1,721,030 research outputs found
Positioning of remotely sensed spectral heterogeneity in the framework of life cycle impact assessment on biodiversity
No full textLife cycle assessment (LCA) is among the most robust, comprehensive and scientifically sound methodologies to unravel the potential causes and effects of anthropogenic impacts on the different geobiosphere compartments . In this framework, a major challenge is related to the development of consensual and operational approaches to assess the human pressure on biodiversity. This has recently brought about the attention of the larger community of ecologists and biologists by Souza et al. (2015). They thoroughly examined the practice of Life Cycle Impact Assessment (LCIA) of land use interventions (land occupation and conversion) on biodiversity, identifying several modeling gaps. Among them is the absence of a wider landscape oriented and operational procedure to evaluate the loss of biological diversity. Concerned by the widespread lack in LCIA of cross-fertilization with disciplines traditionally related to biodiversity analysis (e.g. biology and ecology), they proposed a roadmap to address current methodological limitations. They recommended developing impact characterization factors (CFs) at different spatial scales e.g. by replacing land cover maps with continuous environmental information, and including landscape aspects such as habitat fragmentation or connectivity of ecosystems
Boosting the use of spectral heterogeneity in the impact assessment of agricultural land use on biodiversity
No full textNo consensus has been yet achieved among Life Cycle Assessment (LCA) practitioners on how to assess the impact on biodiversity due to land uses and land use changes, in particular with regard to agricultural areas. In the domain of nature conservation and landscape ecology, spectral heterogeneity (SH) derived from remotely sensed imagery is considered a viable proxy for species diversity detection. The assessment rationale is based on the ‘spectral variation hypothesis’: the higher the spectral variability, the higher the ecological heterogeneity and species community diversity, occupying different niches. Our hypothesis is that SH can be effective to improve or complement current Life Cycle Impact Assessment−LCIA practice on biodiversity loss evaluation driven by land useNo consensus has been yet achieved among Life Cycle Assessment (LCA) practitioners on how to assess
the impact on biodiversity due to land uses and land use changes, in particular with regard to agricultural
areas. In the domain of nature conservation and landscape ecology, spectral heterogeneity (SH) derived
from remotely sensed imagery is considered a viable proxy for species diversity detection. The assess-
ment rationale is based on the ‘spectral variation hypothesis’: the higher the spectral variability, the
higher the ecological heterogeneity and species community diversity, occupying different niches. Our
hypothesis is that SH can be effective to improve or complement current Life Cycle Impact Asses-
smentLCIA practice on biodiversity loss evaluation driven by land use. Hence, we aim here to explore
this assumption by computing SH at a local scale of crops cultivation in Southern Alps (Trentino province,
Italy), and then combining this information with land use over 30 years. We observe and analyse the
relationships between land cover maps and habitat heterogeneity at different time and spatial resolu-
tions. This allows us to argue about the robustness of SH to be a potential surrogate of environmental
nuances for species variability detection in LCIA.
. Hence, we aim here to explore this assumptiNo consensus has been yet achieved among Life Cycle Assessment (LCA) practitioners on how to assess
the impact on biodiversity due to land uses and land use changes, in particular with regard to agricultural
areas. In the domain of nature conservation and landscape ecology, spectral heterogeneity (SH) derived
from remotely sensed imagery is considered a viable proxy for species diversity detection. The assess-
ment rationale is based on the ‘spectral variation hypothesis’: the higher the spectral variability, the
higher the ecological heterogeneity and species community diversity, occupying different niches. Our
hypothesis is that SH can be effective to improve or complement current Life Cycle Impact Asses-
smentLCIA practice on biodiversity loss evaluation driven by land use. Hence, we aim here to explore
this assumption by computing SH at a local scale of crops cultivation in Southern Alps (Trentino province,
Italy), and then combining this information with land use over 30 years. We observe and analyse the
relationships between land cover maps and habitat heterogeneity at different time and spatial resolu-
tions. This allows us to argue about the robustness of SH to be a potential surrogate of environmental
nuances for species variability detection in LCIA.
on by computing SH at a local scale of crops cNo consensus has been yet achieved among Life Cycle Assessment (LCA) practitioners on how to assess
the impact on biodiversity due to land uses and land use changes, in particular with regard to agricultural
areas. In the domain of nature conservation and landscape ecology, spectral heterogeneity (SH) derived
from remotely sensed imagery is considered a viable proxy for species diversity detection. The assess-
ment rationale is based on the ‘spectral variation hypothesis’: the higher the spectral variability, the
higher the ecological heterogeneity and species community diversity, occupying different niches. Our
hypothesis is that SH can be effective to improve or complement current Life Cycle Impact Asses-
smentLCIA practice on biodiversity loss evaluation driven by land use. Hence, we aim here to explore
this assumption by computing SH at a local scale of crops cultivation in Southern Alps (Trentino province,
Italy), and then combining this information with land use over 30 years. We observe and analyse the
relationships between land cover maps and habitat heterogeneity at different time and spatial resolu-
tions. This allows us to argue about the robustness of SH to be a potential surrogate of environmental
nuances for species variability detection in LCIA.
