Dematerialization: Long-term trends in the intensity of use of materials and energy

Bernardini, O.; Galli, R.

doi:10.1016/0016-3287(93)90005-e

Cited by 160 publications

(71 citation statements)

References 4 publications

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“…Some have identified the spontaneous shift from material-intensive industry to less material-intensive services in the course of economic growth as a key factor driving the decoupling economic prosperity from environmental degradation (22)(23)(24). Nevertheless, for CO2 emissions Shanfik and Bandyopadhay (25) found an opposing trend.…”

Section: Discussion and Outlookmentioning

confidence: 99%

Are Services Better for Climate Change?

Suh

2006

Environ. Sci. Technol.

124

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Embodied greenhouse gas (GHG) emissions and their structure of inducement by the supply-chain networks of 480 goods and services in the United States are analyzed for 44 GHGs. Producing a dollar of a product or service generates an average of 0.36 kg of CO 2 equivalent GHGs onsite, increasing to 0.83 kg when supply-chain-induced emissions are taken into account. Services produce less than 5% of total U.S. GHG emissions directly, and their direct GHG emission intensities per dollar output are much less (0.04 kg CO 2 equiv/$) than those of physical products, even when supply-chain-induced emissions are included (0.47 kg CO 2 equiv/$). When both supply-chain effects and the volume of household expenditures are taken into account, however, household consumption of services excluding electric utilities and transportation services proves to be responsible for 37.6% of total industrial GHG emissions in the United States, almost twice the amount due to household consumption of electric utility and transportation services. Given the current structure of GHG emissions, a shift to a service-oriented economy is shown to entail a decrease in GHG emission intensity per unit GDP but an increase, by necessity, in overall GHG emissions in absolute terms. The results are discussed in the context of U.S. climate change policy.

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Section: Discussion and Outlookmentioning

confidence: 99%

Are Services Better for Climate Change?

Suh

2006

Environ. Sci. Technol.

124

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“…21, 29, 51, 66]. The income level at which such a maximum in intensity is reached tends to decrease over time [6,22] These stylized facts are represented in the model equation for useful energy intensity (Y (t) ) in the form of a (asymmetric) bell-shaped function of the sector-specific per capita economic activity. For each region (R), sector (S) and energy form (F) at time t, this can be expressed as 4 :…”

Section: Useful Energy Intensity Curvementioning

confidence: 99%

“…This means that rapid economic growth leads to a faster decline in AEEI, due to both increased decline in the marginal AEEI and a larger share of the capital stock that is relatively new [64]. The parameter that can be used to calibrate the AEEI is the fraction of GDP growth (F S ) 6 (see [63] for details).…”

Section: Autonomous Energy Efficiency Improvementmentioning

confidence: 99%

“…3, is a dimensionless multiplier defined as: 1−E R,S,F . Vintage modelling of energy demand capital delays the 6 In discussing the results, AEEI is expressed as the average percentage of annual sectoral efficiency improvement, based on the average historic regional GDP per capita growth for the period 1971-2003 5 In our model implementation this is the year 2003, the latest year of the calibration period. impact of the PIEEI, as the current PIEEI is the weighted average of the marginal PIEEI over the capital life time.…”

Section: Price-induced Energy Efficiency Improvementmentioning

confidence: 99%

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Uncertainty from Model Calibration: Applying a New Method to Transport Energy Demand Modelling

Ruijven

Sluijs

Vuuren

et al. 2009

Environ Model Assess

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Uncertainties in energy demand modelling originate from both limited understanding of the real-world system and a lack of data for model development, calibration and validation. These uncertainties allow for the development of different models, but also leave room for different calibrations of a single model. Here, an automated model calibration procedure was developed and tested for transport sector energy use modelling in the TIMER 2.0 global energy model. This model describes energy use on the basis of activity levels, structural change and autonomous and priceinduced energy efficiency improvements. We found that the model could reasonably reproduce historic data under different sets of parameter values, leading to different projections of future energy demand levels. Projected energy use for 2030 shows a range of 44-95% around the best-fit projection. Two different model interpretations of the past can generally be distinguished: (1) high useful energy intensity and major energy efficiency improvements or (2) low useful energy intensity and little efficiency improvement. Generally, the first lead to higher future energy demand levels than the second, but model and insights do not provide decisive arguments to attribute a higher likelihood to one of the alternatives. Keywords

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“…Once upon a time, not so many years ago, the word of the day was dematerialization [1]. Matter flows were going to disappear, and in their place digital bits would, in their binary elegance, provide the driving force, medium, value and content of life.…”

Section: Introductionmentioning

confidence: 99%

Resource convergence and resource power: towards new concepts for material efficiency

Vasara¹,

Rouhiainen²,

Lehtinen³

2013

Phil. Trans. R. Soc. A.

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Material efficiency is not a panacea, but it lies at the intersection of many problems of global sustainability. Reducing the usage of a single material in a value chain may require ingenuity and wide cooperation, but conceptually it is not a problem. When the problem stretches to several resources in changing circumstances, we have to understand the linkages between resources and the power, and the influence of each resource in decision-making in different settings. In this paper, we concisely go from the time dimension to a very short history of resource thinking, and then introduce our resource convergence concept. Using text mining on a sample of articles linking different resources, we show in a ‘visual literature review’ how resource convergence emerges from unrelated texts. To demonstrate one new method under the resource convergence umbrella, we use a case example with our Resource Power Index method. In our opinion, mastering complexity will be the key to solving the challenges we are facing, and mastering resource convergence and translating it into material efficiency is one of the central problems. What we show in this paper is a core framework and some details of the direction we see as worth pursuing.

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Dematerialization: Long-term trends in the intensity of use of materials and energy

Cited by 160 publications

References 4 publications

Are Services Better for Climate Change?

Are Services Better for Climate Change?

Uncertainty from Model Calibration: Applying a New Method to Transport Energy Demand Modelling

Resource convergence and resource power: towards new concepts for material efficiency

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