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1. INFERENCE IN DYNAMIC, NONPARAMETRIC MODELS OF PRODUCTION: CENTRAL LIMIT THEOREMS FOR MALMQUIST INDICES

   https://doi.org/10.1017/S0266466620000237  

   Kneip, Alois; Simar, Léopold; Wilson, Paul W. (February 2019, Econometric Theory)

   null (Ed.)

   The Malmquist index gives a measure of productivity in dynamic settings and has been widely applied in empirical work. The index is typically estimated using envelopment estimators, particularly data envelopment analysis (DEA) estimators. Until now, inference about productivity change measured by Malmquist indices has been problematic, including both inference regarding productivity change experienced by particular firms as well as mean productivity change. This paper establishes properties of a DEA-type estimator of distance to the conical hull of a variable returns to scale production frontier. In addition, properties of DEA estimators of Malmquist indices for individual producers are derived as well as properties of geometric means of these estimators. The latter requires new central limit theorem results, extending the work of Kneip, Simar, and Wilson (2015, Econometric Theory 31, 394–422). Simulation results are provided to give applied researchers an idea of how well inference may work in practice in finite samples. Our results extend easily to other productivity indices, including the Luenberger and Hicks–Moorsteen indices. 

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2. Comparative Analysis of Efficiencies for Renewable Energy Capacities across ISO Regions

   Ogunrinde, O. and (January 2020, IISE transactions)

   L. Cromarty, R. Shirwaiker (Ed.)

   This study investigates the renewable energy adoption across regions covered by Independent System Operators (ISOs) in the U.S. The study employed a deterministic model in the form of Data Envelopment Analysis (DEA) to determine the performance of ten ISO regions over a five-year period from 2013 to 2017. Inputs into the model include the Renewable Portfolio Standard (RPS) targets, fossil fuel capacity additions and the costs of capacity additions. Outputs from the model include renewable energy capacity additions and CO2 emissions per MWh of generated electricity. The results show the regions covered by CAISO, ERCOT, NE-ISO, SPP and the NON-ISO to be on the efficient frontier. For the regions not on the efficient frontier, the results identify their limitations and provide projections both for reductions in excess inputs and improvements in outputs to be on the efficient frontier. For example, we see that the regions covered by NY-ISO and PJM would require, on average, renewable energy capacity expansions of 593.65MW and 230.24MW, respectively, to be on the efficient frontier. These regions would require their average fossil capacity expansions to be limited to 234.83MW and 365.4MW respectively. These findings offer some guidance on approaches to improving the performance of these markets. 

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3. City-Scale Electricity Demand Forecasting using a Gaussian Process Model

   https://doi.org/10.1109/GTSD50082.2020.9303132  

   Nguyen, Phong T.; Manuel, Lance (November 2020, 5th International Conference on Green Technology and Sustainable Development (GTSD 2020))

   Accurate and efficient power demand forecasting in urban settings is essential for making decisions related to planning, managing and operations in electricity supply. This task, however, is complicated due to many sources of uncertainty such as due to the variation in weather conditions and household or other needs that influence the inherent stochastic and nonlinear characteristics of electricity demand. Due to the modeling flexibility and computational efficiency afforded by it, a Gaussian process model is employed in this study for energy demand prediction as a function of temperature. A Gaussian process model is a Bayesian non-parametric regression method that models data using a joint Gaussian distribution with mean and covariance functions. The selected mean function is modeled as a polynomial function of temperature, whereas the covariance function is appropriately selected to reflect the actual data patterns. We employ real data sets of daily temperature and electricity demand from Austin, Texas, USA to assess the effectiveness of the proposed method for load forecasting. The accuracy of the model prediction is evaluated using metrics such as mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE) and 95% confidence interval (95% CI). A numerical study undertaken demonstrates that the proposed method has promise for energy demand prediction. 

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4. Long-term stability of productivity increases with tree diversity in Canadian forests

   https://doi.org/10.1073/pnas.2405108121  

   Ding, Xiaxia; Reich, Peter B; Hisano, Masumi; Chen, Han_Y H (December 2024, Proceedings of the National Academy of Sciences)

   The temporal stability of forest productivity is a key ecosystem function and an essential service to humanity. Plot-scale tree diversity experiments with observations over 10 to 11 y indicate that tree diversity increases stability under various environmental changes. However, it remains unknown whether these small-scale experimental findings are relevant to the longer-term stability of natural forests. Using 7,500 natural forest plots across much of Canada, monitored over three to four decades on average, we provide strong evidence that higher temporal stability (defined as the mean productivity divided by its SD over time) is consistently associated with greater tree functional, phylogenetic, and taxonomic diversity across all lengths of observations. Specifically, increasing functional diversity from its minimum to maximum values increases stability, mean productivity, and the temporal SD of productivity by 14%, 36%, and 28%, respectively. Our results highlight that the promotion of functionally, phylogenetically, and/or taxonomically diverse forests could enhance the long-term productivity and stability of natural forests. 

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5. The Energy Trade-Offs of Transitioning to a Locally Sourced Water Supply Portfolio in the City of Los Angeles

   https://doi.org/10.3390/en13215589  

   Zohrabian, Angineh; Sanders, Kelly T. (November 2020, Energies)

   null (Ed.)

   Predicting the energy needs of future water systems is important for coordinating long-term energy and water management plans, as both systems are interrelated. We use the case study of the Los Angeles City’s Department of Water and Power (LADWP), located in a densely populated, environmentally progressive, and water-poor region, to highlight the trade-offs and tensions that can occur in balancing priorities related to reliable water supply, energy demand for water and greenhouse gas emissions. The city is on its path to achieving higher fractions of local water supplies through the expansion of conservation, water recycling and stormwater capture to replace supply from imported water. We analyze scenarios to simulate a set of future local water supply adoption pathways under average and dry weather conditions, across business as usual and decarbonized grid scenarios. Our results demonstrate that an aggressive local water supply expansion could impact the geospatial distribution of electricity demand for water services, which could place a greater burden on LADWP’s electricity system over the next two decades, although the total energy consumed for the utility’s water supply might not be significantly changed. A decomposition analysis of the major factors driving electricity demand suggests that in most scenarios, a structural change in LADWP’s portfolio of water supply sources affects the electricity demanded for water more than increases in population or water conservation. 

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