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Purpose: Screening social life cycle assessment (S-LCA) can be applied to identify the social hotspots associated with a production activity or supply chain. The objective of this paper is to explore how the quality of the results of a screening S-LCA can be improved, illustrated by a case study of sugarcane production in Brazil, an activity which has been criticized for its records on social sustainability due to issues such as poor working conditions for field workers and treatment of migrant workers. Methods: Cradle-to-gate production of sugarcane in Brazil has been modeled using input-output analysis. The associated social impacts have been modeled using the framework of the Social Hotspots Database (SHDB), which is one of the first databases providing information on social risks along supply chains. The results from the SHDB were complemented with results from a systematic analysis of relevant literature. Content analysis was applied to 38 publications in English relevant to the social impacts of sugarcane production in Brazil, including peer-reviewed articles, "gray literature," non-governmental organization reports, and conference presentations. Qualitative data analysis software NVivo 8 was used to facilitate the analysis of the publications. A deductive category system was established based on the subcategories recommended in the UNEP/SETAC social life cycle assessment guidelines. Social impacts were further aggregated and analyzed by social themes and impact categories. Results and discussion: The social impacts of the sugarcane life cycle in Brazil arise almost exclusively within the Brazilian sugarcane sector itself. Fifteen social themes are identified as hotspots in the SHDB, and nine of them are also identified by content analysis. Health and safety and labor rights and decent work are the impact categories with the highest risks. Besides negative impacts, content analysis is capable of identifying several positive impacts related to sugarcane production. Due to the use of aggregated country-level data, social impacts of manual and mechanical harvesting of sugarcane cannot be differentiated in SHDB; however, this can be achieved by content analysis. Conclusions: SHDB is effective for identifying social impacts at the country level, but the data are inevitably aggregated and only show averages across different technologies and geographical areas; therefore, the database is of limited value in distinguishing between alternative operations and locations. Content analysis can facilitate foreground data collection by differentiating operations and identifying both negative and positive impacts at the level of individual activities. We recommend that S-LCA databases can be integrated with results of content analysis to improve the richness—representativity and specificity—of results from a screening S-LCA, to differentiate between alternative production routes and processes. [ABSTRACT FROM AUTHOR]

Purpose: The wood panel industry is one of the most important forest-based industries in Brazil. The medium density particleboard (MDP) is currently produced and consumed worldwide and represents about 50 % of the wood panel industry in Brazil. Unlike other regions, Brazilian MDP is produced from dedicated eucalyptus plantations and heavy fuel oil is an important energy source in MDP manufacture, which may result in a different environmental profile. This paper presents a life cycle assessment of MDP panel produced in Brazil and suggests improvement opportunities by assessing alternative production scenarios. Methods: The cradle-to-gate assessment of 1 m of MDP produced in Brazil considered two main subsystems: forest and industrial production. Detailed inventories for Brazilian eucalyptus production and MDP industrial production were collected as a result of technical visits to Brazilian MDP producers (foreground systems) as well as literature review (mainly background systems). The potential environmental impacts of MDP were assessed in terms of seven impact categories using CML (abiotic depletion, acidification, global warming, eutrophication, and photochemical oxidation) and USEtox (ecotoxicity and human toxicity) impact assessment methods in order to identify the main hotspots. Results and discussion: The industrial production was responsible for most of the impacts in all impact categories, except ecotoxicity (EC). The main hotspots identified were the use of heavy fuel oil (HFO) as a thermal energy source in MDP manufacture and the production of urea-formaldehyde (UF) resin used as synthetic adhesive. Glyphosate herbicide application in soil in forestry operations was the main responsible for the impacts in EC. Scenarios for HFO substitution were assessed and results showed that substituting HFO for in-mill wood residues or diesel leads to reduced environmental impacts. Conclusions: The identification of the main hotspots in the MDP life cycle can assist the wood panel industry to improve their environmental profile. Further research should focus on UF resin production in order to reduce its environmental impacts as well as the possibility of using alternatives resins. Other sources of wood for MDP production could also be investigated (e.g., pine wood and wood residues) to assess potential improvements. [ABSTRACT FROM AUTHOR]

