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RESEARCH PAPER
Macroeconomic, Sectoral and Fiscal Consequences of Decreasing Energy Intensity in the Polish Economy
 
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SGH Warsaw School of Economics, Poland
 
 
Submission date: 2020-04-06
 
 
Final revision date: 2020-05-27
 
 
Acceptance date: 2020-07-16
 
 
Publication date: 2020-09-30
 
 
Corresponding author
Michał Antoszewski   

Szkoła Główna Handlowa w Warszawie, Al. Niepodległości 162, 02-554, Warszawa, Polska
 
 
GNPJE 2020;303(3):53-81
 
KEYWORDS
JEL CLASSIFICATION CODES
ABSTRACT
The aim of this paper is to assess the implications of an ongoing improvement in the energy efficiency of the Polish economy. Poland is among countries that have been leading the way in reducing energy intensity in recent decades. A counterfactual analysis conducted in this study is based on a computable general equilibrium (CGE) model called GEMPOL and captures six dimensions: the overall economic activity level; the industry pattern of output; the product pattern of foreign trade; energy-related expenditures; the quantity of energy used; and the revenue and expenditure of the public finance sector. An accompanying sensitivity analysis underlines the positive relationship between the expected economic effects of improved energy efficiency and the assumed scale of such technological progress, as well as the positive relationship between the magnitude of those consequences and the assumed substitution elasticity values. The obtained results can constitute an important contribution to a scholarly debate on the long-term impacts of decreasing per-unit energy use on the characteristics of Poland’s economy and resulting policy challenges.
 
REFERENCES (33)
1.
Allan G., Hanley N., McGregor P., Swales J., Turner K. [2006], The Macroeconomic Rebound Effect and the UK Economy, Final Report to the Department of Environment Food and Rural Affairs, Ministry of Agriculture, Fisheries and Food, London.
 
2.
Allan G., Gilmartin M., Turner K., McGregor P., Swales K. [2007], UKERC Review of Evidence for the Rebound Effect. Technical Report 4: Computable general equilibrium modelling studies, Research Report, REF UKERC/WP/TPA/2007/012, UK Energy Research Centre, London.
 
3.
Alexeeva-Talebi V., Böhringer C., Löschel A., Voigt S. [2012], The value-added of sectoral disaggregation: Implications on competitive consequences of climate change policies, Energy Economics, 34, supplement 2: S127‑S142.
 
4.
Ang B. W. [1999], Decomposition methodology in energy demand and environmental analysis, in: van der Bergh J. (eds.), Handbook of Environmental and Resource Economics, Edward Elgar Publishing, Cheltenham.
 
5.
Antosiewicz M., Lewandowski P., Witajewski-Baltvilks J. [2016], Input vs. Output taxation – a DSGE approach to modelling resource decoupling, Sustainability, 8 (4) 352.
 
6.
Antoszewski M. [2019a], Assessment of Energy-Related Technological Shocks Within a CGE Model for the Polish Economy, Gospodarka Narodowa, 297 (1): 9–45.
 
7.
Antoszewski M. [2019b], Wide-range estimation of various substitution elasticities for CES production functions at the sectoral level, Energy Economics, 83: 272–289.
 
8.
Antoszewski M., Boratyński J., Zachłod-Jelec M., Wójtowicz K., Cygler M., Jeszke R., Pyrka M., Sikora P., Böhringer C., Gąska J., Jorgensen E., Kąsek L., Kiuila O., Malarski R., Rabiega W. [2015], CGE model PLACE. Technical documentation for the model version as of December 2014, MF Working Papers, 22.
 
9.
Armington P. [1969], A Theory of Demand for Products Distinguished by Place of Production, International Monetary Fund Staff Papers, 16 (1): 170–201.
 
10.
Beauséjour L., Sheikh M., Williams B. [1995], Potential economic effects of experience-rating the unemployment insurance system using a multi-sector general equilibrium model of Canada, Research Report, Fiscal Policy and Economic Analysis Branch, Department of Finance Canada, Ottawa.
 