ultivation in Southern Alps (Trentino province, Italy), and then combining this information with land use over 30 years. We observe and analyse the relationships between land cover maps and habitat heterogeneity at different time and spatial resolutions. This allows us to argue about the robustness of SH to be a potential surrogate of environmental nuances for species variability detection in LCI
Boosting the use of spectral heterogeneity in the impact assessment of agricultural land use on biodiversity
No full textNo consensus has been yet achieved among Life Cycle Assessment (LCA) practitioners on how to assess the impact on biodiversity due to land uses and land use changes, in particular with regard to agricultural areas. In the domain of nature conservation and landscape ecology, spectral heterogeneity (SH) derived from remotely sensed imagery is considered a viable proxy for species diversity detection. The assessment rationale is based on the ‘spectral variation hypothesis’: the higher the spectral variability, the higher the ecological heterogeneity and species community diversity, occupying different niches. Our hypothesis is that SH can be effective to improve or complement current Life Cycle Impact Assessment−LCIA practice on biodiversity loss evaluation driven by land use. Hence, we aim here to explore this assumption by computing SH at a local scale of crops cultivation in Southern Alps (Trentino province, Italy), and then combining this information with land use over 30 years. We observe and analyse the relationships between land cover maps and habitat heterogeneity at different time and spatial resolutions. This allows us to argue about the robustness of SH to be a potential surrogate of environmental nuances for species variability detection in LCI
Impact of COVID-19 outbreak measures of lockdown on the Italian Carbon Footprint
No full textStringent lockdown measures implemented in Italy to mitigate the spread of COVID-19 are generating unprecedented economic impacts. However, the environmental consequences associated with the temporary shutdown and recovery of industrial and commercial activities are still not fully understood. Using the well-known carbon footprint (CF) indicator, this paper provides a comprehensive estimation of environmental effects due to the COVID-19 outbreak lockdown measures in Italy. Our aim was to quantify the CF associated with the consumption of energy by any economic activity and region in Italy during the lockdown, and then compare these environmental burdens with the CF calculated for analogous periods from 2015 to 2019 (similar to March and April). Complementarily, we also conducted a scenario analysis to estimate the post-lockdown CF impact in Italy. A consumption-based approach was applied according to the principles of the established Life Cycle Assessment method. The CF was therefore quantified as a sum of direct and indirect greenhouse gases (GHGs) released from domestically produced and imported energy metabolism flows, excluding the exports. Our findings indicate that the CF in the lockdown period is similar to-20% lower than themean CF calculated for the past. This means avoided GHGs in between similar to 5.6 and similar to 10.6 Mt CO(2)e. Results further suggest that a tendency occurs towards higher impact savings in the Northern regions, on average similar to 230 kt CO(2)e of GHGs avoided by province (against similar to 110-130 kt CO(2)e in central and Southern provinces). Not surprisingly, these are the utmost industrialized areas of Italy and have been the ones mostly affected by the outbreak. Despite our CF estimates are not free of uncertainties, our research offers quantitative insights to start understanding the magnitude generated by such an exceptional lockdown event in Italy on climate change, and to complement current scientific efforts investigating the relationships between air pollution and the spread of COVID-19. (c) 2020 Elsevier B.V. All rights reserved
Positioning of remotely sensed spectral heterogeneity in the framework of life cycle impact assessment on biodiversity
No full textLife cycle assessment (LCA) is among the most robust, comprehensive and scientifically sound methodologies to unravel the potential causes and effects of anthropogenic impacts on the different geobiosphere compartments . In this framework, a major challenge is related to the development of consensual and operational approaches to assess the human pressure on biodiversity. This has recently brought about the attention of the larger community of ecologists and biologists by Souza et al. (2015). They thoroughly examined the practice of Life Cycle Impact Assessment (LCIA) of land use interventions (land occupation and conversion) on biodiversity, identifying several modeling gaps. Among them is the absence of a wider landscape oriented and operational procedure to evaluate the loss of biological diversity. Concerned by the widespread lack in LCIA of cross-fertilization with disciplines traditionally related to biodiversity analysis (e.g. biology and ecology), they proposed a roadmap to address current methodological limitations. They recommended developing impact characterization factors (CFs) at different spatial scales e.g. by replacing land cover maps with continuous environmental information, and including landscape aspects such as habitat fragmentation or connectivity of ecosystems
Rebound effects due to economic choices when assessing the environmental sustainability of wine
No full textPredicting Sustainable Economic Welfare – Analysis and perspectives for Luxembourg based on energy policy scenarios
