Dynamic stock modelling

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Dynamic stock modelling (DSM) is a new development in material flow accounting and explicitly considers the role of in-use stocks in past, present, and future material use.

Contents

For resource use

In-use stocks of buildings, infrastructure, and (durable) products play several important roles in social metabolism: [1]

Dynamic stock modelling (DSM) explicitly considers these different roles of in-use stocks. DSM has a long tradition in modelling population and fixed capital; over the last twenty years, applications for product and material stocks have been developed. [2] Age-cohort-based models, state-of-the-art in DSM, are of a descriptive nature: Each age-cohort is assigned an expected lifetime and the cohort’s use phase ends when its lifetime elapses. At any given point in time, in-use stocks are composed of different age-cohorts, each with its specific material content and energy efficiency. [3] [4] In DSM, the assumed total stock size is determined by exogenously specified parameters such as population and per capita service level [5] and the age-cohort lifetime model can be used to adjust the inflows into and the outflows from stocks.

Further applications

DSM is the basis for many other types of modelling; examples include integrated assessment models, system dynamics models, population balance models, and dynamic material flow accounting (MFA) models. The latter are an important manner in which the material and technological detail of MFA is enhanced. DSM of materials additionally allows for the modelling of the end-of-life product flow which is the sum of all discarded products leaving the use phase according to the lifetime distribution chosen. This enables forecasting of waste volume and recycling potential and provides essential information for resource and energy use reduction strategies. The connection between dynamic DSM and waste input-output (IO) models, a special IO model type designed for handling waste, is currently under development and will allow for simultaneous assessment of environmental impacts of material production and recycling. [2]

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Electronic waste Discarded electronic devices

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Economy-wide material flow accounts (EW-MFA) is a framework to compile statistics linking flows of materials from natural resources to a national economy. EW-MFA are descriptive statistics, in physical units such as tonnes per year.

Circular economy regenerative system in which resource input and waste, emission, and energy leakage, are minimised

A circular economy is an economic system aimed at eliminating waste and the continual use of resources. Circular systems employ reuse, sharing, repair, refurbishment, remanufacturing and recycling to create a close-loop system, minimising the use of resource inputs and the creation of waste, pollution and carbon emissions. The circular economy aims to keep products, equipment and infrastructure in use for longer, thus improving the productivity of these resources. All 'waste' should become 'food' for another process: either a by-product or recovered resource for another industrial process, or as regenerative resources for nature, e.g. compost. This regenerative approach is in contrast to the traditional linear economy, which has a 'take, make, dispose' model of production.

Nutrient cycle Set of processes exchanging nutrients between parts of a system

A nutrient cycle is the movement and exchange of organic and inorganic matter back into the production of matter. Energy flow is a unidirectional and noncyclic pathway, whereas the movement of mineral nutrients is cyclic. Mineral cycles include the carbon cycle, sulfur cycle, nitrogen cycle, water cycle, phosphorus cycle, oxygen cycle, among others that continually recycle along with other mineral nutrients into productive ecological nutrition.

Social metabolism study of materials and energy flows between nature and society

Social metabolism or socioeconomic metabolism is the set of flows of materials and energy that occur between nature and society, between different societies, and within societies. These human-controlled material and energy flows are a basic feature of all societies but their magnitude and diversity largely depend on specific cultures, or sociometabolic regimes. Social or socioeconomic metabolism is also described as "the self-reproduction and evolution of the biophysical structures of human society. It comprises those biophysical transformation processes, distribution processes, and flows, which are controlled by humans for their purposes. The biophysical structures of society and socioeconomic metabolism together form the biophysical basis of society."

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Martin Faulstich is a German research scientist. He is a professor at the Clausthal University of Technology, chairman of the German Advisory Council on the Environment and the managing director of the Clausthal Institute of Environmental Technology (CUTEC) in Clausthal-Zellerfeld.

Environmental systems analysis (ESA) is a systematic and systems based approach for describing human actions impacting on the natural environment to support decisions and actions aimed at perceived current or future environmental problems. Impacts of different types of objects are studied that ranges from projects, programs and policies, to organizations, and products. Environmental systems analysis encompasses a family of environmental assessment tools and methods, including life cycle assessment (LCA), material flow analysis (MFA) and substance flow analysis (SFA), and environmental impact assessment (EIA), among others.

References

  1. Pauliuk, Stefan; Müller, Daniel B. (2014). "The role of in-use stocks in the social metabolism and in climate change mitigation". Global Environmental Change. 24: 132–42. doi:10.1016/j.gloenvcha.2013.11.006. hdl:11250/2367975.
  2. 1 2 Müller, Esther; Hilty, Lorenz M.; Widmer, Rolf; Schluep, Mathias; Faulstich, Martin (2014). "Modeling Metal Stocks and Flows: A Review of Dynamic Material Flow Analysis Methods". Environmental Science & Technology. 48 (4): 2102–13. Bibcode:2014EnST...48.2102M. doi:10.1021/es403506a. PMID   24494583.
  3. Elshkaki, A (2005). "Dynamic stock modelling: A method for the identification and estimation of future waste streams and emissions based on past production and product stock characteristics". Energy. 30 (8): 1353–63. doi:10.1016/j.energy.2004.02.019.
  4. Van Der Voet, Ester; Kleijn, René; Huele, Ruben; Ishikawa, Masanobu; Verkuijlen, Evert (2002). "Predicting future emissions based on characteristics of stocks". Ecological Economics. 41 (2): 223–34. doi:10.1016/S0921-8009(02)00028-9.
  5. b. Müller, Daniel (2006). "Stock dynamics for forecasting material flows—Case study for housing in the Netherlands". Ecological Economics. 59: 142–56. doi:10.1016/j.ecolecon.2005.09.025.