Abstract

Critical to the environmental success of sustainable innovations is the adoption by consumers. The consensus is that instrumental shortcomings of sustainable innovations inhibit their adoption. However, we argue that the adoption of sustainable innovations does not exclusively depend on their instrumental attributes. In addition, people may be motivated to adopt sustainable innovations because of their positive environmental and symbolic attributes, that is, they benefit the environment and can be used to signal positive characteristics to oneself and others. We studied the significance of instrumental, environmental and symbolic attributes for the adoption of two sustainable innovations: electric cars (Study 1) and local renewable energy systems (Study 2), following two methods. Results showed that when asked directly, participants claimed that instrumental and environmental attributes are most important for their decision to adopt sustainable innovations, while symbolic attributes were rated as less important. Interestingly, evaluations of the symbolic and environmental attributes of sustainable innovations, but not evaluations of their instrumental attributes, predicted different indicators of adoption (i.e., interest in, the acceptability of, and intention to adopt these sustainable innovations), suggesting that the significance of symbolic motives for adopting sustainable innovations may not be recognized by consumers. Moreover, favorable evaluations of symbolic attributes particularly enhanced interest in and acceptability of sustainable innovation when participants evaluated the instrumental attributes more negatively, but not when instrumental attributes were evaluated relatively positively. This suggests that the instrumental drawbacks of sustainable innovations may sometimes strengthen their positive signal, which can promote interest in sustainable innovations. (c) 2014 Elsevier Ltd. All rights reserved.

Abstract

global emission inventory of carbon monoxide (CO) emissions with 1° × 1° latitude-longitude resolution was compiled for 1990 on a sectoral basis. The sectoral sources considered include large-scale biomass burning (29%, of which savanna burning, 18%, and deforestation, 11%), fossil fuel combustion (27%, predominantly in road transport), biofuel combustion (19%, predominantly fuelwood combustion), agricultural waste burning (21%) and industrial process sources (4%). The inventory was compiled using mostly national statistics as activity data, emission factors at global or country level, and specific grid maps to convert, by sector, country total emissions to the 1° × 1° grid. A special effort was made to compile a global inventory of biofuel use, since this was considered to be a significant source on a global level, and a major source in some regions such as India and China. The global anthropogenic source of CO in 1990 is estimated at about 974 Tg CO yr-1. The inventory is available on a sectoral basis on a 1° × 1° grid for input to global atmospheric models and on a regional/country basis for policy analysis. © 1999 Elsevier Science Ltd. All rights reserved.

Abstract

Leakage of hydrocarbon has a large economic and environmental impact. Traditional methods for investigating leakage and resulting pollution, such as drilling, are destructive, time consuming and expensive. Remote sensing is an alternative that is non-destructive and has been been tested extensively for exploration of onshore hydrocarbon reservoirs and detection of hydrocarbons at the Earth’s surface. In this research, a leaking pipeline is investigated through field reflectance spectrometry and the findings are validated with traditional drilling and geophysical measurements. The measurements show a significant increase of vegetation anomalies on the pipeline with respect to areas further away. The observed anomalies are positively related to hydrocarbon pollution through chemical analysis of drillings. Subsurface geophysical measurements show a large correlation with observed surface vegetation stress, enhancing the identification of hydrocarbon-related vegetation stress through spectroscopy.