Embodied energy and economic value in the United States economy: 1963, 1967 and 1972

Costanza, Robert; Herendeen, Robert A.

doi:10.1016/0165-0572(84)90014-8

Cited by 115 publications

(50 citation statements)

References 7 publications

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“…This step is certainly the most controversial, with critics arguing that energy consumption and economic value are not necessarily related (Huettner, 1982). But recent studies provide supportive evidence that total direct and indirect energy consumption (embodied energy) and dollar values are indeed highly correlated in the U.S. economy (Costanza, 1980Cleveland et al, 1984;Costanza and Herendeen, 1984). We therefore use a conversion factor based on these studies to give a crude estimate of the economic value of ecosystem production from their GPP estimates converted to FFE.…”

Section: Habitat Typementioning

confidence: 99%

Valuation and management of wetland ecosystems

Costanza

Färber

Maxwell

1989

Ecological Economics

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306

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Section: Habitat Typementioning

confidence: 99%

Valuation and management of wetland ecosystems

Costanza

Färber

Maxwell

1989

Ecological Economics

Self Cite

306

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“…Then, as NPR economic decreased, and gross power consumption stagnated, the economy expended (decreased) its reserves in order to increase its flow efficiency with increased mutual constraint and decreased conditional entropy. Tables 2 and 3 (Costanza 1980;Costanza and Herendeen 1984). …”

Section: Resultsmentioning

confidence: 99%

Information Theory to Assess Relations Between Energy and Structure of the U.S. Economy Over Time

King

2016

Biophys Econ Resour Qual

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This paper describes the changing structure of the United States' (U.S.) domestic economy by applying information theory-based metrics to the U.S. input-output (I-O) tables from 1947 to 2012. Here the I-O tables are an economic network where the sectors are the nodes. The value of these metrics is that they describe the balance or trade-off between efficiency and redundancy of network flows as well as equality and hierarchy of flows through nodes in a network. I relate these metrics to the U.S. gross power consumption and annual intermediate spending by the food and energy sectors, the latter being a proxy for the inverse of the net power ratio (or net energy) of the economy, to test hypotheses of energy-economy structural linkages. The results of this paper show that increasing gross power consumption, as well as a decreasing share of intermediate expenditures of the food and energy sectors, correlates with increased distribution of money among economic sectors, and vice versa. The information theory metrics indicate two time periods at which major structural shifts occurred. The first was between 1967 and 1972, and the second was around the turn of the twenty-first century when food and energy expenditures no longer continued to decrease after 2002. In response to the latter, it is clear that the U.S. economy did trade off structural reserves (e.g., decreasing metrics of conditional entropy, redundancy, and equality) for structural efficiency (e.g., increasing metrics of efficiency, mutual constraint, and hierarchy) after food and energy expenditures increased post-2002. I also test the structural trends with increasingly simpler (e.g., fewer sectors) representations of the I-O tables, and the results are more consistent for I-O representations that account for inputs and outputs (e.g., value added and gross domestic product) rather than only the intermediate transactions among sectors. The findings of this paper have important implications for economic modeling in at least two ways. First, the paper helps explain how fundamental shifts in resources costs relate to economic structure and economic growth. Second, the paper shows that the number of sectors used to represent economic transactions influences the systemic metrics themselves.

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“…As a result it becomes named for economic sectors or types of technology producing the records, rather than the businesses employing the services causing it. Considerable statistical study has been done based on the recorded energy use accounts to identify benefits of technology, links between economic sectors [4,9,[12][13][14] and their relation to growth [3,4,12,[15][16][17][18]. The data is mostly aggregated by various government agencies, such as the Bureau of Economic Accounting and Census Bureau [1,19] for the US.…”

Section: Background Of Measuring Business Energy Usementioning

confidence: 99%

System Energy Assessment (SEA), Defining a Standard Measure of EROI for Energy Businesses as Whole Systems

Henshaw

King²,

Zarnikau

2011

Sustainability

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A more objective method for measuring the energy needs of businesses, System Energy Assessment (SEA), measures the combined impacts of material supply chains and service supply chains, to assess businesses as whole self-managing net-energy systems. The method is demonstrated using a model Wind Farm, and defines a physical measure of their energy productivity for society (EROI-S), a ratio of total energy delivered to total energy expended. Energy use records for technology and proxy measures for clearly understood but not individually recorded energy uses for services are combined for a whole system estimate of consumption required for production. Current methods count only energy needs for technology. Business services outsource their own energy needs to operate, leaving no traceable record. That uncounted business energy demand is often 80% of the total, an amount of "dark energy" hidden from view, discovered by finding the average energy estimated needs for businesses far below the world average energy consumed per dollar of GDP. Presently for lack of information the energy needs of business services are counted to be "0". Our default assumption is to treat them as "average". The result is a hard measure of total business demand for energy services, a "Scope 4" energy use or GHG impact assessment. Counting recorded energy uses and discounting unrecorded ones misrepresents labor intensive work as highly energy efficient. The result confirms a similar finding by Hall et al. in 1981 [1]. We use exhaustive search for what a business needs to operate as a whole, tracing internal business relationships rather than energy data, to locate its natural physical boundary as a working unit, and so define a business as a physical rather than statistical subject of scientific study. See also online resource materials and notes [2].

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Embodied energy and economic value in the United States economy: 1963, 1967 and 1972

Cited by 115 publications

References 7 publications

Valuation and management of wetland ecosystems

Valuation and management of wetland ecosystems

Information Theory to Assess Relations Between Energy and Structure of the U.S. Economy Over Time

System Energy Assessment (SEA), Defining a Standard Measure of EROI for Energy Businesses as Whole Systems

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