Open dissemination of scientific work requires transparency of methods, reproducibility of results, and reusability of modeling and analytic work. The talks in this session deal with the special case of scientific work focused on questions of environmental sustainability. The opening talk, by Jennifer Baka Penn St Univ and Josh Cousins SUNY-College of environmental science and forestry, introduces the concept of political-industrial ecology and relates it to research methods in the field. The second talk, by Stephanie Pincetl at UCLA discusses an open data project at intersection of public policy, academic research, and the private sector. The final talk is from Stefan Pauliuk, who will introduce the newly developed Industrial Ecology Data Commons and describe its role in an open data infrastructure for the field.
10:30am - 11:00am
Advancing Sustainability Science: A Political-Industrial Ecology Perspective
1Penn State, United States of America; 2SUNY-ESF, United States of America
This paper evaluates how the emerging field of political-industrial ecology (PIE) can advance sustainability science, particularly in terms of methodological and theoretical innovations. Less than a decade old, PIE integrates theories and methods from political and industrial ecology to evaluate how biophysical and political systems are entwined in shaping nature-society relations and processes. Normative in its approach, PIE seeks to better embed societal metabolisms within their broader historic, ecological, and political economic context in pursuit of reducing the environmental impact of industrial ecosystems and resource flows. In the paper, we first outline the theoretical and historical origins of the field before synthesizing the findings of eight PIE case studies which have helped to catalyze the field. We conclude with a discussion on the future prospects of PIE, specifically the methodological and data challenges/opportunities of the field.
11:00am - 11:30am
Building Energy Data Transparency -- the travails of making data accessible
UCLA, United States of America
Building energy use is an important contributor to greenhouse gas emissions and is also strongly tied to thermal comfort. Understanding building energy use at a granular level provides many important insights that are unobtainable without that data. The UCLA Energy Atlas is built on address level billing data, aggregated for customer privacy on the public facing interactive website, but shows building energy use by neighborhood, city and Council of Government. Data is matched to attributes including vintage, square footage, industrial classification code, income, and more. It enables local governments and researchers to discover energy use patterns, target energy efficiency incentives, evaluate programs, and understand equity differences across regions and among residents and industries. Such data is indispensable for the energy transition. This talk explains the process of developing the Energy Atlas and some of the implications for sustainable systems and technology.
11:30am - 12:00pm
A General Data Model for Industrial Ecology and its Implementation in a data commons Prototype
University of Freiburg
Till this day, data in industrial ecology are commonly seen as existing within the domain of particular methods or models, such as input-output, life cycle assessment, urban metabolism, or material flow analysis data.
This artificial division of data into methods contradicts the common phenomena described by those data: the objects and processes in the industrial system, or socioeconomic metabolism. A consequence of this scattered organization of related data across methods is that IE researchers and consultants spend too much time searching for and reformatting data from diverse and incoherent sources, time that could be invested into quality control and analysis of model results instead. This talk outlines a solution to two major barriers to data exchange within industrial ecology: i) the lack of a generic structure for industrial ecology data and ii) the lack of a bespoke platform to exchange industrial ecology datasets.
We present a general data model for socioeconomic metabolism that can be used to structure all data that can be located in the industrial system, including process descriptions, product descriptions, stocks, flows, and coefficients of all kind. We describe a relational database built on the general data model and a user interface to it, both of which are open source and can be implemented by individual researchers, groups, institutions, or the entire community. In the latter case, one could speak of an industrial ecology data commons (IEDC), and we unveil an IEDC prototype containing a diverse set of datasets from the literature.