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Footprint calculators are efficient tools to monitor the environmental impact of private consumption. We present the results of an analysis of data entered into an online Material Footprint calculator undertaken to identify the socioeconomic drivers of the Material Footprint in different areas of consumption, from housing to holidaymaking. We developed regression models to reveal (1) the impact of socioeconomic characteristics on Material Footprints of private households and (2) correlations between the components of Material Footprints for different arrays of consumption. Our results show that an increasing Material Footprint in one array of consumption comes with an increasing Material Footprint in all other arrays, with the exception of housing and holidaymaking. The socioeconomic characteristics of users have a significant impact on their Material Footprints. However, this impact varies by the array of consumption. Households only exhibit generally bigger Material Footprints as a result of higher incomes and larger dwellings. We conclude that indicators which strive to monitor resource efficiency should survey disaggregated data in order to classify the resource use to different population groups and arrays of consumption.
Green Information Systems in general, and footprint calculators in particular, are promising feedback tools to assist people in adopting sustainable behaviour. Therefore, a Material Footprint model for use in an online footprint calculator was developed by identifying the most important predictors of the Material Footprint of the calculator's users. By means of statistical learning, the analysis revealed that 22 of the 95 predictors identified accounted for 74% of the variance in Material Footprints. Ten predictors out of the 95, mainly from the mobility domain, were capable of showing a prediction accuracy of 61%. The authors conclude that 22 predictors from the areas of mobility, housing and nutrition, as well as sociodemographic information, accurately predict a person's Material Footprint. The short and concise Material Footprint model may help developers and researchers to enhance their information systems with additional items while ensuring the data quality of such applications.
The food and agricultural sector will face numerous challenges in the next decades, arising from changing global production and consumption patterns, which currently go along with high resource use, causing ecological and socio-economic impacts. The aim of this paper is to illustrate and evaluate the practical applicability of the Hot Spot Analysis methodology in the context of supply chain management in companies. The HSA is a method to identify social and ecological problems along the entire life cycle of a product. Special emphasis is put on a customized implementation in the value chain beef of McDonald's Germany. The HSA of McDonald's beef value chain shows that the main ecological problems arise in the phase of raw material extraction, whereas the main social problems can be identified in the phase of slaughtering. Finally, the paper shows potentials and shortcomings of such a customized application and how the results can be implemented in the sustainability management of a company.
Nutrition is responsible for about 30% of global natural resource use. In order to limit the negative impact the nutritional sector has on the environment and on society, the consumption and processing of foodstuffs with assumed low negative impact is an important topic in the effort of sustainable development. In professional kitchens, clearly defined indicators assessing the impact of business activities are needed in this effort. The research and development in the NAHGAST project provides groundwork that could be of important assistance in this effort. Two versions of an assessment tool, with indicators of different complexity (NAHGAST Meal-Basic and NAHGAST Meal-Pro), were developed that can be used by kitchen professionals to determine the sustainability performance of their products - the offered meal. An informed selection of indicators, and a discussion of what processes and impacts this indicator relates to in the wider context, are essential and are discussed in this paper. Furthermore, in the selection of indicators for the purpose of our research certain criteria were considered simultaneously: (1) Communicability - What information an indicator can communicate and how comprehensible this information is for different actors; (2) Feasibility and data availability - Whether there is sufficient data for an indicator to be included and whether it is realistic for companies to integrate this indicator in their daily work practice; and (3) Scientific relevance - Whether the indicator is relevant for sustainability efforts on a larger scale and for related discussions in the scientific community. Insights related to these considerations are valuable for future developments in sustainability assessment in out-of-home gastronomy. The tool has been used to evaluate a number of dishes and results are deemed meaningful. However, assessments must not be understood as an accurate measurement but as an approximation of the sustainability of meals. At the level of individual indicators, they allow a detailed analysis and targeted optimization of recipes, while the aggregated results in the form of labels can be communicated well to customers. However, deficiencies and challenges, as discovered in the application phase of the project, demonstrate research gaps in the wider context. Finally, further steps for an integration of the tool in company processes and remaining options for companies to adjust the tool are discussed.
