Hostname: page-component-54dcc4c588-ff9ft Total loading time: 0 Render date: 2025-10-03T15:44:06.242Z Has data issue: false hasContentIssue false
  • English
  • Français

Directed technological change, energy and more: a modern story

Published online by Cambridge University Press:  05 March 2020

Zheng Hou Open the ORCID record for Zheng Hou [Opens in a new window] ,
Catarina Roseta-Palma  and
Joaquim J.S. Ramalho
Zheng Hou*
Affiliation:
Business Research Unit, ISCTE-Instituto Universitario de Lisboa, Lisbon, Portugal
Catarina Roseta-Palma
Affiliation:
Department of Economics and Business Research Unit, ISCTE-Instituto Universitario de Lisboa, Lisbon, Portugal
Joaquim J.S. Ramalho
Affiliation:
Department of Economics and Business Research Unit, ISCTE-Instituto Universitario de Lisboa, Lisbon, Portugal
*
*Corresponding author. E-mail: hzguo@iscte-iul.pt
Get access Rights & Permissions [Opens in a new window]

Abstract

Reliance of modern economic activities on the use of energy, most of which still comes from non-renewable sources, provokes concerns regarding the most efficient utilization of energy inputs in production. While most theory expects directed technological change to be biased towards the non-renewable input, there is rare macro-level evidence that technological change is actually biased towards energy rather than other main inputs. To fill this gap, we apply stochastic frontier analysis to country data regarding output produced with capital, labor and energy, and estimate a set of indicators for technological change. Findings show that technological change is biased the most towards energy in general. In particular, although different groups of countries exhibit various patterns, there is strong evidence that technological change favors energy more than labor. This is in line with the theoretical expectation that technological change ought to be biased towards the non-renewable input rather than the renewable ones.

Keywords

directed technological changeenergyeconomic growthstochastic frontier analysis

JEL classification

Q32: Exhaustible Resources and Economic Development Q43: Energy and the Macroeconomy O33: Technological Change: Choices and Consequences • Diffusion Processes O44: Environment and Growth

