Environmental management intentions: An empirical investigation of Argentina's polluting firms

This paper builds on past behavioural research which explicitly recognises that ‘actions’ are preceded by ‘intentions’ which are in turn determined by individual mindsets, locus of control, principles of governances and context factors. More concretely, it presents a model that investigates the extent to which environmental behavioural intentions are explained by i) managers' core values, basic assumptions, and beliefs, ii) individual and socio-cognitive frames, iii) principles of governance, and iv) context factors. Context factors include obstacles and drivers of greener behaviours, market pressures, and firm demographics. The resulting theoretical framework is tested empirically through regression analyses that use data gathered from a survey of 536 Argentinean firms in polluting industries. The model performs well, explaining approximately 50% of the variations in the (pro) environmental behaviour of firms. Policy implications are briefly discussed.     

Vegetation cover and rainfall seasonality impact nutrient loss via runoff and erosion in the Colombian Andes

Regional Environmental Change - Mountain ecosystems provide key services to a large portion of the population in the tropics. However, they are particularly vulnerable to regional environmental...     

Atmospheric pollution in Beijing, China, as recorded in sediments of the Kunming Lake

The fluxes of heavy metals and magnetite, a fly-ash component, from coal burning, to sediments of the Kunming Lake in Beijing have increased measurably over the last fifty years. Ages were introduced to the sediment strata through Pb-210 geochronologies. Even with a doubling of coal usage by the year 2000, the anthropogenic metal fluxes will still be less than those recorded in metropolitan Tokyo and U.S. lakes.     

Chemical looping with oxygen uncoupling: an advanced biomass combustion technology to avoid CO2 emissions

Mitigation and Adaptation Strategies for Global Change - Bioenergy with carbon dioxide (CO2) capture and storage (BECCS) technologies represent an interesting option to reach negative carbon...     

Soil moisture determines the effectiveness of two urease inhibitors to decrease N<Subscript>2</Subscript>O emission

Among the mitigation strategies to prevent nitrogen (N) losses from ureic fertilizers, urease inhibitors (UIs) have been demonstrated to promote high N use efficiency by reducing ammonia (NH₃) volatilization. In the last few years, some field experiments have also shown its effectiveness in reducing nitrous oxide (N₂O) losses from fertilized soils under conditions of low soil moisture. An incubation experiment was carried out with the aim of assessing the main biotic mechanisms behind N₂O emissions once that the UIs N-(n-butyl) thiophosphoric triamid (NBPT) and phenil phosphorodiamidate (PPDA) were applied with Urea (U) under different soil moisture conditions (40, 60 and 80 % water-filled pore space, WFPS). In the same study we tried to analyze to what extent soil WFPS regulates the effect of these inhibitors on N₂O emissions. The use of PPDA in our study allowed us to compare the effect of NBPT with that of another commercially available urease inhibitor, aiming to see if the results were inhibitor-specific or not. Based on the results from this experiment, a WFPS (i.e. 60 %) was chosen for a second study (i.e. mesocosm experiment) aiming to assess the efficiency of the UIs to indirectly affect N₂O emissions through influencing the pool of soil mineral N. The N₂O emissions at 40 % WFPS were almost negligible, being significantly lower from all fertilized treatments than that produced at 60 and 80 % WFPS. When compared to U alone, NBPT+U reduced the N₂O emissions at 60 % WFPS but had no effect at 80 % WFPS. The application of PPDA significantly increased the emissions with respect to U at 80 % WFPS whereas no significant effect was found at 60 %. At 80 % WFPS, denitrification was the main source of N₂O emissions for all treatments. In the mesocosm study, the application of NBPT+U was an effective strategy to reduce N₂O emissions (75 % reduction compared to U alone), due to a lower soil ammonium (NH₄ ⁺) content induced by the inhibitor. These results suggest that adequate management of the UI NBPT could provide, under certain soil conditions, an opportunity for mitigation of N₂O emissions from fertilized soils.     

Revisiting the Life Cycle Assessment of mussels from a sectorial perspective

Mussels as a commercial product involve a range of activities which can be included within four different sub-sectors: culture, dispatch centres, canning factories and, finally, cooking plants. This paper deals with the environmental evaluation of the whole mussel sector from a Life Cycle Assessment approach. The use of exhaustive inventories led to the subsequent environmental characterization of the mussel sector in terms of the contribution observed for each of the sub-sectors. In this sense, the sub-sector associated with dispatch centres presented the largest contributions to the potential environmental impacts, clearly ahead of mussel farming. Furthermore, the sub-sectors of mussel cooking plants and canning factories showed a much lower contribution to the potential environmental impacts. Several improvement potentials were identified from the characterization results, stressing the minimization of the electric energy consumption in dispatch centres. A comparative LCA was performed in order to contrast the environmental performance of the three main commercial mussel products: fresh mussels, canned mussels and frozen mussels. The analysis showed that fresh mussels have the least favourable environmental profile.     