Since human nutrition is responsible for about 30 % of the global natural resource use and in order to decrease resource use to a level in line with planetary boundaries, Lukas et al. (2016) proposed a re-source use reduction in the nutrition sector by a factor 2 (Material Footprint).
The catering sector needs clearly defined indicators to assess their business activities' impact on ecology, social aspects, economic value, and health status. Within the project NAHGAST two sets of indicators, called NAHGAST Meal-Basis and NAHGAST Meal-Pro were developed. The indicator sets are proposed to measure several, with sustainability-associated challenges, such as such as the ecological, social and economical effects, which may come along with the production and the consumption of a meal. Basically, the NAHGAST Meal-Basis deals with qualitative indicators, such as the amount of organic food per serving or the percentage of food wasted. This set is supposed to enable leaders to assess the sustainability of their meals and to visualize future improvements on a simplistic level. The NAHGAST Meal-Pro deals with a more sophisticated set of indicators, such as the carbon and material footprint or the cost recovery per meal. Both sets are underpinned with sus-tainable targets and elaborated as an Excel-based assessment tool, which is tested within a one-year case study. The usefulness and the limits of the tool, as well as current results of the implementation including pro-posed challenges, are discussed.
The concept Material Input per Service Unit (MIPS) was developed 20 years ago as a measure for the overall natural resource use of products and services. The material intensity analysis is used to calculate the material footprint of any economic activities in production and consumption. Environmental assessment has developed extensive databases for life cycle inventories, which can additionally be adopted for material intensity analysis. Based on practical experience in measuring material footprints on the micro level, this paper presents the current state of research and methodology development: it shows the international discussions on the importance of accounting methodologies to measure progress in resource efficiency. The MIPS approach is presented and its micro level application for assessing value chains, supporting business management, and operationalizing sustainability strategies is discussed. Linkages to output-oriented Life Cycle Assessment as well as to Material Flow Analysis (MFA) at the macro level are pointed out. Finally we come to the conclusion that the MIPS approach provides relevant knowledge on resource and energy input at the micro level for fact-based decision-making in science, policy, business, and consumption.
Recent research on the natural resource use of private consumption suggests a sustainable Material Footprint of 8 tons per capita by 2050 in industrialised countries. We analyse the Material Footprint in Germany from 2015 to 2020 in order to test whether the Material Footprint decreases accordingly. We studied the Material Footprint of 113,559 users of an online footprint calculator and predicted the Material Footprint by seasonally decomposed autoregressive (STL-ARIMA) and exponential smoothing (STL-ETS) algorithms. We find a relatively stable Material Footprint for private consumption. The overall Material Footprint decreased by 0.4% per year between 2015 and 2020 on average. The predictions do not suggest that the Material Footprint of private consumption follows the reduction path of 3.3% per year that will lead to the sustainable consumption of natural resource
Measure or management? : Resource use indicators for policymakers based on microdata by households
(2018)
Sustainable Development Goal 12 (SDG 12) requires sustainable production and consumption. One indicator named in the SDG for resource use is the (national) material footprint. A method and disaggregated data basis that differentiates the material footprint for production and consumption according to, e.g., sectors, fields of consumption as well as socioeconomic criteria does not yet exist. We present two methods and its results for analyzing resource the consumption of private households based on microdata: (1) an indicator based on representative expenditure data in Germany and (2) an indicator based on survey data from a web tool. By these means, we aim to contribute to monitoring the Sustainable Development Goals, especially the sustainable management and efficient use of natural resources. Indicators based on microdata ensure that indicators can be disaggregated by socioeconomic characteristics like age, sex, income, or geographic location. Results from both methods show a right-skewed distribution of the Material Footprint in Germany and, for instance, an increasing Material Footprint with increasing household income. The methods enable researchers and policymakers to evaluate trends in resource use and to differentiate between lifestyles and along socioeconomic characteristics. This, in turn, would allow us to tailor sustainable consumption policies to household needs and restrictions.