scholarly journals A bottom-up cost analysis of a high concentration PV module

Author(s):  
Kelsey A. W. Horowitz ◽  
Michael Woodhouse ◽  
Hohyun Lee ◽  
Greg P. Smestad
2021 ◽  
Author(s):  
Shrivardhan Hulswar ◽  
George Manville ◽  
Rafel Simo ◽  
Marti Gali ◽  
Thomas G. Bell ◽  
...  

<p>An updated estimation of the bottom-up global surface seawater dimethyl sulphide (DMS) climatology, DMS-Rev3, is the third of its kind and includes five significant changes from the last climatology, ‘L11’ (Lana et al., 2011) that was released about a decade ago. The first change is the inclusion of new observations that have become available over the last decade, i.e., the total number of observations included in DMS- Rev3 are 865,109 as compared to 47,313 data points used in the last estimation (~1728% increase in raw data). The second was significant improvements in data handling, processing, filtering, to avoid bias due to different observation frequencies. Thirdly, we incorporated the dynamic seasonal changes observed in the ocean biogeochemical provinces and their variable geographic boundaries. Fourth change was refinements in the interpolation algorithm used to fill up the missing data. And finally, an upgraded smoothing algorithm based on observed DMS variability length scales (VLS) which helped reproduce a more realistic distribution of the DMS concentration data. The results show that DMS-Rev3 estimates the global annual mean DMS concentration at 2.34 nM, 4% lower than the current bottom-up ‘L11’ climatology. However, significant regional differences of more than 100% are observed. The largest changes are observed in high concentration regions such as the polar oceans, although oceanic regions which were under-sampled in the past also show large differences. DMS-Rev3 reduces the previously observed patchiness in high productivity regions.</p>


2021 ◽  
Author(s):  
Shrivardhan Hulswar ◽  
Rafel Simo ◽  
Martí Galí ◽  
Thomas Bell ◽  
Arancha Lana ◽  
...  

Abstract. This paper presents an updated estimation of the bottom-up global surface seawater dimethyl sulfide (DMS) climatology. This update, called DMS-Rev3, is the third of its kind and includes five significant changes from the last climatology, ‘L11’ (Lana et al., 2011) that was released about a decade ago. The first change is the inclusion of new observations that have become available over the last decade, creating a database of 872,427 observations leading to a ~18-fold increase in raw data as compared to the last estimation The second is significant improvements in data handling, processing, and filtering, to avoid biases due to different observation frequencies which results from different measurement techniques. Thirdly, we incorporate the dynamic seasonal changes observed in the geographic boundaries of the ocean biogeochemical provinces. The fourth change involves the refinement of the interpolation algorithm used to fill in the missing data. And finally, an upgraded smoothing algorithm based on observed DMS variability length scales (VLS) helps to reproduce a more realistic distribution of the DMS concentration data. The results show that DMS-Rev3 estimates the global annual mean DMS concentration to be ~1.87 nM (2.35 nM without a sea-ice mask), i.e., about 4 % lower than the previous bottom-up ‘L11’ climatology. However, significant regional differences of more than 100 % as compared to L11 are observed. The global sea to air flux of DMS is estimated at ~27 TgS yr−1 which is about 4 % lower than L11, although, like the DMS distribution, large regional differences were observed. The largest changes are observed in high concentration regions such as the polar oceans, although oceanic regions that were under-sampled in the past also show large differences between revisions of the climatology. Finally, DMS-Rev3 reduces the previously observed patchiness in high productivity regions.  


Author(s):  
Martin Zurek ◽  
Lars Heinrich

AbstractIn a recent discussion about efficient ways to combine multiple firm characteristics into a multifactor portfolio, a distinction was made between the bottom-up and top-down approach. Both approaches integrate characteristics with equal weights and ignore interaction effects from differences in informational content and correlations between the firm characteristics. The authors complement the bottom-up approach for the missing interaction effects by implementing a linear alpha forecasting framework. Bottom-up versus top-down factor investing is typically discussed using the assumption that all characteristics are equally priced, but the pricing impact of different firm characteristics can vary tremendously. The alpha forecasting perspective provides a theoretical motivation for factor investing and helps to compare the bottom-up and top-down approach with regard to the difference of informational content and interaction effects between firm characteristics. Taking into account the difference in informational content between firm characteristics leads to significant performance improvement in factor models with a high concentration of informational content. Equally weighted characteristics result in related performance irrespective of whether the bottom-up or top-down approach is applied.


2015 ◽  
Vol 24 (1) ◽  
pp. 58-68 ◽  
Author(s):  
Yongjoo Chung ◽  
Hugon Kim ◽  
Chunhyun Paik ◽  
Young Jin Kim

2013 ◽  
Vol 16 (3) ◽  
pp. A176 ◽  
Author(s):  
E. Nicod ◽  
T. Jackson ◽  
F. Grimaccia ◽  
A. Angelis ◽  
P. Kanavos

2014 ◽  
Vol 52 (1) ◽  
pp. 18 ◽  
Author(s):  
Min-Young Kim ◽  
Ha-Na Choi ◽  
Ho-Sung Shin

2015 ◽  
Vol 8 (12) ◽  
pp. 3395-3408 ◽  
Author(s):  
Douglas M. Powell ◽  
Ran Fu ◽  
Kelsey Horowitz ◽  
Paul A. Basore ◽  
Michael Woodhouse ◽  
...  

Using a bottom-up cost model, we assess the impact of initial factory capital expenditure (capex) on photovoltaic (PV) module minimum sustainable price (MSP) and industry-wide trends, including sustainable growth rate and barriers to innovation.


Author(s):  
Gaurav Kumar ◽  
Piyush Pal ◽  
Pulkit Agarwal ◽  
Rahul Dev ◽  
Akhilesh Kumar Chauhan

Author(s):  
Sejal Patel ◽  
Melanie Lindenberg ◽  
Maroeska M. Rovers ◽  
Wim H. van Harten ◽  
Theo J.M. Ruers ◽  
...  

Background: Over the past decade, many hospitals have adopted hybrid operating rooms (ORs). As resources are limited, these ORs have to prove themselves in adding value. Current estimations on standard OR costs show great variety, while cost analyses of hybrid ORs are lacking. Therefore, this study aims to identify the cost drivers of a conventional and hybrid OR and take a first step in evaluating the added value of the hybrid OR. Methods: A comprehensive bottom-up cost analysis was conducted in five Dutch hospitals taking into account: construction, inventory, personnel and overhead costs by means of interviews and hospital specific data. The costs per minute for both ORs were calculated using the utilization rates of the ORs. Cost drivers were identified by sensitivity analyses. Results: The costs per minute for the conventional OR and the hybrid OR were €9.45 (€8.60-€10.23) and €19.88 (€16.10- €23.07), respectively. Total personnel and total inventory costs had most impact on the conventional OR costs. For the hybrid OR the costs were mostly driven by utilization rate, total inventory and construction costs. The results were incorporated in an open access calculation model to enable adjustment of the input parameters to a specific hospital or country setting. Conclusion: This study estimated a cost of €9.45 (€8.60-€10.23) and €19.88 (€16.10-€23.07) for the conventional and hybrid OR, respectively. The main factors influencing the OR costs are: total inventory costs, total construction costs, utilization rate, and total personnel costs. Our analysis can be used as a basis for future research focusing on evaluating value for money of this promising innovative OR. Furthermore, our results can inform surgeons, and decision and policy-makers in hospitals on the adoption and optimal utilization of new (hybrid) ORs.


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