Abstract This article presents a novel multi-criteria decision analysis (MCDA) model for performing robust indicator weighting in Life Cycle Assessment (LCA) and Social Life Cycle Assessment (S-LCA). This model integrates stochastic weights analysis with preference information that utilizes the value judgements of decision makers, benefitting from the diversity of interests and familiarities of decision makers regarding each indicator. The model considers all decision makers on an equal basis but does not assume they have the same importance. The MCDA model was applied to support the evaluation of the overall environmental and social impacts of manual and mechanical sugarcane harvesting in Brazil based on LCA and S-LCA. Brazilian experts were surveyed on the weights of relevant environmental and social indicators. The novel MCDA approach explores all the possible convex combinations of the weights provided by the surveyed group. The results of the MCDA model show that mechanical harvesting compared to manual harvesting had lower environmental life cycle impacts at the end-point level and better social impacts for all these convex combinations. Decision-making based on environmental impacts at the mid-point level is less clear: manual harvesting is more likely (67% of the convex combinations of the weights) to be considered better than mechanical harvesting; but the advantage of mechanical harvesting over manual harvesting can be greater than the reverse (almost twice as large). This article recommends presenting both mid-point and end-point LCIA results for a thoroughly informed decision-making. The MCDA model developed in this article can also be used to support weighting in future comparative LCA or S-LCA studies. Highlights • A novel MCDA approach integrating stochastic weights with preference information is proposed. • LCA and S-LCA impacts of manual and mechanical harvesting of sugarcane are compared. • Mechanical harvesting has better end-point environmental and social impacts. • Mid-point LCIA results do not allow identifying a clear winner. • Mid- and end-point LCIA results led to different conclusions. [ABSTRACT FROM AUTHOR]

The present article aims to compare sustainability maturity results among ten agri-food industries located in Brazil and Kenya. Integrated nexus analyses are missing from previous models to perform this type of comparison. Most of existing models focus on macro-analysis issues rather than micro-analysis, which includes evaluating organizations' production practices. A novel model was developed integrating the water-energy-food nexus with business excellence models, resulting in the following evaluation items: drivers, agents, and processes. Through interviews, documentation analysis, and direct observation, ten agri-food industries were evaluated, five in Brazil and five in Kenya. The results obtained are expressed using a sustainability maturity model with a 7-point scale (ranging from one to seven). For drivers, Brazilian companies received an average score of 2.98, compared to 3.68 for Kenyan companies. Brazilian agri-food businesses received a score of 3.33, whereas Kenyan businesses obtained an average score of 3.73 in the agent item. Finally, for processes, Brazilian companies received 2.8, while Kenyan companies scored 3.27. Overall, Kenyan companies performed better than Brazilian companies. With these results, it was possible to conclude that the developed evaluation model can be used to evaluate an agri-food company sustainable status as well as to establish comparisons among companies and countries, with benchmarking potential. • A nexus model with three assessment dimensions is presented. • Results expressed as sustainability maturity evaluation scores. • Kenyan companies outperformed Brazilian organizations. • Transition of agri-food companies to more sustainable practices. [ABSTRACT FROM AUTHOR]

