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Register a new userDeeply decarbonizing residential and urban central districts through photovoltaics plus electric vehicle applications
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With the costs of renewable energy technologies declining, new forms of urban energy systems are emerging that can be established in a cost-effective way. The SolarEV City concept has been proposed that uses rooftop Photovoltaics (PV) to its maximum extent, combined with Electric Vehicle (EV) with bi-directional charging for energy storage. Urban environments consist of various areas, such as residential and commercial districts, with different energy consumption patterns, building structures, and car parks. The cost effectiveness and decarbonization potentials of PV + EV and PV (+ battery) systems vary across these different urban environments and change over time as cost structures gradually shift. To evaluate these characteristics, we performed techno-economic analyses of PV, battery, and EV technologies for a residential area in Shinchi, Fukushima and the central commercial district of Kyoto, Japan between 2020 and 2040. We found that PV + EV and PV only systems in 2020 are already cost competitive relative to existing energy systems (grid electricity and gasoline car). In particular, the PV + EV system rapidly increases its economic advantage over time, particularly in the residential district which has larger PV capacity and EV battery storage relative to the size of energy demand. Electricity exchanges between neighbors (e.g., peer-to-peer or microgrid) further enhanced the economic value (net present value) and decarbonization potential of PV + EV systems up to 23 percent and 7 percent in 2030, respectively. These outcomes have important strategic implications for urban decarbonization over the coming decades.
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The compact city, as a sustainable concept, is intended to augment the efficiency of urban function. However, previous studies have concentrated more on morphology than on structure. The present study focuses on urban structural elements, i.e., urban hotspots consisting of high-density and high-intensity socioeconomic zones, and explores the economic performance associated with their spatial structure. We use nighttime luminosity (NTL) data and the Loubar method to identify and extract the hotspot and ultimately draw two conclusions. First, with population increasing, the hotspot number scales sublinearly with an exponent of approximately 0.50~0.55, regardless of the location in China, the EU or the US, while the intersect values are totally different, which is mainly due to different economic developmental level. Secondly, we demonstrate that the compactness of hotspots imposes an inverted U-shaped influence on economic growth, which implies that an optimal compactness coefficient does exist. These findings are helpful for urban planning.
This paper proposes a public-private insurance scheme for earthquakes and floods in Italy in which property-owners, the insurer and the government co-operate in risk financing. Our model departs from the existing literature by describing a public-private insurance intended to relieve the financial burden that natural events place on governments, while at the same time assisting individuals and protecting the insurance business. Hence, the business is aiming at maximizing social welfare rather than profits. Given the limited amount of data available on natural risks, expected losses per individual have been estimated through risk-modeling. In order to evaluate the insurers loss profile, spatial correlation among insured assets has been evaluated by means of the Hoeffding bound for r-dependent random variables. Though earthquakes generate expected losses that are almost six times greater than floods, we found that the amount of public funds needed to manage the two perils is almost the same. We argue that this result is determined by a combination of the risk aversion of individuals and the shape of the loss distribution. Lastly, since earthquakes and floods are uncorrelated, we tested whether jointly managing the two perils can counteract the negative impact of spatial correlation. Some benefit from risk diversification emerged, though the probability of the government having to inject further capital might be considerable. Our findings suggest that, when not supported by the government, private insurance might either financially over-expose the insurer or set premiums so high that individuals would fail to purchase policies.
The rapid early spread of COVID-19 in the U.S. was experienced very differently by different socioeconomic groups and business industries. In this study, we study aggregate mobility patterns of New York City and Chicago to identify the relationship between the amount of interpersonal contact between people in urban neighborhoods and the disparity in the growth of positive cases among these groups. We introduce an aggregate Contact Exposure Index (CEI) to measure exposure due to this interpersonal contact and combine it with social distancing metrics to show its effect on positive case growth. With the help of structural equations modeling, we find that the effect of exposure on case growth was consistently positive and that it remained consistently higher in lower-income neighborhoods, suggesting a causal path of income on case growth via contact exposure. Using the CEI, schools and restaurants are identified as high-exposure industries, and the estimation suggests that implementing specific mobility restrictions on these point-of-interest categories are most effective. This analysis can be useful in providing insights for government officials targeting specific population groups and businesses to reduce infection spread as reopening efforts continue to expand across the nation.
In the Paris agreement of 2015, it was decided to reduce the CO2 emissions of the energy sector to zero by 2050 and to restrict the global mean temperature increase to 1.5 degree Celcius above the pre-industrial level. Such commitments are possible only with practically CO2-free power generation based on variable renewable technologies. Historically, the main point of criticism regarding renewable power is the variability driven by weather dependence. Power-to-X systems, which convert excess power to other stores of energy for later use, can play an important role in offsetting the variability of renewable power production. In order to do so, however, these systems have to be scheduled properly to ensure they are being powered by low-carbon technologies. In this paper, we introduce a graphical approach for scheduling power-to-X plants in the day-ahead market by minimizing carbon emissions and electricity costs. This graphical approach is simple to implement and intuitively explain to stakeholders. In a simulation study using historical prices and CO2 intensity for four different countries, we find that the price and CO2 intensity tends to decrease with increasing scheduling horizon. The effect diminishes when requiring an increasing amount of full load hours per year. Additionally, investigating the trade-off between optimizing for price or CO2 intensity shows that it is indeed a trade-off: it is not possible to obtain the lowest price and CO2 intensity at the same time.
The transition to a low-carbon economy is one of the ambitions of the European Union for 2030. Biobased industries play an essential role in this transition. However, there has been an on-going discussion about the actual benefit of using biomass to produce biobased products, specifically the use of agricultural materials (e.g., corn and sugarcane). This paper presents the environmental impact assessment of 30% and 100% biobased PET (polyethylene terephthalate) production using EU biomass supply chains (e.g., sugar beet, wheat, and Miscanthus). An integral assessment between the life cycle assessment methodology and the global sensitivity assessment is presented as an early-stage support tool to propose and select supply chains that improve the environmental performance of biobased PET production. From the results, Miscanthus is the best option for the production of biobased PET: promoting EU local supply chains, reducing greenhouse gas (GHG) emissions (process and land-use change), and generating lower impacts in midpoint categories related to resource depletion, ecosystem quality, and human health. This tool can help improving the environmental performance of processes that could boost the shift to a low-carbon economy.
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