Dynamics in the global protected-area estate since 2004

Nations of the world have committed to a number of goals and targets to address global environmental challenges. Protected areas have for centuries been a key strategy in conservation and play a major role in addressing current challenges. The most important tool used to track progress on protected-area commitments is the World Database on Protected Areas (WDPA). Periodic assessments of the world's protected-area estate show steady growth over the last 2 decades. However, the current method, which uses the latest version of the WDPA, does not show the true dynamic nature of protected areas over time and does not provide information on sites removed from the WDPA. In reality, this method can only show growth or remain stable. We used GIS tools in an approach to assess protected-area change over time based on 12 temporally distinct versions of the WDPA that quantify area added and removed from the WDPA annually from 2004 to 2016. Both the narrative of continual growth of protected area and the counter-narrative of protected area removal were overly simplistic. The former because growth was almost entirely in the marine realm and the latter because some areas removed were reprotected in later years. On average 2.5 million km² was added to the WDPA annually and 1.1 million km² was removed. Reasons for the inclusion and removal of protected areas in the WDPA database were in part due to data-quality issues but also to on-the-ground changes. To meet the 17% protected-area component of Aichi Biodiversity Target 11 by 2020, which stood at 14.7% in 2016, either the rate of protected-area removal must decrease or the rate of protected-area designation and addition to the WDPA must increase.     

Sampling strategies in the assessment of long‐term exposures to toxic substances in air

The decision to mitigate exposures from vapor intrusion (VI) is typically based on limited data from 24‐hour air samples. It is well documented that these data do not accurately represent long‐term average exposures linked to adverse health effects. Limited decision guidance is currently available to determine the most appropriate sampling strategy, considering the cost of sampling alternatives along with the economic consequences of exposure‐related health effects. We present a decision model that introduces economic and statistical considerations in evaluating alternative VI sampling methods. The model characterizes the best sampling method by factoring economic and health consequences of exposure, the variability of exposure, the cost of sampling and mitigation, and the likelihood of false‐negatives and false‐positives. Decision‐makers can use results to select the sample size that maximizes net benefit. Conceptual and mathematical models are presented linking biological, statistical, and economic considerations to assess the cost and effectiveness of different sampling strategies. The model relates an average exposure concentration, determined statistically, to abatement costs and to the monetary value of health deterioration. The value of the information provided by different strategies is calculated and used to select the optimum sampling method. Simulations show that longer‐term sampling methods tend to be more accurate and cost‐effective than short‐term samples. The ideal sampling strategy shows significant seasonal variation (it is typically optimal to use longer samples in the winter) and also varies significantly with the stringency of regulatory standards. Longer‐term sample collection provides a more accurate representation of average VI exposure and reduces the likelihood of type I and type II errors. This reduces expected costs of mitigation and exposure (e.g., health consequences, legal and regulatory penalties), which in some cases can be quite significant. The model herein shows how these savings are balanced against the additional costs of longer‐term sampling.     

Spatio-temporal log-Gaussian Cox processes for modelling wildfire occurrence: the case of Catalonia, 1994–2008

Wildfires have become one of the principal environmental problems in the Mediterranean basin. While fire plays an important role in most terrestrial plant ecosystems, the potential hazard that it represents for human lives and property has led to the application of fire exclusion policies that, in the long term, have caused severe damage, mainly due to the increase of fuel loadings in forested areas, in some forest systems. The lack of an easy solution to forest fire management highlights the importance of preventive tasks. The observed spatio-temporal pattern of wildfire occurrences may be idealized as a realization of some stochastic process. In particular, we may use a space–time point pattern approach for the analysis and inference process. We studied wildfires in Catalonia, a region in the north-east of the Iberian Peninsula, and we analyzed the spatio-temporal patterns produced by those wildfire incidences by considering the influence of covariates on trends in the intensity of wildfire locations. A total of 3,166 wildfires from 1994–2008 have been recorded. We specified spatio-temporal log-Gaussian Cox process models. Models were estimated using Bayesian inference for Gaussian Markov Random Field through the integrated nested Laplace approximation algorithm. The results of our analysis have provided statistical evidence that areas closer to humans have more human induced wildfires, areas farther have more naturally occurring wildfires. We believe the methods presented in this paper may contribute to the prevention and management of those wildfires which are not random in space or time.