Information

Type
Research Article
Information
Environment and Development Economics , Volume 25 , Special Issue 6: The Environment, Resources and Pollution – New Challenges for Economic Development , December 2020 , pp. 611 - 633
Copyright
Copyright © The Author(s), (2020). Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Acemoglu, D (2002) Directed technical change. Review of Economic Studies 69, 781–809.CrossRefGoogle Scholar
Acemoglu, D (2007) Equilibrium bias of technology. Econometrica 75, 1371–1409.CrossRefGoogle Scholar
Acemoglu, D (2010) When does labor scarcity encourage innovation? Journal of Political Economy 118, 1037–1078.CrossRefGoogle Scholar
Acemoglu, D, Aghion, P, Bursztyn, L and Hemous, D (2012) The environment and directed technical change. American Economic Review 102, 131–166.CrossRefGoogle ScholarPubMed
Aigner, D, Lovell, C and Schmidt, P (1977) Formulation and estimation of stochastic frontier production functions. Journal of Econometrics 6, 21–37.CrossRefGoogle Scholar
Anderson, K (1972) Optimal growth when the stock of resources is finite and depletable. Journal of Economic Theory 4, 256–267.CrossRefGoogle Scholar
André, F and Smulders, S (2014) Fueling growth when oil peaks: directed technological change and the limits to efficiency. European Economic Review 69, 18–39.CrossRefGoogle Scholar
Baron, A (2011) Measuring human capital. Strategic HR Review 10, 30–35.Google Scholar
Battese, G and Coelli, T (1995) A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical Economics 20, 325–332.CrossRefGoogle Scholar
Benchekroun, H and Withagen, C (2011) The optimal depletion of exhaustible resources: a complete characterization. Resource and Energy Economics 33, 612–636.Google Scholar
Boyd, GA and Lee, JM (2019) Measuring plant level energy efficiency and technical change in the U.S. metal-based durable manufacturing sector using stochastic frontier analysis. Energy Economics 81, 159–174.CrossRefGoogle Scholar
Chen, Y, Schmidt, P and Wang, H (2014) Consistent estimation of the fixed effects stochastic frontier model. Journal of Econometrics 18, 65–76.CrossRefGoogle Scholar
Dasgupta, P and Heal, G (1974) The optimal depletion of exhaustible resources. Review of Economic Studies 41, 3–28. Symposium on the Economics of Exhaustible Resources.CrossRefGoogle Scholar
Diamond, P (1965) Disembodied technical change in a two-sector model. Review of Economic Studies 32, 161–168.Google Scholar
Di Maria, C and Valente, S (2008) Hicks meets Hotelling: the direction of technical change in capital–resource economies. Environment and Development Economics 13, 691–717.CrossRefGoogle Scholar
Dissou, Y, Karnizova, L and Sun, Q (2014) Industry-level econometric estimates of energy-capital-labor substitution with a nested CES production function. Atlantic Economic Journal 43, 107–121.Google Scholar
Dong, Z, Guo, Y, Wang, L and Dai, J (2013) The direction of technical change: a study based on the inter-provincial panel data of China. Asian Journal of Technology Innovation 21, 317–333.Google Scholar
Duman, YS and Kasman, A (2018) Environmental technical efficiency in EU member and candidate countries: a parametric hyperbolic distance function approach. Energy 147, 297–307.Google Scholar
Garg, P and Sweeney, J (1978) Optimal growth with depletable resources. Resources and Energy 1, 43–56.CrossRefGoogle Scholar
Greene, W (2005) Fixed and random effects in stochastic frontier models. Journal of Productivity Analysis 23, 7–32.CrossRefGoogle Scholar
Grimaud, A and Rougé, L (2003) Non-renewable resources and growth with vertical innovations: optimum, equilibrium and economic policies. Journal of Environmental Economics and Management 45, 433–453.CrossRefGoogle Scholar
Grimaud, A, Lafforgue, G and Magné, B (2011) Climate change mitigation options and directed technical change: a decentralized equilibrium analysis? Resource and Energy Economics 33, 938–962.CrossRefGoogle Scholar
Groth, C and Schou, P (2002) Can non-renewable resources alleviate the knife-edge character of endogenous growth? Oxford Economic Papers 54, 386–411.CrossRefGoogle Scholar
Groth, C and Schou, P (2007) Growth and non-renewable resources: the different roles of capital and resource taxes. Journal of Environmental Economics and Management 53, 80–98.CrossRefGoogle Scholar
Grubb, M and Ulph, D (2002) Energy, the environment, and innovation. Oxford Review of Economic Policy 18, 92–106.Google Scholar
Hartwick, J (1977) Intergenerational equity and the investing of rents from exhaustible resources. The American Economic Review 67, 972–974.Google Scholar
Heshmati, A and Kumbhakar, S (2011) Technical change and total factor productivity growth: the case of Chinese provinces. Technological Forecasting & Social Change 78, 575–590.CrossRefGoogle Scholar
Hicks, J (1932) The Theory of Wages. London: Macmillan.Google Scholar
Hogan, W and Jorgenson, D (1991) Productivity trends and the cost of reducing CO2 emissions. The Energy Journal 12, 67–85.CrossRefGoogle Scholar
Hotelling, H (1931) The economics of exhaustible resources. The Journal of Political Economy 39, 137–175.Google Scholar
Hou, Z, Roseta-Palma, C and Ramalho, J (2020) Firm-level evidence for directed technological change involving energy inputs. Working Paper.Google Scholar
IEA (2019) World Energy Balances 2019. Paris: International Energy Agency. Available at https://www.iea.org/reports/world-energy-balances-2019.Google Scholar
Ingham, A and Simmons, P (1975) Natural resources and growing population. The Review of Economic Studies 42, 191–206.CrossRefGoogle Scholar
Jaffe, AB, Newell, RG and Stavins, RN (2005) A tale of two market failures: technology and environmental policy. Ecological Economics 54, 164–174.Google Scholar
