Greenhouse gas footprinting for small businesses:The use of input-output data

Berners-Lee, M. and Howard, D. C. and Moss, J. and Kaivanto, K. and Scott, W. A. (2011) Greenhouse gas footprinting for small businesses:The use of input-output data. Science of the Total Environment, 409 (5). pp. 883-891. ISSN 0048-9697

Abstract

To mitigate anthropogenic climate change greenhouse gas emissions (GHG) must be reduced; their major source is man's use of energy. A key way to manage emissions is for the energy consumer to understand their impact and the consequences of changing their activities. This paper addresses the challenge of delivering relevant, practical and reliable greenhouse gas 'footprint' information for small and medium sized businesses. The tool we describe is capable of ascribing parts of the total footprint to specific actions to which the business can relate and is sensitive enough to reflect the consequences of change. It provides a comprehensive description of all emissions for each business and sets them in the context of local, national and global statistics. It includes the GHG costs of all goods and services irrespective of their origin and without double accounting.We describe the development and use of the tool, which draws upon both national input-output data and process-based life cycle analysis techniques; a hybrid model. The use of national data sets the output in context and makes the results consistent with national and global targets, while the life cycle techniques provide a means of reflecting the dynamics of actions. The model is described in some detail along with a rationale and a short discussion of validity. As the tool is designed for small commercial users, we have taken care to combine rigour with practicality; parameterising from readily available client data whilst being clear about uncertainties. As an additional incentive, we also report on the potential costs or savings of switching activities.For users to benefit from the tool, they need to understand the output and know how much confidence they should place in the results. We not only describe an application of non-parametric statistics to generate confidence intervals, but also offer users the option of and guidance on adjusting figures to examine the sensitivity of the model to its components. It is important that the user does not see the model as a calculator that will generate one truth, but as a method of gaining insight and informing management decisions.We describe its application in tourism businesses in North West England as a demonstrator for the service sector remote from simple primary production, with brief case studies. We discuss its success compared to traditional approaches and outline further development work.