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The Emission Trading System has been promoted as a tool for providing financial and cost-effective incentives to carbon emitters to apply emission reduction measures. The mechanism of Emission Trading System has been applied in many energy-intensive industries such as electric power industry. However, it appears that Emission Trading System finds limited application in building sector due to the unique characteristics of buildings. This study presents an identification and analysis on the factors affecting the implementation of Emission Trading System in the building sector within the context of China. Research data are collected from semi-structured interviews with a group of carefully selected experts. As a result, fifteen representative factors have been identified, and discussions on their representativeness have been conducted. The intricate interrelationships between the identified factors have been examined based on a hierarchy structure established by using the Interpretive Structural Modeling method. Furthermore, these factors have been classified into four categories: autonomous factors, dependent factors, linkage factors, and driving factors, which is based on the calculation of the factors' driving/dependence power by applying the Matrice d’Impacts croises-multipication applique a classement (MICMAC) technique. This classification provides a different profile between individual factors from that by traditional study where the relative importance is generally given between factors. The findings on these factors provide valuable references for helping policy designers and practitioners adopt effective policies and measures to promote the development of ETS in the Chinese building sector.

Circular Economy involves strategies for sustainable development, environmental protection, clean production, ecological consumption, waste regeneration and reuse, aligning social equity, environmental quality and economic well-being. The present research aimed to propose a model for measuring the circular economy in the pig farming chain. To that end, a systematic review was performed, which addressed the study of specific situations related to the interaction between the circular economy and the management of pig waste. The proposal was based on the assessment and correlation of GRI indicators with the aspects of pig farming chain present in the researched literature. A statistical model was developed using the Spearman’s correlation analysis. It was identified 35 correlations, and the questions from the social dimension presented the highest correlation indices, with a representativeness of 82.86% of interaction. It was not found in the existing literature a model for measuring the circular economy that considered several aspects (environmental, economic and social) simultaneously. With regard to the characteristics of the circular economy in the pig farming chain, the proposed model could facilitate decision making on the environmental and social aspects, as well as regarding property costs, thus contributing to environmental preservation and to the practice of circular economy.

European and United States' (U.S.) regulatory schemes mandate the use of biofuels based on estimates of life cycle GHG emissions that indicate their environmental superiority compared with conventional fuels. This paper analyzes the quality of the activity data used in calculating these estimates for Brazilian sugarcane and U.S. corn ethanol, in terms of completeness, reliability, and transparency. The findings demonstrate that it is impossible to determine the completeness of the data because the regulation's texts and related documents, and their sources fail to provide information on the representativeness or origins of the data. Moreover, there is no transparency about the sample from which data is extracted. To obtain this information it is necessary to review the literary sources referenced in the regulation's documents. Among the 37 sources of data examined in this analysis, 23 are no longer available, and only 3 sources contain information allowing for the characterization of the adequacy of the sample. In addition, none of the data items analyzed were subject to a verification process. These findings highlight the urgent need to modify biofuel regulation to set minimum standards for data quality and the reporting of data sources, paying particular attention to default activity data.

The role of spatial planning for sustainable development has been widely acknowledged in academia and in policy. Monitoring spatial plans increases knowledge of whether they achieved their goals in guaranteeing sustainable development on a territorial level. Indicators are suited for this task and could play an important role in determining the applicability or effectiveness of plans. The contribution of indicator sets for effective strategic sustainability monitoring needs to be supported by an analysis of consistency and meaning at each stage of indicator development, from design to implementation. Indicator selection is usually done by experts and/or through participatory approaches and often little is known about the robustness of the selection stage, its utility, accuracy, validity, feasibility and redundancy. The main goal of this research is to identify, through a participatory approach and data reduction techniques, a set of sustainability indicators for strategic monitoring of regional spatial plans. Decision makers and planning practitioners from regional and local authorities, as well as academics, were asked to assess through a scoring procedure the importance of a base set of sustainability indicators to monitor the effects of a spatial plan. Principal Components Analysis, followed by sensitivity analysis with Monte Carlo simulation were used in the data reduction phase. This approach was tested on a set of outcomes/effects indicators developed for the Algarve's regional spatial plan (southern Portugal). It was possible to highlight the importance of involving different groups of stakeholders and to identify relevant indicator selection criteria, as well as a final set of indicators that are more representative of different stakeholders' opinions and more able to translate, in an integrated way, overall changes in sustainability performance. Factors that can influence the results of the approach are discussed, such as stakeholder representativeness and data availability. Recommendations regarding the use of the approach and its results are also presented, for example concerning the use of information conveyed by indicators in an aggregated way, in the form of an index. The approach could be useful to guide the collection of regional sustainability data, contributing to the reporting and assessment of spatial plans, tailored to each particular territorial reality and stakeholders' perceptions, values, needs and concerns.

Trout farming is the main fish production system in France. This article describes a system to classify trout farms based on environmental impacts calculated by life cycle assessment and technical and economic indicators. Since the number of surveyed farms was too small for a robust assessment, we combined principal component analysis (PCA) with a non-parametric bootstrap technique. French trout farms were surveyed to collect technical and economic indicators. The representativeness of the survey was verified by comparing it to a national inventory. Life cycle assessment was used to estimate environmental impacts of farms and the contribution of each production stage to impacts. PCA was used to evaluate both technical-economic and environmental indicators of the trout farms, which were separated into three groups based on the size of fish produced (pan-size, large and mixed-size, and very large). Non-parametric bootstrap was used to compare the groups and to test the significance of PCA results. Results validated the fish-farm classification system based on the size of fish produced and indicated that farm operations and fish feeding contributed the most to environmental impacts. The PCA method distinguished three groups via their technical indicators, with non-significant differences among the groups in environmental impacts. However, environmental indicators showed strong links with technical and economic indicators. In conclusion, bootstrapped PCA offers the ability to assess groups of trout production system when the sample size is too small and provides more conservative results by considering uncertainty. Future studies should focus on providing reliable data to reduce uncertainty.[br/]

Since the revision of EMAS (EMAS II), a central role within this EU-regulated environmental management system has been assigned to the assessment of significant environmental aspects as the basis for identification of an organisation's environmental targets. However, EMAS only provides some general guidelines on the assessment of environmental aspects. To comply with the requirements of EMAS II, Volkswagen AG initiated the development of a comprehensive systematic approach which is now to be applied throughout Volkswagen's production sites. This article describes the elements of this approach. A methodological section discusses issues concerning the scientific assessment of environmental aspects, taking into account reproducibility, scientific acceptance and geographical representativeness of existing methods as well as time consumption and understandability for decision-makers. In addition, practical procedures to define environmental targets were worked out during two audits of Volkswagen's production sites, focusing on collaborative workshops with environmental experts, process experts and decision-makers to identify production-integrated improvements and enhance environmental sensitivity throughout the company.

Ensuring policy coherence across environmental, social and economic goals is a key challenge to sustainable development. The United Nations Sustainable Development Goals (SDGs) and their constituent targets and indicators provide a framework to track progress of the multiple dimensions that characterise sustainability. Though the SDGs are all expressly equally important, they vary in complexity, the level of agreement on key concepts and definitions, representativeness of indicators, and availability of data. Here, by analysing quantitatively the implementation of the SDGs in the European Union, we show that the environmental goals are by some distance the most complex and least coherent of the SDGs. We highlight the need to improve data availability and prioritise both monitoring and strengthening of coherence within and among biodiversity and climate SDGs in particular. Our findings inform critical areas for financing sustainable development and provide solutions for designing post-2030 Agendas with improved potential for achieving policy coherence.