Consumer Willingness to Pay for Eco-labeled Refrigerators

The United States Environmental Protection Agency has used environmental information provision as a policy tool to take advantage of consumer preferences for products that meet higher environmental standards. Such environmental programs include a variety of policies ranging from eco-labeling programs to voluntary environmental agreements between governments and manufacturers. This study analyzes the effects of two such programs - the ENERGY STAR program, an eco-labeling program, and the Climate Leaders program, a voluntary environmental agreement program - on consumer preferences for a household appliance. The study estimates consumer willingness to pay (WTP) for the two programs and examines factors that motivate WTP. A particular interest for the ENERGY STAR program is in determining how the offer of a mail-in rebate affects these preferences. Data used for this study was collected from an online survey conducted in the United States during March and April, 2009. Conditional and random parameter logit models, with product attributes only and with demographic and other individual characteristics as interaction terms, are used to analyze the data. Findings from this study imply that consumers are willing to pay a premium equivalent to a significant portion of the purchase prices for the products approved by either program. Also, it is found that consumers who are more concerned about environmental issues, such as global climate change, and who have confidence in the effects of collective action, are more likely to engage in the purchase of such environmentally friendly products. These results should help government agencies and manufacturers evaluate the effectiveness of environmental information provision programs

The Error Covariance Matrix Inflation in Ensemble Kalman Filter

The estimation accuracy of ensemble forecast errors is crucial to the assimilation results for all ensemble-based schemes. The ensemble Kalman filter (EnKF) is a widely used scheme in land surface data assimilation, without using the adjoint of a dynamical model. In EnKF, the forecast error covariance matrix is estimated as the sampling covariance matrix of the ensemble forecast states. However, past researches on EnKF have found that it can generally lead to an underestimate of the forecast error covariance matrix, due to the limited ensemble size, as well as the poor initial perturbations and model error. This can eventually result in filter divergence. Therefore, using inflation to further adjust the forecast error covariance matrix becomes increasingly important. In this chapter, a new structure of the forecast error covariance matrix is proposed to mitigate the problems with limited ensemble size and model error. An adaptive procedure equipped with a second-order least squares method is applied to estimate the inflation factors of forecast and observational error covariance matrices. The proposed method is tested on the well-known atmosphere-like Lorenz-96 model with spatially correlated observational systems. The experiment results show that the new structure of the forecast error covariance matrix and the adaptive estimation procedure lead to improvement of the analysis states

Factors Influencing Consumer Likelihood of Purchasing a Flexible-Fuel or Hybrid Automobile

Developing fuels and vehicles that reduce our reliance on fossil fuels has become a priority due to the threat of global climate change and desire for reduced dependence on oil imports. Flexible-fuel vehicles that can run on ethanol/gasoline blends of up to 85% ethanol and hybrid electric vehicles present two such opportunities. While production of both flexible-fuel and hybrid vehicles is increasing, there is still a great deal of uncertainty about how consumers will respond to these products. To address this uncertainty, data was collected through an online survey of automobile owners that asked respondents how likely they were to choose either a flexible-fuel or hybrid vehicle as their next vehicle. A bivariate probit model was used to jointly analyze responses to these two questions. The results show that, while there was some overlap in the factors correlated with perceived likelihood of choosing one of these two types of automobiles, there were also clear differences. These results should benefit policymakers, marketers and academics seeking a better understanding of the respective markets for these vehicles.flexible-fuel vehicles, ethanol, E85, hybrid electric vehicles, Demand and Price Analysis, Environmental Economics and Policy, Resource /Energy Economics and Policy,

Optimization of terrestrial ecosystem model parameters using atmospheric CO2 concentration data with the Global Carbon Assimilation System (GCAS)