No full textAmbitious energy policies have been established in Luxembourg, which has one of the highest Gross Domestic Products (GDP) per capita in the world but still much depends on imported fuels and electricity. Born as an alternative to GDP, the Index of Sustainable Economic Welfare (ISEW) is applied in this study as a framework to predict socio-economic and environmental performances of Luxembourg in relation to energy policy scenarios. The ISEW for the 1960–2010 timeframe is firstly calculated and compared with GDP in order to disclose the impact of factors differently considered by the two indices, e.g. consumption trends, equity, air pollution, carbon emissions, consumer durables expenditures, investments, etc. A forecasting model to predict the ISEW trend until 2030 is then proposed to assess the relevance of national energy policies. The analysis of historical time-series shows that the ISEW grows over time at much slower pace than GDP, mostly due to increases in defensive expenditures. This gap may decline in the future by implementing those energy policies, providing a slight but tangible recovery of the economic welfare over the next 10–15 years. Several insights are ultimately given on the benefits and drawbacks of using the ISEW framework to assess long-term sustainability issues
Modelling the relationships between urban land cover change and local climate regulation to estimate urban heat island effect
No full textUrban land covers affect the thermal characteristics of the city, such as the urban heat island (UHI) effect, potentially increasing energy demand to maintain comfortable indoor and outdoor temperatures. As the land patterns change, the capacity of the landscape to regulate the UHI can change. The aim of this paper is to explore how simulating land cover changes (LCC) may affect UHI using an ecosystem service matrix approach. A LCC model, illustrated in the case study of Lisbon, Portugal, was implemented to estimate the UHI effects over time starting from the modelling of land cover changes associated with the supply of local climate regulation service. Our results show that the capacity of urban landscape to mitigate the UHI effect has decreased since 1990, and will continue to decrease slightly until 2022 although more smoothly than between 1990 and 2000. This is because no substantial land cover changes have occurred after 2000 that required the transition between highest to lowest ecosystem service supplier landscapes. The proposed modelling approach may be refined and used to aiding the decision making process for urban planners in the placement of built structures and green spaces that have the capacity to regulate local climate
A spatiotemporally differentiated product system modelling framework for consequential life cycle assessment
No full textConsequential life cycle assessment (CLCA) applied to systems under continuous evolution, such as cities, usually disregard interactions among local components, how policy decisions influence them, and the value of spatial data. As a result, local environmental impacts might not be well-considered. This paper investigates how a spatially explicit system dynamics (SD) modelling approach can overcome such limitation, contributing to the advancement of CLCA. First, a novel CLCA conceptual framework is presented combining consequential life cycle inventories, SD principles and the use of spatially explicit data. Second, its innovative value is demonstrated through a proof-of-concept SD-CLCA model. This model evaluates the environmental impacts of changes in electricity supply-demand in the market due to an increasing adoption of solar photovoltaic panels (SPV) in residential buildings. It allows traceability of both system changes and their environmental consequences. For demonstration purposes, the SD-CLCA model is applied to Lisbon municipality and the broader electricity market of Portugal. Results showcase how SD-CLCA models could provide a closer representation of the real effects of predicted changes in the electricity market due to different SPV adoption scenarios. They also illustrate that changes in gross domestic product, population and precipitation provide a diversified set of impact scores. As a methodological advancement, and to the net of a few shortcomings, the proposed SD-CLCA model is able to capture the complexity of cause-effect dynamics determining environmental impacts, which currently represents a research gap in LCA
Decrease in life expectancy due to COVID-19 disease not offset by reduced environmental impacts associated with lockdowns in Italy
Full text linkThe consequence of the lockdowns implemented to address the COVID-19 pandemic on human health damage due to air pollution and other environmental issues must be better understood. This paper analyses the effect of reducing energy demand on the evolution of environmental impacts during the occurrence of 2020-lockdown periods in Italy, with a specific focus on life expectancy. An energy metabolism analysis is conducted based on the life cycle assessment (LCA) of all monthly energy consumptions, by sector, category and province area in Italy between January 2015 to December 2020. Results show a general decrease (by -5% on average) of the LCA midpoint impact categories (global warming, stratospheric ozone depletion, fine particulate matter formation, etc.) over the entire year 2020 when compared to past years. These avoided impacts, mainly due to reductions in fossil energy consumptions, are meaningful during the first lockdown phase between March and May 2020 (by -21% on average). Regarding the LCA endpoint damage on human health, -66 Disability Adjusted Life Years (DALYs) per 100,000 inhabitants are estimated to be saved. The analysis shows that the magnitude of the officially recorded casualties is substantially larger than the estimated gains in human lives due to the environmental impact reductions. Future research could therefore investigate the complex cause-effect relationships between the deaths occurred in 2020 imputed to COVID-19 disease and co-factors other than the SARS-CoV-2 virus
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