Karanfil, F and Yeddir-Tamsamani, Y (2010) Is technological change biased toward energy? A multisectoral analysis for the French economy. Energy Policy 38, 1842–1850.Google Scholar
Kemfert, C and Welsch, H (2000) Energy-capital-labor substitution and the economic effects of CO2 abatement: evidence for Germany. Journal of Policy Modelling 22, 641–660.CrossRefGoogle Scholar
Kim, J and Heo, E (2013) Asymmetric substitutability between energy and capital: evidence from the manufacturing sectors in 10 OECD countries. Energy Economics 40, 81–89.CrossRefGoogle Scholar
Klump, R, McAdam, P and Willman, A (2007) Factor substitution and factor-augmenting technical progress in the United States: a normalized supply-side system approach. The Review of Economics and Statistics 89, 183–192.Google Scholar
Kodde, D and Palm, F (1986) Wald criteria for jointly testing equality and inequality restrictions. Econometrica 54, 1243–1248.CrossRefGoogle Scholar
Kumar, S and Managi, S (2009) Energy price-induced and exogenous technological change: assessing the economic and environmental outcomes. Resource and Energy Economics 31, 334–353.CrossRefGoogle Scholar
Kumbhakar, S (1990) Production frontiers, panel data, and time-varying technical inefficiency. Journal of Econometrics 46, 201–211.CrossRefGoogle Scholar
Kumbhakar, S and Wang, H (2005) Estimation of growth convergence using a stochastic production frontier approach. Economic Letters 88, 300–305.CrossRefGoogle Scholar
Kumbhakar, S, Denny, M and Fuss, M (2000) Estimation and decomposition of productivity change when production is not efficient: a panel data approach. Econometric Reviews 19, 425–460.CrossRefGoogle Scholar
Kumbhakar, S, Wang, H and Horncastle, A (2015) A Practitioner's Guide to Stochastic Frontier Analysis Using Stata. Cambridge: Cambridge University Press.Google Scholar
Linn, J (2008) Energy prices and the adoption of energy-saving technology. The Economic Journal 118, 1986–2012.CrossRefGoogle Scholar
Liu, XY, Pollitt, MG, Xie, BC and Liu, LQ (2019) Does environmental heterogeneity affect the productive efficiency of grid utilities in China? Energy Economics 83, 333–344.CrossRefGoogle Scholar
Meeusen, W and Van den Broeck, J (1977) Efficiency estimation from Cobb-Douglas production functions with composed error. International Economic Review 18, 435–444.CrossRefGoogle Scholar
Newell, R, Jaffe, A and Stavins, R (1999) The induced innovation hypothesis and energy-saving technological change. The Quarterly Journal of Economics 114, 941–975.CrossRefGoogle Scholar
Otto, V, Loschel, A and Dellink, R (2007) Energy biased technical change: a CGE analysis. Resource and Energy Economics 29, 137–158.Google Scholar
Parmeter, C and Kumbhakar, S (2014) Efficiency analysis: a primer on recent advances. Foundations and Trends in Econometrics 7, 191–385.CrossRefGoogle Scholar
Popp, D (2002) Induced innovation and energy prices. The American Economic Review 92, 160–180.CrossRefGoogle Scholar
Sanstad, A, Roy, J and Sathaye, J (2006) Estimating energy-augmenting technological change in developing country industries. Energy Economics 28, 720–729.Google Scholar
Shao, S, Luan, R, Yang, Z and Li, C (2016) Does directed technological change get greener: empirical evidence from Shanghai's industrial green development transformation. Ecological Indicators 69, 758–770.CrossRefGoogle Scholar
Smulders, S and De Nooij, M (2003) The impact of energy conservation on technology and economic growth. Resource and Energy Economics 25, 59–79.Google Scholar
Solow, R (1974) Intergenerational equity and exhaustible resources. Review of Economic Studies 41, 29–46. Symposium on the Economics of Exhaustible Resources.Google Scholar
Stiglitz, J (1974) Growth with exhaustible natural resources: efficient and optimal growth paths. The Review of Economic Studies 41, 123–137. Symposium on the Economics of Exhaustible Resources.Google Scholar
Stroombergen, A, Rose, D and Nana, G (2002) Review of the Statistical Measurement of Human Capital. Statistics New Zealand.Google Scholar
Su, X, Zhou, W, Nakagami, K, Ren, H and Mu, H (2012) Capital stock-labor-energy substitution and production efficiency study for China. Energy Economics 34, 1208–1213.CrossRefGoogle Scholar
United Nations (2017) World Economic Situation and Prospects 2018. New York: United Nations.. Available at https://www.un.org/development/desa/dpad/wp-content/uploads/sites/45/publication/WESP2018/Full/Web-1.pdf.Google Scholar
Wang, H (2002) Heteroscedasticity and non-monotonic efficiency effects of a stochastic frontier model. Journal of Productivity Analysis 18, 241–253.CrossRefGoogle Scholar
Wang, H and Schmidt, P (2002) One-step and two-step estimation of the effects of exogenous variables on technical efficiency levels. Journal of Productivity Analysis 18, 129–144.Google Scholar
Wesseh, P and Lin, B (2016) Output and substitution elasticities of energy and implications for renewable energy expansion in the ECOWAS region. Energy Policy 89, 125–137.CrossRefGoogle Scholar
Yang, Z, Shao, S, Yang, L and Miao, Z (2018) Improvement pathway of energy consumption structure in China's industrial sector: from the perspective of directed technical change. Energy Economics 72, 166–176.Google Scholar
Zha, D, Kavuri, AS and Si, S (2017) Energy biased technology change: focused on Chinese energy-intensive industries. Applied Energy 190, 1081–1089.CrossRefGoogle Scholar
Zha, D, Kavuri, AS and Si, S (2018) Energy-biased technical change in the Chinese industrial sector with CES production functions. Energy 148, 896–903.CrossRefGoogle Scholar