Author Posting. © American Geophysical Union, 2017. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Biogeosciences 122 (2017): 3218–3237, doi:10.1002/2016JG003716.The Global Carbon Assimilation System that assimilates ground-based atmospheric CO2 data is used to estimate several key parameters in a terrestrial ecosystem model for the purpose of improving carbon cycle simulation. The optimized parameters are the leaf maximum carboxylation rate at 25°C (V25 max), the temperature sensitivity of ecosystem respiration (Q10), and the soil carbon pool size. The optimization is performed at the global scale at 1° resolution for the period from 2002 to 2008. The results indicate that vegetation from tropical zones has lower V25 max values than vegetation in temperate regions. Relatively high values of Q10 are derived over high/midlatitude regions. Both V25 max and Q10 exhibit pronounced seasonal variations at middle-high latitudes. The maxima in V25 max occur during growing seasons, while the minima appear during nongrowing seasons. Q10 values decrease with increasing temperature. The seasonal variabilities of V25 max and Q10 are larger at higher latitudes. Optimized V25 max and Q10 show little seasonal variabilities at tropical regions. The seasonal variabilities of V25 max are consistent with the variabilities of LAI for evergreen conifers and broadleaf evergreen forests. Variations in leaf nitrogen and leaf chlorophyll contents may partly explain the variations in V25 max. The spatial distribution of the total soil carbon pool size after optimization is compared favorably with the gridded Global Soil Data Set for Earth System. The results also suggest that atmospheric CO2 data are a source of information that can be tapped to gain spatially and temporally meaningful information for key ecosystem parameters that are representative at the regional and global scales.National Key R&D Program of China Grant Number: 2016YFA0600204; National Natural Science Foundation of China Grant Number: 415713382018-06-2

Soil Moisture Assimilation Using a Modified Ensemble Transform Kalman Filter Based on Station Observations in the Hai River Basin

Assimilating observations to a land surface model can further improve soil moisture estimation accuracy. However, assimilation results largely rely on forecast error and generally cannot maintain a water budget balance. In this study, shallow soil moisture observations are assimilated into Common Land Model (CoLM) to estimate the soil moisture in different layers. A proposed forecast error inflation and water balance constraint are adopted in the Ensemble Transform Kalman Filter to reduce the analysis error and water budget residuals. The assimilation results indicate that the analysis error is reduced and the water imbalance is mitigated with this approach

Preliminary studies of the tardigrada communities from a polymetallic nodule area of the deep South China Sea

Knowledge about marine tardigrades from the South China Sea is very scarce, with only four species from shallow waters recorded to date. The present study investigated the structure and diversity of tardigrade communities from the deep sea (1517-1725 m) at 8 stations in a polymetallic nodule area of the northern South China Sea. A total of 151 arthrotardigrades were collected belonging to 11 genera (Angursa, Batillipes, Coronarctus, Euclavarctus, Exoclavarctus, Halechiniscus, Moebjergarctus, Raiarctus, Rhomboarctus, Tanarctus and Tholoarctus), representing 17 species. Two Angursa species (Angursa sp. 4 and Angursa sp. 3) were the most abundant (25.2% and 14.6%, respectively), followed by Moebjergarctus sp. (13.9%). Specimens were mostly (90.7%) distributed in the upper layer of the sandy-mud sediment (0-1 cm). The SIMPROF test showed that the composition of tardigrade communities at all stations was not significantly different. At different stations, the number of species, Shannon-Wiener diversity index and Pielou’s evenness index ranged from 4 to 10, 1.94 to 2.87, and 0.75 to 1.00, respectively. The average taxonomic distinctness (D+) ranged from 72.50 to 90.00, and the variation in taxonomic distinctness (L+) ranged from 316.67 to 1181.25. This study provides some basic information about the biodiversity of the marine tardigrade community in the South China Sea.info:eu-repo/semantics/publishedVersio

Influence of Different Carboxylic Acid Ligands on Luminescent Properties of Eu(Lc) 3

A series of rare earth europium complexes with different carboxylic acid ligands Eu(Lc)3phen (Lc = MAA, AA, BA, SA) were synthesized. The complexes were characterized by FTIR, TG-DSC, XRD, UV absorption spectra, and photoluminescence spectra (PL) to study the structure, thermal stability, the energy absorption, and luminescent properties of the complexes. The results showed that the series complexes are all with good crystallization and relatively high thermal stability. The differences of the luminescent properties of complexes are caused by the different ligand structures. The absorption intensity of the carboxylic acid ligands, BA, was the strongest, followed by the MAA and AA and SA was the weakest. Therefore, the fluorescence intensity of the Eu(BA)3phen was the strongest, followed by the Eu(MAA)3phen and Eu(AA)3phen and the Eu2(SA)3phen2 was the weakest. All complexes showed good luminescence properties