Bioethanol from sugarcane can be produced using first-generation (1G) or second-generation (2G) technologies. 2G technologies can increase the capacity of production per sugarcane mass input and are expected to have a key role in future reductions of environmental impacts of sugarcane bioethanol. A hybrid Input-Output (IO) framework is developed for Brazil coupling the System of National Accounts and the National Energy Balance, which is extended to assess Greenhouse Gas (GHG) emissions. Life-cycle based estimates for two sugarcane cultivation systems, two 1G and eight 2G bioethanol production scenarios, are coupled in the IO framework. A multi-objective linear programming (MOLP) model is formulated based on this framework for energy-economic-environmental analysis of the Brazilian economic system and domestic bioethanol supply in prospective scenarios. Twenty-four solutions are computed: four “extreme” solutions resulting from the individual optimization of each objective function (GDP, employment level, total energy consumption and total GHG emissions - 1G scenario), ten compromise solutions minimizing the distance of the feasible region to the ideal solution (1G, 1G-optimized and prospective 1G + 2G scenarios), and ten solutions maximizing the total bioethanol production (1G, 1G-optimized and prospective 1G + 2G scenarios). Higher diesel oil and lubricants consumption in the mechanical harvesting process has counterbalanced the positive effects of more efficient trucks leading to higher energy consumption and GHG emissions. Lower overall employment level in the 1G + 2G scenarios is achieved such that policies linked to reabsorption of sugarcane cutters in alternative activities are positive. Indirect effects from maximizing the bioethanol production increase the total energy consumption and the GHG emissions thus requiring efficiency measures and fossil energy substitution by cleaner sources. The integrated- or country-based analysis of the whole economic system has complemented the process design and process-based analysis, contributing to identify direct and indirect effects that can offset the benefits. Direct and indirect effects on the whole economic system have to be considered in policies and technological choices for prospective bioethanol production, since positive direct effects of 1G + 2G plants can be counterbalanced by indirect impacts on other sectors, mainly from chemicals in the process. [ABSTRACT FROM AUTHOR]

An interactive method devoted to multi-objective linear programming (MOLP) models is used to assess the trade-offs between economic, energy and environmental (E3) objectives in the Brazilian economic system. The MOLP model is based on a hybrid input–output (IO) framework, with monetary (R$) and physical (tons of oil equivalent) units, developed from the Brazilian IO table and the National Energy Balance. This framework is extended to assess different Greenhouse Gas (GHG) emissions, which are then aggregated into a single indicator (CO 2 eq). The model includes 444 variables, 597 constraints and 3 objective functions: maximization of Gross Domestic Product (GDP), minimization of energy consumption and minimization of GHG emissions. The interactive decision support tool enables a progressive and selective search of non-dominated solutions making the most of graphical displays, namely the parametric diagram associated with the objective function “weights”, to provide insightful information to the Decision Maker. A representative sample of non-dominated solutions has been computed in the interactive process, allowing to identify three main regions corresponding to solutions with different characteristics, i.e. different patterns of trade-offs between the conflicting objective functions. Illustrative results indicate that the maximization of GDP leads to an increase of both energy consumption and GHG emissions, while the minimization of either GHG emissions or energy consumption cause negative impacts on GDP. [ABSTRACT FROM AUTHOR]

A multi-objective linear programming (MOLP) model based on a hybrid Input–Output (IO) framework is presented. This model aims at assessing the trade-offs between economic, energy, environmental (E3) and social objectives in the Brazilian economic system. This combination of multi-objective models with Input–Output Analysis (IOA) plays a supplementary role in understanding the interactions between the economic and energy systems, and the corresponding impacts on the environment, offering a consistent framework for assessing the effects of distinct policies on these systems. Firstly, the System of National Accounts (SNA) is reorganized to include the National Energy Balance, creating a hybrid IO framework that is extended to assess Greenhouse Gas (GHG) emissions and the employment level. The objective functions considered are the maximization of GDP (gross domestic product) and employment levels, as well as the minimization of energy consumption and GHG emissions. An interactive method enabling a progressive and selective search of non-dominated solutions with distinct characteristics and underlying trade-offs is utilized. Illustrative results indicate that the maximization of GDP and the employment levels lead to an increase of both energy consumption and GHG emissions, while the minimization of either GHG emissions or energy consumption cause negative impacts on GDP and employment. [ABSTRACT FROM AUTHOR]