11.
Bukowski M., Gąska J., Jackl F., Kassenberg A., Pankowiec A., Śniegocki A., Śpionek A., Karaczun Z., Szpor A. [2013], Niskoemisyjna Polska 2050. Podróż do niskoemisyjnej przyszłości, Research Report, Warsaw Institute of Economic Studies, Warszawa.
 
12.
Caron J. [2012], Estimating carbon leakage and the efficiency of border adjustments in general equilibrium – Does sectoral aggregation matter?, Energy Economics, 34, Supplement 2: S111‑S126.
 
13.
Central Statistical Office [2014], Supply and use tables in 2010, CSO, Warszawa.
 
14.
Central Statistical Office [2020], Roczne wskaźniki makroekonomiczne [access: Jan. 5, 2020], CSO, Warszawa, http://www.stat.gov.pl/wskazni....
 
15.
Deaton A., Muellbauer J. [1980], An almost ideal demand system, The American Economic Review, 70 (3): 312–326.
 
16.
Dietzenbacher E., Lenzen M., Los B., Guan D., Lahr M. L., Sancho F., Suh S., Yang C. [2013], Input–output analysis: The next 25 years, Economic Systems Research, 25 (4): 369–389.
 
17.
European Commission [2015], The 2015 Ageing Report. Underlying assumptions and projection methodologies, European Economy, No. 8/2014, EC, Brussels.
 
18.
European Commission [2016], EU Reference Scenario 2016 – Energy, transport and GHG emissions – Trends to 2050, EC, Brussels.
 
19.
Gillingham K., Rapson D., Wagner G. [2016], The Rebound Effect and Energy Efficiency Policy, Review of Environmental Economics and Policy, 10 (1): 68–88.
 
20.
Gurgul H., Lach Ł. [2016], Simulating evolution of interindustry linkages in endogenous dynamic IO model with layers of techniques, Metroeconomica, 67 (4): 632–666.
 
21.
Gurgul H., Lach Ł. [2019a], Eco-efficiency analysis in generalized IO models: Methods and examples, Munich Personal RePEc Archive Paper, 96604.
 
22.
Gurgul H., Lach Ł. [2019b], On approximating the accelerator part in dynamic input–output models. Central European Journal of Operations Research, 27 (1): 219–239.
 
23.
International Energy Agency [2014], Capturing the Multiple Benefits of Energy Efficiency, Staff Paper, OECD/IEA, Paris.
 
24.
International Energy Agency [2017], IEA Headline Global Energy Data (2017 edition) [access: Nov. 15, 2017], https://www.iea.org/statistics....
 
25.
Kaldor N. [1957], A model of economic growth, The Economic Journal, 67 (268): 591–624.
 
26.
Lach Ł. [2020], Tracing key sectors and important input-output coefficients: Methods and applications, C. H. Beck, Warszawa.
 
27.
McKibbin W., Wilcoxen P. [1999], The theoretical and empirical structure of the G-Cubed model, Economic Modelling, 16 (1): 123–148.
 
28.
Plich M., Skrzypek J. [2016], Trendy energochłonności polskiej gospodarki, Wiadomości Statystyczne, 7: 16–38.
 
29.
Rutherford T. [2010], GTAP7inGAMS, mimeo, ETH, Zürich.
 
30.
Stone R. [1954], Linear expenditure systems and demand analysis: an application to the pattern of British demand, The Economic Journal, 64 (255): 511–527.
 
31.
Timmer M. P., Dietzenbacher E., Los B., Stehrer R., de Vries G. J. [2015], An Illustrated User Guide to the World Input-Output Database: The Case of Global Automotive Production, Review of International Economics, 23 (3): 575–605.
 
32.
Voigt S., de Cian E., Schymura M., Verdolini E. [2014], Energy intensity developments in 40 major economies: Structural change or technology improvement?, Energy Economics, 41 (1): 47–62.
 
33.
World Bank [2018], GDP, PPP (constant 2011 international $) [access: March 2, 2018], https://data.worldbank.org/ind....
 
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