Environmental impact of urban consumption patterns: Drivers and focus points

Abstract The purpose of our study is to analyse how urban lifestyles impact on the environment to offer knowledge based inspiration for effective environmental policies relating to contemporary Danish consumption patterns. The application of a Personal Metabolism (PM) coupled Life Cycle Assessment (LCA) approach supported by cluster analysis facilitated the identification of consumption-related clusters based on central demographic and life style parameters such as income, diet, transport, and age. The environmental performance of the assessed consumption patterns were calculated in a full life cycle perspective and covering all relevant environmental impacts both on midpoint and endpoint levels by applying the ReCiPe 2008 Life Cycle Impact Assessment (LCIA) methodology. The results of the contribution analysis revealed that climate change, particulate matter, human toxicity, fossil depletion and ionizing radiation contribute most to the three endpoints covered by ReCiPe 2008. Results of cluster analysis indicated that demographic parameters such as income level and age of the respondents has a strong influence on the environmental impacts. The influence of lifestyle aspects such as choice of diet, use of private car and household size was also investigated. These three parameters were found to significantly influence the consumption related environmental impacts of urban Danish residents. Overall our study identify drivers and focus points of consumption and provides a contemporary picture of Danish urban consumption-related environmental impacts.

[1]  Jannick Højrup Schmidt,et al.  The carbon footprint of Danish production and consumption: Literature review and model calculations , 2014 .

[2]  Gert Van Hoof,et al.  Indicator selection in life cycle assessment to enable decision making: issues and solutions , 2013, The International Journal of Life Cycle Assessment.

[3]  Edgar G. Hertwich,et al.  Global Climate targets and future consumption level: An evaluation of the required GHG intensity , 2013 .

[4]  Pradip P. Kalbar,et al.  Weighting and Aggregation in Life Cycle Assessment: Do Present Aggregated Single Scores Provide Correct Decision Support? , 2017 .

[5]  M. Sutton,et al.  Food choices, health and environment: Effects of cutting Europe's meat and dairy intake , 2014 .

[6]  E. Hertwich,et al.  Climate policy through changing consumption choices: Options and obstacles for reducing greenhouse gas emissions , 2014 .

[7]  P A Lant,et al.  The connection between water and energy in cities: a review. , 2011, Water science and technology : a journal of the International Association on Water Pollution Research.

[8]  F. Chapin,et al.  A safe operating space for humanity , 2009, Nature.

[9]  G. Norris The requirement for congruence in normalization , 2001 .

[10]  Paolo D'Odorico,et al.  Environmental impact food labels combining carbon, nitrogen, and water footprints , 2016 .

[11]  Thomas P. Seager,et al.  Normalization in Comparative Life Cycle Assessment to Support Environmental Decision Making , 2017 .

[12]  Klaus Hubacek,et al.  The Impact of Social Factors and Consumer Behavior on Carbon Dioxide Emissions in the United Kingdom , 2010 .

[13]  E. Hertwich,et al.  Corrigendum: Global climate targets and future consumption level: an evaluation of the required GHG intensity , 2013 .

[14]  P. Gober,et al.  Determinants of Small-Area Water Consumption for the City of Phoenix, Arizona , 2007 .

[15]  Michael Lettenmeier,et al.  Natural resource consumption caused by Finnish households , 2007 .

[16]  F. Chapin,et al.  Planetary boundaries: Exploring the safe operating space for humanity , 2009 .

[17]  D. Meyer,et al.  The Determinants of Urban Resource Consumption , 2012 .

[18]  Clemens Reimann,et al.  Statistical data analysis explained : applied environmental statics with R , 2008 .

[19]  Linfield Brown,et al.  Statistics for Environmental Engineers , 2002 .

[20]  Reinout Heijungs,et al.  Identifying best existing practice for characterization modeling in life cycle impact assessment , 2012, The International Journal of Life Cycle Assessment.

[21]  Anders Bjørn,et al.  Introducing carrying capacity-based normalisation in LCA: framework and development of references at midpoint level , 2015, The International Journal of Life Cycle Assessment.

[22]  Tim Jackson,et al.  The carbon footprint of UK households 1990–2004: A socio-economically disaggregated, quasi-multi-regional input–output model , 2009 .

[23]  Thomas P Seager,et al.  Environmental decision-making using life cycle impact assessment and stochastic multiattribute decision analysis: a case study on alternative transportation fuels. , 2009, Environmental science & technology.

[24]  S. Barr,et al.  Behavioural attitudes towards water saving? Evidence from a study of environmental actions , 2006 .

[25]  Karen Allacker,et al.  Current trends and limitations of life cycle assessment applied to the urban scale: critical analysis and review of selected literature , 2019, The International Journal of Life Cycle Assessment.

[26]  Xiaoli Zhao,et al.  Residential energy consumption in urban China: A decomposition analysis , 2012 .

[27]  Morten Birkved,et al.  Personal Metabolism (PM) coupled with Life Cycle Assessment (LCA) model: Danish Case Study. , 2016, Environment international.

[28]  Brian Everitt,et al.  Optimization Clustering Techniques , 2011 .