinity settings can lead to significant uneven aging of cores on a CPU. We observe that infrastructure threads, used in a wide spectrum of network, storage, and virtualization sub-systems, exercise their affinitized cores up to 23× more when compared to typical 𝜇s-scale application threads. In addition, we observe that the affinitized infrastructure threads generate regional heat hot spots and preclude CPUs from being used with the expected lifetime. Finally, we discuss design options to tackle the unbalanced core-aging problem to improve the overall sustainability of CPUs and call for more attention to sustainabilityaware affinity and mitigation of such problems. 

Continued advances in technology have led to falling costs and a dramatic increase in the aggregate amount of solar capacity installed across the world. A drawback of increased solar penetration is the potential for supply-demand mismatches in the grid due to the intermittent nature of solar generation. While energy storage can be used to mask such problems, we argue that there is also a need to explicitly control the rate of solar generation of each solar array in order to achieve high penetration while also handling supply-demand mismatches. To address this issue, we present the notion of smart solar arrays that can actively modulate their solar output based on the notion of proportional fairness. We present a decentralized algorithm based on Lagrangian optimization that enables each smart solar array to make local decisions on its fair share of solar power it can inject into the grid and then present a sense-broadcast-respond protocol to implement our decentralized algorithm into smart solar arrays. We also study the benefits of using energy storage when we rate control solar. To do so, we present a decentralized algorithm to charge and discharge batteries for each smart solar. Our evaluation on a city-scale dataset shows that our approach enables 2.6× more solar penetration while causing smart arrays to reduce their output by as little as 12.4%. By employing an adaptive gradient approach, our decentralized algorithm has 3 to 30× faster convergence. Finally, we demonstrate energy storage can help netmeter more solar energy while ensuring fairness and grid constraints are met.