Factors influencing the diffusion of electric arc furnace steelmaking technology

Labson, B. Stephen; Gooday, Peter

doi:10.1080/00036849400000053

Cited by 15 publications

(7 citation statements)

References 14 publications

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“…3. Conventional economic factors such as prices do not seem to explain the observed patterns of EAF diffusion [10,18].…”

Section: Diffusion Of New Steel Technologies As Illustration Of Explamentioning

confidence: 81%

“…Here, neither relative scrap prices nor energy prices do explain the empirical diffusion pattern [10,18,19]. Important influencing factors are complementarities with innovations in finished steel production and the changes in institutional conditions after the first oil crisis (for a detailed account see [4]).…”

Section: Diffusion Of New Steel Technologies As Illustration Of Explamentioning

confidence: 99%

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Diffusion as time-dependent result of technological evolution, competition, and policies: the case of cleaner iron and steel technologies

Nill

2008

Journal of Cleaner Production

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“…3. Conventional economic factors such as prices do not seem to explain the observed patterns of EAF diffusion [10,18].…”

Section: Diffusion Of New Steel Technologies As Illustration Of Explamentioning

confidence: 81%

Section: Diffusion Of New Steel Technologies As Illustration Of Explamentioning

confidence: 99%

Diffusion as time-dependent result of technological evolution, competition, and policies: the case of cleaner iron and steel technologies

Nill

2008

Journal of Cleaner Production

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“…After using this procedure to get refinery-specific estimates of β's, similar to the first model, the second stage is to regress resulting estimates of β's on various refinery and market-related variables. The earlier-mentioned specification of the growth process was first suggested by Chow (1969), and was used by Labson and Gooday (1994) to model the diffusion of electric arc Downloaded by [University of Saskatchewan Library] at 13:56 08 October 2012 furnace steelmaking technology in West Europe, US, and Japan. Recently, Crompton (2001) also used this approach to model and forecast the growth in the electric arc furnace share of total crude steel production in US and Japan.…”

Section: Geographical Factorsmentioning

confidence: 99%

Technology adoption in response to changes in market conditions: Evidence from the US petroleum refining industry

Chen

2005

Economics of Innovation and New Technology

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This paper analyzes the determinants of the levels and rates of technology adoption for petroleum refineries that survived the period 1980-1989, during which the conditions of product demand and crude oil supply changed significantly. Regression models are specified to investigate the growth of technology-related capacity, the growth of technology complexity, and the rates of adoption estimated from a diffuse model of technology use. Both levels and rates of adoption are hypothesized to be affected by refinery size, regulatory status, elements of local markets, and geographical factors. Empirical results generally suggest that compared with the supply-side factors of crude oil sources and regulatory subsidies, refinery size and demand-side factors, such as total consumption, consumption growth and fluctuation, and changes of the consumption mix, are responsible for the most part of the determination of technology adoption for refineries surviving the 1980s.Technology adoption, Petroleum refining industry,

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“…There are also some diffusion studies on the electric arc furnace [25][26][27]. Moya [8] proves that scrap prices, energy prices and steel price are enough to explain the empirical diffusion pattern of this technology in Japan using a simple ARMAX model.…”

Section: Application Of This New Approach To Model the Diffusion Of Imentioning

confidence: 99%

Where Diffusion of Clean Technologies and Barriers to Innovation Clash: Application to the Global Diffusion of the Electrical Arc Furnace

Moya

2017

Energies

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This paper analyses the role of barriers preventing the worldwide take-up of a clean technology: the electrical arc furnace. It also identifies which barriers affect a parameter that summarises the combined effect of all of them. The first step, determination of the combined effect of the barriers, is carried out using a novel approach to model the diffusion of innovations. This new approach is composed only by terms that account for the driver of innovations and the parameter that summarises the effect of barriers. The objective quantification of the effect of barriers in the diffusion of innovations opens up new opportunities for designing policies to overcome the barriers identified as the most relevant, for identifying the effect of existing policies, for relating innovation indicators with those barriers or for better incorporating the effect of barriers in bottom-up models that forecast the technological evolution of the economy.

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Factors influencing the diffusion of electric arc furnace steelmaking technology

Cited by 15 publications

References 14 publications

Diffusion as time-dependent result of technological evolution, competition, and policies: the case of cleaner iron and steel technologies

Diffusion as time-dependent result of technological evolution, competition, and policies: the case of cleaner iron and steel technologies

Technology adoption in response to changes in market conditions: Evidence from the US petroleum refining industry

Where Diffusion of Clean Technologies and Barriers to Innovation Clash: Application to the Global Diffusion of the Electrical Arc Furnace

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